regularized logistic regression: Topics by Science.gov

Sample records for regularized logistic regression

Nonconvex Sparse Logistic Regression With Weakly Convex Regularization

NASA Astrophysics Data System (ADS)

Shen, Xinyue; Gu, Yuantao

2018-06-01

In this work we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem. The idea is based on the finding that a weakly convex function as an approximation of the $\\ell_0$ pseudo norm is able to better induce sparsity than the commonly used $\\ell_1$ norm. For a class of weakly convex sparsity inducing functions, we prove the nonconvexity of the corresponding sparse logistic regression problem, and study its local optimality conditions and the choice of the regularization parameter to exclude trivial solutions. Despite the nonconvexity, a method based on proximal gradient descent is used to solve the general weakly convex sparse logistic regression, and its convergence behavior is studied theoretically. Then the general framework is applied to a specific weakly convex function, and a necessary and sufficient local optimality condition is provided. The solution method is instantiated in this case as an iterative firm-shrinkage algorithm, and its effectiveness is demonstrated in numerical experiments by both randomly generated and real datasets.
Unitary Response Regression Models

ERIC Educational Resources Information Center

Lipovetsky, S.

2007-01-01

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…
Rank-Optimized Logistic Matrix Regression toward Improved Matrix Data Classification.

PubMed

Zhang, Jianguang; Jiang, Jianmin

2018-02-01

While existing logistic regression suffers from overfitting and often fails in considering structural information, we propose a novel matrix-based logistic regression to overcome the weakness. In the proposed method, 2D matrices are directly used to learn two groups of parameter vectors along each dimension without vectorization, which allows the proposed method to fully exploit the underlying structural information embedded inside the 2D matrices. Further, we add a joint [Formula: see text]-norm on two parameter matrices, which are organized by aligning each group of parameter vectors in columns. This added co-regularization term has two roles-enhancing the effect of regularization and optimizing the rank during the learning process. With our proposed fast iterative solution, we carried out extensive experiments. The results show that in comparison to both the traditional tensor-based methods and the vector-based regression methods, our proposed solution achieves better performance for matrix data classifications.
Large unbalanced credit scoring using Lasso-logistic regression ensemble.

PubMed

Wang, Hong; Xu, Qingsong; Zhou, Lifeng

2015-01-01

Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data.
Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble

PubMed Central

Wang, Hong; Xu, Qingsong; Zhou, Lifeng

2015-01-01

Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data. PMID:25706988
Supporting Regularized Logistic Regression Privately and Efficiently.

PubMed

Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei

2016-01-01

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.
Supporting Regularized Logistic Regression Privately and Efficiently

PubMed Central

Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei

2016-01-01

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc. PMID:27271738
Regularization Paths for Conditional Logistic Regression: The clogitL1 Package.

PubMed

Reid, Stephen; Tibshirani, Rob

2014-07-01

We apply the cyclic coordinate descent algorithm of Friedman, Hastie, and Tibshirani (2010) to the fitting of a conditional logistic regression model with lasso [Formula: see text] and elastic net penalties. The sequential strong rules of Tibshirani, Bien, Hastie, Friedman, Taylor, Simon, and Tibshirani (2012) are also used in the algorithm and it is shown that these offer a considerable speed up over the standard coordinate descent algorithm with warm starts. Once implemented, the algorithm is used in simulation studies to compare the variable selection and prediction performance of the conditional logistic regression model against that of its unconditional (standard) counterpart. We find that the conditional model performs admirably on datasets drawn from a suitable conditional distribution, outperforming its unconditional counterpart at variable selection. The conditional model is also fit to a small real world dataset, demonstrating how we obtain regularization paths for the parameters of the model and how we apply cross validation for this method where natural unconditional prediction rules are hard to come by.
Regularization Paths for Conditional Logistic Regression: The clogitL1 Package

PubMed Central

Reid, Stephen; Tibshirani, Rob

2014-01-01

We apply the cyclic coordinate descent algorithm of Friedman, Hastie, and Tibshirani (2010) to the fitting of a conditional logistic regression model with lasso (ℓ1) and elastic net penalties. The sequential strong rules of Tibshirani, Bien, Hastie, Friedman, Taylor, Simon, and Tibshirani (2012) are also used in the algorithm and it is shown that these offer a considerable speed up over the standard coordinate descent algorithm with warm starts. Once implemented, the algorithm is used in simulation studies to compare the variable selection and prediction performance of the conditional logistic regression model against that of its unconditional (standard) counterpart. We find that the conditional model performs admirably on datasets drawn from a suitable conditional distribution, outperforming its unconditional counterpart at variable selection. The conditional model is also fit to a small real world dataset, demonstrating how we obtain regularization paths for the parameters of the model and how we apply cross validation for this method where natural unconditional prediction rules are hard to come by. PMID:26257587
A regularization corrected score method for nonlinear regression models with covariate error.

PubMed

Zucker, David M; Gorfine, Malka; Li, Yi; Tadesse, Mahlet G; Spiegelman, Donna

2013-03-01

Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and interval estimates of the regression coefficients. We present here a new general method for adjusting for covariate error. Our method consists of an approximate version of the Stefanski-Nakamura corrected score approach, using the method of regularization to obtain an approximate solution of the relevant integral equation. We develop the theory in the setting of classical likelihood models; this setting covers, for example, linear regression, nonlinear regression, logistic regression, and Poisson regression. The method is extremely general in terms of the types of measurement error models covered, and is a functional method in the sense of not involving assumptions on the distribution of the true covariate. We discuss the theoretical properties of the method and present simulation results in the logistic regression setting (univariate and multivariate). For illustration, we apply the method to data from the Harvard Nurses' Health Study concerning the relationship between physical activity and breast cancer mortality in the period following a diagnosis of breast cancer. Copyright © 2013, The International Biometric Society.
Classification of mislabelled microarrays using robust sparse logistic regression.

PubMed

Bootkrajang, Jakramate; Kabán, Ata

2013-04-01

Previous studies reported that labelling errors are not uncommon in microarray datasets. In such cases, the training set may become misleading, and the ability of classifiers to make reliable inferences from the data is compromised. Yet, few methods are currently available in the bioinformatics literature to deal with this problem. The few existing methods focus on data cleansing alone, without reference to classification, and their performance crucially depends on some tuning parameters. In this article, we develop a new method to detect mislabelled arrays simultaneously with learning a sparse logistic regression classifier. Our method may be seen as a label-noise robust extension of the well-known and successful Bayesian logistic regression classifier. To account for possible mislabelling, we formulate a label-flipping process as part of the classifier. The regularization parameter is automatically set using Bayesian regularization, which not only saves the computation time that cross-validation would take, but also eliminates any unwanted effects of label noise when setting the regularization parameter. Extensive experiments with both synthetic data and real microarray datasets demonstrate that our approach is able to counter the bad effects of labelling errors in terms of predictive performance, it is effective at identifying marker genes and simultaneously it detects mislabelled arrays to high accuracy. The code is available from http://cs.bham.ac.uk/∼jxb008. Supplementary data are available at Bioinformatics online.
Application of L1/2 regularization logistic method in heart disease diagnosis.

PubMed

Zhang, Bowen; Chai, Hua; Yang, Ziyi; Liang, Yong; Chu, Gejin; Liu, Xiaoying

2014-01-01

Heart disease has become the number one killer of human health, and its diagnosis depends on many features, such as age, blood pressure, heart rate and other dozens of physiological indicators. Although there are so many risk factors, doctors usually diagnose the disease depending on their intuition and experience, which requires a lot of knowledge and experience for correct determination. To find the hidden medical information in the existing clinical data is a noticeable and powerful approach in the study of heart disease diagnosis. In this paper, sparse logistic regression method is introduced to detect the key risk factors using L(1/2) regularization on the real heart disease data. Experimental results show that the sparse logistic L(1/2) regularization method achieves fewer but informative key features than Lasso, SCAD, MCP and Elastic net regularization approaches. Simultaneously, the proposed method can cut down the computational complexity, save cost and time to undergo medical tests and checkups, reduce the number of attributes needed to be taken from patients.
Developing logistic regression models using purchase attributes and demographics to predict the probability of purchases of regular and specialty eggs.

PubMed

Bejaei, M; Wiseman, K; Cheng, K M

2015-01-01

Consumers' interest in specialty eggs appears to be growing in Europe and North America. The objective of this research was to develop logistic regression models that utilise purchaser attributes and demographics to predict the probability of a consumer purchasing a specific type of table egg including regular (white and brown), non-caged (free-run, free-range and organic) or nutrient-enhanced eggs. These purchase prediction models, together with the purchasers' attributes, can be used to assess market opportunities of different egg types specifically in British Columbia (BC). An online survey was used to gather data for the models. A total of 702 completed questionnaires were submitted by BC residents. Selected independent variables included in the logistic regression to develop models for different egg types to predict the probability of a consumer purchasing a specific type of table egg. The variables used in the model accounted for 54% and 49% of variances in the purchase of regular and non-caged eggs, respectively. Research results indicate that consumers of different egg types exhibit a set of unique and statistically significant characteristics and/or demographics. For example, consumers of regular eggs were less educated, older, price sensitive, major chain store buyers, and store flyer users, and had lower awareness about different types of eggs and less concern regarding animal welfare issues. However, most of the non-caged egg consumers were less concerned about price, had higher awareness about different types of table eggs, purchased their eggs from local/organic grocery stores, farm gates or farmers markets, and they were more concerned about care and feeding of hens compared to consumers of other eggs types.
Feature Clustering for Accelerating Parallel Coordinate Descent

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scherrer, Chad; Tewari, Ambuj; Halappanavar, Mahantesh

2012-12-06

We demonstrate an approach for accelerating calculation of the regularization path for L1 sparse logistic regression problems. We show the benefit of feature clustering as a preconditioning step for parallel block-greedy coordinate descent algorithms.
Factors That Contributed to Gifted Students' Success on STEM Pathways: The Role of Race, Personal Interests, and Aspects of High School Experience

ERIC Educational Resources Information Center

Steenbergen-Hu, Saiying; Olszewski-Kubilius, Paula

2017-01-01

In this study, we conducted binary logistic regression on survey data collected from 244 past participants of a Talent Search program who attended regular high schools but supplemented their regular high school education with enriched or accelerated math and science learning activities. The participants completed an online survey 4 to 6 years…
Linearized Alternating Direction Method of Multipliers for Constrained Nonconvex Regularized Optimization

DTIC Science & Technology

2016-11-22

structure of the graph, we replace the ℓ1- norm by the nonconvex Capped -ℓ1 norm , and obtain the Generalized Capped -ℓ1 regularized logistic regression...X. M. Yuan. Linearized augmented lagrangian and alternating direction methods for nuclear norm minimization. Mathematics of Computation, 82(281):301...better approximations of ℓ0- norm theoretically and computationally beyond ℓ1- norm , for example, the compressive sensing (Xiao et al., 2011). The
Avoiding overstating the strength of forensic evidence: Shrunk likelihood ratios/Bayes factors.

PubMed

Morrison, Geoffrey Stewart; Poh, Norman

2018-05-01

When strength of forensic evidence is quantified using sample data and statistical models, a concern may be raised as to whether the output of a model overestimates the strength of evidence. This is particularly the case when the amount of sample data is small, and hence sampling variability is high. This concern is related to concern about precision. This paper describes, explores, and tests three procedures which shrink the value of the likelihood ratio or Bayes factor toward the neutral value of one. The procedures are: (1) a Bayesian procedure with uninformative priors, (2) use of empirical lower and upper bounds (ELUB), and (3) a novel form of regularized logistic regression. As a benchmark, they are compared with linear discriminant analysis, and in some instances with non-regularized logistic regression. The behaviours of the procedures are explored using Monte Carlo simulated data, and tested on real data from comparisons of voice recordings, face images, and glass fragments. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.
Prevalence of and factors associated with regular khat chewing among university students in Ethiopia.

PubMed

Astatkie, Ayalew; Demissie, Meaza; Berhane, Yemane; Worku, Alemayehu

2015-01-01

Khat (Catha edulis) is commonly chewed for its psychostimulant and euphorigenic effects in Africa and the Arabian Peninsula. Students use it to help them study for long hours especially during the period of examination. However, how regularly khat is chewed among university students and its associated factors are not well documented. In this article we report on the prevalence of and factors associated with regular khat chewing among university students in Ethiopia. We did a cross-sectional study from May 20, 2014 to June 23, 2014 on a sample of 1,255 regular students recruited from all campuses of Hawassa University, southern Ethiopia. The data were collected using self-administered questionnaires. We analyzed the data to identify factors associated with current regular khat chewing using complex sample adjusted logistic regression analysis. The prevalence of current regular khat chewing was 10.5% (95% confidence interval [CI]: 6.1%-14.9%). After controlling for sex, religion, year of study, having a father who chews khat, cigarette smoking and alcohol drinking in the adjusted logistic regression model, living off-campus in rented houses as compared to living in the university dormitory (adjusted odds ratio [95% CI] =8.09 [1.56-42.01]), and having friends who chew khat (adjusted odds ratio [95% CI] =4.62 [1.98-10.74]) were found to significantly increase the odds of current regular khat use. Students living outside the university campus in rented houses compared to those living in dormitory and those with khat chewing peers are more likely to use khat. A multipronged prevention approach involving students, the university officials, the surrounding community, and regulatory bodies is required.
Prevalence of and factors associated with regular khat chewing among university students in Ethiopia

PubMed Central

Astatkie, Ayalew; Demissie, Meaza; Berhane, Yemane; Worku, Alemayehu

2015-01-01

Purpose Khat (Catha edulis) is commonly chewed for its psychostimulant and euphorigenic effects in Africa and the Arabian Peninsula. Students use it to help them study for long hours especially during the period of examination. However, how regularly khat is chewed among university students and its associated factors are not well documented. In this article we report on the prevalence of and factors associated with regular khat chewing among university students in Ethiopia. Methods We did a cross-sectional study from May 20, 2014 to June 23, 2014 on a sample of 1,255 regular students recruited from all campuses of Hawassa University, southern Ethiopia. The data were collected using self-administered questionnaires. We analyzed the data to identify factors associated with current regular khat chewing using complex sample adjusted logistic regression analysis. Results The prevalence of current regular khat chewing was 10.5% (95% confidence interval [CI]: 6.1%–14.9%). After controlling for sex, religion, year of study, having a father who chews khat, cigarette smoking and alcohol drinking in the adjusted logistic regression model, living off-campus in rented houses as compared to living in the university dormitory (adjusted odds ratio [95% CI] =8.09 [1.56–42.01]), and having friends who chew khat (adjusted odds ratio [95% CI] =4.62 [1.98–10.74]) were found to significantly increase the odds of current regular khat use. Conclusion Students living outside the university campus in rented houses compared to those living in dormitory and those with khat chewing peers are more likely to use khat. A multipronged prevention approach involving students, the university officials, the surrounding community, and regulatory bodies is required. PMID:25750551
Gene selection in cancer classification using sparse logistic regression with Bayesian regularization.

PubMed

Cawley, Gavin C; Talbot, Nicola L C

2006-10-01

Gene selection algorithms for cancer classification, based on the expression of a small number of biomarker genes, have been the subject of considerable research in recent years. Shevade and Keerthi propose a gene selection algorithm based on sparse logistic regression (SLogReg) incorporating a Laplace prior to promote sparsity in the model parameters, and provide a simple but efficient training procedure. The degree of sparsity obtained is determined by the value of a regularization parameter, which must be carefully tuned in order to optimize performance. This normally involves a model selection stage, based on a computationally intensive search for the minimizer of the cross-validation error. In this paper, we demonstrate that a simple Bayesian approach can be taken to eliminate this regularization parameter entirely, by integrating it out analytically using an uninformative Jeffrey's prior. The improved algorithm (BLogReg) is then typically two or three orders of magnitude faster than the original algorithm, as there is no longer a need for a model selection step. The BLogReg algorithm is also free from selection bias in performance estimation, a common pitfall in the application of machine learning algorithms in cancer classification. The SLogReg, BLogReg and Relevance Vector Machine (RVM) gene selection algorithms are evaluated over the well-studied colon cancer and leukaemia benchmark datasets. The leave-one-out estimates of the probability of test error and cross-entropy of the BLogReg and SLogReg algorithms are very similar, however the BlogReg algorithm is found to be considerably faster than the original SLogReg algorithm. Using nested cross-validation to avoid selection bias, performance estimation for SLogReg on the leukaemia dataset takes almost 48 h, whereas the corresponding result for BLogReg is obtained in only 1 min 24 s, making BLogReg by far the more practical algorithm. BLogReg also demonstrates better estimates of conditional probability than the RVM, which are of great importance in medical applications, with similar computational expense. A MATLAB implementation of the sparse logistic regression algorithm with Bayesian regularization (BLogReg) is available from http://theoval.cmp.uea.ac.uk/~gcc/cbl/blogreg/

Modeling Students' Problem Solving Performance in the Computer-Based Mathematics Learning Environment

ERIC Educational Resources Information Center

Lee, Young-Jin

2017-01-01

Purpose: The purpose of this paper is to develop a quantitative model of problem solving performance of students in the computer-based mathematics learning environment. Design/methodology/approach: Regularized logistic regression was used to create a quantitative model of problem solving performance of students that predicts whether students can…
Regular Exercise and Depressive Symptoms in Community-Dwelling Elders in Northern Taiwan.

PubMed

Chang, Shu-Hung; Chien, Nai-Hui; Chen, Miao-Chuan

2016-12-01

According to World Health Organization, depressive disorder will be a Top 2 disease in the world by 2020. In light of Taiwan's rapidly increasing elderly population, elderly psychological health is expected to become an increasingly important issue in healthcare. This study examines the association between regular exercise and depressive symptoms in community-dwelling older adults by gender in northern Taiwan. The participants were selected using a probability-proportional-to-size procedure from community-dwelling adults who were aged 65 years or older and living in northern Taiwan. A cross-sectional study and interviews were used to collect information about their exercise behaviors, depressive symptoms, and the factors influencing the depressive symptoms. Percentage, chi-square, t test, and logistic regression were used to analyze the data. One thousand twenty elderly individuals completed the questionnaires. Among the participants with the average age of 73.5 years, 44.5% were men, and 55.5% were women. Two hundred seventeen of the participants (21.3%) had depressive symptoms. Five hundred eighty-five of the participants (57.4%) exercised regularly. The result of logistic regression showed that regular exercise was a significant predictor of depressive symptoms in elderly individuals (odds ratio = 3.54, 95% confidence interval [1.76, 7.12]). Other factors such as gender, chronicle diseases, and health status were not related to depressive symptoms. Moreover, both for male and female individuals, regular exercise was a significant predictor of depressive symptoms (odds ratio = 4.76, 95% confidence interval [1.65, 13.72] and odds ratio = 3.03, 95% confidence interval [1.18, 7.69], respectively). Other factors were not related to depressive symptoms. This study shows regular exercise to be a significant predictor of depressive symptoms in both men and women. Therefore, senior citizens should be encouragedto exercise regularly as a way to promote good mental health.
Associations of family meal frequency with family meal habits and meal preparation characteristics among families of youth with type 1 diabetes

PubMed Central

Kornides, M. L.; Nansel, T. R.; Quick, V.; Haynie, D. L.; Lipsky, L. M.; Laffel, L. M. B.; Mehta, S. N.

2014-01-01

Background While benefits of family mealtimes, such as improved dietary quality and increased family communication, have been well-documented in the general population, less is known about family meal habits that contribute to more frequent family meals in youth with type 1 diabetes. Methods This cross-sectional study surveyed 282 youth ages 8–18 years with type 1 diabetes and their parents on measures regarding diabetes-related and dietary behaviours. T-tests determined significant differences in youth's diet quality, adherence to diabetes management and glycaemic control between those with and without regular family meals (defined as ≥5 meals per week). Logistic regression analyses determined unadjusted and adjusted associations of age, socio-demographics, family meal habits, and family meal preparation characteristics with regular family meals. Results 57% of parents reported having regular family meals. Families with regular family meals had significantly better diet quality as measured by the Healthy Eating Index (P < 0.05) and the NRF9.3 (P < 0.01), and adherence to diabetes management (P < 0.001); the difference in glycaemic control approached statistical significance (P = 0.06). Priority placed on, pleasant atmosphere and greater structure around family meals were each associated with regular family meals (P < 0.05). Meals prepared at home were positively associated with regular family meals, while convenience and fast foods were negatively associated (P < 0.05). Families in which at least one parent worked part-time or stayed at home were significantly more likely to have regular family meals than families in which both parents worked full-time (P < 0.05). In the multivariate logistic regression model, greater parental priority given to family mealtimes (P < 0.001) and more home-prepared meals (P < 0.001) predicted occurrence of regular family meals; adjusting for parent work status and other family meal habits. Conclusions Strategies for promoting families meals should not only highlight the benefits of family meals, but also facilitate parents' skills for and barriers to home-prepared meals. PMID:23731337
Associations of family meal frequency with family meal habits and meal preparation characteristics among families of youth with type 1 diabetes.

PubMed

Kornides, M L; Nansel, T R; Quick, V; Haynie, D L; Lipsky, L M; Laffel, L M B; Mehta, S N

2014-05-01

While benefits of family mealtimes, such as improved dietary quality and increased family communication, have been well-documented in the general population, less is known about family meal habits that contribute to more frequent family meals in youth with type 1 diabetes. This cross-sectional study surveyed 282 youth ages 8-18 years with type 1 diabetes and their parents on measures regarding diabetes-related and dietary behaviours. T-tests determined significant differences in youth's diet quality, adherence to diabetes management and glycaemic control between those with and without regular family meals (defined as ≥ 5 meals per week). Logistic regression analyses determined unadjusted and adjusted associations of age, socio-demographics, family meal habits, and family meal preparation characteristics with regular family meals. 57% of parents reported having regular family meals. Families with regular family meals had significantly better diet quality as measured by the Healthy Eating Index (P < 0.05) and the NRF9.3 (P < 0.01), and adherence to diabetes management (P < 0.001); the difference in glycaemic control approached statistical significance (P = 0.06). Priority placed on, pleasant atmosphere and greater structure around family meals were each associated with regular family meals (P < 0.05). Meals prepared at home were positively associated with regular family meals, while convenience and fast foods were negatively associated (P < 0.05). Families in which at least one parent worked part-time or stayed at home were significantly more likely to have regular family meals than families in which both parents worked full-time (P < 0.05). In the multivariate logistic regression model, greater parental priority given to family mealtimes (P < 0.001) and more home-prepared meals (P < 0.001) predicted occurrence of regular family meals; adjusting for parent work status and other family meal habits. Strategies for promoting families meals should not only highlight the benefits of family meals, but also facilitate parents' skills for and barriers to home-prepared meals. © 2013 John Wiley & Sons Ltd.
Breast cancer screening among shift workers: a nationwide population-based survey in Korea.

PubMed

Son, Heesook; Kang, Youngmi

2017-04-01

We aimed to examine the association between shift work types and participation in breast cancer screening (BCS) programs by comparing rates of participation for BCS among regular daytime workers and alternative shift workers using data from a nationally representative, population-based survey conducted in Korea. In addition, the results were analyzed according to sociodemographic factors, including occupation, education, income, private health insurance, age, and number of working hours a week. This secondary cross-sectional analysis used data from the 2012 Korean National Health and Nutritional Examination Survey. The target population included women aged ≥ 40 years who responded as to whether they had undergone BCS in the previous year. Accordingly, we analyzed survey data for a total of 1,193 women and used a multivariate logistic regression analysis to evaluate the differences in factors affecting BCS between regular daytime and alternative shift workers. A logistic regression analysis was performed considering private health insurance as a significant sociodemographic factor for BCS among regular daytime shift workers. In contrast, none of the tested variables could significantly predict adherence to BCS among alternative shift workers. The results of this study suggest the need for the development of comprehensive workplace breast cancer prevention programs by considering shift work types. More attention should be given to female workers with low education levels, those who are uninsured, and young workers to improve the participation rate for BCS at the workplace.
Family socialization of adolescent's self-reported cigarette use: the role of parents' history of regular smoking and parenting style.

PubMed

Foster, Sarah E; Jones, Deborah J; Olson, Ardis L; Forehand, Rex; Gaffney, Cecelia A; Zens, Michael S; Bau, J J

2007-05-01

To examine the main and interactive effects of parental history of regular cigarette smoking and parenting style on adolescent self-reported cigarette use. Predictors of adolescent self-reported cigarette use, including parents' history of regular cigarette smoking and two dimensions of parenting behavior, were analyzed in a sample of 934 predominately Caucasian (96.3%) parent-adolescent dyads. Families were drawn from the control group of a randomized control trial aimed at preventing adolescent substance use. In addition to the main effects of parents' history of regular smoking and parental warmth, logistic regression analysis revealed that the interaction of these two variables was associated with adolescent self-reported cigarette use. Parental warmth was associated with a decreased likelihood of the adolescent ever having smoked a cigarette; however, this was true only if neither parent had a history of regular cigarette smoking. Findings suggest that adolescent smoking prevention programs may be more efficacious if they address both parental history of regular smoking and parenting behavior.
Factors associated with regular dental visits among hemodialysis patients

PubMed Central

Yoshioka, Masami; Shirayama, Yasuhiko; Imoto, Issei; Hinode, Daisuke; Yanagisawa, Shizuko; Takeuchi, Yuko; Bando, Takashi; Yokota, Narushi

2016-01-01

AIM To investigate awareness and attitudes about preventive dental visits among dialysis patients; to clarify the barriers to visiting the dentist. METHODS Subjects included 141 dentate outpatients receiving hemodialysis treatment at two facilities, one with a dental department and the other without a dental department. We used a structured questionnaire to interview participants about their awareness of oral health management issues for dialysis patients, perceived oral symptoms and attitudes about dental visits. Bivariate analysis using the χ2 test was conducted to determine associations between study variables and regular dental check-ups. Binominal logistic regression analysis was used to determine factors associated with regular dental check-ups. RESULTS There were no significant differences in patient demographics between the two participating facilities, including attitudes about dental visits. Therefore, we included all patients in the following analyses. Few patients (4.3%) had been referred to a dentist by a medical doctor or nurse. Although 80.9% of subjects had a primary dentist, only 34.0% of subjects received regular dental check-ups. The most common reasons cited for not seeking dental care were that visits are burdensome and a lack of perceived need. Patients with gum swelling or bleeding were much more likely to be in the group of those not receiving routine dental check-ups (χ2 test, P < 0.01). Logistic regression analysis demonstrated that receiving dental check-ups was associated with awareness that oral health management is more important for dialysis patients than for others and with having a primary dentist (P < 0.05). CONCLUSION Dialysis patients should be educated about the importance of preventive dental care. Medical providers are expected to participate in promoting dental visits among dialysis patients. PMID:27648409
An investigation into the differentiating characteristics between car key burglars and regular burglars.

PubMed

Chapman, Rachel; Smith, Lisa L; Bond, John W

2012-07-01

Car key burglary has recently become the focus of empirical investigation as offenders, no longer able to steal vehicles without first obtaining their keys, resort to "burgling" target properties. Research surrounding the modus operandi of these offenses is beginning to emerge; however, little attention has been paid to investigating the characteristics of car key burglary offenders. Challenging the assumption that car key burglary offenses are perpetrated by regular burglars, this study aims to differentiate between offenders. Logistic regression analysis of 110 car key and 110 regular burglary offenders revealed that car key burglars are more likely to have previous vehicle theft convictions and are also more likely to be detected on information supplied to the police than regular burglars. Regular burglars are more likely to have previous shoplifting convictions. It was concluded that car key burglars are a distinct sample of offenders and the implications of these findings are discussed. © 2012 American Academy of Forensic Sciences.
Intrinsic motivation factors based on the self-determinant theory for regular breast cancer screening.

PubMed

Jung, Su Mi; Jo, Heui-Sug

2014-01-01

The purpose of this study was to identify factors of intrinsic motivation that affect regular breast cancer screening and contribute to development of a program for strategies to improve effective breast cancer screening. Subjects were residing in South Korea Gangwon-Province and were female over 40 and under 69 years of age. For the investigation, the Intrinsic Motivation Inventory (IMI) was modified to the situation of cancer screening and was used to survey 905 inhabitants. Multinominal logistic regression analyses were conducted for regular breast cancer screening (RS), one-time breast cancer screening (OS) and non-breast cancer screening (NS). For statistical analysis, IBM SPSS 20.0 was utilized. The determinant factors between RS and NS were "perceived effort and choice" and "stress and strain" - internal motivations related to regular breast cancer screening. Also, determinant factors between RS and OS are "age" and "perceived effort and choice" for internal motivation related to cancer screening. To increase regular screening, strategies that address individual perceived effort and choice are recommended.
Health-related behaviors associated with subjective sleep insufficiency in Japanese workers: A cross-sectional study.

PubMed

Kageyama, Makoto; Odagiri, Keiichi; Mizuta, Isagi; Yamamoto, Makoto; Yamaga, Keiko; Hirano, Takako; Onoue, Kazue; Uehara, Akihiko

2017-03-28

Sleep disturbances are related to somatic and mental disorders, industrial accidents, absenteeism, and retirement because of disability. We aimed to identify health-related behaviors associated with subjective sleep insufficiency in Japanese workers. This cross-sectional study included 5,297 employees (mean age: 43.6±11.3 years; 4,039 men). Multiple logistic regression analysis was used to identify health-related behaviors associated with subjective sleep insufficiency. Overall, 28.2% of participants experienced subjective sleep insufficiency. There was a significant difference between the genders in the proportion of participants with subjective sleep insufficiency (male: 26.4%; female: 34.3%; p<0.001). Multiple logistic regression analysis revealed that being a female or ≥40 years, experiencing a weight change of ≥3 kg during the preceding year, not exercising regularly, not walking quickly, and eating a late-evening or fourth meal were associated with subjective sleep insufficiency. After stratifying by gender, age ≥40 years, not exercising regularly, and eating a late-evening or fourth meal were significantly associated with subjective sleep insufficiency in both genders. Not walking quickly, experiencing a weight change, and eating quickly were positively associated with subjective sleep insufficiency only for males. Females who did not engage in physical activity were more likely to have experienced subjective sleep insufficiency, but this relationship was not observed in males. The results indicated that certain health-related behaviors, specifically not exercising regularly and nocturnal eating habits, were associated with subjective sleep insufficiency in a group of Japanese workers.
Attitude and practice of physical activity and social problem-solving ability among university students.

PubMed

Sone, Toshimasa; Kawachi, Yousuke; Abe, Chihiro; Otomo, Yuki; Sung, Yul-Wan; Ogawa, Seiji

2017-04-04

Effective social problem-solving abilities can contribute to decreased risk of poor mental health. In addition, physical activity has a favorable effect on mental health. These previous studies suggest that physical activity and social problem-solving ability can interact by helping to sustain mental health. The present study aimed to determine the association between attitude and practice of physical activity and social problem-solving ability among university students. Information on physical activity and social problem-solving was collected using a self-administered questionnaire. We analyzed data from 185 students who participated in the questionnaire surveys and psychological tests. Social problem-solving as measured by the Social Problem-Solving Inventory-Revised (SPSI-R) (median score 10.85) was the dependent variable. Multiple logistic regression analysis was employed to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) for higher SPSI-R according to physical activity categories. The multiple logistic regression analysis indicated that the ORs (95% CI) in reference to participants who said they never considered exercising were 2.08 (0.69-6.93), 1.62 (0.55-5.26), 2.78 (0.86-9.77), and 6.23 (1.81-23.97) for participants who did not exercise but intended to start, tried to exercise but did not, exercised but not regularly, and exercised regularly, respectively. This finding suggested that positive linear association between physical activity and social problem-solving ability (p value for linear trend < 0.01). The present findings suggest that regular physical activity or intention to start physical activity may be an effective strategy to improve social problem-solving ability.
Learning investment indicators through data extension

NASA Astrophysics Data System (ADS)

Dvořák, Marek

2017-07-01

Stock prices in the form of time series were analysed using single and multivariate statistical methods. After simple data preprocessing in the form of logarithmic differences, we augmented this single variate time series to a multivariate representation. This method makes use of sliding windows to calculate several dozen of new variables using simple statistic tools like first and second moments as well as more complicated statistic, like auto-regression coefficients and residual analysis, followed by an optional quadratic transformation that was further used for data extension. These were used as a explanatory variables in a regularized logistic LASSO regression which tried to estimate Buy-Sell Index (BSI) from real stock market data.
Associations between dental knowledge, source of dental knowledge and oral health behavior in Japanese university students: A cross-sectional study

PubMed Central

Taniguchi-Tabata, Ayano; Mizutani, Shinsuke; Yamane-Takeuchi, Mayu; Kataoka, Kota; Azuma, Tetsuji; Tomofuji, Takaaki; Iwasaki, Yoshiaki; Morita, Manabu

2017-01-01

The aim of this study was to investigate the associations between dental knowledge, the source of dental knowledge and oral health behavior in a group of students at a university in Japan. A total of 2,220 university students (1,276 males, 944 females) volunteered to undergo an oral examination and answer a questionnaire. The questionnaire assessed dental knowledge, the source of dental knowledge and oral health behavior (e.g., daily frequency of tooth brushing, use of dental floss and regular dental checkups). The odds ratio and 95% confidence interval for oral health behavior based on dental knowledge and source of dental knowledge were calculated using logistic regression models. Of the participants, 1,266 (57.0%) students obtained dental knowledge from dental clinics, followed by school (39.2%) and television (29.1%). Logistic regression analyses indicated that use of dental floss was significantly associated with source of dental knowledge from dental clinics (P = 0.006). Receiving regular dental checkups was significantly associated with source of dental knowledge; the positive source was dental clinic (P < 0.001) and the negative sources were school (P = 0.004) and television (P = 0.018). Dental clinic was the most common source of dental knowledge and associated with better oral health behavior among the Japanese university students in this study. PMID:28594914
Impact of low vision on employment.

PubMed

Mojon-Azzi, Stefania M; Sousa-Poza, Alfonso; Mojon, Daniel S

2010-01-01

We investigated the influence of self-reported corrected eyesight on several variables describing the perception by employees and self-employed persons of their employment. Our study was based on data from the Survey of Health, Ageing and Retirement in Europe (SHARE). SHARE is a multidisciplinary, cross-national database of microdata on health, socioeconomic status, social and family networks, collected on 31,115 individuals in 11 European countries and in Israel. With the help of ordered logistic regressions and binary logistic regressions, we analyzed the influence of perceived visual impairment--corrected by 19 covariates capturing socioeconomic and health-related factors--on 10 variables describing the respondents' employment situation. Based on data covering 10,340 working individuals, the results of the logistic and ordered regressions indicate that respondents with lower levels of self-reported general eyesight were significantly less satisfied with their jobs, felt they had less freedom to decide, less opportunity to develop new skills, less support in difficult situations, less recognition for their work, and an inadequate salary. Respondents with a lower eyesight level more frequently reported that they feared their health might limit their ability to work before regular retirement age and more often indicated that they were seeking early retirement. Analysis of this dataset from 12 countries demonstrates the strong impact of self-reported visual impairment on individual employment, and therefore on job satisfaction, productivity, and well-being. Copyright © 2010 S. Karger AG, Basel.
Microaggressions and marijuana use among college students.

PubMed

Pro, George; Sahker, Ethan; Marzell, Miesha

2017-03-09

This study examines the association between exposure to microaggressions and marijuana use, using original survey data from a sample of racial/ethnic minority college students (n = 332) from a large Division I university in the United States. Nearly all of our sample (96%) reported at least one experience with microaggressions in the past 6 months, while 33% reported using marijuana regularly. We modeled regular use of marijuana using multiple logistic regression, with consideration of sex, age, race/ethnicity, and microaggression scale scores as covariates. Age, sex, the microinvalidations subscale score, and the full microaggression scale score were significantly associated with marijuana use in our full models (p < .01; p = .01; p = .02; p = .03, respectively). With each additional experience of microaggression, the odds of regular marijuana use increase. Academic communities may consider the primary prevention of discriminatory behavior when addressing student substance use.
Physical activity behaviour in men with inflammatory joint disease: a cross-sectional register-based study.

PubMed

Hammer, Nanna Maria; Midtgaard, Julie; Hetland, Merete Lund; Krogh, Niels Steen; Esbensen, Bente Appel

2018-05-01

Physical activity is recommended as an essential part of the non-pharmacological management of inflammatory joint disease, but previous research in this area has predominantly included women. The aim of this study was to examine physical activity behaviour in men with inflammatory joint disease. The study was conducted as a cross-sectional register-based study. Data on physical activity behaviour in men with RA, PsA and AS were matched with sociodemographic and clinical variables extracted from the DANBIO registry. Logistic regression analyses using multiple imputations were performed to investigate demographic and clinical variables associated with regular engagement in physical activity (moderate-vigorous ⩾2 h/week). Descriptive statistics were applied to explore motivation, barriers and preferences for physical activity. A total of 325 men were included of whom 129 (40%) engaged in regular physical activity. In univariate analyses, higher age, visual analogue scale (VAS) for pain, VAS fatigue, VAS patient's global, CRP level, disease activity, functional disability and current smoking were negatively associated with regular engagement in physical activity. In the final multivariable regression model only a high VAS fatigue score (⩾61 mm) (OR = 0.228; CI: 0.119, 0.436) remained significantly independently associated with regular physical activity. A majority of men with inflammatory joint disease do not meet the recommendations of regular physical activity. Both sociodemographic and clinical parameters were associated with engagement in physical activity, and fatigue especially seems to play a pivotal role in explaining suboptimal physical activity behaviour in this patient group.
Socioeconomic Disadvantage and Other Risk Factors for Using Higher-Nicotine/Tar-Yield (Regular Full-Flavor) Cigarettes.

PubMed

Higgins, Stephen T; Redner, Ryan; Priest, Jeff S; Bunn, Janice Y

2017-11-07

Use of machine-estimated higher nicotine/tar yield (regular full-flavor) cigarettes is associated with increased risk of nicotine dependence. The present study examined risk factors for using full-flavor versus other cigarette types, including socioeconomic disadvantage and other risk factors for tobacco use or tobacco-related adverse health impacts. Associations between use of full-flavor cigarettes and risk of nicotine dependence were also examined. Data were obtained from nationally representative samples of adult cigarette smokers from the US National Survey on Drug Use and Health. Logistic regression and classification and regression tree modeling were used to examine associations between use of full-flavor cigarettes and educational attainment, poverty, race/ethnicity, age, sex, mental illness, alcohol abuse/dependence, and illicit drug abuse/dependence. Logistic regression was used to examine risk for nicotine dependence. Each of these risk factors except alcohol abuse/dependence independently predicted increased odds of using full-flavor cigarettes (p < .001), with lower educational attainment the strongest predictor, followed by poverty, male sex, younger age, minority race/ethnicity, mental illness, and drug abuse/dependence, respectively. Use of full-flavor cigarettes was associated with increased odds of nicotine dependence within each of these risk factor groupings (p < .01). Cart modeling identified how prevalence of full-flavor cigarette use can vary from a low of 25% to a high of 66% corresponding to differing combinations of these independent risk factors. Use of full-flavor cigarettes is overrepresented in socioeconomically disadvantaged and other vulnerable populations, and associated with increased risk of nicotine dependence. Greater regulation of this cigarette type may be warranted. Greater regulation of commercially available Regular Full-Flavor Cigarettes may be warranted. Use of this type of cigarette is overrepresented in socioeconomically disadvantaged and other vulnerable populations and associated with increased risk for nicotine dependence. © The Author 2016. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
[A case-control study: association between oral hygiene and oral cancer in non-smoking and non-drinking women].

PubMed

Wu, J F; Lin, L S; Chen, F; Liu, F Q; Huang, J F; Yan, L J; Liu, F P; Qiu, Y; Zheng, X Y; Cai, L; He, B C

2017-08-06

Objective: To evaluate the influence of oral hygiene on risk of oral cancer in non-smoking and non-drinking women. Methods: From September 2010 to February 2016, 242 non-smoking and non-drinking female patients with pathologically confirmed oral cancer were recruited in a hospital of Fuzhou, and another 856 non-smoking and non-drinking healthy women from health examination center in the same hospital were selected as control group. Five oral hygiene related variables including the frequency of teeth brushing, number of teeth lost, poor prosthesis, regular dental visits and recurrent dental ulceration were used to develop oral hygiene index model. Unconditional logistic regression was used to calculate odds ratios ( OR ) and 95% confidence intervals (95 %CI ). The area under the receiver operating characteristic curve (AUROC) was used to evaluate the predictability of the oral hygiene index model. Multivariate logistic regression model was used to analyze the association between oral hygiene index and the incidence of oral cancer. Results: Teeth brushing <2 twice daily, teeth lost ≥5, poor prosthesis, no regular dental visits, recurrent dental ulceration were risk factors for the incidence of oral cancer in non-smoking and non-drinking women, the corresponding OR (95 %CI ) were 1.50 (1.08-2.09), 1.81 (1.15-2.85), 1.51 (1.03-2.23), 1.73 (1.15-2.59), 7.30 (4.00-13.30), respectively. The AUROC of the oral hygiene index model was 0.705 9, indicating a high predictability. Multivariate logistic regression showed that the oral hygiene index was associated with risk of oral cancer. The higher the score, the higher risk was observed. The corresponding OR (95 %CI ) of oral hygiene index scores (score 1, score 2, score 3, score 4-5) were 2.51 (0.84-7.53), 4.68 (1.59-13.71), 6.47 (2.18-19.25), 15.29 (5.08-45.99), respectively. Conclusion: Oral hygiene could influence the incidence of oral cancer in non-smoking and non-drinking women, and oral hygiene index has a certain significance in assessing the combined effects of oral hygiene.
Correlates of regular exercise during pregnancy: the Norwegian Mother and Child Cohort Study.

PubMed

Owe, K M; Nystad, W; Bø, K

2009-10-01

The aims of this study were to describe the level of exercise during pregnancy and to assess factors associated with regular exercise. Using data from the Norwegian Mother and Child Cohort Study conducted by the Norwegian Institute of Public Health, 34 508 pregnancies were included in the present study. Data were collected by self-completed questionnaires in gestational weeks 17 and 30, and analyzed by logistic regression analysis. The results are presented as adjusted odds ratios (aOR) with a 95% confidence interval. The proportion of women exercising regularly was 46.4% before pregnancy and decreased to 28.0 and 20.4% in weeks 17 and 30, respectively. Walking and bicycling were the most frequently reported activities before and during pregnancy. The prevalence of swimming tended to increase from prepregnancy to week 30. Exercising regularly prepregnancy was highly related to regular exercise in week 17, aOR=18.4 (17.1-19.7) and 30, aOR 4.3 (4.1-4.6). Low gestational weight gain was positively associated with regular exercise in week 30, aOR=1.2 (1.1-1.4), whereas being overweight before pregnancy was inversely associated with regular exercise in week 17, aOR=0.8 (0.7-0.8) and 30, aOR=0.7 (0.6-0.7). Also, women experiencing a multiple pregnancy, pelvic girdle pain, or nausea were less likely to exercise regularly.
[Health behaviors by job stress level in large-sized company with male and female workers].

PubMed

Park, Hyunju; Jung, Hye-Sun

2010-12-01

This study was done to investigate differences in health behaviors by job stress level in male and female workers in a large-sized company. Participants were 576 male and 228 female workers who completed questionnaires. Job stress was measured using the 'Short Form Korean Occupational Stress Scale (SF-KOSS)'. Health behaviors included smoking, alcohol consumption, regular exercise, and diet. Frequency, mean, SD, chi-square test, and multivariate logistic regression using SAS version 9.1 were used to analyze data. Smoking, drinking and regular exercise rates were not different by job stress level in male or female workers. Only regular diet was significantly different by job stress level in male and female workers. From multivariate analysis, the alcohol consumption rates for female workers differed by marital status. Regular exercise rate was significantly related to age for male workers and type of employment for female workers. After adjusting for demographic and work-related characteristics, regular diet significantly differed by shift work for male workers and marital status and shift work for female workers. The findings of the study indicate that nursing interventions should be developed to manage job stress to improve diet habits for male and female workers in large-sized companies.

Associations between yoga practice and joint problems: a cross-sectional survey among 9151 Australian women.

PubMed

Lauche, Romy; Schumann, Dania; Sibbritt, David; Adams, Jon; Cramer, Holger

2017-07-01

Yoga exercises have been associated with joint problems recently, indicating that yoga practice might be potentially dangerous for joint health. This study aimed to analyse whether regular yoga practice is associated with the frequency of joint problems in upper middle-aged Australian women. Women aged 62-67 years from the Australian Longitudinal Study on Women's Health (ALSWH) were questioned in 2013 whether they experienced regular joint pain or problems in the past 12 months and whether they regularly practiced yoga. Associations of joint problems with yoga practice were analysed using Chi-squared tests and multiple logistic regression modelling. Of 9151 women, 29.8% reported regular problems with stiff or painful joints, and 15.2, 11.9, 18.1 and 15.9% reported regular problems with shoulders, hips, knees and feet, respectively, in the past 12 months. Yoga was practiced sometimes by 10.1% and often by 8.4% of women. Practicing yoga was not associated with upper or lower limb joint problems. No association between yoga practice and joint problems has been identified. Further studies are warranted for conclusive judgement of benefits and safety of yoga in relation to joint problems.
[Associations between dormitory environment/other factors and sleep quality of medical students].

PubMed

Zheng, Bang; Wang, Kailu; Pan, Ziqi; Li, Man; Pan, Yuting; Liu, Ting; Xu, Dan; Lyu, Jun

2016-03-01

To investigate the sleep quality and related factors among medical students in China, understand the association between dormitory environment and sleep quality, and provide evidence and recommendations for sleep hygiene intervention. A total of 555 undergraduate students were selected from a medical school of an university in Beijing through stratified-cluster random-sampling to conduct a questionnaire survey by using Chinese version of Pittsburgh Sleep Quality Index (PSQI) and self-designed questionnaire. Analyses were performed by using multiple logistic regression model as well as multilevel linear regression model. The prevalence of sleep disorder was 29.1%(149/512), and 39.1%(200/512) of the students reported that the sleep quality was influenced by dormitory environment. PSQI score was negatively correlated with self-reported rating of dormitory environment (γs=-0.310, P<0.001). Logistic regression analysis showed the related factors of sleep disorder included grade, sleep regularity, self-rated health status, pressures of school work and employment, as well as dormitory environment. RESULTS of multilevel regression analysis also indicated that perception on dormitory environment (individual level) was associated with sleep quality with the dormitory level random effects under control (b=-0.619, P<0.001). The prevalence of sleep disorder was high in medical students, which was associated with multiple factors. Dormitory environment should be taken into consideration when the interventions are taken to improve the sleep quality of students.
The association between second-hand smoke exposure and depressive symptoms among pregnant women.

PubMed

Huang, Jingya; Wen, Guoming; Yang, Weikang; Yao, Zhenjiang; Wu, Chuan'an; Ye, Xiaohua

2017-10-01

Tobacco smoking and depression are strongly associated, but the possible association between second-hand smoke (SHS) exposure and depression is unclear. This study aimed to examine the possible relation between SHS exposure and depressive symptoms among pregnant women. A cross-sectional survey was conducted in Shenzhen, China, using a multistage sampling method. The univariable and multivariable logistic regression models were used to explore the associations between SHS exposure and depressive symptoms. Among 2176 pregnant women, 10.5% and 2.0% were classified as having probable and severe depressive symptoms. Both binary and multinomial logistic regression revealed that there were significantly increased risks of severe depressive symptoms corresponding to SHS exposure in homes or regular SHS exposure in workplaces using no exposure as reference. In addition, greater frequency of SHS exposure was significantly associated with the increased risk of severe depressive symptoms. Our findings suggest that SHS exposure is positively associated with depressive symptoms in a dose-response manner among the pregnant women. Copyright © 2017 Elsevier B.V. All rights reserved.
[The effect of self-foot reflexology on the relief of premenstrual syndrome and dysmenorrhea in high school girls].

PubMed

Kim, Yi-Soon; Kim, Min-Za; Jeong, Ihn-Sook

2004-08-01

This study was aimed to identify the effect of self-foot reflexology on the relief of premenstrual syndrome and dysmenorrhea in high school girls. Study subjects was 236 women residing in the community, teachers and nurses who were older than 45 were recruited. Data was collected with self administered questionnaires from July 1st to August 31st, 2003 and analysed using SPSS/WIN 10.0 with Xtest, t-test, and stepwise multiple logistic regression at a significant level of =.05. The breast cancer screening rate was 57.2%, and repeat screening rate was 15.3%. With the multiple logistic regression analysis, factors associated with mammography screening were age and perceived barriers of action, and factors related to the repeat mammography screening were education level and other cancer screening experience. Based on the results, we recommend the development of an intervention program to decrease the perceived barrier of action, to regard mammography as an essential test in regular check-up, and to give active advertisement and education to the public to improve the rates of breast cancer screening and repeat screening.
Encrypted data stream identification using randomness sparse representation and fuzzy Gaussian mixture model

NASA Astrophysics Data System (ADS)

Zhang, Hong; Hou, Rui; Yi, Lei; Meng, Juan; Pan, Zhisong; Zhou, Yuhuan

2016-07-01

The accurate identification of encrypted data stream helps to regulate illegal data, detect network attacks and protect users' information. In this paper, a novel encrypted data stream identification algorithm is introduced. The proposed method is based on randomness characteristics of encrypted data stream. We use a l1-norm regularized logistic regression to improve sparse representation of randomness features and Fuzzy Gaussian Mixture Model (FGMM) to improve identification accuracy. Experimental results demonstrate that the method can be adopted as an effective technique for encrypted data stream identification.
[Sedentary leisure time and food consumption among Brazilian adolescents: the Brazilian National School-Based Adolescent Health Survey (PeNSE), 2009].

PubMed

Camelo, Lidyane do Valle; Rodrigues, Jôsi Fernandes de Castro; Giatti, Luana; Barreto, Sandhi Maria

2012-11-01

The objective of this paper was to investigate whether sedentary leisure time was associated with increased regular consumption of unhealthy foods, independently of socio-demographic indicators and family context. The analysis included 59,809 students from the Brazilian National School-Based Adolescent Health Survey (PeNSE) in 2009. The response variable was sedentary leisure time, defined as watching more than two hours of TV daily. The target explanatory variables were regular consumption of soft drinks, sweets, cookies, and processed meat. Odds ratios (OR) and 95% confidence limits (95%CI) were obtained by multiple logistic regression. Prevalence of sedentary leisure time was 65%. Regular consumption of unhealthy foods was statistically higher among students reporting sedentary leisure time, before and after adjusting for sex, age, skin color, school administration (public versus private), household assets index, and household composition. The results indicate the need for integrated interventions to promote healthy leisure-time activities and healthy eating habits among young people.
Predictors of regular cigarette smoking among adolescent females: Does body image matter?

PubMed Central

Kaufman, Annette R.; Augustson, Erik M.

2013-01-01

This study examined how factors associated with body image predict regular smoking in adolescent females. Data were from the National Longitudinal Study of Adolescent Health (Add Health), a study of health-related behaviors in a nationally representative sample of adolescents in grades 7 through 12. Females in Waves I and II (n=6,956) were used for this study. Using SUDAAN to adjust for the sampling frame, univariate and multivariate analyses were performed to investigate if baseline body image factors, including perceived weight, perceived physical development, trying to lose weight, and self-esteem, were predictive of regular smoking status 1 year later. In univariate analyses, perceived weight (p<.01), perceived physical development (p<.0001), trying to lose weight (p<.05), and self-esteem (p<.0001) significantly predicted regular smoking 1 year later. In the logistic regression model, perceived physical development (p<.05), and self-esteem (p<.001) significantly predicted regular smoking. The more developed a female reported being in comparison to other females her age, the more likely she was to be a regular smoker. Lower self-esteem was predictive of regular smoking. Perceived weight and trying to lose weight failed to reach statistical significance in the multivariate model. This current study highlights the importance of perceived physical development and self-esteem when predicting regular smoking in adolescent females. Efforts to promote positive self-esteem in young females may be an important strategy when creating interventions to reduce regular cigarette smoking. PMID:18686177
The effect of high leverage points on the logistic ridge regression estimator having multicollinearity

NASA Astrophysics Data System (ADS)

Ariffin, Syaiba Balqish; Midi, Habshah

2014-06-01

This article is concerned with the performance of logistic ridge regression estimation technique in the presence of multicollinearity and high leverage points. In logistic regression, multicollinearity exists among predictors and in the information matrix. The maximum likelihood estimator suffers a huge setback in the presence of multicollinearity which cause regression estimates to have unduly large standard errors. To remedy this problem, a logistic ridge regression estimator is put forward. It is evident that the logistic ridge regression estimator outperforms the maximum likelihood approach for handling multicollinearity. The effect of high leverage points are then investigated on the performance of the logistic ridge regression estimator through real data set and simulation study. The findings signify that logistic ridge regression estimator fails to provide better parameter estimates in the presence of both high leverage points and multicollinearity.
Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression.

PubMed

Jovanovic, Milos; Radovanovic, Sandro; Vukicevic, Milan; Van Poucke, Sven; Delibasic, Boris

2016-09-01

Quantification and early identification of unplanned readmission risk have the potential to improve the quality of care during hospitalization and after discharge. However, high dimensionality, sparsity, and class imbalance of electronic health data and the complexity of risk quantification, challenge the development of accurate predictive models. Predictive models require a certain level of interpretability in order to be applicable in real settings and create actionable insights. This paper aims to develop accurate and interpretable predictive models for readmission in a general pediatric patient population, by integrating a data-driven model (sparse logistic regression) and domain knowledge based on the international classification of diseases 9th-revision clinical modification (ICD-9-CM) hierarchy of diseases. Additionally, we propose a way to quantify the interpretability of a model and inspect the stability of alternative solutions. The analysis was conducted on >66,000 pediatric hospital discharge records from California, State Inpatient Databases, Healthcare Cost and Utilization Project between 2009 and 2011. We incorporated domain knowledge based on the ICD-9-CM hierarchy in a data driven, Tree-Lasso regularized logistic regression model, providing the framework for model interpretation. This approach was compared with traditional Lasso logistic regression resulting in models that are easier to interpret by fewer high-level diagnoses, with comparable prediction accuracy. The results revealed that the use of a Tree-Lasso model was as competitive in terms of accuracy (measured by area under the receiver operating characteristic curve-AUC) as the traditional Lasso logistic regression, but integration with the ICD-9-CM hierarchy of diseases provided more interpretable models in terms of high-level diagnoses. Additionally, interpretations of models are in accordance with existing medical understanding of pediatric readmission. Best performing models have similar performances reaching AUC values 0.783 and 0.779 for traditional Lasso and Tree-Lasso, respectfully. However, information loss of Lasso models is 0.35 bits higher compared to Tree-Lasso model. We propose a method for building predictive models applicable for the detection of readmission risk based on Electronic Health records. Integration of domain knowledge (in the form of ICD-9-CM taxonomy) and a data-driven, sparse predictive algorithm (Tree-Lasso Logistic Regression) resulted in an increase of interpretability of the resulting model. The models are interpreted for the readmission prediction problem in general pediatric population in California, as well as several important subpopulations, and the interpretations of models comply with existing medical understanding of pediatric readmission. Finally, quantitative assessment of the interpretability of the models is given, that is beyond simple counts of selected low-level features. Copyright © 2016 Elsevier B.V. All rights reserved.
Can use of healthcare services among 15-16-year-olds predict an increased level of high school dropout? A longitudinal community study.

PubMed

Homlong, Lisbeth; Rosvold, Elin O; Haavet, Ole R

2013-09-19

To study associations between healthcare seeking in 15-16-year-olds and high school dropout 5 years later. Longitudinal community study. Data from a comprehensive youth health survey conducted in 2000-2004, linked to data from national registries up to 2010. 13 964 10th grade secondary school students in six Norwegian counties. Logistic regression was used to compute ORs for high school dropout. The total proportion of students not completing high school 5 years after registering was 29% (girls 24%, boys 34%). Frequent attenders to school health services and youth health clinics at age 15-16 years had a higher dropout rate (37/48% and 45/71%), compared with those with no or moderate use. Adolescents referred to mental health services were also more likely to drop out (47/62%). Boys with moderate use of a general practitioner (GP) had a lower dropout rate (30%). A multiple logistic regression analysis, in which we adjusted for selected health indicators and sociodemographic background variables, revealed that seeking help from the youth health clinic and consulting mental health services, were associated with increased level of high school dropout 5 years later. Frequent attenders (≥4 contacts) had the highest odds of dropping out. Yet, boys who saw a GP and girls attending the school health services regularly over the previous year were less likely than their peers to drop out from high school. Adolescents who seek help at certain healthcare services can be at risk of dropping out of high school later. Health workers should pay particular attention to frequent attenders and offer follow-up when needed. However, boys who attended a GP regularly were more likely to continue to high school graduation, which may indicate a protective effect of having a regular and stable relationship with a GP.
Associations between food insecurity and healthy behaviors among Korean adults

PubMed Central

Chun, In-Ae; Park, Jong; Ro, Hee-Kyung; Han, Mi-Ah

2015-01-01

BACKGROUND/OBJECTIVES Food insecurity has been suggested as being negatively associated with healthy behaviors and health status. This study was performed to identify the associations between food insecurity and healthy behaviors among Korean adults. SUBJECTS/METHODS The data used were the 2011 Community Health Survey, cross-sectional representative samples of 253 communities in Korea. Food insecurity was defined as when participants reported that their family sometimes or often did not get enough food to eat in the past year. Healthy behaviors were considered as non-smoking, non-high risk drinking, participation in physical activities, eating a regular breakfast, and maintaining a normal weight. Multiple logistic regression and multinomial logistic regression analyses were used to identify the association between food insecurity and healthy behaviors. RESULTS The prevalence of food insecurity was 4.4% (men 3.9%, women 4.9%). Men with food insecurity had lower odds ratios (ORs) for non-smoking, 0.75 (95% CI: 0.68-0.82), participation in physical activities, 0.82 (95% CI: 0.76-0.90), and eating a regular breakfast, 0.66 (95% CI: 0.59-0.74), whereas they had a higher OR for maintaining a normal weight, 1.19 (95% CI: 1.09-1.30), than men with food security. Women with food insecurity had lower ORs for non-smoking, 0.77 (95% CI: 0.66-0.89), and eating a regular breakfast, 0.79 (95% CI: 0.72-0.88). For men, ORs for obesity were 0.78 (95% CI: 0.70-0.87) for overweight and 0.56 (95% CI: 0.39-0.82) for mild obesity. For women, the OR for moderate obesity was 2.04 (95% CI: 1.14-3.63) as compared with normal weight. CONCLUSIONS Food insecurity has a different impact on healthy behaviors. Provision of coping strategies for food insecurity might be critical to improve healthy behaviors among the population. PMID:26244083
Can a Toy Encourage Lower Calorie Meal Bundle Selection in Children? A Field Experiment on the Reinforcing Effects of Toys on Food Choice.

PubMed

Reimann, Martin; Lane, Kristen

2017-01-01

The goal of this research was to test whether including an inexpensive nonfood item (toy) with a smaller-sized meal bundle (420 calories), but not with the regular-sized meal bundle version (580 calories), would incentivize children to choose the smaller-sized meal bundle, even among children with overweight and obesity. Logistic regression was used to evaluate the effect in a between-subjects field experiment of a toy on smaller-sized meal choice (here, a binary choice between a smaller-sized or regular-sized meal bundles). A random sample of 109 elementary school children from two schools in the Tucson, Arizona metropolitan area (55 females; Mage = 8.53 years, SDage = 2.14; MBMI = 18.30, SDBMI = 4.42) participated. Children's height and weight were measured and body-mass-index (BMI) was calculated, adjusting for age and sex. In our sample, 21 children were considered to be either overweight or obese. Logistic regression was used to evaluate the effect of a toy on smaller-sized meal choice. Results revealed that the inclusion of a toy with a smaller-sized meal, but not with the regular-sized version, predicted smaller-sized meal choice (P < .001), suggesting that children can be incentivized to choose less food when such is paired with a toy. BMI neither moderated nor nullified the effect of toy on smaller-sized meal choice (P = .125), suggesting that children with overweight and obesity can also be incentivized to choose less. This article is the first to suggest that fast-food restaurant chains may well utilize toys to motivate children to choose smaller-sized meal bundles. Our findings may be relevant for consumers, health advocates, policy makers, and marketers who would benefit from a strategy that presents healthier, but still desirable, meal bundle options.
Can a Toy Encourage Lower Calorie Meal Bundle Selection in Children? A Field Experiment on the Reinforcing Effects of Toys on Food Choice

PubMed Central

2017-01-01

The goal of this research was to test whether including an inexpensive nonfood item (toy) with a smaller-sized meal bundle (420 calories), but not with the regular-sized meal bundle version (580 calories), would incentivize children to choose the smaller-sized meal bundle, even among children with overweight and obesity. Logistic regression was used to evaluate the effect in a between-subjects field experiment of a toy on smaller-sized meal choice (here, a binary choice between a smaller-sized or regular-sized meal bundles). A random sample of 109 elementary school children from two schools in the Tucson, Arizona metropolitan area (55 females; Mage = 8.53 years, SDage = 2.14; MBMI = 18.30, SDBMI = 4.42) participated. Children’s height and weight were measured and body-mass-index (BMI) was calculated, adjusting for age and sex. In our sample, 21 children were considered to be either overweight or obese. Logistic regression was used to evaluate the effect of a toy on smaller-sized meal choice. Results revealed that the inclusion of a toy with a smaller-sized meal, but not with the regular-sized version, predicted smaller-sized meal choice (P < .001), suggesting that children can be incentivized to choose less food when such is paired with a toy. BMI neither moderated nor nullified the effect of toy on smaller-sized meal choice (P = .125), suggesting that children with overweight and obesity can also be incentivized to choose less. This article is the first to suggest that fast-food restaurant chains may well utilize toys to motivate children to choose smaller-sized meal bundles. Our findings may be relevant for consumers, health advocates, policy makers, and marketers who would benefit from a strategy that presents healthier, but still desirable, meal bundle options. PMID:28085904
Sample size determination for logistic regression on a logit-normal distribution.

PubMed

Kim, Seongho; Heath, Elisabeth; Heilbrun, Lance

2017-06-01

Although the sample size for simple logistic regression can be readily determined using currently available methods, the sample size calculation for multiple logistic regression requires some additional information, such as the coefficient of determination ([Formula: see text]) of a covariate of interest with other covariates, which is often unavailable in practice. The response variable of logistic regression follows a logit-normal distribution which can be generated from a logistic transformation of a normal distribution. Using this property of logistic regression, we propose new methods of determining the sample size for simple and multiple logistic regressions using a normal transformation of outcome measures. Simulation studies and a motivating example show several advantages of the proposed methods over the existing methods: (i) no need for [Formula: see text] for multiple logistic regression, (ii) available interim or group-sequential designs, and (iii) much smaller required sample size.
A comparison of Cox and logistic regression for use in genome-wide association studies of cohort and case-cohort design.

PubMed

Staley, James R; Jones, Edmund; Kaptoge, Stephen; Butterworth, Adam S; Sweeting, Michael J; Wood, Angela M; Howson, Joanna M M

2017-06-01

Logistic regression is often used instead of Cox regression to analyse genome-wide association studies (GWAS) of single-nucleotide polymorphisms (SNPs) and disease outcomes with cohort and case-cohort designs, as it is less computationally expensive. Although Cox and logistic regression models have been compared previously in cohort studies, this work does not completely cover the GWAS setting nor extend to the case-cohort study design. Here, we evaluated Cox and logistic regression applied to cohort and case-cohort genetic association studies using simulated data and genetic data from the EPIC-CVD study. In the cohort setting, there was a modest improvement in power to detect SNP-disease associations using Cox regression compared with logistic regression, which increased as the disease incidence increased. In contrast, logistic regression had more power than (Prentice weighted) Cox regression in the case-cohort setting. Logistic regression yielded inflated effect estimates (assuming the hazard ratio is the underlying measure of association) for both study designs, especially for SNPs with greater effect on disease. Given logistic regression is substantially more computationally efficient than Cox regression in both settings, we propose a two-step approach to GWAS in cohort and case-cohort studies. First to analyse all SNPs with logistic regression to identify associated variants below a pre-defined P-value threshold, and second to fit Cox regression (appropriately weighted in case-cohort studies) to those identified SNPs to ensure accurate estimation of association with disease.
[Risk factors for the kidney stones: a hospital-based case-control study in a distric hospital in Beijing].

PubMed

Wang, Jiao; Luo, Gong-tang; Niu, Wei-jing; Gong, Man-man; Liu, Lu; Zhou, Jie; Zhou, Xue-wei; He, Li-hua

2013-12-18

To explore the risk and protective factors of kidney calculi in order to put forward theoretical basis for preventive and control measures. A 1:1 matched case-control study was performed using data from a hospital in Beijing. The case group included 100 inpatients who were diagnosed kidney calculi using B ultrasonic, X-ray and intravenous pyelography during the survey while other 100 urolithiasis and endocrine disease excluded inpatients who shared the same sex, within five years gap to the case group inpatients were for the control group. A face-to-face survey was conducted with self-made questionnaires which covered demographic characteristics, water issues, dietary habits, genetic and medical history. Epidata 3.0 was used to build the database and SPSS 19.0 for the statistical analysis. In the univariate Logistic regression analysis, ten variables were found showing statistical significance. For the multivariate Logistic regression analysis, variables left in the model were labor intensity (OR=0.622, 95%CI: 0.435-0.889), preferring to drink after dinner (OR=0.316, 95%CI: 0.122-0.815), loving drinking (OR=0.232, 95%CI: 0.084-0.642), drinking tea regularly (OR=1.463, 95%CI: 1.033-2.071), eating more vegetables (OR=0.571, 95%CI: 0.328-0.993), the history of the urolithiasis (OR=2.127, 95%CI: 1.065-90.145). Drinking tea regularly, urolithiasis history and brain work are the risk factors of kidney calculi while loving drinking and eating more vegetables for the protection.
Influence of the usual motivation for dental attendance on dental status and oral health-related quality of life.

PubMed

Montero, Javier; Albaladejo, Alberto; Zalba, José-Ignacio

2014-05-01

To evaluate the influence of dental visiting patterns on the dental status and Oral Health-related Quality of Life (OHQoL) of patients visiting the University Clinic of Salamanca (Spain). This cross-sectional study consisted of a clinical oral examination and a questionnaire-based interviewin a consecutive sample of patients seeking a dental examination. Patients were classified as problem-based dental attendees(PB) and regular dental attendees(RB). Clinical and OHQoL(OHIP-14 & OIDP)data were compared betweengroups. Pair-wise comparisons were performed and a Logistic Regression Model was fitted for predicting the Odds Ratio (OR) of being a PB patient. The sample was composed of 255 patients aged 18 to 87 years (mean age: 63.1 ± 12.7; women: 51.8%). The PB patients had a poorer dental status (i.e. caries, periodontal and prosthetic needs), brushed their teethless,and were significantly more impaired in their OHQoL according to both instruments.The logistic regression coefficients demonstrated that on average the OR of being a PB patient was high in this dental patient sample, but this OR increased significantly if the patient was a male (OR= 1.1-5.0) or referred pain-related impacts according to the OHIP and, additionally, the OR decreased significantly as a function of the number of healthy fillings and the number of sextants coded as CPI=0. Regular dental check-ups are associated with better dental status and a better OHQoL after controlling for potentially related confounding factors.
The crux of the method: assumptions in ordinary least squares and logistic regression.

PubMed

Long, Rebecca G

2008-10-01

Logistic regression has increasingly become the tool of choice when analyzing data with a binary dependent variable. While resources relating to the technique are widely available, clear discussions of why logistic regression should be used in place of ordinary least squares regression are difficult to find. The current paper compares and contrasts the assumptions of ordinary least squares with those of logistic regression and explains why logistic regression's looser assumptions make it adept at handling violations of the more important assumptions in ordinary least squares.
Network-based regularization for matched case-control analysis of high-dimensional DNA methylation data.

PubMed

Sun, Hokeun; Wang, Shuang

2013-05-30

The matched case-control designs are commonly used to control for potential confounding factors in genetic epidemiology studies especially epigenetic studies with DNA methylation. Compared with unmatched case-control studies with high-dimensional genomic or epigenetic data, there have been few variable selection methods for matched sets. In an earlier paper, we proposed the penalized logistic regression model for the analysis of unmatched DNA methylation data using a network-based penalty. However, for popularly applied matched designs in epigenetic studies that compare DNA methylation between tumor and adjacent non-tumor tissues or between pre-treatment and post-treatment conditions, applying ordinary logistic regression ignoring matching is known to bring serious bias in estimation. In this paper, we developed a penalized conditional logistic model using the network-based penalty that encourages a grouping effect of (1) linked Cytosine-phosphate-Guanine (CpG) sites within a gene or (2) linked genes within a genetic pathway for analysis of matched DNA methylation data. In our simulation studies, we demonstrated the superiority of using conditional logistic model over unconditional logistic model in high-dimensional variable selection problems for matched case-control data. We further investigated the benefits of utilizing biological group or graph information for matched case-control data. We applied the proposed method to a genome-wide DNA methylation study on hepatocellular carcinoma (HCC) where we investigated the DNA methylation levels of tumor and adjacent non-tumor tissues from HCC patients by using the Illumina Infinium HumanMethylation27 Beadchip. Several new CpG sites and genes known to be related to HCC were identified but were missed by the standard method in the original paper. Copyright © 2012 John Wiley & Sons, Ltd.
Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

ERIC Educational Resources Information Center

Azen, Razia; Traxel, Nicole

2009-01-01

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…

A multi-country comparison of reasons for dental non-attendance

PubMed Central

Listl, Stefan; Moeller, John; Manski, Richard

2013-01-01

The purpose of this study was to describe cross-country differences with respect to the reasons for dental non-attendance by Europeans currently aged 50 yr and older. The analyses were based on retrospective life-history data from the Survey of Health, Ageing and Retirement in Europe and included information about various reasons why respondents from 13 European countries had never had regular dental visits in their lifetimes. A series of logistic regression models was estimated to identify reasons for dental non-attendance across different welfare state regimes. The highest percentage of respondents without any regular dental attendance throughout their lifetimes was found for the Southern welfare state regime, followed by the Eastern, the Bismarckian, and the Scandinavian welfare state regimes. Factors such as patients’ perception that regular dental treatment is ‘not necessary’ or ‘not usual’ appear to be the predominant reason for non-attendance in all welfare state regimes. Within the Southern, Eastern, and Bismarckian welfare state regimes, the health system level factor ‘no place to receive this type of care close to home’ and the perception of regular dental treatment as ‘not necessary’ were more often referred to than in Scandinavia. This could be relevant information for health care decision makers in order to prioritize interventions towards increasing rates of regular dental attendance. PMID:24147428
Applying Kaplan-Meier to Item Response Data

ERIC Educational Resources Information Center

McNeish, Daniel

2018-01-01

Some IRT models can be equivalently modeled in alternative frameworks such as logistic regression. Logistic regression can also model time-to-event data, which concerns the probability of an event occurring over time. Using the relation between time-to-event models and logistic regression and the relation between logistic regression and IRT, this…
Body size satisfaction and physical activity levels among men and women.

PubMed

Kruger, Judy; Lee, Chong-Do; Ainsworth, Barbara E; Macera, Caroline A

2008-08-01

Body size satisfaction may be an important factor associated with physical activity. We analyzed data from the 2002 National Physical Activity and Weight Loss Survey (NPAWLS), a population-based cross-sectional telephone survey of US adults. Multiple logistic regression models were used to examine the association of body size satisfaction on being regularly active. Participants were aged > or =18 years with complete data on weight, race/ethnicity, physical activity level, and body size satisfaction (n = 10,021). More than half of men (55.8%) and women (53.3%) who reported being very satisfied with the body size were regularly active. After adjustment for covariates, participants who reported being somewhat or not satisfied with their body size had a 13 and 44% lower odds of being regularly active, respectively, compared with those very satisfied with their body size. When stratified by race/ethnicity, this association remained in whites (P for trend <0.001), but became weaker and nonsignificant in blacks, Hispanics, or other racial/ethnic groups. Irrespective of actual weight, those who were satisfied with their body size were more likely to engage in regular physical activity than those less satisfied. Further research is needed to explore predictors of physical activity to reduce health disparities.
Regular use of pain medication due to musculoskeletal disorders in the general working population: Cross-sectional study among 10,000 workers.

PubMed

Sundstrup, Emil; Jakobsen, Markus D; Brandt, Mikkel; Jay, Kenneth; Ajslev, Jeppe Z N; Andersen, Lars L

2016-11-01

We aimed to determine the association between work, health, and lifestyle with regular use of pain medication due to musculoskeletal disorders in the general working population. Currently employed wage earners (N = 10,024) replied to questions about health, work, and lifestyle. The odds for regularly using medication for musculoskeletal disorders were modeled using logistic regression controlled for various confounders. Pain intensity increased the odds for using pain medication in a dose-response fashion. With seated work as reference, the odds for using pain medication were 1.26 (95%CI: 1.09-1.47) for workers engaged in standing or walking work that is not strenuous and 1.59 (95%CI: 1.39-1.82) for workers engaged in standing or walking work with lifting tasks or heavy and fast strenuous work. Workers with higher levels of physical activity at work are more likely to use pain medication on a regular basis for musculoskeletal disorders, even when adjusting for pain intensity, lifestyle, and influence at work. Am. J. Ind. Med. 59:934-941, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
Inverse Ising Inference Using All the Data

NASA Astrophysics Data System (ADS)

Aurell, Erik; Ekeberg, Magnus

2012-03-01

We show that a method based on logistic regression, using all the data, solves the inverse Ising problem far better than mean-field calculations relying only on sample pairwise correlation functions, while still computationally feasible for hundreds of nodes. The largest improvement in reconstruction occurs for strong interactions. Using two examples, a diluted Sherrington-Kirkpatrick model and a two-dimensional lattice, we also show that interaction topologies can be recovered from few samples with good accuracy and that the use of l1 regularization is beneficial in this process, pushing inference abilities further into low-temperature regimes.
Regular-Soda Intake Independent of Weight Status Is Associated with Asthma among US High School Students

PubMed Central

Park, Sohyun; Blanck, Heidi M.; Sherry, Bettylou; Jones, Sherry Everett; Pan, Liping

2015-01-01

Limited research shows an inconclusive association between soda intake and asthma, potentially attributable to certain preservatives in sodas. This cross-sectional study examined the association between regular (nondiet)-soda intake and current asthma among a nationally representative sample of high school students. Analysis was based on the 2009 national Youth Risk Behavior Survey and included 15,960 students (grades 9 through 12) with data for both regular-soda intake and current asthma status. The outcome measure was current asthma (ie, told by doctor/nurse that they had asthma and still have asthma). The main exposure variable was regular-soda intake (ie, drank a can/bottle/glass of soda during the 7 days before the survey). Multivariable logistic regression was used to estimate the adjusted odds ratios for regular-soda intake with current asthma after controlling for age, sex, race/ethnicity, weight status, and current cigarette use. Overall, 10.8% of students had current asthma. In addition, 9.7% of students who did not drink regular soda had current asthma, and 14.7% of students who drank regular soda three or more times per day had current asthma. Compared with those who did not drink regular soda, odds of having current asthma were higher among students who drank regular soda two times per day (adjusted odds ratio = 1.28; 95% CI 1.02 to 1.62) and three or more times per day (adjusted odds ratio = 1.64; 95% CI 1.25 to 2.16). The association between high regular-soda intake and current asthma suggests efforts to reduce regular-soda intake among youth might have benefits beyond improving diet quality. However, this association needs additional research, such as a longitudinal examination. PMID:23260727
Regular-soda intake independent of weight status is associated with asthma among US high school students.

PubMed

Park, Sohyun; Blanck, Heidi M; Sherry, Bettylou; Jones, Sherry Everett; Pan, Liping

2013-01-01

Limited research shows an inconclusive association between soda intake and asthma, potentially attributable to certain preservatives in sodas. This cross-sectional study examined the association between regular (nondiet)-soda intake and current asthma among a nationally representative sample of high school students. Analysis was based on the 2009 national Youth Risk Behavior Survey and included 15,960 students (grades 9 through 12) with data for both regular-soda intake and current asthma status. The outcome measure was current asthma (ie, told by doctor/nurse that they had asthma and still have asthma). The main exposure variable was regular-soda intake (ie, drank a can/bottle/glass of soda during the 7 days before the survey). Multivariable logistic regression was used to estimate the adjusted odds ratios for regular-soda intake with current asthma after controlling for age, sex, race/ethnicity, weight status, and current cigarette use. Overall, 10.8% of students had current asthma. In addition, 9.7% of students who did not drink regular soda had current asthma, and 14.7% of students who drank regular soda three or more times per day had current asthma. Compared with those who did not drink regular soda, odds of having current asthma were higher among students who drank regular soda two times per day (adjusted odds ratio=1.28; 95% CI 1.02 to 1.62) and three or more times per day (adjusted odds ratio=1.64; 95% CI 1.25 to 2.16). The association between high regular-soda intake and current asthma suggests efforts to reduce regular-soda intake among youth might have benefits beyond improving diet quality. However, this association needs additional research, such as a longitudinal examination. Published by Elsevier Inc.
Perceived individual, social, and environmental factors for physical activity and walking.

PubMed

Granner, Michelle L; Sharpe, Patricia A; Hutto, Brent; Wilcox, Sara; Addy, Cheryl L

2007-07-01

Few studies have explored associations of individual, social, and environmental factors with physical activity and walking behavior. A random-digit-dial questionnaire, which included selected individual, social, and environmental variables, was administered to 2025 adults, age 18 y and older, in two adjacent counties in a southeastern state. Logistic regressions were conducted adjusting for age, race, sex, education, and employment. In multivariate models, somewhat different variables were associated with physical activity versus regular walking. Self-efficacy (OR = 19.19), having an exercise partner (OR = 1.47), recreation facilities (OR = 1.54), and safety of trails from crime (OR = 0.72) were associated with physical activity level; while self-efficacy (OR = 4.22), known walking routes (OR = 1.54), recreation facilities (OR = 1.57-1.59), and safety of trails from crime (OR = 0.69) were associated with regular walking behavior. Physical activity and walking behaviors were associated with similar variables in this study.
Cannabis use and destructive periodontal diseases among adolescents.

PubMed

López, Rodrigo; Baelum, Vibeke

2009-03-01

The aim of this experiment was to investigate the association between cannabis use and destructive periodontal disease among adolescents. Data from a population screening examination carried out among Chilean high school students from the Province of Santiago were used to determine whether there was an association between the use of cannabis and signs of periodontal diseases as defined by (1) the presence of necrotizing ulcerative gingival (NUG) lesions or (2) the presence of clinical attachment loss (CAL) > or =3 mm. The cannabis exposures variables considered were "Ever use of cannabis" (yes/no) and "Regular use of cannabis" (yes/no). The associations were investigated using multiple logistic regression analyses adjusted for age, gender, paternal income, paternal education, frequency of tooth-brushing and time since last dental visit. Multiple logistic regression analyses showed that "Ever use of cannabis" was significantly negatively associated with the presence of NUG lesions (OR=0.47 [0.2;0.9]) among non-smokers only. No significant associations were observed between the presence of CAL > or =3 mm and cannabis use in either of the smoking groups. There was no evidence to suggest that the use of cannabis is positively associated with periodontal diseases in this adolescent population.
Majorization Minimization by Coordinate Descent for Concave Penalized Generalized Linear Models

PubMed Central

Jiang, Dingfeng; Huang, Jian

2013-01-01

Recent studies have demonstrated theoretical attractiveness of a class of concave penalties in variable selection, including the smoothly clipped absolute deviation and minimax concave penalties. The computation of the concave penalized solutions in high-dimensional models, however, is a difficult task. We propose a majorization minimization by coordinate descent (MMCD) algorithm for computing the concave penalized solutions in generalized linear models. In contrast to the existing algorithms that use local quadratic or local linear approximation to the penalty function, the MMCD seeks to majorize the negative log-likelihood by a quadratic loss, but does not use any approximation to the penalty. This strategy makes it possible to avoid the computation of a scaling factor in each update of the solutions, which improves the efficiency of coordinate descent. Under certain regularity conditions, we establish theoretical convergence property of the MMCD. We implement this algorithm for a penalized logistic regression model using the SCAD and MCP penalties. Simulation studies and a data example demonstrate that the MMCD works sufficiently fast for the penalized logistic regression in high-dimensional settings where the number of covariates is much larger than the sample size. PMID:25309048
Relationship between caregivers' nutritional knowledge and children's dietary behavior in Chinese rural areas.

PubMed

Zeng, Rong; Luo, Jiayou; Tan, Cai; DU, Qiyun; Zhang, Weimin; Li, Yanping

2012-11-01

To explore the relationship between caregivers' nutritional knowledge and children's dietary behavior in rural areas of China. A cross-sectional study was conducted. 3361 rural caregivers and their children, aged 2 to 7 years old, were selected randomly and surveyed by questionnaire. Logistic regression models were used to identify the relationship between caregivers' nutritional knowledge and the children's dietary behaviors. The awareness level of nutritional knowledge among rural caregivers was 57.9%; among the children surveyed, 79.6% did not like to drink milk, 66.0% were considered choosy of food, 84.1% regularly snacked, 24.4% frequently skipped breakfast, and 13.7% did not come to meals on time. Logistic regression models indicated that a caregiver with a low level of nutritional knowledge is a risk factor for a child's unhealth dietary behaviors (snacking excepted): the odds ratios (OR) of not liking to drink milk, being choosy about food, skipping breakfast or not having meals on time are 1.665, 1.338, 1.330 and 1.582, respectively. Caregivers' nutritional knowledge is strongly associated with children's dietary behavior. Nutrition education programs are urgently wanted to improve caregiver's knowledge and thus to improve children's dietary behavior in rural areas of China.
NIDA-Drug Addiction Treatment Outcome Study (DATOS) Relapse as a Function of Spirituality/Religiosity

PubMed Central

Schoenthaler, Stephen J.; Blum, Kenneth; Braverman, Eric R.; Giordano, John; Thompson, Ben; Oscar-Berman, Marlene; Badgaiyan, Rajendra D.; Madigan, Margaret A.; Dushaj, Kristina; Li, Mona; Demotrovics, Zsolt; Waite, Roger L.; Gold, Mark S.

2015-01-01

Background The connection between religion/spirituality and deviance, like substance abuse, was first made by Durkheim who defined socially expected behaviors as norms. He explained that deviance is due in large part to their absence (called anomie), and concluded that spirituality lowers deviance by preserving norms and social bonds. Impairments in brain reward circuitry, as observed in Reward Deficiency Syndrome (RDS), may also result in deviance and as such we wondered if stronger belief in spirituality practice and religious belief could lower relapse from drugs of abuse. Methods The NIDA Drug Addiction Treatment Outcome Study data set was used to examine post hoc relapse rates among 2,947 clients who were interviewed at 12 months after intake broken down by five spirituality measures. Results Our main findings strongly indicate, that those with low spirituality have higher relapse rates and those with high spirituality have higher remission rates with crack use being the sole exception. We found significant differences in terms of cocaine, heroin, alcohol, and marijuana relapse as a function of strength of religious beliefs (x2 = 15.18, p = 0.028; logistic regression = 10.65, p = 0.006); frequency of attending religious services (x2 = 40.78, p < 0.0005; logistic regression = 30.45, p < 0.0005); frequency of reading religious books (x2 = 27.190, p < 0.0005; logistic regression = 17.31, p < 0.0005); frequency of watching religious programs (x2 = 19.02, p = 0.002; logistic regression = ns); and frequency of meditation/prayer (x2 = 11.33, p = 0.045; logistic regression = 9.650, p = 0.002). Across the five measures of spirituality, the spiritual participants reported between 7% and 21% less alcohol, cocaine, heroin, and marijuana use than the non-spiritual subjects. However, the crack users who reported that religion was not important reported significantly less crack use than the spiritual participants. The strongest association between remission and spirituality involves attending religious services weekly, the one marker of the five that involves the highest social interaction/social bonding consistent with Durkheim’s social bond theory. Conclusions Stronger spiritual/religious beliefs and practices are directly associated with remission from abused drugs except crack. Much like the value of having a sponsor, for clients who abuse drugs, regular spiritual practice, particularly weekly attendance at the religious services of their choice is associated with significantly higher remission. These results demonstrate the clinically significant role of spirituality and the social bonds it creates in drug treatment programs. PMID:26052556
A Method for Calculating the Probability of Successfully Completing a Rocket Propulsion Ground Test

NASA Technical Reports Server (NTRS)

Messer, Bradley P.

2004-01-01

Propulsion ground test facilities face the daily challenges of scheduling multiple customers into limited facility space and successfully completing their propulsion test projects. Due to budgetary and schedule constraints, NASA and industry customers are pushing to test more components, for less money, in a shorter period of time. As these new rocket engine component test programs are undertaken, the lack of technology maturity in the test articles, combined with pushing the test facilities capabilities to their limits, tends to lead to an increase in facility breakdowns and unsuccessful tests. Over the last five years Stennis Space Center's propulsion test facilities have performed hundreds of tests, collected thousands of seconds of test data, and broken numerous test facility and test article parts. While various initiatives have been implemented to provide better propulsion test techniques and improve the quality, reliability, and maintainability of goods and parts used in the propulsion test facilities, unexpected failures during testing still occur quite regularly due to the harsh environment in which the propulsion test facilities operate. Previous attempts at modeling the lifecycle of a propulsion component test project have met with little success. Each of the attempts suffered form incomplete or inconsistent data on which to base the models. By focusing on the actual test phase of the tests project rather than the formulation, design or construction phases of the test project, the quality and quantity of available data increases dramatically. A logistic regression model has been developed form the data collected over the last five years, allowing the probability of successfully completing a rocket propulsion component test to be calculated. A logistic regression model is a mathematical modeling approach that can be used to describe the relationship of several independent predictor variables X(sub 1), X(sub 2),..,X(sub k) to a binary or dichotomous dependent variable Y, where Y can only be one of two possible outcomes, in this case Success or Failure. Logistic regression has primarily been used in the fields of epidemiology and biomedical research, but lends itself to many other applications. As indicated the use of logistic regression is not new, however, modeling propulsion ground test facilities using logistic regression is both a new and unique application of the statistical technique. Results from the models provide project managers with insight and confidence into the affectivity of rocket engine component ground test projects. The initial success in modeling rocket propulsion ground test projects clears the way for more complex models to be developed in this area.
NIDA-Drug Addiction Treatment Outcome Study (DATOS) Relapse as a Function of Spirituality/Religiosity.

PubMed

Schoenthaler, Stephen J; Blum, Kenneth; Braverman, Eric R; Giordano, John; Thompson, Ben; Oscar-Berman, Marlene; Badgaiyan, Rajendra D; Madigan, Margaret A; Dushaj, Kristina; Li, Mona; Demotrovics, Zsolt; Waite, Roger L; Gold, Mark S

The connection between religion/spirituality and deviance, like substance abuse, was first made by Durkheim who defined socially expected behaviors as norms. He explained that deviance is due in large part to their absence (called anomie), and concluded that spirituality lowers deviance by preserving norms and social bonds. Impairments in brain reward circuitry, as observed in Reward Deficiency Syndrome (RDS), may also result in deviance and as such we wondered if stronger belief in spirituality practice and religious belief could lower relapse from drugs of abuse. The NIDA Drug Addiction Treatment Outcome Study data set was used to examine post hoc relapse rates among 2,947 clients who were interviewed at 12 months after intake broken down by five spirituality measures. Our main findings strongly indicate, that those with low spirituality have higher relapse rates and those with high spirituality have higher remission rates with crack use being the sole exception. We found significant differences in terms of cocaine, heroin, alcohol, and marijuana relapse as a function of strength of religious beliefs (x 2 = 15.18, p = 0.028; logistic regression = 10.65, p = 0.006); frequency of attending religious services (x 2 = 40.78, p < 0.0005; logistic regression = 30.45, p < 0.0005); frequency of reading religious books (x 2 = 27.190, p < 0.0005; logistic regression = 17.31, p < 0.0005); frequency of watching religious programs (x 2 = 19.02, p = 0.002; logistic regression = ns); and frequency of meditation/prayer (x 2 = 11.33, p = 0.045; logistic regression = 9.650, p = 0.002). Across the five measures of spirituality, the spiritual participants reported between 7% and 21% less alcohol, cocaine, heroin, and marijuana use than the non-spiritual subjects. However, the crack users who reported that religion was not important reported significantly less crack use than the spiritual participants. The strongest association between remission and spirituality involves attending religious services weekly, the one marker of the five that involves the highest social interaction/social bonding consistent with Durkheim's social bond theory. Stronger spiritual/religious beliefs and practices are directly associated with remission from abused drugs except crack. Much like the value of having a sponsor, for clients who abuse drugs, regular spiritual practice, particularly weekly attendance at the religious services of their choice is associated with significantly higher remission. These results demonstrate the clinically significant role of spirituality and the social bonds it creates in drug treatment programs.
Influence of Health Behaviors and Occupational Stress on Prediabetic State among Male Office Workers.

PubMed

Ryu, Hosihn; Moon, Jihyeon; Jung, Jiyeon

2018-06-14

This study examined the influence of health behaviors and occupational stress on the prediabetic state of male office workers, and identified related risks and influencing factors. The study used a cross-sectional design and performed an integrative analysis on data from regular health checkups, health questionnaires, and a health behavior-related survey of employees of a company, using Spearman’s correlation coefficients and multiple logistic regression analysis. The results showed significant relationships of prediabetic state with health behaviors and occupational stress. Among health behaviors, a diet without vegetables and fruits (Odds Ratio (OR) = 3.74, 95% Confidence Interval (CI) = 1.93⁻7.66) was associated with a high risk of prediabetic state. In the subscales on occupational stress, organizational system in the 4th quartile (OR = 4.83, 95% CI = 2.40⁻9.70) was significantly associated with an increased likelihood of prediabetic state. To identify influencing factors of prediabetic state, the multiple logistic regression was performed using regression models. The results showed that dietary habits (β = 1.20, p = 0.002), total occupational stress score (β = 1.33, p = 0.024), and organizational system (β = 1.13, p = 0.009) were significant influencing factors. The present findings indicate that active interventions are needed at workplace for the systematic and comprehensive management of health behaviors and occupational stress that influence prediabetic state of office workers.
Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?

NASA Astrophysics Data System (ADS)

Lin, Yingzhi; Deng, Xiangzheng; Li, Xing; Ma, Enjun

2014-12-01

Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.
Confirming the validity of the CONUT system for early detection and monitoring of clinical undernutrition: comparison with two logistic regression models developed using SGA as the gold standard.

PubMed

González-Madroño, A; Mancha, A; Rodríguez, F J; Culebras, J; de Ulibarri, J I

2012-01-01

To ratify previous validations of the CONUT nutritional screening tool by the development of two probabilistic models using the parameters included in the CONUT, to see if the CONUT´s effectiveness could be improved. It is a two step prospective study. In Step 1, 101 patients were randomly selected, and SGA and CONUT was made. With data obtained an unconditional logistic regression model was developed, and two variants of CONUT were constructed: Model 1 was made by a method of logistic regression. Model 2 was made by dividing the probabilities of undernutrition obtained in model 1 in seven regular intervals. In step 2, 60 patients were selected and underwent the SGA, the original CONUT and the new models developed. The diagnostic efficacy of the original CONUT and the new models was tested by means of ROC curves. Both samples 1 and 2 were put together to measure the agreement degree between the original CONUT and SGA, and diagnostic efficacy parameters were calculated. No statistically significant differences were found between sample 1 and 2, regarding age, sex and medical/surgical distribution and undernutrition rates were similar (over 40%). The AUC for the ROC curves were 0.862 for the original CONUT, and 0.839 and 0.874, for model 1 and 2 respectively. The kappa index for the CONUT and SGA was 0.680. The CONUT, with the original scores assigned by the authors is equally good than mathematical models and thus is a valuable tool, highly useful and efficient for the purpose of Clinical Undernutrition screening.
Factors associated with a continuous regular analgesic use - a population-based study of more than 45,000 Danish women and men 18-45 years of age.

PubMed

Hargreave, Marie; Andersen, Tina Veje; Nielsen, Ann; Munk, Christian; Liaw, Kai-Li; Kjaer, Susanne K

2010-01-01

Widespread use of and serious adverse effects associated with use of analgesics accentuates the need to consider factors related to analgesic use. The objective of this study was to describe continuous regular analgesics use and examine factors associated with a continuous regular analgesic use. The study was based on data from two surveys and included a random sample of women and men aged 18-45 years from the general Danish population. Information on analgesics use, self-rated health, demographic and lifestyle factors was collected using a self-administered questionnaire. A total of 28,000 women and 33 000 men were invited to participate and 22,199 women (response-rate 81.4%) and 23,080 men (response-rate 71.0%), respectively, were included in the study. Data were analyzed using multivariate logistic regression. We found that 27% of the women and 18% of the men reported a regular monthly use of at least seven analgesic tablets during the last year (continuous regular analgesics use). Besides poor self-rated health we found in both sexes that increasing age, poor self-rated fitness, and smoking were related to a continuous regular analgesics use. Nulliparity, low level of education, overweight/obesity, binge drinking, and abstinence were associated with a continuous regular analgesics use for women, while underweight and marital/cohabiting status were associated with a continuous regular analgesics use only for men. Regular monthly analgesic use during the last year was generally prevalent. Besides self-rated health, several socio-demographic and lifestyle factors were associated with a continuous regular analgesic use, although with some gender differences.
Factors contributing to attrition behavior in diabetes self-management programs: A mixed method approach

PubMed Central

Gucciardi, Enza; DeMelo, Margaret; Offenheim, Ana; Stewart, Donna E

2008-01-01

Background Diabetes self-management education is a critical component in diabetes care. Despite worldwide efforts to develop efficacious DSME programs, high attrition rates are often reported in clinical practice. The objective of this study was to examine factors that may contribute to attrition behavior in diabetes self-management programs. Methods We conducted telephone interviews with individuals who had Type 2 diabetes (n = 267) and attended a diabetes education centre. Multivariable logistic regression was performed to identify factors associated with attrition behavior. Forty-four percent of participants (n = 118) withdrew prematurely from the program and were asked an open-ended question regarding their discontinuation of services. We used content analysis to code and generate themes, which were then organized under the Behavioral Model of Health Service Utilization. Results Working full and part-time, being over 65 years of age, having a regular primary care physician or fewer diabetes symptoms were contributing factors to attrition behaviour in our multivariable logistic regression. The most common reasons given by participants for attrition from the program were conflict between their work schedules and the centre's hours of operation, patients' confidence in their own knowledge and ability when managing their diabetes, apathy towards diabetes education, distance to the centre, forgetfulness, regular physician consultation, low perceived seriousness of diabetes, and lack of familiarity with the centre and its services. There was considerable overlap between our quantitative and qualitative results. Conclusion Reducing attrition behaviour requires a range of strategies targeted towards delivering convenient and accessible services, familiarizing individuals with these services, increasing communication between centres and their patients, and creating better partnerships between centres and primary care physicians. PMID:18248673
Standards for Standardized Logistic Regression Coefficients

ERIC Educational Resources Information Center

Menard, Scott

2011-01-01

Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…

A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.

PubMed

Schörgendorfer, Angela; Branscum, Adam J; Hanson, Timothy E

2013-06-01

Logistic regression is a popular tool for risk analysis in medical and population health science. With continuous response data, it is common to create a dichotomous outcome for logistic regression analysis by specifying a threshold for positivity. Fitting a linear regression to the nondichotomized response variable assuming a logistic sampling model for the data has been empirically shown to yield more efficient estimates of odds ratios than ordinary logistic regression of the dichotomized endpoint. We illustrate that risk inference is not robust to departures from the parametric logistic distribution. Moreover, the model assumption of proportional odds is generally not satisfied when the condition of a logistic distribution for the data is violated, leading to biased inference from a parametric logistic analysis. We develop novel Bayesian semiparametric methodology for testing goodness of fit of parametric logistic regression with continuous measurement data. The testing procedures hold for any cutoff threshold and our approach simultaneously provides the ability to perform semiparametric risk estimation. Bayes factors are calculated using the Savage-Dickey ratio for testing the null hypothesis of logistic regression versus a semiparametric generalization. We propose a fully Bayesian and a computationally efficient empirical Bayesian approach to testing, and we present methods for semiparametric estimation of risks, relative risks, and odds ratios when parametric logistic regression fails. Theoretical results establish the consistency of the empirical Bayes test. Results from simulated data show that the proposed approach provides accurate inference irrespective of whether parametric assumptions hold or not. Evaluation of risk factors for obesity shows that different inferences are derived from an analysis of a real data set when deviations from a logistic distribution are permissible in a flexible semiparametric framework. © 2013, The International Biometric Society.
Propensity score estimation: machine learning and classification methods as alternatives to logistic regression

PubMed Central

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-01-01

Summary Objective Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this Review was to assess machine learning alternatives to logistic regression which may accomplish the same goals but with fewer assumptions or greater accuracy. Study Design and Setting We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. Results We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (CART), and meta-classifiers (in particular, boosting). Conclusion While the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and to a lesser extent decision trees (particularly CART) appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. PMID:20630332
Robust mislabel logistic regression without modeling mislabel probabilities.

PubMed

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.
Effect of Physical and Academic Stress on Illness and Injury in Division 1 College Football Players.

PubMed

Mann, J Bryan; Bryant, Kirk R; Johnstone, Brick; Ivey, Patrick A; Sayers, Stephen P

2016-01-01

Stress-injury models of health suggest that athletes experience more physical injuries during times of high stress. The purpose of this study was to evaluate the effect of increased physical and academic stress on injury restrictions for athletes (n = 101) on a division I college football team. Weeks of the season were categorized into 3 levels: high physical stress (HPS) (i.e., preseason), high academic stress (HAS) (i.e., weeks with regularly scheduled examinations such as midterms, finals, and week before Thanksgiving break), and low academic stress (LAS) (i.e., regular season without regularly scheduled academic examinations). During each week, we recorded whether a player had an injury restriction, thereby creating a longitudinal binary outcome. The data were analyzed using a hierarchical logistic regression model to properly account for the dependency induced by the repeated observations over time within each subject. Significance for regression models was accepted at p ≤ 0.05. We found that the odds of an injury restriction during training camp (HPS) were the greatest compared with weeks of HAS (odds ratio [OR] = 2.05, p = 0.0003) and LAS (OR = 3.65, p < 0.001). However, the odds of an injury restriction during weeks of HAS were nearly twice as high as during weeks of LAS (OR = 1.78, p = 0.0088). Moreover, the difference in injury rates reported in all athletes during weeks of HPS and weeks of HAS disappeared when considering only athletes that regularly played in games (OR = 1.13, p = 0.75) suggesting that HAS may affect athletes that play to an even greater extent than HPS. Coaches should be aware of both types of stressors and consider carefully the types of training methods imposed during times of HAS when injuries are most likely.
Fungible weights in logistic regression.

PubMed

Jones, Jeff A; Waller, Niels G

2016-06-01

In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.

PubMed

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-08-01

Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this review was to assess machine learning alternatives to logistic regression, which may accomplish the same goals but with fewer assumptions or greater accuracy. We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (classification and regression trees [CART]), and meta-classifiers (in particular, boosting). Although the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and, to a lesser extent, decision trees (particularly CART), appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. Copyright (c) 2010 Elsevier Inc. All rights reserved.
Intergenerational transmission of family meal patterns from adolescence to parenthood: longitudinal associations with parents' dietary intake, weight-related behaviours and psychosocial well-being.

PubMed

Berge, Jerica M; Miller, Jonathan; Watts, Allison; Larson, Nicole; Loth, Katie A; Neumark-Sztainer, Dianne

2018-02-01

The present study examined longitudinal associations between four family meal patterns (i.e. never had regular family meals, started having regular family meals, stopped having regular family meals, maintained having regular family meals) and young adult parents' dietary intake, weight-related behaviours and psychosocial well-being. In addition, family meal patterns of parents were compared with those of non-parents. Analysis of data from the longitudinal Project EAT (Eating and Activity in Adolescents and Young Adults) study. Linear and logistic regressions were used to examine the associations between family meal patterns and parents' dietary intake, weight-related behaviours and psychosocial well-being. School and in-home settings. At baseline (1998; EAT-I), adolescents (n 4746) from socio-economically and racially/ethnically diverse households completed a survey and anthropometric measurements at school. At follow-up (2015; EAT-IV), participants who were parents (n 726) and who were non-parents with significant others (n 618) completed an online survey. Young adult parents who reported having regular family meals as an adolescent and as a parent ('maintainers'), or who started having regular family meals with their own families ('starters'), reported more healthful dietary, weight-related and psychosocial outcomes compared with young adults who never reported having regular family meals ('nevers'; P<0·05). In addition, parents were more likely to be family meal starters than non-parents. Results suggest that mental and physical health benefits of having regular family meals may be realized as a parent whether the routine of regular family meals is carried forward from adolescence into parenthood, or if the routine is started in parenthood.
Regular source of primary care and emergency department use of children in Victoria.

PubMed

Turbitt, Erin; Freed, Gary Lee

2016-03-01

The aim of this paper was to study the prevalence of a regular source of primary care for Victorian children attending one of four emergency departments (EDs) and to determine associated characteristics, including ED use. Responses were collected via an electronic survey from parents attending EDs with their child (≤9 years of age) for a lower-urgency condition. Single, multiple choice, and Likert scale responses were analysed using bivariate and logistic regression tests. Of the 1146 parents who provided responses, 80% stated their child has a regular source of primary care. Of these, care is mostly received by a general practitioner (GP) (95%) in GP group practices (71%). Approximately 20% have changed where their child receives primary care in the last year. No associations were observed between having a regular source of primary care and frequency of ED attendance in the past 12 months, although parents whose child did not have a regular source of primary care were more likely to view the ED as a more convenient place to receive care than the primary care provider (39% without regular source vs. 18% with regular source; P < 0.0001). Children were less likely to have a regular source of primary care if their parents were younger, had a lower household income, lower education, and were visiting a hospital in a lower socio-economic indexes for areas rank. Policy options to improve continuity of care for children may require investigation. Increasing the prevalence of regular source of primary care for children may in turn reduce ED visits. © 2015 The Authors. Journal of Paediatrics and Child Health © 2015 Paediatrics and Child Health Division (Royal Australasian College of Physicians).
Should metacognition be measured by logistic regression?

PubMed

Rausch, Manuel; Zehetleitner, Michael

2017-03-01

Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.
Social capital and administrative contextual determinants of lack of access to a regular doctor: a multilevel analysis in southern Sweden.

PubMed

Lindström, Martin; Axén, Elin; Lindström, Christine; Beckman, Anders; Moghaddassi, Mahnaz; Merlo, Juan

2006-12-01

The aim of this study was to investigate the influence of contextual (social capital and administrative/neo-materialist) and individual factors on lack of access to a regular doctor. The 2000 public health survey in Scania is a cross-sectional study. A total of 13,715 persons answered a postal questionnaire, which is 59% of the random sample. A multilevel logistic regression model, with individuals at the first level and municipalities at the second, was performed. The effect (intra-class correlations, cross-level modification and odds ratios) of individual and municipality (social capital and health care district) factors on lack of access to a regular doctor was analysed using simulation method. The Deviance Information Criterion (DIC) was used as information criterion for the models. The second level municipality variance in lack of access to a regular doctor is substantial even in the final models with all individual and contextual variables included. The model that results in the largest reduction in DIC is the model including age, sex and individual social participation (which is a network aspect of social capital), but the models which include administrative and social capital second level factors also reduced the DIC values. This study suggests that both administrative health care district and social capital may partly explain the individual's self reported lack of access to a regular doctor.
A multi-country comparison of reasons for dental non-attendance.

PubMed

Listl, Stefan; Moeller, John; Manski, Richard

2014-02-01

The purpose of this study was to describe differences across countries with respect to the reasons for dental non-attendance by Europeans currently 50 yr of age and older. The analyses were based on retrospective life-history data from the Survey of Health, Ageing, and Retirement in Europe and included information on various reasons why respondents from 13 European countries had never had regular dental visits in their lifetime. A series of logistic regression models was estimated to identify reasons for dental non-attendance across different welfare-state regimes. The highest proportion of respondents without any regular dental attendance throughout their lifetime was found for the Southern welfare-state regime, followed by the Eastern, the Bismarckian, and the Scandinavian welfare-state regimes. Factors such as patients' perception that regular dental treatment is 'not necessary' or 'not usual' appear to be the predominant reason for non-attendance in all welfare-state regimes. The health system-level factor 'no place to receive this type of care close to home' and the perception of regular dental treatment as 'not necessary' were more often referred to within the Southern, Eastern, and Bismarckian welfare-state regimes than in Scandinavia. This could be relevant information for health-care decision makers in order to prioritize interventions towards increasing rates of regular dental attendance. © 2013 Eur J Oral Sci.
London Measure of Unplanned Pregnancy: guidance for its use as an outcome measure

PubMed Central

Hall, Jennifer A; Barrett, Geraldine; Copas, Andrew; Stephenson, Judith

2017-01-01

Background The London Measure of Unplanned Pregnancy (LMUP) is a psychometrically validated measure of the degree of intention of a current or recent pregnancy. The LMUP is increasingly being used worldwide, and can be used to evaluate family planning or preconception care programs. However, beyond recommending the use of the full LMUP scale, there is no published guidance on how to use the LMUP as an outcome measure. Ordinal logistic regression has been recommended informally, but studies published to date have all used binary logistic regression and dichotomized the scale at different cut points. There is thus a need for evidence-based guidance to provide a standardized methodology for multivariate analysis and to enable comparison of results. This paper makes recommendations for the regression method for analysis of the LMUP as an outcome measure. Materials and methods Data collected from 4,244 pregnant women in Malawi were used to compare five regression methods: linear, logistic with two cut points, and ordinal logistic with either the full or grouped LMUP score. The recommendations were then tested on the original UK LMUP data. Results There were small but no important differences in the findings across the regression models. Logistic regression resulted in the largest loss of information, and assumptions were violated for the linear and ordinal logistic regression. Consequently, robust standard errors were used for linear regression and a partial proportional odds ordinal logistic regression model attempted. The latter could only be fitted for grouped LMUP score. Conclusion We recommend the linear regression model with robust standard errors to make full use of the LMUP score when analyzed as an outcome measure. Ordinal logistic regression could be considered, but a partial proportional odds model with grouped LMUP score may be required. Logistic regression is the least-favored option, due to the loss of information. For logistic regression, the cut point for un/planned pregnancy should be between nine and ten. These recommendations will standardize the analysis of LMUP data and enhance comparability of results across studies. PMID:28435343
Logistic models--an odd(s) kind of regression.

PubMed

Jupiter, Daniel C

2013-01-01

The logistic regression model bears some similarity to the multivariable linear regression with which we are familiar. However, the differences are great enough to warrant a discussion of the need for and interpretation of logistic regression. Copyright © 2013 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.
Self-efficacy, pros, and cons as variables associated with adjacent stages of change for regular exercise in Japanese college students.

PubMed

Horiuchi, Satoshi; Tsuda, Akira; Kobayashi, Hisanori; Fallon, Elizabeth A; Sakano, Yuji

2017-07-01

This study examined self-efficacy (confidence to exercise), pros (exercise's advantages), and cons (exercise's disadvantages) as variables associated across the transtheoretical model's six stages of change in 403 Japanese college students. A series of logistic regression analyses were conducted. Results showed that higher pros and lower cons were associated with being in contemplation compared to precontemplation. Lower cons were associated with being in preparation compared to contemplation. Higher self-efficacy was associated with being in action compared to preparation as well as being in maintenance compared to action. Lower cons were associated with being in termination compared to maintenance.
[The Bambuí Project: a population-based study of factors associated with regular utilization of dental services in adults].

PubMed

Matos, D L; Lima-Costa, M F; Guerra, H L; Marcenes, W

2001-01-01

A cross-sectional study was conducted in Bambuí, Minas Gerais, to identify factors associated with regular use of dental services. Participants were interviewed with a structured questionnaire and previously validated questions. 999/1,221 (81.8%) randomly selected individuals aged > 18 years participated in the Bambuí dental survey. Of these, 654 out of 656 individuals who had at least one natural tooth and had visited a dentist during their lifetime participated in the study. Results adjusted by multiple logistic regression showed that regular use of dental services was significantly related to having > 8 and 4-7 years of schooling (OR = 9.90; 95% CI = 2.90-33.77 and OR = 3.87; 95% CI = 1.11-13.51, respectively), having a preference for restorative treatment rather than extraction (OR = 4.91; 95% CI = 2.23-10.79), having no present need of dental treatment (OR = 4.87; 95% CI = 3.17-7.49), and belief that visiting the dentist prevents tooth decay and gum disease (OR = 1.73; 95% CI = 1.13-2.65). The results show that regular use of dental services was related to factors distributed in the Andersen and Newman model (1973) explaining use of dentistry services.
Gender and age inequalities in regular sports participation: a cross-national study of 25 European countries.

PubMed

Van Tuyckom, Charlotte; Scheerder, Jeroen; Bracke, Piet

2010-08-01

This article provides a unique opportunity to compare gender inequalities in sports participation across Europe, and the extent to which this varies by age using large, cross-sections of the population. The Eurobarometer Survey 62.0 (carried out in 2004 at the request of the European Commission and covering the adult population of 25 European member states, N = 23,909) was used to analyse differences in regular sports participation by gender and by age in the different countries. For the majority of countries, the occurrence of regular sporting activity was less than 40%. Additionally, binary logistic regression analyses identified significant gender differences in sports participation in 12 countries. In Belgium, France, Greece, Latvia, Lithuania, Slovakia, Spain, and the UK, men were more likely to report being regularly active in sports than women, whereas in Denmark, Finland, Sweden, and the Netherlands the opposite was true. Moreover, the extent to which these gender inequalities differ by age varies considerably across countries. The results imply that: (i) in some European countries more efforts must be undertaken to promote the original goals of the Sport for All Charter, and (ii) to achieve more female participation in sports will require different policy responses in the diverse European member states.
Vitamin D deficiency and leisure time activities in the elderly: are all pastimes the same?

PubMed

De Rui, Marina; Toffanello, Elena Debora; Veronese, Nicola; Zambon, Sabina; Bolzetta, Francesco; Sartori, Leonardo; Musacchio, Estella; Corti, Maria Chiara; Baggio, Giovannella; Crepaldi, Gaetano; Perissinotto, Egle; Manzato, Enzo; Sergi, Giuseppe

2014-01-01

Optimal vitamin D status is important for overall health and well-being, particularly in the elderly. Although vitamin D synthesis in the skin declines with age, exposure to sunlight still seems to help older-aged adults to achieve adequate serum 25-hydroxyvitamin D (25OHD) levels. Elderly people would therefore benefit from outdoor leisure activities, but the effects of different types of pastime on serum 25OHD levels have yet to be thoroughly investigated. To assess the association of different pastimes with 25OHD deficiency in elderly subjects. A sample of 2,349 community-dwelling elderly individuals (1,389 females and 960 males) enrolled in the Progetto Veneto Anziani was analyzed. Brisk walking, cycling, gardening and fishing were classed as outdoor activities, and dancing and gym workouts as indoor pastimes. Any activities undertaken for at least 1 hour/week during the previous month were considered as being practiced regularly. Logistic regression models were used to estimate the association between different pastimes and 25OHD deficiency. Serum 25OHD levels were significantly higher in individuals who engaged in outdoor pastimes (+25% in women, +27.7% in men) compared to those who did not. In particular, subjects regularly practicing gardening or cycling had higher serum 25OHD levels than those who did not, whereas 25OHD levels differed little between subjects who did or did not undertake indoor activities. Among the outdoor pastimes considered, logistic regression analysis confirmed a lower likelihood of vitamin D deficiency (25OHD<50 nmol/L) for cyclists (OR 0.51, 95% CI 0.37-0.69 in women; OR 0.50, 95% CI 0.29-0.87 in men) and gardeners (OR 0.62, 95% CI 0.47-0.83 in women; OR 0.46, 95% CI 0.26-0.80), but not for brisk walkers. Regular cycling and gardening reduce the likelihood of inadequate vitamin D status in Caucasian elderly people, irrespective of their age, BMI and comorbidities, and of the season of the year.
Use of Opioid Analgesics in Older Australians.

PubMed

Veal, Felicity C; Bereznicki, Luke R E; Thompson, Angus J; Peterson, Gregory M

2015-08-01

To identify potential medication management issues associated with opioid use in older Australians. Retrospective cross-sectional review of the utilization of analgesics in 19,581 people who underwent a medication review in Australia between 2010 and 2012. Australian residents living in the community deemed at risk for adverse medication outcomes or any resident living fulltime in an aged care facility. Patient characteristics in those taking regularly dosed opioids and not and those taking opioid doses >120 mg and ≤120 mg MEQ/day were compared. Multivariable binary logistic regression was used to analyze the association between regular opioid and high dose opioid usage and key variables. Additionally, medication management issues associated with opioids were identified. Opioids were taken by 31.8% of patients, with 22.1% taking them regularly. Several major medication management issues were identified. There was suboptimal use of multimodal analgesia, particularly a low use of non-opioid analgesics, in patients taking regular opioids. There was extensive use (45%) of concurrent anxiolytics/hypnotics among those taking regular opioid analgesics. Laxative use in those prescribed opioids regularly was low (60%). Additionally, almost 12% of patients were taking doses of opioid that exceeded Australian recommendations. A significant evidence to practice gap exists regarding the use of opioids amongst older Australians. These findings highlight the need for a quick reference guide to support prescribers in making appropriate decisions regarding pain management in older patients with persistent pain. This should also be combined with patient and caregiver education about the importance of regular acetaminophen to manage persistent pain. Wiley Periodicals, Inc.
Differences in antibiotic use between patients with and without a regular doctor in Hong Kong.

PubMed

Lam, Tai Pong; Wun, Yuk Tsan; Lam, Kwok Fai; Sun, Kai Sing

2015-12-15

Literature shows that continuity of care from a primary care physician is associated with better patient satisfaction and preventive care. This may also have an effect on patients' use of antibiotics. This study investigated the differences in antibiotic use between patients with and without a regular doctor in a pluralistic health care system. A cross-sectional telephone questionnaire survey using randomly selected household phone numbers was conducted in Hong Kong. Several key areas about antibiotic use were compared between the respondents with a regular doctor and those without. The response rate was 68.3 %. Of the 2,471 respondents, 1,450 (58.7 %) had a regular doctor, 942 (38.1 %) without, and 79 (3.2 %) did not give a clear answer. The respondents with a regular doctor were more likely to report that they always finished the full course of antibiotics (74.2 % vs 62.4 %), as well as using antibiotics for their last upper respiratory tract infections (17.4 % vs 10.1 %). The association with antibiotic use remained significant in the multivariable logistic regression analysis after adjusting for other confounding factors (P < 0.001, OR = 1.76, 95 % CI:(1.27, 2.48)). While patients with a regular doctor, compared to those without, were more likely to report finishing the full course of antibiotics, they also had nearly twice the chance of reporting antibiotic use for upper respiratory tract infections. This challenges the common belief of the benefits in having a regular doctor.
Psychosocial factors influencing smokeless tobacco use by teen-age military dependents.

PubMed

Lee, S; Raker, T; Chisick, M C

1994-02-01

Using bivariate and logistic regression analysis, we explored psychosocial correlates of smokeless tobacco (SLT) use in a sample of 2,257 teenage military dependents. We built separate regression models for males and females to explain triers and users of SLT. Results show female and male triers share five factors regarding SLT use--parental and peer approval, trying smoking, relatives using SLT, and athletic team membership. Male trial of SLT was additionally associated with race, difficulty in purchasing SLT, relatives who smoke, current smoking, and belief that SLT can cause mouth cancer. Male use of SLT was associated with race, seeing a dentist regularly, SLT counseling by a dentist, parental approval, trying and current smoking, and grade level. In all models, trying smoking was the strongest explanatory variable. Relatives and peers exert considerable influence on SLT use. Few triers or users had received SLT counseling from their dentist despite high dental utilization rates.

[How medical students perform academically by admission types?].

PubMed

Kim, Se-Hoon; Lee, Keumho; Hur, Yera; Kim, Ji-Ha

2013-09-01

Despite the importance of selecting students whom are capable for medical education and to become a good doctor, not enough studies have been done in the category. This study focused on analysing the medical students' academic performance (grade point average, GPA) differences, flunk and dropout rates by admission types. From 2004 to 2010, we gathered 369 Konyang University College of Medicine's students admission data and analyzed the differences between admission method and academic achievement, differences in failure and dropout rates. Analysis of variance (ANOVA), ordinary least square, and logistic regression were used. The rolling students showed higher academic achievement from year 1 to 3 than regular students (p < 0.01). Using admission type variable as control variable in multiple regression model similar results were shown. But unlike the results of ANOVA, GPA differences by admission types were shown not only in lower academic years but also in year 6 (p < 0.01). From the regression analysis of flunk and dropout rate by admission types, regular admission type students showed higher drop out rate than the rolling ones which demonstrates admission types gives significant effect on flunk or dropout rates in medical students (p < 0.01). The rolling admissions type students tend to show lower flunk rate and dropout rates and perform better academically. This implies selecting students primarily by Korean College Scholastic Ability Test does not guarantee their academic success in medical education. Thus we suggest a more in-depth comprehensive method of selecting students that are appropriate to individual medical school's educational goal.
Application of the transtheoretical model: exercise behavior in Korean adults with metabolic syndrome.

PubMed

Kim, Chun-Ja; Kim, Bom-Taeck; Chae, Sun-Mi

2010-01-01

Although regular exercise has been recommended to reduce the risk of cardiovascular disease (CVD) among people with metabolic syndrome, little information is available about psychobehavioral strategies in this population. The purpose of this study was to identify the stages, processes of change, decisional balance, and self-efficacy of exercise behavior and to determine the significant predictors explaining regular exercise behavior in adults with metabolic syndrome. This descriptive, cross-sectional survey design enrolled a convenience sample of 210 people with metabolic syndrome at a university hospital in South Korea. Descriptive statistics were used to analyze demographic characteristics, metabolic syndrome risk factors, and transtheoretical model-related variables. A multivariate logistic regression analysis was used to determine the most important predictors of regular exercise stages. Action and maintenance stages comprised 51.9% of regular exercise stages, whereas 48.1% of non-regular exercise stages were precontemplation, contemplation, and preparation stages. Adults with regular exercise stages displayed increased high-density lipoprotein cholesterol level, were more likely to use consciousness raising, self-reevaluation, and self-liberation strategies, and were less likely to evaluate the merits/disadvantages of exercise, compared with those in non-regular exercise stages. In this study of regular exercise behavior and transtheoretical model-related variables, consciousness raising, self-reevaluation, and self-liberation were associated with a positive effect on regular exercise behavior in adults with metabolic syndrome. Our findings could be used to develop strategies and interventions to maintain regular exercise behavior directed at Korean adults with metabolic syndrome to reduce CVD risk. Further prospective intervention studies are needed to investigate the effect of regular exercise program on the prevention and/or reduction of CVD risk among this population. Health care providers, especially nurses, are optimally positioned to help their clients initiate and maintain regular exercise behavior in clinical and community settings.
Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model

NASA Astrophysics Data System (ADS)

Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami

2017-06-01

A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.
PARAMETRIC AND NON PARAMETRIC (MARS: MULTIVARIATE ADDITIVE REGRESSION SPLINES) LOGISTIC REGRESSIONS FOR PREDICTION OF A DICHOTOMOUS RESPONSE VARIABLE WITH AN EXAMPLE FOR PRESENCE/ABSENCE OF AMPHIBIANS

EPA Science Inventory

The purpose of this report is to provide a reference manual that could be used by investigators for making informed use of logistic regression using two methods (standard logistic regression and MARS). The details for analyses of relationships between a dependent binary response ...
Predicting U.S. Army Reserve Unit Manning Using Market Demographics

DTIC Science & Technology

2015-06-01

develops linear regression , classification tree, and logistic regression models to determine the ability of the location to support manning requirements... logistic regression model delivers predictive results that allow decision-makers to identify locations with a high probability of meeting unit...manning requirements. The recommendation of this thesis is that the USAR implement the logistic regression model. 14. SUBJECT TERMS U.S
Analyzing Student Learning Outcomes: Usefulness of Logistic and Cox Regression Models. IR Applications, Volume 5

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2005-01-01

Logistic and Cox regression methods are practical tools used to model the relationships between certain student learning outcomes and their relevant explanatory variables. The logistic regression model fits an S-shaped curve into a binary outcome with data points of zero and one. The Cox regression model allows investigators to study the duration…
An appraisal of convergence failures in the application of logistic regression model in published manuscripts.

PubMed

Yusuf, O B; Bamgboye, E A; Afolabi, R F; Shodimu, M A

2014-09-01

Logistic regression model is widely used in health research for description and predictive purposes. Unfortunately, most researchers are sometimes not aware that the underlying principles of the techniques have failed when the algorithm for maximum likelihood does not converge. Young researchers particularly postgraduate students may not know why separation problem whether quasi or complete occurs, how to identify it and how to fix it. This study was designed to critically evaluate convergence issues in articles that employed logistic regression analysis published in an African Journal of Medicine and medical sciences between 2004 and 2013. Problems of quasi or complete separation were described and were illustrated with the National Demographic and Health Survey dataset. A critical evaluation of articles that employed logistic regression was conducted. A total of 581 articles was reviewed, of which 40 (6.9%) used binary logistic regression. Twenty-four (60.0%) stated the use of logistic regression model in the methodology while none of the articles assessed model fit. Only 3 (12.5%) properly described the procedures. Of the 40 that used the logistic regression model, the problem of convergence occurred in 6 (15.0%) of the articles. Logistic regression tends to be poorly reported in studies published between 2004 and 2013. Our findings showed that the procedure may not be well understood by researchers since very few described the process in their reports and may be totally unaware of the problem of convergence or how to deal with it.
Explaining match outcome in elite Australian Rules football using team performance indicators.

PubMed

Robertson, Sam; Back, Nicole; Bartlett, Jonathan D

2016-01-01

The relationships between team performance indicators and match outcome have been examined in many team sports, however are limited in Australian Rules football. Using data from the 2013 and 2014 Australian Football League (AFL) regular seasons, this study assessed the ability of commonly reported discrete team performance indicators presented in their relative form (standardised against their opposition for a given match) to explain match outcome (Win/Loss). Logistic regression and decision tree (chi-squared automatic interaction detection (CHAID)) analyses both revealed relative differences between opposing teams for "kicks" and "goal conversion" as the most influential in explaining match outcome, with two models achieving 88.3% and 89.8% classification accuracies, respectively. Models incorporating a smaller performance indicator set displayed a slightly reduced ability to explain match outcome (81.0% and 81.5% for logistic regression and CHAID, respectively). However, both were fit to 2014 data with reduced error in comparison to the full models. Despite performance similarities across the two analysis approaches, the CHAID model revealed multiple winning performance indicator profiles, thereby increasing its comparative feasibility for use in the field. Coaches and analysts may find these results useful in informing strategy and game plan development in Australian Rules football, with the development of team-specific models recommended in future.
Logistic Regression: Concept and Application

ERIC Educational Resources Information Center

Cokluk, Omay

2010-01-01

The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…
Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.
Who uses height-adjustable desks? - Sociodemographic, health-related, and psycho-social variables of regular users.

PubMed

Wallmann-Sperlich, Birgit; Bipp, Tanja; Bucksch, Jens; Froboese, Ingo

2017-03-06

Sit-to-stand height-adjustable desks (HAD) may promote workplace standing, as long as workers use them on a regular basis. The aim of this study was to investigate (i) how common HAD in German desk-based workers are, and how frequently HADs are used, (ii) to identify sociodemographic, health-related, and psycho-social variables of workday sitting including having a HAD, and (iii) to analyse sociodemographic, health-related, and psycho-social variables of users and non-users of HADs. A cross-sectional sample of 680 participants (51.9% men; 41.0 ± 13.1 years) in a desk-based occupation was interviewed by telephone about their occupational sitting and standing proportions, having and usage of a HAD, and answered questions concerning psycho-social variables of occupational sitting. The proportion of workday sitting was calculated for participants having an HAD (n = 108) and not-having an HAD (n = 573), as well as for regular users of HAD (n = 54), and irregular/non-users of HAD (n = 54). Linear regressions were conducted to calculate associations between socio-demographic, health-related, psychosocial variables and having/not having an HAD, and the proportion of workday sitting. Logistic regressions were executed to examine the association of mentioned variables and participants' usage of HADs. Sixteen percent report that they have an HAD, and 50% of these report regular use of HAD. Having an HAD is not a correlate of the proportion of workday sitting. Further analysis restricted to participants having available a HAD highlights that only the 'perceived advantages of sitting less' was significantly associated with HAD use in the fully adjusted model (OR 1.75 [1.09; 2.81], p < 0.05). The present findings indicate that accompanying behavioral action while providing an HAD is promising to increase the regular usage of HAD. Hence, future research needs to address the specificity of behavioral actions in order to enhance regular HAD use, and needs to give more fundamental insights into these associations.
An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

PubMed Central

Weiss, Brandi A.; Dardick, William

2015-01-01

This article introduces an entropy-based measure of data–model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data–model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Carlo simulation results are used to demonstrate the application of entropy in logistic regression. Entropy should be used in conjunction with other measures of data–model fit to assess how well logistic regression models classify cases into observed categories. PMID:29795897
Logistic regression applied to natural hazards: rare event logistic regression with replications

NASA Astrophysics Data System (ADS)

Guns, M.; Vanacker, V.

2012-06-01

Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.
An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression.

PubMed

Weiss, Brandi A; Dardick, William

2016-12-01

This article introduces an entropy-based measure of data-model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data-model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Carlo simulation results are used to demonstrate the application of entropy in logistic regression. Entropy should be used in conjunction with other measures of data-model fit to assess how well logistic regression models classify cases into observed categories.
Regular intake of non-opioid analgesics is associated with an increased risk of restless legs syndrome in patients maintained on antidepressants.

PubMed

Leutgeb, U; Martus, P

2002-08-30

To evaluate prevalence and risk factors of restless legs syndrome (RLS) in secondary-care patients maintained on tricyclic and serotonin reuptake inhibiting antidepressants. A total of 243 subjects with affective and anxiety disorders were interviewed for symptoms of RLS before and after at least 6 months of antidepressant pharmacotherapy within a naturalistic study. Logistic regression analysis was applied to adjust for the effects of age, gender, comorbidities, and the most frequent co-medications. The overall prevalence of RLS was 27%. In the RLS-affected patients, regular use or overuse of non-opioid analgesics frequently combined with caffeine was the major risk factor which significantly correlated with psychiatric and medical comorbidity. In a subsample of 172 patients who had seldom taken analgesics the prevalence of RLS was 9%, which corresponds with its prevalence in the general population. Neither antidepressants nor neuroleptics but non-opioid analgesics appear to be a major risk factor of RLS. Their regular use should be considered in studies of RLS-patients on psychotropic or other drugs. In this sample of secondary-care patients, ICD-10 classified depression or anxiety per se did not appear to be a risk factor of RLS.
Socioeconomic factors affecting colorectal, breast and cervical cancer screening in an Asian urban low-income setting at baseline and post-intervention.

PubMed

Wee, Liang En; Koh, Gerald Choon-Huat; Chin, Run Ting; Yeo, Wei Xin; Seow, Branden; Chua, Darren

2012-07-01

Inequalities in cancer screening are little studied in Asian societies. We determined whether area and individual measures of socio-economic status (SES) affected cancer screening participation in Singapore and prospectively evaluated an access-enhancing community-based intervention. The study population involved all residents aged >40 years in two housing estates comprising of owner-occupied (high-SES area) and rental (low-SES area) flats. From 2009 to 2011, non-adherents to regular screening for colorectal/breast/cervical cancer were offered free convenient screening over six months. Pre- and post-intervention screening rates were compared with McNemar's test. Multi-level logistic regression identified factors of regular screening at baseline; Cox regression analysis identified predictors of screening post-intervention. Participation was 78.2% (1081/1383). In the low-SES area, 7.7% (33/427), 20.4% (44/216), and 14.3% (46/321) had regular colorectal, cervical and breast cancer screening respectively. Post-intervention, screening rates in the low-SES area rose significantly to 19.0% (81/427), 25.4% (55/216), and 34.3% (74/216) respectively (p<0.001). Area SES was more consistently associated with screening than individual SES at baseline. Post-intervention, for colorectal cancer screening, those with higher education were more likely to attend (p=0.004); for female cancer screening, the higher-income were less likely to attend (p=0.032). Access-enhancing community-based interventions improve participation among disadvantaged strata of Asian societies. Copyright © 2012 Elsevier Inc. All rights reserved.
Effect of acculturation and access to care on colorectal cancer screening in low-income Latinos.

PubMed

Savas, Lara S; Vernon, Sally W; Atkinson, John S; Fernández, Maria E

2015-06-01

Latinos have lower colorectal cancer screening (CRCS) and survival rates compared to other race/ethnic groups. This cross-sectional study examines relationships between acculturation, access to and utilization of healthcare services, and CRCS in low-income Latinos. Bilingual data collectors conducted structured interviews with 544 Latino men and women (>50 years) residing in the Texas-Mexico border area. Using a hierarchical logistic regression model, we examined the relationship between lifetime history of any CRCS test and indicators of acculturation, healthcare utilization and access to care, adjusting for socio-demographic characteristics. Survey results revealed a 34% prevalence of CRCS. Participants reporting a provider recommendation for screening, regular check-ups, higher acculturation level, and health insurance had significantly increased odds of CRCS. Findings indicate CRCS intervention research in Latinos should focus on (1) increasing physicians' recommendations for screening, (2) promoting regular check-ups, (3) and increasing CRC prevention efforts on less acculturated and uninsured groups.
Social network and individual correlates of sexual risk behavior among homeless young men who have sex with men.

PubMed

Tucker, Joan S; Hu, Jianhui; Golinelli, Daniela; Kennedy, David P; Green, Harold D; Wenzel, Suzanne L

2012-10-01

There is growing interest in network-based interventions to reduce HIV sexual risk behavior among both homeless youth and men who have sex with men. The goal of this study was to better understand the social network and individual correlates of sexual risk behavior among homeless young men who have sex with men (YMSM) to inform these HIV prevention efforts. A multistage sampling design was used to recruit a probability sample of 121 homeless YMSM (ages: 16-24 years) from shelters, drop-in centers, and street venues in Los Angeles County. Face-to-face interviews were conducted. Because of the different distributions of the three outcome variables, three distinct regression models were needed: ordinal logistic regression for unprotected sex, zero-truncated Poisson regression for number of sex partners, and logistic regression for any sex trade. Homeless YMSM were less likely to engage in unprotected sex and had fewer sex partners if their networks included platonic ties to peers who regularly attended school, and had fewer sex partners if most of their network members were not heavy drinkers. Most other aspects of network composition were unrelated to sexual risk behavior. Individual predictors of sexual risk behavior included older age, Hispanic ethnicity, lower education, depressive symptoms, less positive condom attitudes, and sleeping outdoors because of nowhere else to stay. HIV prevention programs for homeless YMSM may warrant a multipronged approach that helps these youth strengthen their ties to prosocial peers, develop more positive condom attitudes, and access needed mental health and housing services. Copyright © 2012 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.
Power and Sample Size Calculations for Logistic Regression Tests for Differential Item Functioning

ERIC Educational Resources Information Center

Li, Zhushan

2014-01-01

Logistic regression is a popular method for detecting uniform and nonuniform differential item functioning (DIF) effects. Theoretical formulas for the power and sample size calculations are derived for likelihood ratio tests and Wald tests based on the asymptotic distribution of the maximum likelihood estimators for the logistic regression model.…
A Methodology for Generating Placement Rules that Utilizes Logistic Regression

ERIC Educational Resources Information Center

Wurtz, Keith

2008-01-01

The purpose of this article is to provide the necessary tools for institutional researchers to conduct a logistic regression analysis and interpret the results. Aspects of the logistic regression procedure that are necessary to evaluate models are presented and discussed with an emphasis on cutoff values and choosing the appropriate number of…

Comparison of standard maximum likelihood classification and polytomous logistic regression used in remote sensing

Treesearch

John Hogland; Nedret Billor; Nathaniel Anderson

2013-01-01

Discriminant analysis, referred to as maximum likelihood classification within popular remote sensing software packages, is a common supervised technique used by analysts. Polytomous logistic regression (PLR), also referred to as multinomial logistic regression, is an alternative classification approach that is less restrictive, more flexible, and easy to interpret. To...
Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications

PubMed Central

Huang, Jian; Zhang, Cun-Hui

2013-01-01

The ℓ1-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weighted ℓ1-penalized estimators for convex loss functions of a general form, including the generalized linear models. We study the estimation, prediction, selection and sparsity properties of the weighted ℓ1-penalized estimator in sparse, high-dimensional settings where the number of predictors p can be much larger than the sample size n. Adaptive Lasso is considered as a special case. A multistage method is developed to approximate concave regularized estimation by applying an adaptive Lasso recursively. We provide prediction and estimation oracle inequalities for single- and multi-stage estimators, a general selection consistency theorem, and an upper bound for the dimension of the Lasso estimator. Important models including the linear regression, logistic regression and log-linear models are used throughout to illustrate the applications of the general results. PMID:24348100
Intra-Urban Mobility and its Potential Impact on the Spread of Blood-Borne Infections among Drug Injectors in Tijuana, Mexico

PubMed Central

Brouwer, Kimberly C.; Lozada, Remedios; Weeks, John R.; Magis-Rodríguez, Carlos; Firestone-Cruz, Michelle; Strathdee, Steffanie A.

2013-01-01

We explored intra-urban mobility of Tijuana, Mexico injection drug users (IDUs). In 2005, 222 IDUs underwent behavioral surveys and infectious disease testing. Participants resided in 58 neighborhoods, but regularly injected in 30. From logistic regression, “mobile” IDUs (injecting ≥3 km from their residence) were more likely to cross the Mexico/U.S. border, share needles, and get arrested for carrying syringes - but less likely to identify hepatitis as an injection risk. Mobile participants lived in neighborhoods with less drug activity, treatment centers, or migrants, but higher marriage and home ownership rates. Mobile IDUs should be targeted for outreach and further investigation. PMID:22136446
Missed or Delayed Medical Care Appointments by Older Users of Nonemergency Medical Transportation

PubMed Central

MacLeod, Kara E.; Ragland, David R.; Prohaska, Thomas R.; Smith, Matthew Lee; Irmiter, Cheryl; Satariano, William A.

2015-01-01

Purpose of the Study: This study identified factors associated with canceling nonemergency medical transportation appointments among older adult Medicaid patients. Design and Methods: Data from 125,913 trips for 2,913 Delaware clients were examined. Mediation analyses, as well as, multivariate logistic regressions were conducted. Results: Over half of canceled trips were attributed to client reasons (e.g., no show, refusal). Client characteristics (e.g., race, sex, functional status) were associated with cancelations; however, these differed based on the cancelation reason. Regularly scheduled trips were less likely to be canceled. Implications: The evolving American health care system may increase service availability. Additional policies can improve service accessibility and overcome utilization barriers. PMID:24558264
[Factors determining irregular attendance to follow-up visits among human immunodeficiency virus patients: results of the hospital survey of patients infected with human immunodeficiency virus].

PubMed

Diaz, Asuncion; Ten, Alicia; Marcos, Henar; Gutiérrez, Gonzalo; González-García, Juan; Moreno, Santiago; Barrios, Ana María; Arponen, Sari; Portillo, Álvaro; Serrano, Regino; García, Maria Teresa; Pérez, José Luis; Toledo, Javier; Royo, Maria Carmen; González, Gustavo; Izquierdo, Ana; Viloria, Luis Javier; López, Irene; Elizalde, Lázaro; Martínez, Eva; Castrillejo, Daniel; Aranguren, Rosa; Redondo, Caridad; Diez, Mercedes

2015-05-01

To describe the occurrence of non-regular attendance to follow-up visits among HIV patients and to analyze the determining factors. One-day survey carried out annually (2002-2012) in public hospitals. Epidemiological, clinical and behavioral data are collected in all HIV-infected inpatients and outpatients receiving HIV-related care on the day of the survey. "Non-regular attendance to a follow-up visit" was defined as sporadic attendance to the medical appointments, according to the judgment of the attending physician. Descriptive and bivariate analyses were performed, and factors associated to non-regular attendance to follow-up visits were estimated using logistic regression. A total of 7,304 subjects were included, of whom 13.7% did not attend medical appointments regularly. Factors directly associated with non-regular attendance were: age between 25-49 years; birth in Sub-Saharan Africa or Latin-America; low educational level; being homeless or in prison; living alone or in closed institutions; being unemployed or retired; being an intravenous drug user; not using a condom at last sexual encounter, and injecting drugs in the last 30 days. Conversely, HIV diagnosis within the last year and being men who have sex with men were factors inversely associated with non-regular attendance to follow-up visits. In spite of health care beings free of charge for everyone in Spain, social factors can act as barriers to regular attendance to medical appointments, which, in turn, can endanger treatment effectiveness in some population groups. This should be taken into account when planning HIV policies in Spain. Copyright © 2014 Elsevier España, S.L.U. y Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.
An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

ERIC Educational Resources Information Center

Weiss, Brandi A.; Dardick, William

2016-01-01

This article introduces an entropy-based measure of data-model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify…
What Are the Odds of that? A Primer on Understanding Logistic Regression

ERIC Educational Resources Information Center

Huang, Francis L.; Moon, Tonya R.

2013-01-01

The purpose of this Methodological Brief is to present a brief primer on logistic regression, a commonly used technique when modeling dichotomous outcomes. Using data from the National Education Longitudinal Study of 1988 (NELS:88), logistic regression techniques were used to investigate student-level variables in eighth grade (i.e., enrolled in a…
On the Usefulness of a Multilevel Logistic Regression Approach to Person-Fit Analysis

ERIC Educational Resources Information Center

Conijn, Judith M.; Emons, Wilco H. M.; van Assen, Marcel A. L. M.; Sijtsma, Klaas

2011-01-01

The logistic person response function (PRF) models the probability of a correct response as a function of the item locations. Reise (2000) proposed to use the slope parameter of the logistic PRF as a person-fit measure. He reformulated the logistic PRF model as a multilevel logistic regression model and estimated the PRF parameters from this…
Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

PubMed

Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

2015-08-01

Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.
Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches.

PubMed

Valle, Denis; Lima, Joanna M Tucker; Millar, Justin; Amratia, Punam; Haque, Ubydul

2015-11-04

Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue. A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon. A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression. Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.
Explaining Match Outcome During The Men’s Basketball Tournament at The Olympic Games

PubMed Central

Leicht, Anthony S.; Gómez, Miguel A.; Woods, Carl T.

2017-01-01

In preparation for the Olympics, there is a limited opportunity for coaches and athletes to interact regularly with team performance indicators providing important guidance to coaches for enhanced match success at the elite level. This study examined the relationship between match outcome and team performance indicators during men’s basketball tournaments at the Olympic Games. Twelve team performance indicators were collated from all men’s teams and matches during the basketball tournament of the 2004-2016 Olympic Games (n = 156). Linear and non-linear analyses examined the relationship between match outcome and team performance indicator characteristics; namely, binary logistic regression and a conditional interference (CI) classification tree. The most parsimonious logistic regression model retained ‘assists’, ‘defensive rebounds’, ‘field-goal percentage’, ‘fouls’, ‘fouls against’, ‘steals’ and ‘turnovers’ (delta AIC <0.01; Akaike weight = 0.28) with a classification accuracy of 85.5%. Conversely, four performance indicators were retained with the CI classification tree with an average classification accuracy of 81.4%. However, it was the combination of ‘field-goal percentage’ and ‘defensive rebounds’ that provided the greatest probability of winning (93.2%). Match outcome during the men’s basketball tournaments at the Olympic Games was identified by a unique combination of performance indicators. Despite the average model accuracy being marginally higher for the logistic regression analysis, the CI classification tree offered a greater practical utility for coaches through its resolution of non-linear phenomena to guide team success. Key points A unique combination of team performance indicators explained 93.2% of winning observations in men’s basketball at the Olympics. Monitoring of these team performance indicators may provide coaches with the capability to devise multiple game plans or strategies to enhance their likelihood of winning. Incorporation of machine learning techniques with team performance indicators may provide a valuable and strategic approach to explain patterns within multivariate datasets in sport science. PMID:29238245
Psychosocial predictors of breast self-examination behavior among female students: an application of the health belief model using logistic regression.

PubMed

Didarloo, Alireza; Nabilou, Bahram; Khalkhali, Hamid Reza

2017-11-03

Breast cancer is a life-threatening condition affecting women around the world. The early detection of breast lumps using a breast self-examination (BSE) is important for the prevention and control of this disease. The aim of this study was to examine BSE behavior and its predictive factors among female university students using the Health Belief Model (HBM). This investigation was a cross-sectional survey carried out with 334 female students at Urmia University of Medical Sciences in the northwest of Iran. To collect the necessary data, researchers applied a valid and reliable three-part questionnaire. The data were analyzed using descriptive statistics and a chi-square test, in addition to multivariate logistic regression statistics in SPSS software version 16.0 (SPSS Inc., Chicago, IL, USA). The results indicated that 82 of the 334 participants (24.6%) reported practicing BSEs. Multivariate logistic regression analyses showed that high perceived severity [OR = 2.38, 95% CI = (1.02-5.54)], high perceived benefits [OR = 1.94, 95% CI = (1.09-3.46)], and high perceived self-efficacy [OR = 13.15, 95% CI = (3.64-47.51)] were better predictors of BSE behavior (P < 0.05) than low perceived severity, benefits, and self-efficacy. The findings also showed that a high level of knowledge compared to a low level of knowledge [OR = 5.51, 95% CI = (1.79-16.86)] and academic undergraduate and graduate degrees compared to doctoral degrees [OR = 2.90, 95% CI = (1.42-5.92)] of the participants were predictors of BSE performance (P < 0.05). The study revealed that the HBM constructs are able to predict BSE behavior. Among these constructs, self-efficacy was the most important predictor of the behavior. Interventions based on the constructs of perceived self-efficacy, benefits, and severity are recommended for increasing women's regular screening for breast cancer.
Logistic regression for risk factor modelling in stuttering research.

PubMed

Reed, Phil; Wu, Yaqionq

2013-06-01

To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.
Dynamic Dimensionality Selection for Bayesian Classifier Ensembles

DTIC Science & Technology

2015-03-19

learning of weights in an otherwise generatively learned naive Bayes classifier. WANBIA-C is very cometitive to Logistic Regression but much more...classifier, Generative learning, Discriminative learning, Naïve Bayes, Feature selection, Logistic regression , higher order attribute independence 16...discriminative learning of weights in an otherwise generatively learned naive Bayes classifier. WANBIA-C is very cometitive to Logistic Regression but
A review of logistic regression models used to predict post-fire tree mortality of western North American conifers

Treesearch

Travis Woolley; David C. Shaw; Lisa M. Ganio; Stephen Fitzgerald

2012-01-01

Logistic regression models used to predict tree mortality are critical to post-fire management, planning prescribed bums and understanding disturbance ecology. We review literature concerning post-fire mortality prediction using logistic regression models for coniferous tree species in the western USA. We include synthesis and review of: methods to develop, evaluate...
Preserving Institutional Privacy in Distributed binary Logistic Regression.

PubMed

Wu, Yuan; Jiang, Xiaoqian; Ohno-Machado, Lucila

2012-01-01

Privacy is becoming a major concern when sharing biomedical data across institutions. Although methods for protecting privacy of individual patients have been proposed, it is not clear how to protect the institutional privacy, which is many times a critical concern of data custodians. Built upon our previous work, Grid Binary LOgistic REgression (GLORE)1, we developed an Institutional Privacy-preserving Distributed binary Logistic Regression model (IPDLR) that considers both individual and institutional privacy for building a logistic regression model in a distributed manner. We tested our method using both simulated and clinical data, showing how it is possible to protect the privacy of individuals and of institutions using a distributed strategy.
Covariate Imbalance and Adjustment for Logistic Regression Analysis of Clinical Trial Data

PubMed Central

Ciolino, Jody D.; Martin, Reneé H.; Zhao, Wenle; Jauch, Edward C.; Hill, Michael D.; Palesch, Yuko Y.

2014-01-01

In logistic regression analysis for binary clinical trial data, adjusted treatment effect estimates are often not equivalent to unadjusted estimates in the presence of influential covariates. This paper uses simulation to quantify the benefit of covariate adjustment in logistic regression. However, International Conference on Harmonization guidelines suggest that covariate adjustment be pre-specified. Unplanned adjusted analyses should be considered secondary. Results suggest that that if adjustment is not possible or unplanned in a logistic setting, balance in continuous covariates can alleviate some (but never all) of the shortcomings of unadjusted analyses. The case of log binomial regression is also explored. PMID:24138438
Differentially private distributed logistic regression using private and public data.

PubMed

Ji, Zhanglong; Jiang, Xiaoqian; Wang, Shuang; Xiong, Li; Ohno-Machado, Lucila

2014-01-01

Privacy protecting is an important issue in medical informatics and differential privacy is a state-of-the-art framework for data privacy research. Differential privacy offers provable privacy against attackers who have auxiliary information, and can be applied to data mining models (for example, logistic regression). However, differentially private methods sometimes introduce too much noise and make outputs less useful. Given available public data in medical research (e.g. from patients who sign open-consent agreements), we can design algorithms that use both public and private data sets to decrease the amount of noise that is introduced. In this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data. We try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios. Logistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee.
Logistic regression analysis of conventional ultrasonography, strain elastosonography, and contrast-enhanced ultrasound characteristics for the differentiation of benign and malignant thyroid nodules

PubMed Central

Deng, Yingyuan; Wang, Tianfu; Chen, Siping; Liu, Weixiang

2017-01-01

The aim of the study is to screen the significant sonographic features by logistic regression analysis and fit a model to diagnose thyroid nodules. A total of 525 pathological thyroid nodules were retrospectively analyzed. All the nodules underwent conventional ultrasonography (US), strain elastosonography (SE), and contrast -enhanced ultrasound (CEUS). Those nodules’ 12 suspicious sonographic features were used to assess thyroid nodules. The significant features of diagnosing thyroid nodules were picked out by logistic regression analysis. All variables that were statistically related to diagnosis of thyroid nodules, at a level of p < 0.05 were embodied in a logistic regression analysis model. The significant features in the logistic regression model of diagnosing thyroid nodules were calcification, suspected cervical lymph node metastasis, hypoenhancement pattern, margin, shape, vascularity, posterior acoustic, echogenicity, and elastography score. According to the results of logistic regression analysis, the formula that could predict whether or not thyroid nodules are malignant was established. The area under the receiver operating curve (ROC) was 0.930 and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 83.77%, 89.56%, 87.05%, 86.04%, and 87.79% respectively. PMID:29228030
Logistic regression analysis of conventional ultrasonography, strain elastosonography, and contrast-enhanced ultrasound characteristics for the differentiation of benign and malignant thyroid nodules.

PubMed

Pang, Tiantian; Huang, Leidan; Deng, Yingyuan; Wang, Tianfu; Chen, Siping; Gong, Xuehao; Liu, Weixiang

2017-01-01

The aim of the study is to screen the significant sonographic features by logistic regression analysis and fit a model to diagnose thyroid nodules. A total of 525 pathological thyroid nodules were retrospectively analyzed. All the nodules underwent conventional ultrasonography (US), strain elastosonography (SE), and contrast -enhanced ultrasound (CEUS). Those nodules' 12 suspicious sonographic features were used to assess thyroid nodules. The significant features of diagnosing thyroid nodules were picked out by logistic regression analysis. All variables that were statistically related to diagnosis of thyroid nodules, at a level of p < 0.05 were embodied in a logistic regression analysis model. The significant features in the logistic regression model of diagnosing thyroid nodules were calcification, suspected cervical lymph node metastasis, hypoenhancement pattern, margin, shape, vascularity, posterior acoustic, echogenicity, and elastography score. According to the results of logistic regression analysis, the formula that could predict whether or not thyroid nodules are malignant was established. The area under the receiver operating curve (ROC) was 0.930 and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 83.77%, 89.56%, 87.05%, 86.04%, and 87.79% respectively.

Prevalence and Determinants of Preterm Birth in Tehran, Iran: A Comparison between Logistic Regression and Decision Tree Methods.

PubMed

Amini, Payam; Maroufizadeh, Saman; Samani, Reza Omani; Hamidi, Omid; Sepidarkish, Mahdi

2017-06-01

Preterm birth (PTB) is a leading cause of neonatal death and the second biggest cause of death in children under five years of age. The objective of this study was to determine the prevalence of PTB and its associated factors using logistic regression and decision tree classification methods. This cross-sectional study was conducted on 4,415 pregnant women in Tehran, Iran, from July 6-21, 2015. Data were collected by a researcher-developed questionnaire through interviews with mothers and review of their medical records. To evaluate the accuracy of the logistic regression and decision tree methods, several indices such as sensitivity, specificity, and the area under the curve were used. The PTB rate was 5.5% in this study. The logistic regression outperformed the decision tree for the classification of PTB based on risk factors. Logistic regression showed that multiple pregnancies, mothers with preeclampsia, and those who conceived with assisted reproductive technology had an increased risk for PTB ( p < 0.05). Identifying and training mothers at risk as well as improving prenatal care may reduce the PTB rate. We also recommend that statisticians utilize the logistic regression model for the classification of risk groups for PTB.
Borderline personality disorder and regularly drinking alcohol before sex.

PubMed

Thompson, Ronald G; Eaton, Nicholas R; Hu, Mei-Chen; Hasin, Deborah S

2017-07-01

Drinking alcohol before sex increases the likelihood of engaging in unprotected intercourse, having multiple sexual partners and becoming infected with sexually transmitted infections. Borderline personality disorder (BPD), a complex psychiatric disorder characterised by pervasive instability in emotional regulation, self-image, interpersonal relationships and impulse control, is associated with substance use disorders and sexual risk behaviours. However, no study has examined the relationship between BPD and drinking alcohol before sex in the USA. This study examined the association between BPD and regularly drinking before sex in a nationally representative adult sample. Participants were 17 491 sexually active drinkers from Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. Logistic regression models estimated effects of BPD diagnosis, specific borderline diagnostic criteria and BPD criterion count on the likelihood of regularly (mostly or always) drinking alcohol before sex, adjusted for controls. Borderline personality disorder diagnosis doubled the odds of regularly drinking before sex [adjusted odds ratio (AOR) = 2.26; confidence interval (CI) = 1.63, 3.14]. Of nine diagnostic criteria, impulsivity in areas that are self-damaging remained a significant predictor of regularly drinking before sex (AOR = 1.82; CI = 1.42, 2.35). The odds of regularly drinking before sex increased by 20% for each endorsed criterion (AOR = 1.20; CI = 1.14, 1.27) DISCUSSION AND CONCLUSIONS: This is the first study to examine the relationship between BPD and regularly drinking alcohol before sex in the USA. Substance misuse treatment should assess regularly drinking before sex, particularly among patients with BPD, and BPD treatment should assess risk at the intersection of impulsivity, sexual behaviour and substance use. [Thompson Jr RG, Eaton NR, Hu M-C, Hasin DS Borderline personality disorder and regularly drinking alcohol before sex Drug Alcohol Rev 2017;36:540-545]. © 2017 Australasian Professional Society on Alcohol and other Drugs.
The association between peer, parental influence and tobacco product features and earlier age of onset of regular smoking among adults in 27 European countries.

PubMed

Filippidis, Filippos T; Agaku, Israel T; Vardavas, Constantine I

2015-10-01

Factors that influence smoking initiation and age of smoking onset are important considerations in tobacco control. We evaluated European Union (EU)-wide differences in the age of onset of regular smoking, and the potential role of peer, parental and tobacco product design features on the earlier onset of regular smoking among adults <40 years old in 27 EU countries. We analysed data from 4442 current and former smokers aged 15-39 years, collected for the Eurobarometer 77.1 survey (2012). Respondents reported their age at regular smoking onset and factors that influenced their decision to start smoking, including peer influence, parental influence and features of tobacco products. Multi-variable logistic regression, adjusted for age; geographic region; education; difficulty to pay bills; and gender, was used to assess the role of the various pro-tobacco influences on early onset of regular smoking (i.e. <18 years). Among ever smokers, the mean age of onset of regular smoking was 16.6 years, ranging from 15.8 to 18.8 years in member countries. 68.1% responded that they started smoking regularly when they were <18 years old. Ever smokers who reported they were influenced by peers (OR = 1.70; 95%CI 1.30-2.20) or parents (OR = 1.60; 95%CI 1.21-2.12) were more likely to have started smoking regularly <18 years old. No significant association between design and marketing features of tobacco products and an early initiation of regular smoking was observed (OR = 1.04; 95%CI 0.83-1.31). We identified major differences in smoking initiation patterns among EU countries, which may warrant different approaches in the prevention of tobacco use. © The Author 2015. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.
Young women's perceptions and experiences with contraception supply in community pharmacies.

PubMed

Fakih, Souhiela; Batra, Peter; Gatny, Heather H; Kusunoki, Yasamin; Barber, Jennifer S; Farris, Karen B

2015-01-01

Unintended pregnancy is a major public health problem in the United States.Correct contraceptive use can reduce the rate of unintended pregnancy. Community pharmacies are well positioned to provide contraceptives and advice about contraception. To determine young women's perceptions and experiences with contraception supply in community pharmacies and to identify whether pharmacy characteristics predicted very positive experiences. This study comprised two cross-sectional surveys including an online women's pharmacy perceptions and experiences (PPE) survey and a faxed/observed survey of community pharmacies. One county in Michigan. Young women and community pharmacies. The two surveys were merged to explore pharmacy characteristics that may impact women's perceptions and experiences with community pharmacies. Multiple logistic regression analysis was used to explore relationships between pharmacy characteristics and positive outcomes. The response rate for the PPE survey was 54% (n = 343/637). Data from all community pharmacies in the county was retrieved via fax (n = 41/94, 43.6%) or observation (n = 53/94, 56.4%). Women were included in this analysis if they indicated a regular pharmacy (one they go to most often) in the county of interest (n = 210). More than 50% of women (n = 125/210) visited a pharmacy more than once per month. Sixty percent of women were currently using something to prevent pregnancy (n = 124/210, 60.8%). Thirty-five percent of women had a positive experience (n = 73/210, 34.8%). In the multiple logistic regression, women who visited a chain pharmacy had almost 65% lower odds of an overall positive experience with their regular pharmacy compared with women who visited a grocery or mass merchandise pharmacy (odds ratio 0.35 [95% CI 0.16], P = 0.75). Young women visit community pharmacies and use contraceptives frequently. Interventions need to be developed and implemented to improve young women's perceptions and experiences with contraception at community pharmacies.
How do drug market changes affect characteristics of injecting initiation and subsequent patterns of drug use? Findings from a cohort of regular heroin and methamphetamine injectors in Melbourne, Australia.

PubMed

Horyniak, Danielle; Stoové, Mark; Degenhardt, Louisa; Aitken, Campbell; Kerr, Thomas; Dietze, Paul

2015-01-01

Changes in drug market characteristics have been shown to affect drug use patterns but few studies have examined their impacts on injecting initiation experiences and subsequent patterns of injecting drug use (IDU). We collected data on self-reported injecting initiation experiences and past-month patterns of IDU from 688 regular heroin and methamphetamine injectors in Melbourne, Australia, who initiated injecting across three different drug market periods (prior to the Australian heroin shortage ('high heroin')/immediately following the shortage ('low heroin')/'contemporary' markets (fluctuating heroin and methamphetamine availability)). We used univariable and multivariable logistic regression to examine the relationship between period of injecting initiation and first drug injected, and multinomial logistic regression for the relationship between period of injecting initiation and current injecting patterns. 425 participants (62%) reported initiating injecting in the high heroin period, 146 (21%) in the low heroin period, and 117 (17%) in the contemporary period. Participants who initiated injecting during the low heroin period were twice as likely to initiate injecting using a drug other than heroin (AOR: 1.94, 95% CI: 1.27-2.95). The most common patterns of drug use among study participants in the month preceding interview were polydrug use (44%) and primary heroin use (41%). Injecting initiation period was either non-significantly or weakly associated with current drug use pattern, which was more strongly associated with other socio-demographic and drug use characteristics, particularly self-reported drug of choice. The drug market period in which injecting initiation occurred influenced the first drug injected and influenced some aspects of subsequent drug use. In the context of highly dynamic drug markets in which polydrug use is common there is a need for broad harm reduction and drug treatment services which are flexible and responsive to changing patterns of drug use. Copyright © 2014 Elsevier B.V. All rights reserved.
Young women’s perceptions and experiences with contraception supply in community pharmacies

PubMed Central

Fakih, Souhiela; Batra, Peter; Gatny, Heather H; Kusunoki, Yasamin; Barber, Jennifer S.; Farris, Karen B.

2015-01-01

Background Unintended pregnancy is a major public health problem in the United States.. Correct contraceptive use can reduce the rate of unintended pregnancy. Community pharmacies are well positioned to provide contraceptives and advise about contraception. Objectives (1) determine young women’s perceptions and experiences with contraception supply in community pharmacies and (2) identify whether very pharmacy characteristics predicted positive experiences. Design This study was comprised of two cross-sectional surveys including an online women’s pharmacy perceptions and experiences (PPE) survey and a faxed/observed survey of community pharmacies. Setting One County in Michigan, USA Participants Young women and community pharmacies Main outcome measure The two surveys were merged to explore pharmacy characteristics that may impact women’s perceptions and experiences with community pharmacies. Multiple logistic regression analysis was used to explore relationships between pharmacy characteristics and positive outcomes. Results The response rate for the PPE survey was 54% (n= 334/637). Data from all community pharmacies in the county was retrieved via fax (n= 41/94, 43.6%) or observation (n= 53/94, 56.4%). Women were included in this analysis if they indicated a regular (most commonly used) pharmacy in the county of interest (n=210). Over 50% of women (n= 125/210) visited a pharmacy more than once per month. Sixty percent of women were currently using something to prevent pregnancy (n=124/210, 60.8%). Thirty-five percent of women had a positive experience (n=73/210, 34.8%). In the multiple logistic regression, women who visited a chain pharmacy had almost 65% lower odds of an overall positive experience with their regular pharmacy, compared to women who visited a grocery or mass merchandise pharmacy (OR: 0.35, 95% CI: 0.16, 0.75). Conclusion Young women visit community pharmacies and use contraceptives frequently. Interventions need to be developed and implemented to improve young women’s perceptions and experiences with contraception at community pharmacies. PMID:26003156
Regular Use of Aspirin or Non-Aspirin Nonsteroidal Anti-Inflammatory Drugs Is Not Associated With Risk of Incident Pancreatic Cancer in Two Large Cohort Studies.

PubMed

Khalaf, Natalia; Yuan, Chen; Hamada, Tsuyoshi; Cao, Yin; Babic, Ana; Morales-Oyarvide, Vicente; Kraft, Peter; Ng, Kimmie; Giovannucci, Edward; Ogino, Shuji; Stampfer, Meir; Cochrane, Barbara B; Manson, JoAnn E; Clish, Clary B; Chan, Andrew T; Fuchs, Charles S; Wolpin, Brian M

2018-04-01

Use of aspirin and/or non-aspirin nonsteroidal anti-inflammatory drugs (NSAIDs) reduces the risk of several cancers, but it is not clear if use of these drugs is associated with risk of pancreatic cancer. We evaluated aspirin and non-aspirin NSAID use and risk of pancreatic adenocarcinoma in 141,940 participants from the Health Professionals Follow-up Study and Nurses' Health Study using multivariable-adjusted Cox proportional hazards regression. We considered several exposure classifications to model differing lag times between NSAID exposure and cancer development. We also conducted a nested case-control study of participants from 3 prospective cohorts using conditional logistic regression to evaluate pre-diagnosis levels of plasma salicylurate, a major metabolite of aspirin, in 396 pancreatic cancer cases and 784 matched individuals without pancreatic cancer (controls). In the prospective cohort study, 1122 participants developed pancreatic adenocarcinoma over 4.2 million person-years. Use of aspirin or non-aspirin NSAIDs was not associated with pancreatic cancer risk, even after considering several latency exposure classifications. In a pre-planned subgroup analysis, regular aspirin use was associated with reduced pancreatic cancer risk among participants with diabetes (relative risk, 0.71; 95% CI, 0.54-0.94). In the nested case-control study, pre-diagnosis levels of salicylurate were not associated with pancreatic cancer risk (odds ratio, 1.08; 95% CI, 0.72-1.61; P trend 0.81; comparing participants in the highest quintile with those in the lowest quintile of plasma salicylurate). Regular aspirin or non-aspirin NSAID use was not associated with future risk of pancreatic cancer in participants from several large prospective cohort studies. A possible reduction in risk for pancreatic cancer among people with diabetes who regularly use aspirin should be further examined in preclinical and human studies. Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.
Brazilian immigrants' oral health literacy and participation in oral health care in Canada.

PubMed

Calvasina, Paola; Lawrence, Herenia P; Hoffman-Goetz, Laurie; Norman, Cameron D

2016-02-15

Inadequate functional health literacy is a common problem in immigrant populations. The aim of this study was to investigate the association between oral (dental) health literacy (OHL) and participation in oral health care among Brazilian immigrants in Toronto, Ontario, Canada. The study used a cross-sectional design and a convenience sample of 101 Brazilian immigrants selected through the snowball sampling technique. Data were analyzed using descriptive statistics and logistic regression modeling. Most of the sample had adequate OHL (83.1 %). Inadequate/marginal OHL was associated with not visiting a dentist in the preceding year (OR = 3.61; p = 0.04), not having a dentist as the primary source of dental information (OR = 5.55; p < 0.01), and not participating in shared dental treatment decision making (OR = 1.06; p = 0.05; OHL as a continuous variable) in multivariate logistic regressions controlling for covariates. A low average annual family income was associated with two indicators of poor participation in oral health care (i.e., not having visited a dentist in the previous year, and not having a dentist as regular source of dental information). Limited OHL was linked to lower participation in the oral health care system and with barriers to using dental services among a sample of Brazilian immigrants. More effective knowledge transfer will be required to help specific groups of immigrants to better navigate the Canadian dental care system.
Arteriovenous fistula maturation in patients with permanent access created prior to or after hemodialysis initiation.

PubMed

Duque, Juan C; Martinez, Laisel; Tabbara, Marwan; Dvorquez, Denise; Mehandru, Sushil K; Asif, Arif; Vazquez-Padron, Roberto I; Salman, Loay H

2017-05-15

Multiple factors and comorbidities have been implicated in the ability of arteriovenous fistulas (AVF) to mature, including vessel anatomy, advanced age, and the presence of coronary artery disease or peripheral vascular disease. However, little is known about the role of uremia on AVF primary failure. In this study, we attempt to evaluate the effect of uremia on AVF maturation by comparing AVF outcomes between pre-dialysis chronic kidney disease (CKD) stage five patients and those who had their AVF created after hemodialysis (HD) initiation. We included 612 patients who underwent AVF creation between 2003 and 2015 at the University of Miami Hospital and Jackson Memorial Hospital. Effects of uremia on primary failure were evaluated using univariate statistical comparisons and multivariate logistic regression analyses. Primary failure occurred in 28.1% and 26.3% of patients with an AVF created prior to or after HD initiation, respectively (p = 0.73). The time of HD initiation was not associated with AVF maturation in multivariate logistic regression analysis (p = 0.57). In addition, pre-operative blood urea nitrogen (p = 0.78), estimated glomerular filtration rate (p = 0.66), and serum creatinine levels (p = 0.14) were not associated with AVF primary failure in pre-dialysis patients. Our results show that clearance of uremia with regular HD treatments prior to AVF creation does not improve the frequency of vascular access maturation.
Predictors of dental visits among primary school children in the rural Australian community of Lithgow.

PubMed

John, James Rufus; Mannan, Haider; Nargundkar, Subrat; D'Souza, Mario; Do, Loc Giang; Arora, Amit

2017-04-11

Regular dental attendance is significant in maintaining and improving children's oral health and well-being. This study aims to determine the factors that predict and influence dental visits in primary school children residing in the rural community of Lithgow, New South Wales (NSW), Australia. All six primary schools of Lithgow were approached to participate in a cross-sectional survey prior to implementing water fluoridation in 2014. Children aged 6-13 years (n = 667) were clinically examined for their oral health status and parents were requested to complete a questionnaire on fluoride history, diet, last dental visit, and socio-demographic characteristics. Multiple logistic regression analyses were employed to examine the independent predictors of a 6-monthly and a yearly dental visit. Overall, 53% of children visited a dentist within six months and 77% within twelve months. In multiple logistic regression analyses, age of the child and private health insurance coverage were significantly associated with both 6-monthly and twelve-month dental visits. In addition, each serve of chocolate consumption was significantly associated with a 27% higher odds (OR = 1.27, 95% CI: 1.05-1.54) of a 6-monthly dental visit. It is imperative that the socio-demographic and dietary factors that influence child oral health must be effectively addressed when developing the oral health promotion policies to ensure better oral health outcomes.
Logistic regression for dichotomized counts.

PubMed

Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W

2016-12-01

Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.
Predicting 30-day Hospital Readmission with Publicly Available Administrative Database. A Conditional Logistic Regression Modeling Approach.

PubMed

Zhu, K; Lou, Z; Zhou, J; Ballester, N; Kong, N; Parikh, P

2015-01-01

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Hospital readmissions raise healthcare costs and cause significant distress to providers and patients. It is, therefore, of great interest to healthcare organizations to predict what patients are at risk to be readmitted to their hospitals. However, current logistic regression based risk prediction models have limited prediction power when applied to hospital administrative data. Meanwhile, although decision trees and random forests have been applied, they tend to be too complex to understand among the hospital practitioners. Explore the use of conditional logistic regression to increase the prediction accuracy. We analyzed an HCUP statewide inpatient discharge record dataset, which includes patient demographics, clinical and care utilization data from California. We extracted records of heart failure Medicare beneficiaries who had inpatient experience during an 11-month period. We corrected the data imbalance issue with under-sampling. In our study, we first applied standard logistic regression and decision tree to obtain influential variables and derive practically meaning decision rules. We then stratified the original data set accordingly and applied logistic regression on each data stratum. We further explored the effect of interacting variables in the logistic regression modeling. We conducted cross validation to assess the overall prediction performance of conditional logistic regression (CLR) and compared it with standard classification models. The developed CLR models outperformed several standard classification models (e.g., straightforward logistic regression, stepwise logistic regression, random forest, support vector machine). For example, the best CLR model improved the classification accuracy by nearly 20% over the straightforward logistic regression model. Furthermore, the developed CLR models tend to achieve better sensitivity of more than 10% over the standard classification models, which can be translated to correct labeling of additional 400 - 500 readmissions for heart failure patients in the state of California over a year. Lastly, several key predictor identified from the HCUP data include the disposition location from discharge, the number of chronic conditions, and the number of acute procedures. It would be beneficial to apply simple decision rules obtained from the decision tree in an ad-hoc manner to guide the cohort stratification. It could be potentially beneficial to explore the effect of pairwise interactions between influential predictors when building the logistic regression models for different data strata. Judicious use of the ad-hoc CLR models developed offers insights into future development of prediction models for hospital readmissions, which can lead to better intuition in identifying high-risk patients and developing effective post-discharge care strategies. Lastly, this paper is expected to raise the awareness of collecting data on additional markers and developing necessary database infrastructure for larger-scale exploratory studies on readmission risk prediction.
Frequent condom use with casual partners varies by sexual position among younger gay and bisexual men in New Zealand: national behavioural surveillance 2006-2011.

PubMed

Lachowsky, Nathan J; Saxton, Peter J W; Hughes, Anthony J; Dickson, Nigel P; Milhausen, Robin R; Dewey, Cate E; Summerlee, Alastair J S

2016-02-01

Background Condom promotion remains a cornerstone of HIV/STI control, but must be informed by evidence of uptake and address disparities in use. This study sought to determine the prevalence of, and demographic, behavioural and relational factors associated with, condom use during insertive and receptive anal intercourse with casual partners among younger gay, bisexual and other men who have sex with men (YMSM) in New Zealand. The 2006-2011 national HIV behavioural surveillance data for YMSM aged 16-29 years was pooled. Separately for each sexual position, frequent (always/almost always) versus infrequent condom use was regressed onto explanatory variables using manual backward stepwise multivariable logistic regression analysis. Three-quarters of YMSM reported frequent condom use during insertive (76.0%) and receptive (73.8%) anal intercourse. YMSM who were exclusively insertive were more likely to report frequent condom use than versatile YMSM. Factors positively associated with frequent condom use, irrespective of sexual position were: in-person versus web-based recruitment, testing HIV negative versus never testing or testing HIV positive, having no recent sex with women, reporting two to five versus one male sexual partner in the past 6 months, reporting no current regular partner, but if in a regular relationship, reporting a boyfriend-type versus fuckbuddy-type partner, and frequent versus infrequent regular partner condom use. Pacific ethnicity and less formal education were negatively associated with frequent condom use only during receptive anal intercourse. The findings from this study demonstrate that condom norms can be actively established and maintained among YMSM. Condom promotion efforts must increase YMSM's capacity, agency and skills to negotiate condom use, especially for the receptive partner.
Predicting and explaining inflammation in Crohn's disease patients using predictive analytics methods and electronic medical record data.

PubMed

Reddy, Bhargava K; Delen, Dursun; Agrawal, Rupesh K

2018-01-01

Crohn's disease is among the chronic inflammatory bowel diseases that impact the gastrointestinal tract. Understanding and predicting the severity of inflammation in real-time settings is critical to disease management. Extant literature has primarily focused on studies that are conducted in clinical trial settings to investigate the impact of a drug treatment on the remission status of the disease. This research proposes an analytics methodology where three different types of prediction models are developed to predict and to explain the severity of inflammation in patients diagnosed with Crohn's disease. The results show that machine-learning-based analytic methods such as gradient boosting machines can predict the inflammation severity with a very high accuracy (area under the curve = 92.82%), followed by regularized regression and logistic regression. According to the findings, a combination of baseline laboratory parameters, patient demographic characteristics, and disease location are among the strongest predictors of inflammation severity in Crohn's disease patients.
Negative perceptions of illness and health are associated with frequent use of physiotherapy in primary healthcare.

PubMed

Opseth, Gro; Wahl, Astrid Klopstad; Bjørke, Gustav; Mengshoel, Anne Marit

2018-03-01

There is growing concern that an ageing population and increasing number of patients with chronic illnesses in the future will foster a need for health services beyond the resources available in society. Patients with chronic illnesses are reported to be frequent users of physicians' services in the primary health sector. Therapies for patients with chronic musculoskeletal illnesses are delivered by physiotherapists in this sector. However, we know little about the use of physiotherapy services and the factors that may explain their use. The aim of the present study was to examine the association between the regular/non-regular use of physiotherapy services, impacts of illness, and perceptions of illness and health. A cross-sectional survey included patients between 18 and 70 years of age who visited a physiotherapy outpatient clinic in Oslo during one randomly chosen week. Patient characteristics and use of physiotherapy were mapped. The Brief Illness Perception Questionnaire (BIPQ), a single item of the Short Form Health Survey (SF-12) and the Ørebro Musculoskeletal Pain Questionnaire (ØMPQ) were used to assess perceptions of illness and health, and impacts of illness. Data were analysed using independent sample t-tests and logistic regression analysis. A total of 507 patients with a mean age of 46 (standard deviation 12) years participated, of whom 54% were regular users of physiotherapy. BIPQ (p = 0.02; β = 0.03) and the single-item on general health perception (p = 0.001; β = 0.44,) were the only significant variables in the final equation associated with regular use of physiotherapy. About half of the participants were regular, high consumers of physiotherapy, and negative perceptions of illness and health were associated with this regular use. Copyright © 2017 John Wiley & Sons, Ltd.
Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.
Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.

PubMed

Choi, Seung Hoan; Labadorf, Adam T; Myers, Richard H; Lunetta, Kathryn L; Dupuis, Josée; DeStefano, Anita L

2017-02-06

Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.
Associations Between Alcohol Use and Intimate Partner Violence Among Men Who Have Sex with Men.

PubMed

Davis, Alissa; Kaighobadi, Farnaz; Stephenson, Rob; Rael, Christine; Sandfort, Theodorus

2016-12-01

Intimate partner violence (IPV) research among men who have sex with men (MSM) has primarily focused on the prevalence of IPV victimization and perpetration. Although alcohol use is a known trigger of IPV in opposite sex relationships, less is known about alcohol use and IPV perpetration and victimization in same-sex couples. The aim of this study was to examine associations between alcohol use and different types of IPV victimization and perpetration among MSM. MSM in New York City were recruited at gay-friendly venues and events to participate in an online survey assessing sociodemographics, alcohol use, and victimization/perpetration of IPV with both regular and casual sex partners. Logistic regression was used to examine associations between alcohol use and different types of IPV victimization and perpetration. Among 189 participants, 103 (54.5%) reported experiencing at least one incidence of IPV perpetrated by a regular partner and 92 (48.7%) reported having perpetrated IPV against a regular partner in the past 12 months. Higher levels of alcohol use were significantly associated with (1) physical/sexual and HIV-related IPV victimization by a regular partner, (2) physical/sexual, monitoring, and controlling IPV victimization by a casual partner, (3) physical/sexual, emotional, controlling, and HIV-related IPV perpetration against a regular partner, and (4) physical/sexual and emotional IPV perpetration against a casual partner. The association of high levels of alcohol use with different types of IPV perpetration and IPV victimization suggests a need for targeted services that address the co-occurring issues of alcohol use and IPV.
Differentially private distributed logistic regression using private and public data

PubMed Central

2014-01-01

Background Privacy protecting is an important issue in medical informatics and differential privacy is a state-of-the-art framework for data privacy research. Differential privacy offers provable privacy against attackers who have auxiliary information, and can be applied to data mining models (for example, logistic regression). However, differentially private methods sometimes introduce too much noise and make outputs less useful. Given available public data in medical research (e.g. from patients who sign open-consent agreements), we can design algorithms that use both public and private data sets to decrease the amount of noise that is introduced. Methodology In this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data. Experiments and results We try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios. Conclusion Logistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee. PMID:25079786
A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy.

PubMed

Park, Ji Hyun; Kim, Hyeon-Young; Lee, Hanna; Yun, Eun Kyoung

2015-12-01

This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Performance and strategy comparisons of human listeners and logistic regression in discriminating underwater targets.

PubMed

Yang, Lixue; Chen, Kean

2015-11-01

To improve the design of underwater target recognition systems based on auditory perception, this study compared human listeners with automatic classifiers. Performances measures and strategies in three discrimination experiments, including discriminations between man-made and natural targets, between ships and submarines, and among three types of ships, were used. In the experiments, the subjects were asked to assign a score to each sound based on how confident they were about the category to which it belonged, and logistic regression, which represents linear discriminative models, also completed three similar tasks by utilizing many auditory features. The results indicated that the performances of logistic regression improved as the ratio between inter- and intra-class differences became larger, whereas the performances of the human subjects were limited by their unfamiliarity with the targets. Logistic regression performed better than the human subjects in all tasks but the discrimination between man-made and natural targets, and the strategies employed by excellent human subjects were similar to that of logistic regression. Logistic regression and several human subjects demonstrated similar performances when discriminating man-made and natural targets, but in this case, their strategies were not similar. An appropriate fusion of their strategies led to further improvement in recognition accuracy.
Simulating land-use changes by incorporating spatial autocorrelation and self-organization in CLUE-S modeling: a case study in Zengcheng District, Guangzhou, China

NASA Astrophysics Data System (ADS)

Mei, Zhixiong; Wu, Hao; Li, Shiyun

2018-06-01

The Conversion of Land Use and its Effects at Small regional extent (CLUE-S), which is a widely used model for land-use simulation, utilizes logistic regression to estimate the relationships between land use and its drivers, and thus, predict land-use change probabilities. However, logistic regression disregards possible spatial autocorrelation and self-organization in land-use data. Autologistic regression can depict spatial autocorrelation but cannot address self-organization, while logistic regression by considering only self-organization (NElogistic regression) fails to capture spatial autocorrelation. Therefore, this study developed a regression (NE-autologistic regression) method, which incorporated both spatial autocorrelation and self-organization, to improve CLUE-S. The Zengcheng District of Guangzhou, China was selected as the study area. The land-use data of 2001, 2005, and 2009, as well as 10 typical driving factors, were used to validate the proposed regression method and the improved CLUE-S model. Then, three future land-use scenarios in 2020: the natural growth scenario, ecological protection scenario, and economic development scenario, were simulated using the improved model. Validation results showed that NE-autologistic regression performed better than logistic regression, autologistic regression, and NE-logistic regression in predicting land-use change probabilities. The spatial allocation accuracy and kappa values of NE-autologistic-CLUE-S were higher than those of logistic-CLUE-S, autologistic-CLUE-S, and NE-logistic-CLUE-S for the simulations of two periods, 2001-2009 and 2005-2009, which proved that the improved CLUE-S model achieved the best simulation and was thereby effective to a certain extent. The scenario simulation results indicated that under all three scenarios, traffic land and residential/industrial land would increase, whereas arable land and unused land would decrease during 2009-2020. Apparent differences also existed in the simulated change sizes and locations of each land-use type under different scenarios. The results not only demonstrate the validity of the improved model but also provide a valuable reference for relevant policy-makers.
Differentially Private Empirical Risk Minimization

PubMed Central

Chaudhuri, Kamalika; Monteleoni, Claire; Sarwate, Anand D.

2011-01-01

Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the ε-differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance. PMID:21892342
Work, leisure-time physical activity, and risk of preeclampsia and gestational hypertension.

PubMed

Saftlas, Audrey F; Logsden-Sackett, Nyla; Wang, Wenquan; Woolson, Robert; Bracken, Michael B

2004-10-15

Few studies of preeclampsia have assessed physical activity level, yet recent evidence suggests that the pathologic mechanisms in preeclampsia are similar to those in cardiovascular disease, for which physical activity is shown to be protective. The authors assessed the independent and combined effects of work and regular leisure-time physical activity (LTPA) during early pregnancy on risk of de novo preeclampsia (n = 44) and gestational hypertension (n = 172) among women recruited from 13 obstetric practices in the New Haven, Connecticut, area between 1988 and 1991. Control subjects were normotensive throughout pregnancy (n = 2,422). Information on time at work spent sitting, standing, and walking and on LTPA before and during pregnancy was collected via face-to-face interviews. Logistic regression analyses suggested that women who engaged in any regular LTPA regardless of caloric expenditure (adjusted odds ratio (aOR) = 0.66, 95% confidence interval (CI): 0.35, 1.22), were unemployed (aOR = 0.64, 95% CI: 0.21, 2.00), or had nonsedentary jobs (aOR = 0.71, 95% CI: 0.37, 1.36) were at decreased risk of preeclampsia. Analyses of gestational hypertension showed no indication of a protective effect of workplace activity, LTPA, or unemployment. Consistent with other studies, these data suggest that regular physical activity during pregnancy may reduce preeclampsia risk.
Binary logistic regression-Instrument for assessing museum indoor air impact on exhibits.

PubMed

Bucur, Elena; Danet, Andrei Florin; Lehr, Carol Blaziu; Lehr, Elena; Nita-Lazar, Mihai

2017-04-01

This paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The prediction of the impact on the exhibits during certain pollution scenarios (environmental impact) was calculated by a mathematical model based on the binary logistic regression; it allows the identification of those environmental parameters from a multitude of possible parameters with a significant impact on exhibitions and ranks them according to their severity effect. Air quality (NO 2 , SO 2 , O 3 and PM 2.5 ) and microclimate parameters (temperature, humidity) monitoring data from a case study conducted within exhibition and storage spaces of the Romanian National Aviation Museum Bucharest have been used for developing and validating the binary logistic regression method and the mathematical model. The logistic regression analysis was used on 794 data combinations (715 to develop of the model and 79 to validate it) by a Statistical Package for Social Sciences (SPSS 20.0). The results from the binary logistic regression analysis demonstrated that from six parameters taken into consideration, four of them present a significant effect upon exhibits in the following order: O 3 >PM 2.5 >NO 2 >humidity followed at a significant distance by the effects of SO 2 and temperature. The mathematical model, developed in this study, correctly predicted 95.1 % of the cumulated effect of the environmental parameters upon the exhibits. Moreover, this model could also be used in the decisional process regarding the preventive preservation measures that should be implemented within the exhibition space. The paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The mathematical model developed on the environmental parameters analyzed by the binary logistic regression method could be useful in a decision-making process establishing the best measures for pollution reduction and preventive preservation of exhibits.
Determining factors influencing survival of breast cancer by fuzzy logistic regression model.

PubMed

Nikbakht, Roya; Bahrampour, Abbas

2017-01-01

Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.
Mixed conditional logistic regression for habitat selection studies.

PubMed

Duchesne, Thierry; Fortin, Daniel; Courbin, Nicolas

2010-05-01

1. Resource selection functions (RSFs) are becoming a dominant tool in habitat selection studies. RSF coefficients can be estimated with unconditional (standard) and conditional logistic regressions. While the advantage of mixed-effects models is recognized for standard logistic regression, mixed conditional logistic regression remains largely overlooked in ecological studies. 2. We demonstrate the significance of mixed conditional logistic regression for habitat selection studies. First, we use spatially explicit models to illustrate how mixed-effects RSFs can be useful in the presence of inter-individual heterogeneity in selection and when the assumption of independence from irrelevant alternatives (IIA) is violated. The IIA hypothesis states that the strength of preference for habitat type A over habitat type B does not depend on the other habitat types also available. Secondly, we demonstrate the significance of mixed-effects models to evaluate habitat selection of free-ranging bison Bison bison. 3. When movement rules were homogeneous among individuals and the IIA assumption was respected, fixed-effects RSFs adequately described habitat selection by simulated animals. In situations violating the inter-individual homogeneity and IIA assumptions, however, RSFs were best estimated with mixed-effects regressions, and fixed-effects models could even provide faulty conclusions. 4. Mixed-effects models indicate that bison did not select farmlands, but exhibited strong inter-individual variations in their response to farmlands. Less than half of the bison preferred farmlands over forests. Conversely, the fixed-effect model simply suggested an overall selection for farmlands. 5. Conditional logistic regression is recognized as a powerful approach to evaluate habitat selection when resource availability changes. This regression is increasingly used in ecological studies, but almost exclusively in the context of fixed-effects models. Fitness maximization can imply differences in trade-offs among individuals, which can yield inter-individual differences in selection and lead to departure from IIA. These situations are best modelled with mixed-effects models. Mixed-effects conditional logistic regression should become a valuable tool for ecological research.
Advanced colorectal neoplasia risk stratification by penalized logistic regression.

PubMed

Lin, Yunzhi; Yu, Menggang; Wang, Sijian; Chappell, Richard; Imperiale, Thomas F

2016-08-01

Colorectal cancer is the second leading cause of death from cancer in the United States. To facilitate the efficiency of colorectal cancer screening, there is a need to stratify risk for colorectal cancer among the 90% of US residents who are considered "average risk." In this article, we investigate such risk stratification rules for advanced colorectal neoplasia (colorectal cancer and advanced, precancerous polyps). We use a recently completed large cohort study of subjects who underwent a first screening colonoscopy. Logistic regression models have been used in the literature to estimate the risk of advanced colorectal neoplasia based on quantifiable risk factors. However, logistic regression may be prone to overfitting and instability in variable selection. Since most of the risk factors in our study have several categories, it was tempting to collapse these categories into fewer risk groups. We propose a penalized logistic regression method that automatically and simultaneously selects variables, groups categories, and estimates their coefficients by penalizing the [Formula: see text]-norm of both the coefficients and their differences. Hence, it encourages sparsity in the categories, i.e. grouping of the categories, and sparsity in the variables, i.e. variable selection. We apply the penalized logistic regression method to our data. The important variables are selected, with close categories simultaneously grouped, by penalized regression models with and without the interactions terms. The models are validated with 10-fold cross-validation. The receiver operating characteristic curves of the penalized regression models dominate the receiver operating characteristic curve of naive logistic regressions, indicating a superior discriminative performance. © The Author(s) 2013.
Using Logistic Regression To Predict the Probability of Debris Flows Occurring in Areas Recently Burned By Wildland Fires

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.

2003-01-01

Logistic regression was used to predict the probability of debris flows occurring in areas recently burned by wildland fires. Multiple logistic regression is conceptually similar to multiple linear regression because statistical relations between one dependent variable and several independent variables are evaluated. In logistic regression, however, the dependent variable is transformed to a binary variable (debris flow did or did not occur), and the actual probability of the debris flow occurring is statistically modeled. Data from 399 basins located within 15 wildland fires that burned during 2000-2002 in Colorado, Idaho, Montana, and New Mexico were evaluated. More than 35 independent variables describing the burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows were delineated from National Elevation Data using a Geographic Information System (GIS). (2) Data describing the burn severity, geology, land surface gradient, rainfall, and soil properties were determined for each basin. These data were then downloaded to a statistics software package for analysis using logistic regression. (3) Relations between the occurrence/non-occurrence of debris flows and burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated and several preliminary multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combination produced the most effective model. The multivariate model that best predicted the occurrence of debris flows was selected. (4) The multivariate logistic regression model was entered into a GIS, and a map showing the probability of debris flows was constructed. The most effective model incorporates the percentage of each basin with slope greater than 30 percent, percentage of land burned at medium and high burn severity in each basin, particle size sorting, average storm intensity (millimeters per hour), soil organic matter content, soil permeability, and soil drainage. The results of this study demonstrate that logistic regression is a valuable tool for predicting the probability of debris flows occurring in recently-burned landscapes.
Serum 25-hydroxyvitamin D3 is related to fish intake and exercise in Korean adult men.

PubMed

Lym, Youl Lee; Joh, Hee-Kyung

2009-01-01

Vitamin D is an important factor for bone health. It is uncertain which lifestyle is associated with vitamin D status, especially in healthy middle aged Asian men. A cross-sectional analysis was performed in 149 men aged 40-69 years who visited a health check-up center in Korea. Serum vitamin D (25-OHD3) was measured and smoking, alcohol, exercise, occupation, frequency of fish and dairy intake were estimated by self-administered questionnaire. The mean (+/-SD) 25-OHD3 concentration was 96.5+/-30.7 nmol/L. Higher and lower 25-OHD3 groups were generated with the median concentration as the cut-off point. By univariate analysis, exercise status and fish intake frequency were significantly different between two 25-OHD3 groups (p=0.012, 0.019 respectively). After multivariable logistic regression, higher fish intake frequency and regular exercise were associated with higher levels of 25-OHD3 (p for trend=0.017 and 0.02 respectively). In conclusion, frequent fish intake and regular exercise are positively associated with serum 25-OHD3 concentrations in healthy Korean men.
The goalkeeper influence on ball possession effectiveness in futsal

PubMed Central

Lago-Peñas, Carlos

2016-01-01

Abstract The aim of this study was to identify which variables were the best predictors of success in futsal ball possession when controlling for space and task related indicators, situational variables and the participation of the goalkeeper as a regular field player or not (5 vs. 4 or 4 vs. 4). The sample consisted of 326 situations of ball possession corresponding to 31 matches played by a team from the Spanish Futsal League during the 2010–2011, 2011–2012 and 2012–2013 seasons. Multidimensional qualitative data obtained from 10 ordered categorical variables were used. Data were analysed using chi-square analysis and multiple logistic regression analysis. Overall, the highest ball possession effectiveness was achieved when the goalkeeper participated as a regular field player (p<0.01), the duration of the ball possession was less than 10 s (p<0.01), the ball possession ended in the penalty area (p<0.01) and the defensive pressure was low (p<0.01). The information obtained on the relative effectiveness of offensive playing tactics can be used to improve team’s goal-scoring and goal preventing abilities. PMID:28149385
Associations between working environment and weight control efforts among workers with obesity in Korea.

PubMed

Hyun, Hye Sun; Kim, Yunyoung

2018-06-01

Objective The aim of this study was to investigate the relationship between working environment and weight control efforts among obese workers in Korea. Methods This study was based on the 2011 3rd Korean Working Conditions Survey, which was conducted on workers aged 15 years or older. A sample of 484 obese workers was included in the study. Multivariable logistic regression analysis was used to investigate the relationship between working environment and weight control efforts after controlling for individual variables. Adjusted odds ratios (ORs) and 95% confidence intervals were calculated. Results Of the participants, 63.4% reported that they made efforts to control their weight. After controlling for personal factors, the OR of weight control efforts for individuals working 40-49 hours per week was 2.4 times that for individuals working 60 hours or more per week. The OR of regular employment workers was 2.2 times that of non-regular workers. Conclusion We established that working hours and employment type were significantly related to weight control efforts. Therefore, we recommend that working conditions should be considered in designing effective workplace health promotion programs.
Regular use of aspirin and pancreatic cancer risk

PubMed Central

Menezes, Ravi J; Huber, Kenneth R; Mahoney, Martin C; Moysich, Kirsten B

2002-01-01

Background Regular use of aspirin and other non-steroidal anti-inflammatory drugs (NSAIDs) has been consistently associated with reduced risk of colorectal cancer and adenoma, and there is some evidence for a protective effect for other types of cancer. As experimental studies reveal a possible role for NSAIDs is reducing the risk of pancreatic cancer, epidemiological studies examining similar associations in human populations become more important. Methods In this hospital-based case-control study, 194 patients with pancreatic cancer were compared to 582 age and sex-matched patients with non-neoplastic conditions to examine the association between aspirin use and risk of pancreatic cancer. All participants received medical services at the Roswell Park Cancer Institute in Buffalo, NY and completed a comprehensive epidemiologic questionnaire that included information on demographics, lifestyle factors and medical history as well as frequency and duration of aspirin use. Patients using at least one tablet per week for at least six months were classified as regular aspirin users. Unconditional logistic regression was used to compute crude and adjusted odds ratios (ORs) with 95% confidence intervals (CIs). Results Pancreatic cancer risk in aspirin users was not changed relative to non-users (adjusted OR = 1.00; 95% CI 0.72–1.39). No significant change in risk was found in relation to greater frequency or prolonged duration of use, in the total sample or in either gender. Conclusions These data suggest that regular aspirin use may not be associated with lower risk of pancreatic cancer. PMID:12213184
Hepatitis B testing among Vietnamese American men.

PubMed

Taylor, Victoria M; Yasui, Yutaka; Burke, Nancy; Nguyen, Tung; Chen, Anthony; Acorda, Elizabeth; Choe, John H; Jackson, J Carey

2004-01-01

Vietnamese American men are over 10 times more likely to be diagnosed with liver cancer than their white counterparts. This health disparity is attributable to high rates of hepatitis B virus (HBV) infection. Our study objective was to examine factors associated with HBV testing among Vietnamese men. A population-based survey was conducted in Seattle. The questionnaire content was guided by an earlier qualitative study and the Health Behavior Framework. The survey was completed by 345 men (response rate: 80%). About one-third (34%) of the respondents reported they had not been tested for HBV. The following factors were associated (P < 0.01) with previous testing in bivariate comparisons: having a regular source of care and regular provider; knowing that HBV can be spread during childbirth; believing HBV can cause liver cancer; and doctor(s) had recommended testing as well as had asked doctor(s) for testing. Three variables were independently associated with HBV testing in a logistic regression model: regular source of care (OR = 4.5; 95% CI = 2.6-7.9), physician recommendation (OR = 2.3, 95% CI = 1.3-4.0), and knowing HBV can be spread during childbirth (OR = 2.1; 95% CI = 1.2-3.9). Low levels of HBV testing remain a public health problem in some Vietnamese American sub-groups. Health education about HBV transmission may stimulate patients to seek testing. Intervention programs should specifically target Vietnamese men without a regular source of health care and physicians who serve Vietnamese communities.
Regular Benzodiazepine and Z-Substance Use and Risk of Dementia: An Analysis of German Claims Data.

PubMed

Gomm, Willy; von Holt, Klaus; Thomé, Friederike; Broich, Karl; Maier, Wolfgang; Weckbecker, Klaus; Fink, Anne; Doblhammer, Gabriele; Haenisch, Britta

2016-09-06

While acute detrimental effects of benzodiazepine (BDZ), and BDZ and related z-substance (BDZR) use on cognition and memory are known, the association of BDZR use and risk of dementia in the elderly is controversially discussed. Previous studies on cohort or claims data mostly show an increased risk for dementia with the use of BDZs or BDZRs. For Germany, analyses on large population-based data sets are missing. To evaluate the association between regular BDZR use and incident any dementia in a large German claims data set. Using longitudinal German public health insurance data from 2004 to 2011 we analyzed the association between regular BDZR use (versus no BDZR use) and incident dementia in a case-control design. We examined patient samples aged≥60 years that were free of dementia at baseline. To address potential protopathic bias we introduced a lag time between BDZR prescription and dementia diagnosis. Odds ratios were calculated applying conditional logistic regression, adjusted for potential confounding factors such as comorbidities and polypharmacy. The regular use of BDZRs was associated with a significant increased risk of incident dementia for patients aged≥60 years (adjusted odds ratio [OR] 1.21, 95% confidence interval [CI] 1.13-1.29). The association was slightly stronger for long-acting substances than for short-acting ones. A trend for increased risk for dementia with higher exposure was observed. The restricted use of BDZRs may contribute to dementia prevention in the elderly.
Factors Associated with Substance Use in Adolescents with Eating Disorders

PubMed Central

Mann, Andrea P; Accurso, Erin C.; Stiles-Shields, Colleen; Capra, Lauren; Labuschagne, Zandre; Karnik, Niranjan S.; Grange, Daniel Le

2014-01-01

Purpose To examine the prevalence and potential risk factors associated with substance use in adolescents with eating disorders (EDs). Methods This cross-sectional study included 290 adolescents, ages 12 –18 years, who presented for an initial ED evaluation at The Eating Disorders Program at The University of Chicago Medicine (UCM) between 2001 and 2012. Several factors, including DSM-5 diagnosis, diagnostic scores, and demographic characteristics were examined. Multinomial logistic regression was used to test associations between several factors and patterns of drug use for alcohol, cannabis, tobacco, and any substance. Results Lifetime prevalence of any substance use was found to be 24.6% in those with anorexia nervosa (AN), 48.7% in bulimia nervosa (BN), and 28.6% in eating disorder not otherwise specified (EDNOS). Regular substance use (monthly, daily, and bingeing behaviors) or a substance use disorder (SUD) was found in 27.9% of all patients. Older age was the only factor associated with regular use of any substance in the final multinomial model. Older age and non-White race was associated with greater alcohol and cannabis use. Although binge-purge frequency and BN diagnosis were associated with regular substance use in bivariate analyses, gender, race and age were more robustly associated with substance use in the final multinomial models. Conclusions Co-morbid substance use in adolescents with EDs is an important issue. Interventions targeting high-risk groups reporting regular substance use or SUDs are needed. PMID:24656448
Non-melanoma skin cancer and NSAID use in women with a history of skin cancer in the Women's Health Initiative.

PubMed

Wysong, Ashley; Ally, Mina S; Gamba, Christina S; Desai, Manisha; Swetter, Susan M; Seiffert-Sinha, Kristina; Sinha, Animesh A; Stefanick, Marcia L; Tang, Jean Y

2014-12-01

Evidence for the effect of non-steroidal anti-inflammatory drugs (NSAIDs) on non-melanoma skin cancer (NMSC) risk is inconsistent. We prospectively examined whether regular, inconsistent, or no/low-use of NSAIDs is associated with lower NMSC risk among 54,728 postmenopausal Caucasian women in the Women's Health Initiative Observational Study enrolled between 1993 and 1998. Logistic regression models were used to assess odds of NMSC after adjusting for skin type, sun exposure history and indication for NSAID use. There were 7652 incident cases of NMSC (median follow-up: 6.9years). There was no association between regular NSAID-use and NMSC risk relative to no/low-users. However, in a subgroup analysis of 5325 women with a history of skin cancer (incident NMSC: 1897), odds of NMSC were lower among regular NSAID users whether <5years (OR 0.82, 95% CI: 0.70-0.95) or ≥5years (OR 0.82, 95% CI: 0.69-0.98) of use compared to no/low-users. Inconsistent NSAID use and acetaminophen use were not associated with NMSC risk. Overall, NSAID use was not associated with NMSC risk. However, in women with a history of skin cancer, regular NSAID use was associated with 18% lower odds of NMSC. Future studies on potential chemopreventative effects of NSAIDs should focus on subjects with prior history of NMSC. Copyright © 2014 Elsevier Inc. All rights reserved.
E-cigarette Use Related to Demographic Factors in Hawai'i.

PubMed

Seto, Jason C; Davis, James W; Taira, Deborah A

2016-10-01

E-cigarette use is rapidly expanding in the United States and is projected to be a $3 billion industry by the end of this year. E-cigarette use in Hawai'i is significantly higher than national averages. The goal of this study was to examine the relationship in Hawai'i between demographic characteristics and several aspects of e-cigarette use including percentage of residents trying e-cigarettes, reasons for use, and perceived effects on health. Survey data were collected from a random sample of Hawai'i residents via the telephone in the summer of 2015, using methodology similar to that of the Hawai'i Health Survey. Chi-squared tests found e-cigarette use to be significantly associated with age ( P =.001), gender ( P =.03), ethnicity ( P <.001), and education ( P <.001). Among e-cigarette users, 12% said they started smoking regular cigarettes after starting e-cigarettes, 21% said their use of regular cigarettes did not change, 6% said they reduced use of regular cigarettes, and 20% said they completely stopped smoking regular cigarettes. Multivariable logistic regression results suggest Native Hawaiians (OR=29.1, P =.01) and Filipinos (OR=24.3, P =.01) were significantly more likely to report perceived improved health due to e-cigarette use compared to Caucasians. Given existing health disparities for Native Hawaiians and Filipinos, the fact that these groups are significantly more likely than other ethnic/racial groups to report that e-cigarettes improved their health bears notice and highlights the need for additional research in this area.
Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis

PubMed Central

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Background: Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. Methods: In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. Results: The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Conclusion: Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended. PMID:26793655
Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

PubMed

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

Predictors of course in obsessive-compulsive disorder: logistic regression versus Cox regression for recurrent events.

PubMed

Kempe, P T; van Oppen, P; de Haan, E; Twisk, J W R; Sluis, A; Smit, J H; van Dyck, R; van Balkom, A J L M

2007-09-01

Two methods for predicting remissions in obsessive-compulsive disorder (OCD) treatment are evaluated. Y-BOCS measurements of 88 patients with a primary OCD (DSM-III-R) diagnosis were performed over a 16-week treatment period, and during three follow-ups. Remission at any measurement was defined as a Y-BOCS score lower than thirteen combined with a reduction of seven points when compared with baseline. Logistic regression models were compared with a Cox regression for recurrent events model. Logistic regression yielded different models at different evaluation times. The recurrent events model remained stable when fewer measurements were used. Higher baseline levels of neuroticism and more severe OCD symptoms were associated with a lower chance of remission, early age of onset and more depressive symptoms with a higher chance. Choice of outcome time affects logistic regression prediction models. Recurrent events analysis uses all information on remissions and relapses. Short- and long-term predictors for OCD remission show overlap.
Estimating the exceedance probability of rain rate by logistic regression

NASA Technical Reports Server (NTRS)

Chiu, Long S.; Kedem, Benjamin

1990-01-01

Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.
Comparison of naïve Bayes and logistic regression for computer-aided diagnosis of breast masses using ultrasound imaging

NASA Astrophysics Data System (ADS)

Cary, Theodore W.; Cwanger, Alyssa; Venkatesh, Santosh S.; Conant, Emily F.; Sehgal, Chandra M.

2012-03-01

This study compares the performance of two proven but very different machine learners, Naïve Bayes and logistic regression, for differentiating malignant and benign breast masses using ultrasound imaging. Ultrasound images of 266 masses were analyzed quantitatively for shape, echogenicity, margin characteristics, and texture features. These features along with patient age, race, and mammographic BI-RADS category were used to train Naïve Bayes and logistic regression classifiers to diagnose lesions as malignant or benign. ROC analysis was performed using all of the features and using only a subset that maximized information gain. Performance was determined by the area under the ROC curve, Az, obtained from leave-one-out cross validation. Naïve Bayes showed significant variation (Az 0.733 +/- 0.035 to 0.840 +/- 0.029, P < 0.002) with the choice of features, but the performance of logistic regression was relatively unchanged under feature selection (Az 0.839 +/- 0.029 to 0.859 +/- 0.028, P = 0.605). Out of 34 features, a subset of 6 gave the highest information gain: brightness difference, margin sharpness, depth-to-width, mammographic BI-RADs, age, and race. The probabilities of malignancy determined by Naïve Bayes and logistic regression after feature selection showed significant correlation (R2= 0.87, P < 0.0001). The diagnostic performance of Naïve Bayes and logistic regression can be comparable, but logistic regression is more robust. Since probability of malignancy cannot be measured directly, high correlation between the probabilities derived from two basic but dissimilar models increases confidence in the predictive power of machine learning models for characterizing solid breast masses on ultrasound.
[Logistic regression model of noninvasive prediction for portal hypertensive gastropathy in patients with hepatitis B associated cirrhosis].

PubMed

Wang, Qingliang; Li, Xiaojie; Hu, Kunpeng; Zhao, Kun; Yang, Peisheng; Liu, Bo

2015-05-12

To explore the risk factors of portal hypertensive gastropathy (PHG) in patients with hepatitis B associated cirrhosis and establish a Logistic regression model of noninvasive prediction. The clinical data of 234 hospitalized patients with hepatitis B associated cirrhosis from March 2012 to March 2014 were analyzed retrospectively. The dependent variable was the occurrence of PHG while the independent variables were screened by binary Logistic analysis. Multivariate Logistic regression was used for further analysis of significant noninvasive independent variables. Logistic regression model was established and odds ratio was calculated for each factor. The accuracy, sensitivity and specificity of model were evaluated by the curve of receiver operating characteristic (ROC). According to univariate Logistic regression, the risk factors included hepatic dysfunction, albumin (ALB), bilirubin (TB), prothrombin time (PT), platelet (PLT), white blood cell (WBC), portal vein diameter, spleen index, splenic vein diameter, diameter ratio, PLT to spleen volume ratio, esophageal varices (EV) and gastric varices (GV). Multivariate analysis showed that hepatic dysfunction (X1), TB (X2), PLT (X3) and splenic vein diameter (X4) were the major occurring factors for PHG. The established regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4. The accuracy of model for PHG was 79.1% with a sensitivity of 77.2% and a specificity of 80.8%. Hepatic dysfunction, TB, PLT and splenic vein diameter are risk factors for PHG and the noninvasive predicted Logistic regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4.
Variable Selection in Logistic Regression.

DTIC Science & Technology

1987-06-01

23 %. AUTIOR(.) S. CONTRACT OR GRANT NUMBE Rf.i %Z. D. Bai, P. R. Krishnaiah and . C. Zhao F49620-85- C-0008 " PERFORMING ORGANIZATION NAME AND AOORESS...d I7 IOK-TK- d 7 -I0 7’ VARIABLE SELECTION IN LOGISTIC REGRESSION Z. D. Bai, P. R. Krishnaiah and L. C. Zhao Center for Multivariate Analysis...University of Pittsburgh Center for Multivariate Analysis University of Pittsburgh Y !I VARIABLE SELECTION IN LOGISTIC REGRESSION Z- 0. Bai, P. R. Krishnaiah
Multinomial Logistic Regression Predicted Probability Map To Visualize The Influence Of Socio-Economic Factors On Breast Cancer Occurrence in Southern Karnataka

NASA Astrophysics Data System (ADS)

Madhu, B.; Ashok, N. C.; Balasubramanian, S.

2014-11-01

Multinomial logistic regression analysis was used to develop statistical model that can predict the probability of breast cancer in Southern Karnataka using the breast cancer occurrence data during 2007-2011. Independent socio-economic variables describing the breast cancer occurrence like age, education, occupation, parity, type of family, health insurance coverage, residential locality and socioeconomic status of each case was obtained. The models were developed as follows: i) Spatial visualization of the Urban- rural distribution of breast cancer cases that were obtained from the Bharat Hospital and Institute of Oncology. ii) Socio-economic risk factors describing the breast cancer occurrences were complied for each case. These data were then analysed using multinomial logistic regression analysis in a SPSS statistical software and relations between the occurrence of breast cancer across the socio-economic status and the influence of other socio-economic variables were evaluated and multinomial logistic regression models were constructed. iii) the model that best predicted the occurrence of breast cancer were identified. This multivariate logistic regression model has been entered into a geographic information system and maps showing the predicted probability of breast cancer occurrence in Southern Karnataka was created. This study demonstrates that Multinomial logistic regression is a valuable tool for developing models that predict the probability of breast cancer Occurrence in Southern Karnataka.
Comparison of Logistic Regression and Artificial Neural Network in Low Back Pain Prediction: Second National Health Survey

PubMed Central

Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

2012-01-01

Background: The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Methods: Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. Results: The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Conclusions: Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant. PMID:23113198
Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey.

PubMed

Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

2012-01-01

The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant.
Modelling of binary logistic regression for obesity among secondary students in a rural area of Kedah

NASA Astrophysics Data System (ADS)

Kamaruddin, Ainur Amira; Ali, Zalila; Noor, Norlida Mohd.; Baharum, Adam; Ahmad, Wan Muhamad Amir W.

2014-07-01

Logistic regression analysis examines the influence of various factors on a dichotomous outcome by estimating the probability of the event's occurrence. Logistic regression, also called a logit model, is a statistical procedure used to model dichotomous outcomes. In the logit model the log odds of the dichotomous outcome is modeled as a linear combination of the predictor variables. The log odds ratio in logistic regression provides a description of the probabilistic relationship of the variables and the outcome. In conducting logistic regression, selection procedures are used in selecting important predictor variables, diagnostics are used to check that assumptions are valid which include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers and a test statistic is calculated to determine the aptness of the model. This study used the binary logistic regression model to investigate overweight and obesity among rural secondary school students on the basis of their demographics profile, medical history, diet and lifestyle. The results indicate that overweight and obesity of students are influenced by obesity in family and the interaction between a student's ethnicity and routine meals intake. The odds of a student being overweight and obese are higher for a student having a family history of obesity and for a non-Malay student who frequently takes routine meals as compared to a Malay student.
Predicting Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer with Textural Features Derived from Pretreatment 18F-FDG PET/CT Imaging.

PubMed

Beukinga, Roelof J; Hulshoff, Jan B; van Dijk, Lisanne V; Muijs, Christina T; Burgerhof, Johannes G M; Kats-Ugurlu, Gursah; Slart, Riemer H J A; Slump, Cornelis H; Mul, Véronique E M; Plukker, John Th M

2017-05-01

Adequate prediction of tumor response to neoadjuvant chemoradiotherapy (nCRT) in esophageal cancer (EC) patients is important in a more personalized treatment. The current best clinical method to predict pathologic complete response is SUV max in 18 F-FDG PET/CT imaging. To improve the prediction of response, we constructed a model to predict complete response to nCRT in EC based on pretreatment clinical parameters and 18 F-FDG PET/CT-derived textural features. Methods: From a prospectively maintained single-institution database, we reviewed 97 consecutive patients with locally advanced EC and a pretreatment 18 F-FDG PET/CT scan between 2009 and 2015. All patients were treated with nCRT (carboplatin/paclitaxel/41.4 Gy) followed by esophagectomy. We analyzed clinical, geometric, and pretreatment textural features extracted from both 18 F-FDG PET and CT. The current most accurate prediction model with SUV max as a predictor variable was compared with 6 different response prediction models constructed using least absolute shrinkage and selection operator regularized logistic regression. Internal validation was performed to estimate the model's performances. Pathologic response was defined as complete versus incomplete response (Mandard tumor regression grade system 1 vs. 2-5). Results: Pathologic examination revealed 19 (19.6%) complete and 78 (80.4%) incomplete responders. Least absolute shrinkage and selection operator regularization selected the clinical parameters: histologic type and clinical T stage, the 18 F-FDG PET-derived textural feature long run low gray level emphasis, and the CT-derived textural feature run percentage. Introducing these variables to a logistic regression analysis showed areas under the receiver-operating-characteristic curve (AUCs) of 0.78 compared with 0.58 in the SUV max model. The discrimination slopes were 0.17 compared with 0.01, respectively. After internal validation, the AUCs decreased to 0.74 and 0.54, respectively. Conclusion: The predictive values of the constructed models were superior to the standard method (SUV max ). These results can be considered as an initial step in predicting tumor response to nCRT in locally advanced EC. Further research in refining the predictive value of these models is needed to justify omission of surgery. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.
Understanding logistic regression analysis.

PubMed

Sperandei, Sandro

2014-01-01

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.
Predicting space telerobotic operator training performance from human spatial ability assessment

NASA Astrophysics Data System (ADS)

Liu, Andrew M.; Oman, Charles M.; Galvan, Raquel; Natapoff, Alan

2013-11-01

Our goal was to determine whether existing tests of spatial ability can predict an astronaut's qualification test performance after robotic training. Because training astronauts to be qualified robotics operators is so long and expensive, NASA is interested in tools that can predict robotics performance before training begins. Currently, the Astronaut Office does not have a validated tool to predict robotics ability as part of its astronaut selection or training process. Commonly used tests of human spatial ability may provide such a tool to predict robotics ability. We tested the spatial ability of 50 active astronauts who had completed at least one robotics training course, then used logistic regression models to analyze the correlation between spatial ability test scores and the astronauts' performance in their evaluation test at the end of the training course. The fit of the logistic function to our data is statistically significant for several spatial tests. However, the prediction performance of the logistic model depends on the criterion threshold assumed. To clarify the critical selection issues, we show how the probability of correct classification vs. misclassification varies as a function of the mental rotation test criterion level. Since the costs of misclassification are low, the logistic models of spatial ability and robotic performance are reliable enough only to be used to customize regular and remedial training. We suggest several changes in tracking performance throughout robotics training that could improve the range and reliability of predictive models.
Clinical management provided by board-certificated physiatrists in early rehabilitation is a significant determinant of functional improvement in acute stroke patients: a retrospective analysis of Japan rehabilitation database.

PubMed

Kinoshita, Shoji; Kakuda, Wataru; Momosaki, Ryo; Yamada, Naoki; Sugawara, Hidekazu; Watanabe, Shu; Abo, Masahiro

2015-05-01

Early rehabilitation for acute stroke patients is widely recommended. We tested the hypothesis that clinical outcome of stroke patients who receive early rehabilitation managed by board-certificated physiatrists (BCP) is generally better than that provided by other medical specialties. Data of stroke patients who underwent early rehabilitation in 19 acute hospitals between January 2005 and December 2013 were collected from the Japan Rehabilitation Database and analyzed retrospectively. Multivariate linear regression analysis using generalized estimating equations method was performed to assess the association between Functional Independence Measure (FIM) effectiveness and management provided by BCP in early rehabilitation. In addition, multivariate logistic regression analysis was also performed to assess the impact of management provided by BCP in acute phase on discharge destination. After setting the inclusion criteria, data of 3838 stroke patients were eligible for analysis. BCP provided early rehabilitation in 814 patients (21.2%). Both the duration of daily exercise time and the frequency of regular conferencing were significantly higher for patients managed by BCP than by other specialties. Although the mortality rate was not different, multivariate regression analysis showed that FIM effectiveness correlated significantly and positively with the management provided by BCP (coefficient, .35; 95% confidence interval [CI], .012-.059; P < .005). In addition, multivariate logistic analysis identified clinical management by BCP as a significant determinant of home discharge (odds ratio, 1.24; 95% CI, 1.08-1.44; P < .005). Our retrospective cohort study demonstrated that clinical management provided by BCP in early rehabilitation can lead to functional recovery of acute stroke. Copyright © 2015 National Stroke Association. Published by Elsevier Inc. All rights reserved.
Drinking Patterns and Victimization among Male and Female Students in Mexico

PubMed Central

Strunin, Lee; Díaz-Martínez, L. Rosa; Díaz-Martínez, Alejandro; Heeren, Timothy; Winter, Michael; Kuranz, Seth; Hernández–Ávila, Carlos A.; Fernández-Varela, Héctor; Solís-Torres, Cuauhtémoc

2015-01-01

Aims: The purpose of this study is to estimate the prevalence of alcohol use and alcohol-related consequences, identify drinking profiles using latent profile analysis (LPA), and investigate associations between profiles and violent victimization among young people in Mexico. Methods: LPA identified profiles of drinking behavior in a survey of entering first year university students. Multinomial and logistic regression examined associations between drinking patterns, socio-demographic variables and violent victimization. Results: The LPA identified five profiles of behaviors and consequences among the 22,224 current, former and never drinkers: Non/Infrequent-No Consequences, Occasional-Few Consequences, Regular-Some Consequences, Heavy-Many Consequences and Excessive-Many Consequences drinkers. The Occasional-Few Consequences profile comprised the largest, and the Excessive-Many Consequences profile the smallest, group of drinkers. Multinomial regression showed males and older students more likely to be Heavy or Excessive-Many Consequences drinkers. Living alone was associated with higher odds, and higher maternal education with lower odds, of being a Non/Infrequent-No Consequences drinker. Heavier drinking profiles were more likely to experience violent victimization adverse consequences. Logistic regression showed male and female Heavy and Excessive-Many Consequences drinkers had the highest odds, and Non/Infrequent drinkers the lowest odds, of experiencing any victimization. Conclusion: Findings suggest changes in male and female drinking behavior and a continuation of the established pattern of infrequent but high consumption among Mexican youths. Both male and female Heavy and Excessive-Many Consequences drinkers were at elevated risk for experiencing victimization. Identifying cultural gender norms about drinking including drinker expectations and drinking context that contribute to these patterns can inform prevention efforts. PMID:25534933
Comparing Methodologies for Developing an Early Warning System: Classification and Regression Tree Model versus Logistic Regression. REL 2015-077

ERIC Educational Resources Information Center

Koon, Sharon; Petscher, Yaacov

2015-01-01

The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by…
Acoustic Multipurpose Cargo Transfer Bag

NASA Technical Reports Server (NTRS)

Baccus, Shelley

2015-01-01

The Logistics Reduction (LR) project within the Advanced Exploration Systems (AES) program is tasked with reducing logistical mass and repurposing logistical items. Multipurpose Cargo Transfer Bags (MCTB) are designed to be the same external volume as a regular cargo transfer bag, the common logistics carrier for the International Space Station. After use as a cargo bag, the MCTB can be unzipped and unfolded to be reused. This Acoustic MCTBs transform into acoustic blankets after the initial logistics carrying objective is complete.
Using Multiple and Logistic Regression to Estimate the Median WillCost and Probability of Cost and Schedule Overrun for Program Managers

DTIC Science & Technology

2017-03-23

PUBLIC RELEASE; DISTRIBUTION UNLIMITED Using Multiple and Logistic Regression to Estimate the Median Will- Cost and Probability of Cost and... Cost and Probability of Cost and Schedule Overrun for Program Managers Ryan C. Trudelle Follow this and additional works at: https://scholar.afit.edu...afit.edu. Recommended Citation Trudelle, Ryan C., "Using Multiple and Logistic Regression to Estimate the Median Will- Cost and Probability of Cost and
Expression of Proteins Involved in Epithelial-Mesenchymal Transition as Predictors of Metastasis and Survival in Breast Cancer Patients

DTIC Science & Technology

2013-11-01

Ptrend 0.78 0.62 0.75 Unconditional logistic regression was used to estimate odds ratios (OR) and 95 % confidence intervals (CI) for risk of node...Ptrend 0.71 0.67 Unconditional logistic regression was used to estimate odds ratios (OR) and 95 % confidence intervals (CI) for risk of high-grade tumors... logistic regression was used to estimate odds ratios (OR) and 95 % confidence intervals (CI) for the associations between each of the seven SNPs and
Logistic LASSO regression for the diagnosis of breast cancer using clinical demographic data and the BI-RADS lexicon for ultrasonography.

PubMed

Kim, Sun Mi; Kim, Yongdai; Jeong, Kuhwan; Jeong, Heeyeong; Kim, Jiyoung

2018-01-01

The aim of this study was to compare the performance of image analysis for predicting breast cancer using two distinct regression models and to evaluate the usefulness of incorporating clinical and demographic data (CDD) into the image analysis in order to improve the diagnosis of breast cancer. This study included 139 solid masses from 139 patients who underwent a ultrasonography-guided core biopsy and had available CDD between June 2009 and April 2010. Three breast radiologists retrospectively reviewed 139 breast masses and described each lesion using the Breast Imaging Reporting and Data System (BI-RADS) lexicon. We applied and compared two regression methods-stepwise logistic (SL) regression and logistic least absolute shrinkage and selection operator (LASSO) regression-in which the BI-RADS descriptors and CDD were used as covariates. We investigated the performances of these regression methods and the agreement of radiologists in terms of test misclassification error and the area under the curve (AUC) of the tests. Logistic LASSO regression was superior (P<0.05) to SL regression, regardless of whether CDD was included in the covariates, in terms of test misclassification errors (0.234 vs. 0.253, without CDD; 0.196 vs. 0.258, with CDD) and AUC (0.785 vs. 0.759, without CDD; 0.873 vs. 0.735, with CDD). However, it was inferior (P<0.05) to the agreement of three radiologists in terms of test misclassification errors (0.234 vs. 0.168, without CDD; 0.196 vs. 0.088, with CDD) and the AUC without CDD (0.785 vs. 0.844, P<0.001), but was comparable to the AUC with CDD (0.873 vs. 0.880, P=0.141). Logistic LASSO regression based on BI-RADS descriptors and CDD showed better performance than SL in predicting the presence of breast cancer. The use of CDD as a supplement to the BI-RADS descriptors significantly improved the prediction of breast cancer using logistic LASSO regression.
Accretion onto some well-known regular black holes

NASA Astrophysics Data System (ADS)

Jawad, Abdul; Shahzad, M. Umair

2016-03-01

In this work, we discuss the accretion onto static spherically symmetric regular black holes for specific choices of the equation of state parameter. The underlying regular black holes are charged regular black holes using the Fermi-Dirac distribution, logistic distribution, nonlinear electrodynamics, respectively, and Kehagias-Sftesos asymptotically flat regular black holes. We obtain the critical radius, critical speed, and squared sound speed during the accretion process near the regular black holes. We also study the behavior of radial velocity, energy density, and the rate of change of the mass for each of the regular black holes.

Social capital and physical activity among Croatian high school students.

PubMed

Novak, D; Doubova, S V; Kawachi, I

2016-06-01

To examine factors associated with regular physical activity in Croatian adolescents. A cross-sectional survey among high school students was carried out in the 2013/14 school year. A survey was conducted among 33 high schools in Zagreb City, Croatia. Participants were students aged 17-18 years. The dependent variables were regular moderate to vigorous physical activity (MVPA) and overall physical activity measured by the short version of International Physical Activity Questionnaire and defined as 60 min or more of daily physical activity. The independent variables included family, neighborhood, and high school social capital. Other study covariates included: socio-economic status, self-rated health, psychological distress and nutritional status. The associations between physical activity and social capital variables were assessed separately for boys and girls through multiple logistic regression and inverse probability weighting in order to correct for missing data bias. A total of 1689 boys and 1739 girls responded to the survey. A higher percentage of boys reported performing regular vigorous and moderate physical activity (59.4%) and overall physical activity (83.4%), comparing with the girls (35.4% and 70%, respectively). For boys, high family social capital and high informal social control were associated with increased odds of regular MVPA (1.49, 95%CI: 1.18 - 1.90 and 1.26, 95%CI: 1.02 - 1.56, respectively), compared to those with low social capital. For girls, high informal social control was associated with regular overall physical activity (OR 1.38, 95% CI: 1.09 - 1.76). High social capital is associated with regular MVPA in boys and regular overall activity in girls. Intervention and policies that leverage community social capital might serve as an avenue for promotion of physical activity in youth. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
Work-related violence and inconsistent condom use with non-paying partners among female sex workers in Adama City, Ethiopia.

PubMed

Mooney, Alyssa; Kidanu, Aklilu; Bradley, Heather M; Kumoji, Evelyn Kuor; Kennedy, Caitlin E; Kerrigan, Deanna

2013-08-23

Although reported condom use between female sex workers and their clients is high in Ethiopia, condom use with regular, non-paying partners remains low, posing a substantial risk of HIV infection to sex workers, their partners and the general population. Previous studies have identified the synergistic effects of substance abuse, violence and HIV risk, but few have examined these inter-relationships among female sex workers and their regular, non-paying partners. This study explored the associations between work-related violence, alcohol abuse and inconsistent condom use among establishment-based female sex workers and their regular, non-paying partners in Adama City, Ethiopia. A cross-sectional survey was conducted with 350 establishment-based female sex workers, aged 15-35, at 63 bars, hotels and nightclubs. Multivariate logistic regression analysis was conducted to test the association between work-related violence and condom use with regular, non-paying partners, controlling for age, overall income, education and sex workers' total number of sexual partners in the past week. Alcohol abuse was explored as an effect modifier. Respondents reported a high prevalence of work-related violence (59%) and alcohol abuse (51%). Work-related violence was statistically significantly associated with unprotected sex with regular, non-paying partners among those who abused alcohol (OR: 6.34, 95% CI: 2.43-16.56) and among those who did not (OR: 2.98, 95% CI: 1.36-6.54). Alcohol abuse was not associated with inconsistent condom use within these partnerships, though it may strengthen the effect of work-related violence on unprotected sex. Findings suggest violence against establishment-based female sex workers is associated with HIV risk within regular, non-paying partnerships. Qualitative work is needed to better understand the links between a violent work environment and condom use with regular, non-paying partners and how interventions can be implemented in this context to prevent violence against sex workers and reduce HIV transmission.
The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams.

PubMed

Yu, Yuanyuan; Li, Hongkai; Sun, Xiaoru; Su, Ping; Wang, Tingting; Liu, Yi; Yuan, Zhongshang; Liu, Yanxun; Xue, Fuzhong

2017-12-28

Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM) were compared. The "do-calculus" was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal strategy was to adjust for the parent nodes of outcome, which obtained the highest precision. All adjustment strategies through logistic regression were biased for causal effect estimation, while IPW-based-MSM could always obtain unbiased estimation when the adjusted set satisfied G-admissibility. Thus, IPW-based-MSM was recommended to adjust for confounders set.
Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center

PubMed Central

Mehra, Tarun; Müller, Christian Thomas Benedikt; Volbracht, Jörk; Seifert, Burkhardt; Moos, Rudolf

2015-01-01

Principles Case weights of Diagnosis Related Groups (DRGs) are determined by the average cost of cases from a previous billing period. However, a significant amount of cases are largely over- or underfunded. We therefore decided to analyze earning outliers of our hospital as to search for predictors enabling a better grouping under SwissDRG. Methods 28,893 inpatient cases without additional private insurance discharged from our hospital in 2012 were included in our analysis. Outliers were defined by the interquartile range method. Predictors for deficit and profit outliers were determined with logistic regressions. Predictors were shortlisted with the LASSO regularized logistic regression method and compared to results of Random forest analysis. 10 of these parameters were selected for quantile regression analysis as to quantify their impact on earnings. Results Psychiatric diagnosis and admission as an emergency case were significant predictors for higher deficit with negative regression coefficients for all analyzed quantiles (p<0.001). Admission from an external health care provider was a significant predictor for a higher deficit in all but the 90% quantile (p<0.001 for Q10, Q20, Q50, Q80 and p = 0.0017 for Q90). Burns predicted higher earnings for cases which were favorably remunerated (p<0.001 for the 90% quantile). Osteoporosis predicted a higher deficit in the most underfunded cases, but did not predict differences in earnings for balanced or profitable cases (Q10 and Q20: p<0.00, Q50: p = 0.10, Q80: p = 0.88 and Q90: p = 0.52). ICU stay, mechanical and patient clinical complexity level score (PCCL) predicted higher losses at the 10% quantile but also higher profits at the 90% quantile (p<0.001). Conclusion We suggest considering psychiatric diagnosis, admission as an emergencay case and admission from an external health care provider as DRG split criteria as they predict large, consistent and significant losses. PMID:26517545
Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center.

PubMed

Mehra, Tarun; Müller, Christian Thomas Benedikt; Volbracht, Jörk; Seifert, Burkhardt; Moos, Rudolf

2015-01-01

Case weights of Diagnosis Related Groups (DRGs) are determined by the average cost of cases from a previous billing period. However, a significant amount of cases are largely over- or underfunded. We therefore decided to analyze earning outliers of our hospital as to search for predictors enabling a better grouping under SwissDRG. 28,893 inpatient cases without additional private insurance discharged from our hospital in 2012 were included in our analysis. Outliers were defined by the interquartile range method. Predictors for deficit and profit outliers were determined with logistic regressions. Predictors were shortlisted with the LASSO regularized logistic regression method and compared to results of Random forest analysis. 10 of these parameters were selected for quantile regression analysis as to quantify their impact on earnings. Psychiatric diagnosis and admission as an emergency case were significant predictors for higher deficit with negative regression coefficients for all analyzed quantiles (p<0.001). Admission from an external health care provider was a significant predictor for a higher deficit in all but the 90% quantile (p<0.001 for Q10, Q20, Q50, Q80 and p = 0.0017 for Q90). Burns predicted higher earnings for cases which were favorably remunerated (p<0.001 for the 90% quantile). Osteoporosis predicted a higher deficit in the most underfunded cases, but did not predict differences in earnings for balanced or profitable cases (Q10 and Q20: p<0.00, Q50: p = 0.10, Q80: p = 0.88 and Q90: p = 0.52). ICU stay, mechanical and patient clinical complexity level score (PCCL) predicted higher losses at the 10% quantile but also higher profits at the 90% quantile (p<0.001). We suggest considering psychiatric diagnosis, admission as an emergency case and admission from an external health care provider as DRG split criteria as they predict large, consistent and significant losses.
Use and interpretation of logistic regression in habitat-selection studies

USGS Publications Warehouse

Keating, Kim A.; Cherry, Steve

2004-01-01

Logistic regression is an important tool for wildlife habitat-selection studies, but the method frequently has been misapplied due to an inadequate understanding of the logistic model, its interpretation, and the influence of sampling design. To promote better use of this method, we review its application and interpretation under 3 sampling designs: random, case-control, and use-availability. Logistic regression is appropriate for habitat use-nonuse studies employing random sampling and can be used to directly model the conditional probability of use in such cases. Logistic regression also is appropriate for studies employing case-control sampling designs, but careful attention is required to interpret results correctly. Unless bias can be estimated or probability of use is small for all habitats, results of case-control studies should be interpreted as odds ratios, rather than probability of use or relative probability of use. When data are gathered under a use-availability design, logistic regression can be used to estimate approximate odds ratios if probability of use is small, at least on average. More generally, however, logistic regression is inappropriate for modeling habitat selection in use-availability studies. In particular, using logistic regression to fit the exponential model of Manly et al. (2002:100) does not guarantee maximum-likelihood estimates, valid probabilities, or valid likelihoods. We show that the resource selection function (RSF) commonly used for the exponential model is proportional to a logistic discriminant function. Thus, it may be used to rank habitats with respect to probability of use and to identify important habitat characteristics or their surrogates, but it is not guaranteed to be proportional to probability of use. Other problems associated with the exponential model also are discussed. We describe an alternative model based on Lancaster and Imbens (1996) that offers a method for estimating conditional probability of use in use-availability studies. Although promising, this model fails to converge to a unique solution in some important situations. Further work is needed to obtain a robust method that is broadly applicable to use-availability studies.
Modeling Governance KB with CATPCA to Overcome Multicollinearity in the Logistic Regression

NASA Astrophysics Data System (ADS)

Khikmah, L.; Wijayanto, H.; Syafitri, U. D.

2017-04-01

The problem often encounters in logistic regression modeling are multicollinearity problems. Data that have multicollinearity between explanatory variables with the result in the estimation of parameters to be bias. Besides, the multicollinearity will result in error in the classification. In general, to overcome multicollinearity in regression used stepwise regression. They are also another method to overcome multicollinearity which involves all variable for prediction. That is Principal Component Analysis (PCA). However, classical PCA in only for numeric data. Its data are categorical, one method to solve the problems is Categorical Principal Component Analysis (CATPCA). Data were used in this research were a part of data Demographic and Population Survey Indonesia (IDHS) 2012. This research focuses on the characteristic of women of using the contraceptive methods. Classification results evaluated using Area Under Curve (AUC) values. The higher the AUC value, the better. Based on AUC values, the classification of the contraceptive method using stepwise method (58.66%) is better than the logistic regression model (57.39%) and CATPCA (57.39%). Evaluation of the results of logistic regression using sensitivity, shows the opposite where CATPCA method (99.79%) is better than logistic regression method (92.43%) and stepwise (92.05%). Therefore in this study focuses on major class classification (using a contraceptive method), then the selected model is CATPCA because it can raise the level of the major class model accuracy.
Wheelchair-related accidents: relationship with wheelchair-using behavior in active community wheelchair users.

PubMed

Chen, Wan-Yin; Jang, Yuh; Wang, Jung-Der; Huang, Wen-Ni; Chang, Chan-Chia; Mao, Hui-Fen; Wang, Yen-Ho

2011-06-01

To report the prevalence, mechanisms, self-perceived causes, consequences, and wheelchair-using behaviors associated with wheelchair-related accidents. A case-control study. Community. A sample of experienced, community-dwelling, active manual and powered wheelchair users (N=95) recruited from a hospital assistive technology service center. Not applicable. Wheelchair-using behaviors, wheelchair-related accidents over a 3-year period, and the mechanisms and consequences of the accidents. Among the 95 participants, 52 (54.7%) reported at least 1 accident and 16 (16.8%) reported 2 or more accidents during the 3 years prior to the interview. A total of 74 accidents, were categorized into tips and falls (87.8%), accidental contact (6.8%), and dangerous operations (5.4%). A logistic regression found individuals who failed to maintain their wheelchairs regularly (odds ratio [OR]=11.28; 95% confidence interval [CI], 2.62-48.61) and used a wheelchair not prescribed by professionals (OR=4.31; 95% CI, 1.10-16.82) had significantly greater risks of accidents. In addition to the risk factor, lack of regular wheelchair maintenance, the Poisson regression corroborated the other risk factor, seat belts not used (incident rate ratio=2.14; 95% CI, 1.08-4.14), for wheelchair-related accidents. Wheelchair-related accidents are closely related to their wheelchair-using behaviors. Services including professional evaluation, repair, maintenance, and an educational program on proper wheelchair use may decrease the risks of wheelchair accidents. Copyright © 2011 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.
Regular analgesic use and risk of multiple myeloma.

PubMed

Moysich, Kirsten B; Bonner, Mathew R; Beehler, Gregory P; Marshall, James R; Menezes, Ravi J; Baker, Julie A; Weiss, Joli R; Chanan-Khan, Asher

2007-04-01

Analgesic use has been implicated in the chemoprevention of a number of solid tumors, but to date no previous research has focused on the role of analgesics in the etiology of multiple myeloma (MM). We conducted a hospital-based case-control study of 117 patients with primary, incident MM and 483 age and residence matched controls without benign or malignant neoplasms. All participants received medical services at Roswell Park Cancer Institute in Buffalo, NY, and completed a comprehensive epidemiological questionnaire. Participants who reported analgesic use at least once a week for at least 6 months were classified as regular users; individuals who did not use analgesics regularly served as the reference group throughout the analyses. We used unconditional logistic regression analyses to compute crude and adjusted odds ratios (ORs) with corresponding 95% confidence intervals (CIs). Compared to non-users, regular aspirin users were not at reduced risk of MM (adjusted OR=0.99; 95% CI 0.65-1.49), nor were participants with the highest frequency or duration of aspirin use. A significant risk elevation was found for participants who were regular acetaminophen users (adjusted OR=2.95; 95% CI 1.72-5.08). Further, marked increases in risk of MM were noted with both greater frequency (>7 tablets weekly; adjusted OR=4.36; 95% CI 1.70-11.2) and greater duration (>10 years; adjusted OR=3.26; 95% CI 1.52-7.02) of acetaminophen use. We observed no evidence of a chemoprotective effect of aspirin on MM risk, but observed significant risk elevations with various measures of acetaminophen use. Our results warrant further investigation in population-based case-control and cohort studies and should be interpreted with caution in light of the limited sample size and biases inherent in hospital-based studies.
Excessive work and risk of haemorrhagic stroke: a nationwide case-control study.

PubMed

Kim, Beom Joon; Lee, Seung-Hoon; Ryu, Wi-Sun; Kim, Chi Kyung; Chung, Jong-Won; Kim, Dohoung; Park, Hong-Kyun; Bae, Hee-Joon; Park, Byung-Joo; Yoon, Byung-Woo

2013-10-01

Adverse effect of excessive work on health has been suggested previously, but it was not documented in cerebrovascular diseases. The authors investigated whether excessive working conditions would associate with increased risk of haemorrhagic stroke. A nationwide matched case-control study database, which contains 940 cases of incident haemorrhagic stroke (498 intracerebral haemorrhages and 442 sub-arachnoid haemorrhages) with 1880 gender- and age- (± 5-year) matched controls, was analysed. Work-related information based on the regular job situation, including type of occupation, regular working time, duration of strenuous activity during regular work and shift work, was gathered through face-to-face interviews. Conditional logistic regression analyses were used for the multivariable analyses. Compared with white-collar workers, blue-collar workers had a higher risk for haemorrhagic stroke (odds ratio, 1.33 [95% confidence interval, 1.06-1.66]). Longer regular working time was associated with increased risk of haemorrhagic stroke [odds ratio, 1.38 (95% confidence interval, 1.05-1.81) for 8-12 h/day; odds ratio, 1.95 (95% confidence interval, 1.33-2.86) for ≥ 13 h/day; compared with ≤ 4 h/day]. Exposure to ≥ 8 h/week of strenuous activity also associated haemorrhagic stroke risk [odds ratio, 1.61 (95% confidence interval, 1.26-2.05); compared with no strenuous activity]. Shift work was not associated with haemorrhagic stroke (P = 0.98). Positive associations between working condition indices and haemorrhagic stroke risk were consistent regardless of haemorrhagic stroke sub-types and current employment status. Blue-collar occupation, longer regular working time and extended duration of strenuous activity during work may relate to an increased risk of haemorrhagic stroke. © 2012 The Authors. International Journal of Stroke © 2012 World Stroke Organization.
Perceived risk of regular cannabis use in the United States from 2002 to 2012: differences by sex, age, and race/ethnicity*

PubMed Central

Mauro, Pia M.; Martins, Silvia S.

2015-01-01

Introduction Cannabis is one of the most widely used psychoactive substances in the United States (U.S.). Perceived risk of use is associated with substance use; the recent debate surrounding medicalization and legalization of cannabis in the U.S. has the potential to impact perceived risk of use. Recent estimates are needed to assess temporal changes in, and identify correlates of, perceived risk of cannabis use. Methods Utilizing data from the 2002–2012 survey years of the National Survey on Drug Use and Health, chi-squared statistics and logistic regression were used to describe temporal changes in perceived risk of regular cannabis use (i.e., once or twice a week), to explore correlates of perceived risk, and to report frequency of cannabis use. Results Between 2002–2012, perceived great risk of regular cannabis use varied significantly overall (p<0.001). The prevalence of past year non-daily (p<0.001) and daily use varied significantly during this time (p<0.001). Controlling for survey year and other confounders, characteristics associated with increased odds of perceived great risk of regular cannabis use included: female sex; Non-White race/ethnicity; age 50+; and family income of $20,000–49,999. Characteristics associated with decreased odds of perceived great risk included: ages 12–17 and 18–25; high school education or greater; total family income of $75,000+; past year non-daily and daily cannabis use; and survey years 2008–2012. Conclusions Findings characterize trends of perceived risk of regular cannabis use, and past year non-daily and daily cannabis use. Longitudinal studies of the influence of legal status of cannabis at the state-level are needed. PMID:25735467
Perceived risk of regular cannabis use in the United States from 2002 to 2012: differences by sex, age, and race/ethnicity.

PubMed

Pacek, Lauren R; Mauro, Pia M; Martins, Silvia S

2015-04-01

Cannabis is one of the most widely used psychoactive substances in the United States (U.S.). Perceived risk of use is associated with substance use; the recent debate surrounding medicalization and legalization of cannabis in the U.S. has the potential to impact perceived risk of use. Recent estimates are needed to assess temporal changes in, and identify correlates of, perceived risk of cannabis use. Utilizing data from the 2002-2012 survey years of the National Survey on Drug Use and Health, chi-squared statistics and logistic regression were used to describe temporal changes in perceived risk of regular cannabis use (i.e., once or twice a week), to explore correlates of perceived risk, and to report frequency of cannabis use. Between 2002 and 2012, perceived great risk of regular cannabis use varied significantly overall (p < 0.001). The prevalence of past year non-daily (p < 0.001) and daily use varied significantly during this time (p < 0.001). Controlling for survey year and other confounders, characteristics associated with increased odds of perceived great risk of regular cannabis use included: female sex; Non-White race/ethnicity; age 50+; and family income of $20,000-49,999. Characteristics associated with decreased odds of perceived great risk included: ages 12-17 and 18-25; high school education or greater; total family income of $75,000+; past year non-daily and daily cannabis use; and survey years 2008-2012. Findings characterize trends of perceived risk of regular cannabis use, and past year non-daily and daily cannabis use. Longitudinal studies of the influence of legal status of cannabis at the state-level are needed. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Logistic regression models of factors influencing the location of bioenergy and biofuels plants

Treesearch

T.M. Young; R.L. Zaretzki; J.H. Perdue; F.M. Guess; X. Liu

2011-01-01

Logistic regression models were developed to identify significant factors that influence the location of existing wood-using bioenergy/biofuels plants and traditional wood-using facilities. Logistic models provided quantitative insight for variables influencing the location of woody biomass-using facilities. Availability of "thinnings to a basal area of 31.7m2/ha...
Discrete post-processing of total cloud cover ensemble forecasts

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Haiden, Thomas; Pappenberger, Florian

2017-04-01

This contribution presents an approach to post-process ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical post-processing of ensemble predictions are tested. The first approach is based on multinomial logistic regression, the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on station-wise post-processing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model. Reference Hemri, S., Haiden, T., & Pappenberger, F. (2016). Discrete post-processing of total cloud cover ensemble forecasts. Monthly Weather Review 144, 2565-2577.
Fuzzy multinomial logistic regression analysis: A multi-objective programming approach

NASA Astrophysics Data System (ADS)

Abdalla, Hesham A.; El-Sayed, Amany A.; Hamed, Ramadan

2017-05-01

Parameter estimation for multinomial logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets, Maximum Likelihood (ML) estimation is a satisfactory approach. Unfortunately, ML can fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. In this study, a new approach based on fuzzy concepts is proposed to estimate parameters of the multinomial logistic regression. The study assumes that the parameters of multinomial logistic regression are fuzzy. Based on the extension principle stated by Zadeh and Bárdossy's proposition, a multi-objective programming approach is suggested to estimate these fuzzy parameters. A simulation study is used to evaluate the performance of the new approach versus Maximum likelihood (ML) approach. Results show that the new proposed model outperforms ML in cases of small datasets.
Factors associated with HIV/AIDS treatment dropouts in a special care unit in the City of Rio de Janeiro, RJ, Brazil.

PubMed

Schilkowsky, Louise Bastos; Portela, Margareth Crisóstomo; Sá, Marilene de Castilho

2011-06-01

This study aimed to identify factors associated with the health care of patients with HIV/AIDS who drop out. The study was developed in a specialized health care unit of a University hospital in Rio de Janeiro, Brazil, considering a stratified sample of adult patients including all dropout cases (155) and 44.0% of 790 cases under regular follow-up. Bivariate analyses were used to identify associations between health care dropout and demographic, socioeconomic and clinical variables. Logistic and Cox regression models were used to identify the independent effects of the explanatory variables on risk for dropout, in the latter by incorporating information on the outcome over time. Patients were, on average, 35 years old, predominantly males (66.4%) and of a low socioeconomic level (45.0%). In both models, health care dropout was consistently associated with being unemployed or having an unstable job, using illicit drugs and having psychiatric background--positive association; and with age, having AIDS, and having used multiple antiretroviral regimens--negative association. In the logistic regression, dropping out was also positively associated with time between diagnosis and the first outpatient visit, while in the Cox model, the hazard for dropping out was positively associated with being single, and negatively associated with a higher educational level. The results of this work allow for the identification of HIV/AIDS patients more likely to drop out from health care.
A Primer on Logistic Regression.

ERIC Educational Resources Information Center

Woldbeck, Tanya

This paper introduces logistic regression as a viable alternative when the researcher is faced with variables that are not continuous. If one is to use simple regression, the dependent variable must be measured on a continuous scale. In the behavioral sciences, it may not always be appropriate or possible to have a measured dependent variable on a…
A Solution to Separation and Multicollinearity in Multiple Logistic Regression

PubMed Central

Shen, Jianzhao; Gao, Sujuan

2010-01-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27–38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth’s penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study. PMID:20376286
A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

PubMed

Shen, Jianzhao; Gao, Sujuan

2008-10-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.
[Influences of environmental factors and interaction of several chemokines gene-environmental on systemic lupus erythematosus].

PubMed

Ye, Dong-qing; Hu, Yi-song; Li, Xiang-pei; Huang, Fen; Yang, Shi-gui; Hao, Jia-hu; Yin, Jing; Zhang, Guo-qing; Liu, Hui-hui

2004-11-01

To explore the impact of environmental factors, daily lifestyle, psycho-social factors and the interactions between environmental factors and chemokines genes on systemic lupus erythematosus (SLE). Case-control study was carried out and environmental factors for SLE were analyzed by univariate and multivariate unconditional logistic regression. Interactions between environmental factors and chemokines polymorphism contributing to systemic lupus erythematosus were also analyzed by logistic regression model. There were nineteen factors associated with SLE when univariate unconditional logistic regression was used. However, when multivariate unconditional logistic regression was used, only five factors showed having impacts on the disease, in which drinking well water (OR=0.099) was protective factor for SLE, and multiple drug allergy (OR=8.174), over-exposure to sunshine (OR=18.339), taking antibiotics (OR=9.630) and oral contraceptives were risk factors for SLE. When unconditional logistic regression model was used, results showed that there was interaction between eating irritable food and -2518MCP-1G/G genotype (OR=4.387). No interaction between environmental factors was found that contributing to SLE in this study. Many environmental factors were related to SLE, and there was an interaction between -2518MCP-1G/G genotype and eating irritable food.

A deeper look at two concepts of measuring gene-gene interactions: logistic regression and interaction information revisited.

PubMed

Mielniczuk, Jan; Teisseyre, Paweł

2018-03-01

Detection of gene-gene interactions is one of the most important challenges in genome-wide case-control studies. Besides traditional logistic regression analysis, recently the entropy-based methods attracted a significant attention. Among entropy-based methods, interaction information is one of the most promising measures having many desirable properties. Although both logistic regression and interaction information have been used in several genome-wide association studies, the relationship between them has not been thoroughly investigated theoretically. The present paper attempts to fill this gap. We show that although certain connections between the two methods exist, in general they refer two different concepts of dependence and looking for interactions in those two senses leads to different approaches to interaction detection. We introduce ordering between interaction measures and specify conditions for independent and dependent genes under which interaction information is more discriminative measure than logistic regression. Moreover, we show that for so-called perfect distributions those measures are equivalent. The numerical experiments illustrate the theoretical findings indicating that interaction information and its modified version are more universal tools for detecting various types of interaction than logistic regression and linkage disequilibrium measures. © 2017 WILEY PERIODICALS, INC.
Controlling Type I Error Rates in Assessing DIF for Logistic Regression Method Combined with SIBTEST Regression Correction Procedure and DIF-Free-Then-DIF Strategy

ERIC Educational Resources Information Center

Shih, Ching-Lin; Liu, Tien-Hsiang; Wang, Wen-Chung

2014-01-01

The simultaneous item bias test (SIBTEST) method regression procedure and the differential item functioning (DIF)-free-then-DIF strategy are applied to the logistic regression (LR) method simultaneously in this study. These procedures are used to adjust the effects of matching true score on observed score and to better control the Type I error…
Access disparities to Magnet hospitals for patients undergoing neurosurgical operations

PubMed Central

Missios, Symeon; Bekelis, Kimon

2017-01-01

Background Centers of excellence focusing on quality improvement have demonstrated superior outcomes for a variety of surgical interventions. We investigated the presence of access disparities to hospitals recognized by the Magnet Recognition Program of the American Nurses Credentialing Center (ANCC) for patients undergoing neurosurgical operations. Methods We performed a cohort study of all neurosurgery patients who were registered in the New York Statewide Planning and Research Cooperative System (SPARCS) database from 2009–2013. We examined the association of African-American race and lack of insurance with Magnet status hospitalization for neurosurgical procedures. A mixed effects propensity adjusted multivariable regression analysis was used to control for confounding. Results During the study period, 190,535 neurosurgical patients met the inclusion criteria. Using a multivariable logistic regression, we demonstrate that African-Americans had lower admission rates to Magnet institutions (OR 0.62; 95% CI, 0.58–0.67). This persisted in a mixed effects logistic regression model (OR 0.77; 95% CI, 0.70–0.83) to adjust for clustering at the patient county level, and a propensity score adjusted logistic regression model (OR 0.75; 95% CI, 0.69–0.82). Additionally, lack of insurance was associated with lower admission rates to Magnet institutions (OR 0.71; 95% CI, 0.68–0.73), in a multivariable logistic regression model. This persisted in a mixed effects logistic regression model (OR 0.72; 95% CI, 0.69–0.74), and a propensity score adjusted logistic regression model (OR 0.72; 95% CI, 0.69–0.75). Conclusions Using a comprehensive all-payer cohort of neurosurgery patients in New York State we identified an association of African-American race and lack of insurance with lower rates of admission to Magnet hospitals. PMID:28684152
Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models.

PubMed

Schmidt, Amand F; Klungel, Olaf H; Groenwold, Rolf H H

2016-01-01

Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings. These analyses are often complicated by the number of potential confounders and the possibility of model misspecification. We conducted a simulation study to compare the performance of logistic regression, propensity score, disease risk score, and stabilized inverse probability weighting methods to adjust for confounding. Model misspecification was induced in the independent derivation dataset. We evaluated performance using relative bias confidence interval coverage of the true effect, among other metrics. At low events per coefficient (1.0 and 0.5), the logistic regression estimates had a large relative bias (greater than -100%). Bias of the disease risk score estimates was at most 13.48% and 18.83%. For the propensity score model, this was 8.74% and >100%, respectively. At events per coefficient of 1.0 and 0.5, inverse probability weighting frequently failed or reduced to a crude regression, resulting in biases of -8.49% and 24.55%. Coverage of logistic regression estimates became less than the nominal level at events per coefficient ≤5. For the disease risk score, inverse probability weighting, and propensity score, coverage became less than nominal at events per coefficient ≤2.5, ≤1.0, and ≤1.0, respectively. Bias of misspecified disease risk score models was 16.55%. In settings with low events/exposed subjects per coefficient, disease risk score methods can be useful alternatives to logistic regression models, especially when propensity score models cannot be used. Despite better performance of disease risk score methods than logistic regression and propensity score models in small events per coefficient settings, bias, and coverage still deviated from nominal.
On the use and misuse of scalar scores of confounders in design and analysis of observational studies.

PubMed

Pfeiffer, R M; Riedl, R

2015-08-15

We assess the asymptotic bias of estimates of exposure effects conditional on covariates when summary scores of confounders, instead of the confounders themselves, are used to analyze observational data. First, we study regression models for cohort data that are adjusted for summary scores. Second, we derive the asymptotic bias for case-control studies when cases and controls are matched on a summary score, and then analyzed either using conditional logistic regression or by unconditional logistic regression adjusted for the summary score. Two scores, the propensity score (PS) and the disease risk score (DRS) are studied in detail. For cohort analysis, when regression models are adjusted for the PS, the estimated conditional treatment effect is unbiased only for linear models, or at the null for non-linear models. Adjustment of cohort data for DRS yields unbiased estimates only for linear regression; all other estimates of exposure effects are biased. Matching cases and controls on DRS and analyzing them using conditional logistic regression yields unbiased estimates of exposure effect, whereas adjusting for the DRS in unconditional logistic regression yields biased estimates, even under the null hypothesis of no association. Matching cases and controls on the PS yield unbiased estimates only under the null for both conditional and unconditional logistic regression, adjusted for the PS. We study the bias for various confounding scenarios and compare our asymptotic results with those from simulations with limited sample sizes. To create realistic correlations among multiple confounders, we also based simulations on a real dataset. Copyright © 2015 John Wiley & Sons, Ltd.
[Application of SAS macro to evaluated multiplicative and additive interaction in logistic and Cox regression in clinical practices].

PubMed

Nie, Z Q; Ou, Y Q; Zhuang, J; Qu, Y J; Mai, J Z; Chen, J M; Liu, X Q

2016-05-01

Conditional logistic regression analysis and unconditional logistic regression analysis are commonly used in case control study, but Cox proportional hazard model is often used in survival data analysis. Most literature only refer to main effect model, however, generalized linear model differs from general linear model, and the interaction was composed of multiplicative interaction and additive interaction. The former is only statistical significant, but the latter has biological significance. In this paper, macros was written by using SAS 9.4 and the contrast ratio, attributable proportion due to interaction and synergy index were calculated while calculating the items of logistic and Cox regression interactions, and the confidence intervals of Wald, delta and profile likelihood were used to evaluate additive interaction for the reference in big data analysis in clinical epidemiology and in analysis of genetic multiplicative and additive interactions.
School District Policies and Adolescents’ Soda Consumption

PubMed Central

Miller, Gabrielle F.; Sliwa, Sarah; Brener, Nancy D.; Park, Sohyun; Merlo, Caitlin L.

2016-01-01

Purpose Sugar-sweetened beverages (SSBs) are a significant source of calories and added sugars for youth ages 14–18 years in the United States. This study examined the relationship between district-level policies and practices and students’ consumption of regular soda, one type of SSB, in 12 large urban school districts. Methods Data from the 2012 School Health Policies and Practices Study and 2013 Youth Risk Behavior Surveillance System were linked by district. The outcome variable was soda consumption and exposure variables were district policies. We used multivariable logistic regression analyses to calculate adjusted odds ratios (AORs) and 95% confidence intervals (CIs) after controlling for student characteristics and district free/reduced-price meal eligibility. Results About 18% of students reported consuming regular soda at least once per day. Most districts required high schools to have nutrition education, maintain closed campuses, and required/recommended that schools restrict promotional products and sale of beverages. Fewer districts required/recommended that schools offer healthful alternative beverages. Students in districts that restricted promotional products had lower odds of regular soda consumption (AOR = .84, 95% CI = .71–1.00), as did students in districts that restricted access to SSBs and offered healthful beverages when other beverages were available (AOR = .72, 95% CI = .54–.93, AOR = .76, 95% CI = .63–.91). Conclusions This study demonstrates that certain district-level policies are associated with student consumption of regular soda. These findings add to a growing consensus that policies and practices that influence the availability of healthier foods and beverages are needed across multiple settings. PMID:27021401
School District Policies and Adolescents' Soda Consumption.

PubMed

Miller, Gabrielle F; Sliwa, Sarah; Brener, Nancy D; Park, Sohyun; Merlo, Caitlin L

2016-07-01

Sugar-sweetened beverages (SSBs) are a significant source of calories and added sugars for youth ages 14-18 years in the United States. This study examined the relationship between district-level policies and practices and students' consumption of regular soda, one type of SSB, in 12 large urban school districts. Data from the 2012 School Health Policies and Practices Study and 2013 Youth Risk Behavior Surveillance System were linked by district. The outcome variable was soda consumption and exposure variables were district policies. We used multivariable logistic regression analyses to calculate adjusted odds ratios (AORs) and 95% confidence intervals (CIs) after controlling for student characteristics and district free/reduced-price meal eligibility. About 18% of students reported consuming regular soda at least once per day. Most districts required high schools to have nutrition education, maintain closed campuses, and required/recommended that schools restrict promotional products and sale of beverages. Fewer districts required/recommended that schools offer healthful alternative beverages. Students in districts that restricted promotional products had lower odds of regular soda consumption (AOR = .84, 95% CI = .71-1.00), as did students in districts that restricted access to SSBs and offered healthful beverages when other beverages were available (AOR = .72, 95% CI = .54-.93, AOR = .76, 95% CI = .63-.91). This study demonstrates that certain district-level policies are associated with student consumption of regular soda. These findings add to a growing consensus that policies and practices that influence the availability of healthier foods and beverages are needed across multiple settings. Published by Elsevier Inc.
Regular analgesic use and risk of endometrial cancer.

PubMed

Moysich, Kirsten B; Baker, Julie A; Rodabaugh, Kerry J; Villella, Jeannine A

2005-12-01

Analgesic use has been implicated in the chemoprevention of a number of solid tumors, but thus far, no previous research has focused on the role of aspirin in endometrial cancer etiology. We conducted a hospital-based case-control study of 427 women with primary, incident endometrial cancer, and 427 age- and residence-matched controls without benign or malignant neoplasms. All participants received medical services at Roswell Park Cancer Institute in Buffalo, NY, and completed a comprehensive epidemiologic questionnaire. Women who reported analgesic use at least once a week for at least 6 months were classified as regular users and served as the reference group throughout the analyses. We used unconditional logistic regression analyses to compute crude and adjusted odds ratios (OR) with corresponding 95% confidence intervals (CI). Compared with nonusers, regular aspirin users were not at reduced risk of endometrial cancer (adjusted OR, 0.91; 95% CI, 0.66-1.26), nor were women with the highest frequency, duration, or cumulative lifetime aspirin use. When the sample was divided by body mass index status, regular aspirin use was not associated with risk among women classified as normal weight or overweight, but a significant risk reduction was seen for obese women (adjusted OR, 0.50; 95% CI, 0.27-0.92). Significant decreases in risk were also observed for obese women with the greatest frequency, duration, and cumulative aspirin use. No significant associations in the overall sample or among obese women were noted for acetaminophen use. We observed no evidence of an overall chemoprotective effect of aspirin on endometrial cancer risk, but the significant risk reductions among obese women warrant further investigation.
Consumer perceptions of cigarette pack design in France: a comparison of regular, limited edition and plain packaging.

PubMed

Gallopel-Morvan, Karine; Moodie, Crawford; Hammond, David; Eker, Figen; Beguinot, Emmanuelle; Martinet, Yves

2012-09-01

In the face of comprehensive bans on the marketing of tobacco products, packaging has become an increasingly important promotional tool for the tobacco industry. A ban on the use of branding on tobacco packaging, known as 'plain' packaging, has emerged as a promising regulatory strategy. The current study sought to examine perceptions of cigarette packaging among adults in France. Adult smokers and non-smokers (N=836) were surveyed using computer-assisted personal interviewing to assess perceptions of pack design by comparing 'regular' branded packs and 'limited edition' packs (with novel designs or innovations) with 'plain' versions of these packs with all branding, including colour, removed. Plain packs (PP) were less likely than regular packs, and particularly limited edition packs, to be considered attractive, attention grabbing and likely to motivate youth purchase. PPs were also rated as the most effective in convincing non-smokers not to start and smokers to reduce consumption and quit. Logistic regression showed that smokers motivated to quit, in comparison to smokers not motivated to quit, were significantly more likely to consider the PPs as the packs most likely to motivate cessation. Novel cigarette packaging, in the form of limited edition packs, had the highest ratings of consumer appeal, ahead of regular branded packs and also PPs. Interestingly, PPs were perceived to be the packs most likely to promote cessation among those adults with quitting intentions. Plain packaging, therefore, may be a means of helping existing adult smokers motivated to quit to do so.
A cross-sectional study of health-related behaviors in rural eastern China.

PubMed

Sun, Ye-Huan; Yu, Tak-Sun Ignatius; Tong, Shi-Lu; Zhang, Yan; Shi, Xiao-Ming; Li, Wei

2002-12-01

This study examined the status of health-related behaviors among rural residents and the factors influencing the practice of such behaviors. One thousand and ninety subjects aged 15 years or over in a rural community, Anhui Province, China were surveyed. A questionnaire was used to collect information on the health knowledge, attitude and behavior of the subjects. Information on health behavior included smoking, drinking, dietary habits, regular exercises, sleeping pattern and oral health behavior. The prevalence of smoking and drinking in the male subjects was 46.5% and 46.9%, respectively. There was a positive significant association between smoking and drinking. Only 8.3% of all subjects ate three regular meals a day regularly. Among subjects who ate two meals a day, 89.7% did not have breakfast. Only 1.7% of subjects took part in regular exercise. About 85% of subjects slept 6 to 8 h per day. Only 38.4% of the respondents had the habit of hand washing before eating and after using the lavatory. 79.3% of the subjects brushed their teeth every day, and among them, only 10.6 percent brushed their teeth twice a day. Further analyses showed that 64.8% of subjects had 3-5 items of positive health behaviors out of 8 items and only 16.9% had six or more items. Logistical regression analyses suggested that better health behavior was affected by sex, age, years of education, income and health knowledge. The status of health behaviors among rural residents was generally poor. It is thus urgent to reinforce health education in rural communities in China.
Sweetened Beverages, Coffee, and Tea and Depression Risk among Older US Adults

PubMed Central

Guo, Xuguang; Park, Yikyung; Freedman, Neal D.; Sinha, Rashmi; Hollenbeck, Albert R.; Blair, Aaron; Chen, Honglei

2014-01-01

Sweetened beverages, coffee, and tea are the most consumed non-alcoholic beverages and may have important health consequences. We prospectively evaluated the consumption of various types of beverages assessed in 1995–1996 in relation to self-reported depression diagnosis after 2000 among 263,923 participants of the NIH-AARP Diet and Health Study. Odds ratios (OR) and 95% confidence intervals (CI) were derived from multivariate logistic regressions. The OR (95% CI) comparing ≥4 cans/cups per day with none were 1.30 (95%CI: 1.17–1.44) for soft drinks, 1.38 (1.15–1.65) for fruit drinks, and 0.91 (0.84–0.98) for coffee (all P for trend<0.0001). Null associations were observed for iced-tea and hot tea. In stratified analyses by drinkers of primarily diet versus regular beverages, the ORs were 1.31 (1.16–1.47) for diet versus 1.22 (1.03–1.45) for regular soft drinks, 1.51 (1.18–1.92) for diet versus 1.08 (0.79–1.46) for regular fruit drinks, and 1.25 (1.10–1.41) for diet versus 0.94 (0.83–1.08) for regular sweetened iced-tea. Finally, compared to nondrinkers, drinking coffee or tea without any sweetener was associated with a lower risk for depression, adding artificial sweeteners, but not sugar or honey, was associated with higher risks. Frequent consumption of sweetened beverages, especially diet drinks, may increase depression risk among older adults, whereas coffee consumption may lower the risk. PMID:24743309
Relationships of Musculoskeletal Symptoms, Sociodemographics, and Body Mass Index With Leisure-Time Physical Activity Among Nurses.

PubMed

Nam, Soohyun; Song, MinKyoung; Lee, Soo-Jeong

2018-05-01

Nurses have a high prevalence of musculoskeletal symptoms from patient handling tasks such as lifting, transferring, and repositioning. Comorbidities such as musculoskeletal symptoms may negatively affect engagement in leisure-time physical activity (LTPA). However, limited data are available on the relationship between musculoskeletal symptoms and LTPA among nurses. The purpose of this study was to describe musculoskeletal symptoms and LTPA, and to examine the relationships of musculoskeletal symptoms, sociodemographics, and body mass index with LTPA among nurses. Cross-sectional data on sociodemographics, employment characteristics, musculoskeletal symptoms, body mass index, and LTPA were collected from a statewide random sample of 454 California nurses from January to July 2013. Descriptive statistics, bivariate and multiple logistic regressions were performed. We observed that non-White nurses were less likely to engage in regular aerobic physical activity than White nurses (odds ratio [OR] = 0.61; 95% confidence interval [CI] = [0.40, 0.94]). Currently working nurses were less likely to engage in regular aerobic physical activity than their counterparts (OR = 0.48; 95% CI = [0.25, 0.91]). Nurses with higher body mass index were less likely to perform regular aerobic physical activity (OR = 0.93; 95% CI = [0.89, 0.97]) or muscle-strengthening physical activity (OR = 0.92; 95% CI = [0.88, 0.96]). This study found no evidence that musculoskeletal symptoms may interfere with regular engagement in LTPA. Physical activity promotion interventions should address employment-related barriers, and particularly target racial minority nurses and those who have a high body mass index.
Understanding the Relationship Between Sports-Relevant Gambling and Being At-Risk for a Gambling Problem Among American Adolescents.

PubMed

Marchica, Loredana; Zhao, Yaxi; Derevensky, Jeffrey; Ivoska, William

2017-06-01

Fantasy sports is a growing industry with a reported 56.8 million individuals participating in the United States and Canada alone in 2015. Whereas this activity has attracted considerable public attention, little research has examined its impact on adolescents in spite of their high rates of gambling. The current study examined the relationship between regular participation (more than once a month) in sport-relevant gambling activities among adolescents and those identified as being at-risk for a gambling problem. Questionnaire responses were collected from high school students (N = 6818; 49 % male) in Wood County, Ohio, United States. Statistical analyses revealed that regular involvement in sports betting, fantasy sports betting, and daily fantasy sports betting among adolescents was associated with a higher risk of gambling problems. Further, although males participate more frequently in these activities, females who participate have a stronger likelihood of being at-risk. Students aged 16-19 years old are at a higher risk for developing a gambling problem compared to younger adolescents when regularly engaging in sports-related gambling. Moreover, regularly participating in daily fantasy sports is the strongest predictor of at-risk gambling behavior in 13-15 year old students. A hierarchical logistic regression supports that controlling for gender and age, all forms of sport-relevant gambling activities are significant predictors of at-risk gambling. This study contributes to a more comprehensive understanding of the impact of sports betting and fantasy sports on adolescents and establishes an initial step for future studies to further investigate these relationships.
The association between regular yoga and meditation practice and falls and injuries: Results of a national cross-sectional survey among Australian women.

PubMed

Cramer, Holger; Sibbritt, David; Adams, Jon; Lauche, Romy

2016-02-01

Falls are the leading cause of injuries in women across all ages. While yoga has been shown to increase balance, it has also been associated with injuries due to falls during practice. This study aimed to analyse whether regular yoga or meditation practice is associated with the frequency of falls and fall-related injuries in upper middle-aged Australian women. Women aged 59-64 years from the Australian Longitudinal Study on Women's Health (ALSWH) were queried regarding falls and falls-related injuries; and whether they regularly practiced yoga or meditation. Associations of falls and falls-related injuries with yoga or meditation practice were analysed using chi-squared tests and multiple logistic regression modelling. Of 10,011 women, 4413 (44.1%) had slipped, tripped or stumbled, 2770 (27.7%) had fallen to the ground, 1398 (14.0%) had been injured as a result of falling, and 901 (9.0%) women had sought medical attention for a fall-related injury within the previous 12 months. Yoga or meditation was practiced regularly by 746 (7.5%) women. No associations of falls, fall-related injuries and treatment due to falls-related injury with yoga or meditation practice were found. No association between yoga or meditation practice and falls or fall-related injuries have been found. Further studies are warranted for conclusive judgement of benefits and safety of yoga and meditation in relation to balance, falls and fall-related injuries. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Physical activity and prevalence and incidence of mental disorders in adolescents and young adults.

PubMed

Ströhle, Andreas; Höfler, Michael; Pfister, Hildegard; Müller, Anne-Grit; Hoyer, Jürgen; Wittchen, Hans-Ulrich; Lieb, Roselind

2007-11-01

Although positive effects of physical activity on mental health indicators have been reported, the relationship between physical activity and the development of specific mental disorders is unclear. A cross-sectional (12-month) and prospective-longitudinal epidemiological study over 4 years in a community cohort of 2548 individuals, aged 14-24 years at outset of the study. Physical activity and mental disorders were assessed by the DSM-IV Composite International Diagnostic Interview (CIDI) with an embedded physical activity module. Multiple logistic regression analyses controlling for age, gender and educational status were used to determine the cross-sectional and prospective associations of mental disorders and physical activity. Cross-sectionally, regular physical activity was associated with a decreased prevalence of any and co-morbid mental disorder, due to lower rates of substance use disorders, anxiety disorders and dysthymia. Prospectively, subjects with regular physical activity had a substantially lower overall incidence of any and co-morbid mental disorder, and also a lower incidence of anxiety, somatoform and dysthymic disorder. By contrast, the incidence of bipolar disorder was increased among those with regular physical activity at baseline. In terms of the population attributable fraction (PAF), the potential for preventive effects of physical activity was considerably higher for men than for women. Regular physical activity is associated with a substantially reduced risk for some, but not all, mental disorders and also seems to reduce the degree of co-morbidity. Further examination of the evidently complex mechanisms and pathways underlying these associations might reveal promising new research targets and procedures for targeted prevention.
Drinking Level, Drinking Pattern, and Twenty-Year Total Mortality Among Late-Life Drinkers.

PubMed

Holahan, Charles J; Schutte, Kathleen K; Brennan, Penny L; Holahan, Carole K; Moos, Rudolf H

2015-07-01

Research on moderate drinking has focused on the average level of drinking. Recently, however, investigators have begun to consider the role of the pattern of drinking, particularly heavy episodic drinking, in mortality. The present study examined the combined roles of average drinking level (moderate vs. high) and drinking pattern (regular vs. heavy episodic) in 20-year total mortality among late-life drinkers. The sample comprised 1,121 adults ages 55-65 years. Alcohol consumption was assessed at baseline, and total mortality was indexed across 20 years. We used multiple logistic regression analyses controlling for a broad set of sociodemographic, behavioral, and health status covariates. Among individuals whose high level of drinking placed them at risk, a heavy episodic drinking pattern did not increase mortality odds compared with a regular drinking pattern. Conversely, among individuals who engage in a moderate level of drinking, prior findings showed that a heavy episodic drinking pattern did increase mortality risk compared with a regular drinking pattern. Correspondingly, a high compared with a moderate drinking level increased mortality risk among individuals maintaining a regular drinking pattern, but not among individuals engaging in a heavy episodic drinking pattern, whose pattern of consumption had already placed them at risk. Findings highlight that low-risk drinking requires that older adults drink low to moderate average levels of alcohol and avoid heavy episodic drinking. Heavy episodic drinking is frequent among late-middle-aged and older adults and needs to be addressed along with average consumption in understanding the health risks of late-life drinkers.
No rationale for 1 variable per 10 events criterion for binary logistic regression analysis.

PubMed

van Smeden, Maarten; de Groot, Joris A H; Moons, Karel G M; Collins, Gary S; Altman, Douglas G; Eijkemans, Marinus J C; Reitsma, Johannes B

2016-11-24

Ten events per variable (EPV) is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth's correction, are compared. The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect ('separation'). We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth's correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.
[Effects of a non-face-to-face behavioral intervention on poor sleepers and factors affecting improvement of sleep].

PubMed

Amamoto, Yuko; Adachi, Yoshiko; Kunituka, Kouko; Kumagai, Shuzo

2010-03-01

The purposes of this study were 1) to re-examine effects obtained from previous research of a non-face-to-face behavioral intervention in poorer sleepers and 2) to examine the factors impacting on improvement of sleep. The subjects were 178 poor sleepers who participated in an intervention for sleep improvement. The educational procedures comprised a minimal behavioral self-help package for one month that featured self- learning and self- monitoring of practical target habits for change. It was non face-to-face program conducted by only one member of staff. Subjects were asked to answer a questionnaire before and after the intervention. To reexamine the effects of this program found in our previous research, 9 sleep indices, sleep quality, and sleep-related behaviors were compared between before and after intervention. The sleep indices were total sleep time, sleep onset latency, sleep efficiency etc. Subjects were divided into an improvement group (n = 63) and a non-improvement group (n = 115) using a cutoff value for average change in sleep onset latency and sleep efficiency. After comparison of sleep and behavior between the two groups, logistic regression analysis was conducted to select parameters affecting improvement with this program. Total sleep time was significantly increased from 5.7 h to 6.1 h, sleep onset time decreased 18 minutes, and sleep efficiency improved 5.6 points. With 8 of 9 sleep-related behaviors, the proportion of subjects having an undesirable habit significantly decreased. The mean total number of desirable habit' changes was 2.63 in the improvement group and significantly higher than the 2.06 in the non-improvement group. Logistic regression analysis demonstrated that large sleep onset latency at baseline and beginning of regular exercise significantly affected the improvement of sleep in the subjects, after adjusting for all other parameters. The effects revealed by our previous research were reconfirmed. It is suggested that this program is more useful for persons having severe sleep onset difficulties, and regular exercise is particularly important in improvement of sleep. It is possible that even simple behavioral intervention is feasible with many subjects to improve sleep and related habits in poor sleepers.
4D-Fingerprint Categorical QSAR Models for Skin Sensitization Based on Classification Local Lymph Node Assay Measures

PubMed Central

Li, Yi; Tseng, Yufeng J.; Pan, Dahua; Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Hopfinger, Anton J.

2008-01-01

Currently, the only validated methods to identify skin sensitization effects are in vivo models, such as the Local Lymph Node Assay (LLNA) and guinea pig studies. There is a tremendous need, in particular due to novel legislation, to develop animal alternatives, eg. Quantitative Structure-Activity Relationship (QSAR) models. Here, QSAR models for skin sensitization using LLNA data have been constructed. The descriptors used to generate these models are derived from the 4D-molecular similarity paradigm and are referred to as universal 4D-fingerprints. A training set of 132 structurally diverse compounds and a test set of 15 structurally diverse compounds were used in this study. The statistical methodologies used to build the models are logistic regression (LR), and partial least square coupled logistic regression (PLS-LR), which prove to be effective tools for studying skin sensitization measures expressed in the two categorical terms of sensitizer and non-sensitizer. QSAR models with low values of the Hosmer-Lemeshow goodness-of-fit statistic, χHL2, are significant and predictive. For the training set, the cross-validated prediction accuracy of the logistic regression models ranges from 77.3% to 78.0%, while that of PLS-logistic regression models ranges from 87.1% to 89.4%. For the test set, the prediction accuracy of logistic regression models ranges from 80.0%-86.7%, while that of PLS-logistic regression models ranges from 73.3%-80.0%. The QSAR models are made up of 4D-fingerprints related to aromatic atoms, hydrogen bond acceptors and negatively partially charged atoms. PMID:17226934

MODELING SNAKE MICROHABITAT FROM RADIOTELEMETRY STUDIES USING POLYTOMOUS LOGISTIC REGRESSION

EPA Science Inventory

Multivariate analysis of snake microhabitat has historically used techniques that were derived under assumptions of normality and common covariance structure (e.g., discriminant function analysis, MANOVA). In this study, polytomous logistic regression (PLR which does not require ...
Logistic regression analysis of risk factors for postoperative recurrence of spinal tumors and analysis of prognostic factors.

PubMed

Zhang, Shanyong; Yang, Lili; Peng, Chuangang; Wu, Minfei

2018-02-01

The aim of the present study was to investigate the risk factors for postoperative recurrence of spinal tumors by logistic regression analysis and analysis of prognostic factors. In total, 77 male and 48 female patients with spinal tumor were selected in our hospital from January, 2010 to December, 2015 and divided into the benign (n=76) and malignant groups (n=49). All the patients underwent microsurgical resection of spinal tumors and were reviewed regularly 3 months after operation. The McCormick grading system was used to evaluate the postoperative spinal cord function. Data were subjected to statistical analysis. Of the 125 cases, 63 cases showed improvement after operation, 50 cases were stable, and deterioration was found in 12 cases. The improvement rate of patients with cervical spine tumor, which reached 56.3%, was the highest. Fifty-two cases of sensory disturbance, 34 cases of pain, 30 cases of inability to exercise, 26 cases of ataxia, and 12 cases of sphincter disorders were found after operation. Seventy-two cases (57.6%) underwent total resection, 18 cases (14.4%) received subtotal resection, 23 cases (18.4%) received partial resection, and 12 cases (9.6%) were only treated with biopsy/decompression. Postoperative recurrence was found in 57 cases (45.6%). The mean recurrence time of patients in the malignant group was 27.49±6.09 months, and the mean recurrence time of patients in the benign group was 40.62±4.34. The results were significantly different (P<0.001). Recurrence was found in 18 cases of the benign group and 39 cases of the malignant group, and results were significantly different (P<0.001). Tumor recurrence was shorter in patients with a higher McCormick grade (P<0.001). Recurrence was found in 13 patients with resection and all the patients with partial resection or biopsy/decompression. The results were significantly different (P<0.001). Logistic regression analysis of total resection-related factors showed that total resection should be the preferred treatment for patients with benign tumors, thoracic and lumbosacral tumors, and lower McCormick grade, as well as patients without syringomyelia and intramedullary tumors. Logistic regression analysis of recurrence-related factors revealed that the recurrence rate was relatively higher in patients with malignant, cervical, thoracic and lumbosacral, intramedullary tumors, and higher McCormick grade and patient received partial resection or biopsy. Tumor property, tumor location, McCormick grade, tumor resection, and intramedullary tumors are risk factors for the recurrence of spinal tumors. Clinical assessment of these risk factors may be helpful in selecting appropriate treatment strategies.
Logistic regression analysis of risk factors for postoperative recurrence of spinal tumors and analysis of prognostic factors

PubMed Central

Zhang, Shanyong; Yang, Lili; Peng, Chuangang; Wu, Minfei

2018-01-01

The aim of the present study was to investigate the risk factors for postoperative recurrence of spinal tumors by logistic regression analysis and analysis of prognostic factors. In total, 77 male and 48 female patients with spinal tumor were selected in our hospital from January, 2010 to December, 2015 and divided into the benign (n=76) and malignant groups (n=49). All the patients underwent microsurgical resection of spinal tumors and were reviewed regularly 3 months after operation. The McCormick grading system was used to evaluate the postoperative spinal cord function. Data were subjected to statistical analysis. Of the 125 cases, 63 cases showed improvement after operation, 50 cases were stable, and deterioration was found in 12 cases. The improvement rate of patients with cervical spine tumor, which reached 56.3%, was the highest. Fifty-two cases of sensory disturbance, 34 cases of pain, 30 cases of inability to exercise, 26 cases of ataxia, and 12 cases of sphincter disorders were found after operation. Seventy-two cases (57.6%) underwent total resection, 18 cases (14.4%) received subtotal resection, 23 cases (18.4%) received partial resection, and 12 cases (9.6%) were only treated with biopsy/decompression. Postoperative recurrence was found in 57 cases (45.6%). The mean recurrence time of patients in the malignant group was 27.49±6.09 months, and the mean recurrence time of patients in the benign group was 40.62±4.34. The results were significantly different (P<0.001). Recurrence was found in 18 cases of the benign group and 39 cases of the malignant group, and results were significantly different (P<0.001). Tumor recurrence was shorter in patients with a higher McCormick grade (P<0.001). Recurrence was found in 13 patients with resection and all the patients with partial resection or biopsy/decompression. The results were significantly different (P<0.001). Logistic regression analysis of total resection-related factors showed that total resection should be the preferred treatment for patients with benign tumors, thoracic and lumbosacral tumors, and lower McCormick grade, as well as patients without syringomyelia and intramedullary tumors. Logistic regression analysis of recurrence-related factors revealed that the recurrence rate was relatively higher in patients with malignant, cervical, thoracic and lumbosacral, intramedullary tumors, and higher McCormick grade and patient received partial resection or biopsy. Tumor property, tumor location, McCormick grade, tumor resection, and intramedullary tumors are risk factors for the recurrence of spinal tumors. Clinical assessment of these risk factors may be helpful in selecting appropriate treatment strategies. PMID:29434866
Selecting risk factors: a comparison of discriminant analysis, logistic regression and Cox's regression model using data from the Tromsø Heart Study.

PubMed

Brenn, T; Arnesen, E

1985-01-01

For comparative evaluation, discriminant analysis, logistic regression and Cox's model were used to select risk factors for total and coronary deaths among 6595 men aged 20-49 followed for 9 years. Groups with mortality between 5 and 93 per 1000 were considered. Discriminant analysis selected variable sets only marginally different from the logistic and Cox methods which always selected the same sets. A time-saving option, offered for both the logistic and Cox selection, showed no advantage compared with discriminant analysis. Analysing more than 3800 subjects, the logistic and Cox methods consumed, respectively, 80 and 10 times more computer time than discriminant analysis. When including the same set of variables in non-stepwise analyses, all methods estimated coefficients that in most cases were almost identical. In conclusion, discriminant analysis is advocated for preliminary or stepwise analysis, otherwise Cox's method should be used.
Self-regulation and household routines at age three and obesity at age eleven: longitudinal analysis of the UK Millennium Cohort Study

PubMed Central

Anderson, Sarah E; Sacker, Amanda; Whitaker, Robert C; Kelly, Yvonne

2017-01-01

Objective To examine, in a population-based cohort of three-year-old children, the association between self-regulation and exposure to the household routines of regular bedtime, regular mealtime, and limits on watching television/video; and to determine whether self-regulation and these routines predict the risk of obesity at age 11. Methods Analyses included 10 955 children in the nationally-representative UK Millennium Cohort Study. When children were age 3, parents reported whether children had a regular bedtime and mealtime and the amount of television/video watched. Emotional and cognitive self-regulation at age 3 were assessed by parent-report with the Child Social Behaviour Questionnaire. Children’s height and weight were measured at age 11 and obesity was defined using the International Obesity Task Force (IOTF) criteria. Results At age 3, 41% of children always had a regular bedtime, 47% always had a regular mealtime, and 23% were limited to ≤1 hour television/video daily. At age 11, 6.2% of children were obese. All three household routines were significantly associated with better emotional self-regulation, but not better cognitive self-regulation. In a multi-variable logistic regression model including emotional and cognitive self-regulation, all routines, and controlling for sociodemographic covariates, a 1 unit difference in emotional self-regulation at age 3 was associated with an OR (95% CI) for obesity of 1.38 (1.11, 1.71) at age 11, and inconsistent bedtimes with an OR (95% CI) for obesity of 1.87 (1.39, 2.51) at age 11. There was no evidence that emotional self-regulation mediated the relationship between regular bedtimes and later obesity. Cognitive self-regulation was not associated with later obesity. Conclusions Three-year-old children who had regular bedtimes, mealtimes, and limits on their television/video time had better emotional self-regulation. Lack of a regular bedtime and poorer emotional self-regulation at age 3 were independent predictors of obesity at age 11. PMID:28435162
Modification of the Mantel-Haenszel and Logistic Regression DIF Procedures to Incorporate the SIBTEST Regression Correction

ERIC Educational Resources Information Center

DeMars, Christine E.

2009-01-01

The Mantel-Haenszel (MH) and logistic regression (LR) differential item functioning (DIF) procedures have inflated Type I error rates when there are large mean group differences, short tests, and large sample sizes.When there are large group differences in mean score, groups matched on the observed number-correct score differ on true score,…
The regular general practitioner and sickness absence--a register-based study.

PubMed

Winde, Lee; Haukenes, Inger; Hetlevik, Øystein; Gjesdal, Sturla

2013-01-08

Undertaking research on the role of regular GPs with regard to rates of sickness absence is methodologically challenging, and existing results show a wide divergence. We investigated how long-term sickness absence is affected by the characteristics of doctors and their patient lists. The study encompassed all those vocationally active residents of Oslo and Bergen in 2005-2006 who had the same regular GP throughout 2006 (N = 298,039). Encrypted data on sickness absence for each individual in 2006, as well their age, gender and level of education were merged with data on the regular GPs (N = 568) and their patient lists, and subsequently analysed with the aid of logistic regression. The outcome variable was at least one period of sickness absence which had been paid for by the Norwegian Labour and Welfare Administration (NLWA). The explanatory variables included the age, gender, list length and list status (open/closed) of the regular GPs, as well as variables that characterised the composition of the patient lists. The analyses were stratified by gender and controlled for individual age and education. The age, gender and list length of the regular GPs were not associated with sickness absence paid for by the NLWA. The odds ratio for sickness absence > 16 days was reduced for both women and men when the list contained many highly educated patients, a high proportion of elderly people and few disability pensioners. Men on lists with a high proportion of men and lists with a high proportion of vocationally active patients also had lower odds rates for sickness absence > 16 days. Among women, the rate of sickness absence was lower for those on open lists than for those on closed lists. In addition to well-known individual factors, the study shows that the likelihood of sickness absence is affected by the socio-demographic composition of the patient list to which one belongs.
Childhood self-regulatory skills predict adolescent smoking behavior.

PubMed

deBlois, Madeleine E; Kubzansky, Laura D

2016-01-01

Cigarette smoking is the primary preventable cause of premature death. Better self-regulatory capacity is a key psychosocial factor that has been linked with reduced likelihood of tobacco use. Studies point to the importance of multiple forms of self-regulation, in the domains of emotion, attention, behavior, and social regulation, although no work has evaluated all of these domains in a single prospective study. Considering those four self-regulation domains separately and in combination, this study prospectively investigated whether greater self-regulation in childhood is associated with reduced likelihood of either trying cigarettes or becoming a regular smoker. Hypotheses were tested using longitudinal data from a cohort of 1709 US children participating in the Panel Study of Income Dynamics--Child Development Supplement. Self-regulation was assessed at study baseline when children ranged in age from 6 to 14 years, using parent-reported measures derived from the Behavior Problems Index and Positive Behavior Scale. Children ages 12-19 self-reported their cigarette smoking, defined in two ways: (1) trying and (2) regular use. Separate multiple logistic regression models were used to evaluate odds of trying or regularly using cigarettes, taking account of various potential confounders. Over an average of five years of follow-up, 34.5% of children ever tried cigarettes and 10.6% smoked regularly. Higher behavioral self-regulation was the only domain associated with reduced odds of trying cigarettes (odds ratio (OR) = .85, 95% confidence interval (CI) = .73-.99). Effective regulation in each of the domains was associated with reduced likelihood of regular smoking, although the association with social regulation was not statistically significant (ORs range .70-.85). For each additional domain in which a child was able to regulate successfully, the odds of becoming a regular smoker dropped by 18% (95% CI = .70-.97). These findings suggest that effective childhood self-regulatory skills across multiple domains may reduce future health risk behaviors.
Prevalence of wheezing and atopic diseases in Austrian schoolchildren in conjunction with urban, rural or farm residence.

PubMed

Horak, Elisabeth; Morass, Bernhard; Ulmer, Hanno; Genuneit, Jon; Braun-Fahrländer, Charlotte; von Mutius, Erika

2014-09-01

A large number of studies have consistently shown that children growing up on a farm have a reduced prevalence of allergic disorders. The GABRIEL Advanced Study was conducted in five rural areas of southern Germany, Switzerland, Austria and Poland to shed light on the protective 'farm effect' on asthma and atopic disease. Whereas, the GABRIEL Advanced Study focussed on rural children only, the present study incorporates data from Innsbruck town children also. A screening questionnaire was developed to identify children with and without atopic disease within their living environment. Children were stratified into farm children, rural children and Innsbruck-town children. Within the farming environment, regular exposure to the following key factors of interest was predefined: the animal shed, the hay loft and farm milk. Wheezing in the past 12 months (W12), doctor-diagnosed (dd)-asthma, dd-allergic rhinitis and dd-atopic dermatitis were evaluated by using standardized questions from the International Study of Asthma and Allergies in Childhood (ISAAC) RESULTS: Farm children with regular exposure showed a lower risk for W12 (odds ratios (OR) = 0.3; 95%; confidence interval (CI) 0.2-0.5), dd-asthma (OR = 0.4; 95% CI 0.2-0.9) and dd-hay fever (OR 0.2; 95% CI 0.1-0.4). The protective effect of regular exposure extended to rural children but included W12 and dd-hay fever only. Multivariate logistic regression analysis for children being regularly exposed revealed protective attributes for the animal shed, the hay loft and farm milk. These data show that regular exposure to a farming environment protects against wheezing, asthma and hay fever. Regarding wheezing and hay fever, this effect was not restricted to children living on a farm but also notable in rural children with regular farm contact.
Cannabis Liberalization and Adolescent Cannabis Use: A Cross-National Study in 38 Countries

PubMed Central

Shi, Yuyan; Lenzi, Michela; An, Ruopeng

2015-01-01

Aims To assess the associations between types of cannabis control policies at country level and prevalence of adolescent cannabis use. Setting, Participants and Design Multilevel logistic regressions were performed on 172,894 adolescents 15 year of age who participated in the 2001/2002, 2005/2006, or 2009/2010 cross-sectional Health Behaviour in School-Aged Children (HBSC) survey in 38 European and North American countries. Measures Self-reported cannabis use status was classified into ever use in life time, use in past year, and regular use. Country-level cannabis control policies were categorized into a dichotomous measure (whether or not liberalized) as well as 4 detailed types (full prohibition, depenalization, decriminalization, and partial prohibition). Control variables included individual-level sociodemographic characteristics and country-level economic characteristics. Findings Considerable intra-class correlations (.15-.19) were found at country level. With respect to the dichotomized cannabis control policy, adolescents were more likely to ever use cannabis (odds ratio (OR) = 1.10, p = .001), use in past year (OR = 1.09, p = .007), and use regularly (OR = 1.26, p = .004). Although boys were substantially more likely to use cannabis, the correlation between cannabis liberalization and cannabis use was smaller in boys than in girls. With respect to detailed types of policies, depenalization was associated with higher odds of past-year use (OR = 1.14, p = .013) and regular use (OR = 1.23, p = .038), and partial prohibition was associated with higher odds of regular use (OR = 2.39, p = .016). The correlation between cannabis liberalization and regular use was only significant after the policy had been introduced for more than 5 years. Conclusions Cannabis liberalization with depenalization and partial prohibition policies was associated with higher levels of regular cannabis use among adolescents. The correlations were heterogeneous between genders and between short- and long-terms. PMID:26605550
Satellite rainfall retrieval by logistic regression

NASA Technical Reports Server (NTRS)

Chiu, Long S.

1986-01-01

The potential use of logistic regression in rainfall estimation from satellite measurements is investigated. Satellite measurements provide covariate information in terms of radiances from different remote sensors.The logistic regression technique can effectively accommodate many covariates and test their significance in the estimation. The outcome from the logistical model is the probability that the rainrate of a satellite pixel is above a certain threshold. By varying the thresholds, a rainrate histogram can be obtained, from which the mean and the variant can be estimated. A logistical model is developed and applied to rainfall data collected during GATE, using as covariates the fractional rain area and a radiance measurement which is deduced from a microwave temperature-rainrate relation. It is demonstrated that the fractional rain area is an important covariate in the model, consistent with the use of the so-called Area Time Integral in estimating total rain volume in other studies. To calibrate the logistical model, simulated rain fields generated by rainfield models with prescribed parameters are needed. A stringent test of the logistical model is its ability to recover the prescribed parameters of simulated rain fields. A rain field simulation model which preserves the fractional rain area and lognormality of rainrates as found in GATE is developed. A stochastic regression model of branching and immigration whose solutions are lognormally distributed in some asymptotic limits has also been developed.
Association of aspirin and non-steroidal anti-inflammatory drug use with risk of colorectal cancer according to genetic variants

PubMed Central

Nan, Hongmei; Hutter, Carolyn M.; Lin, Yi; Jacobs, Eric J.; Ulrich, Cornelia M.; White, Emily; Baron, John A.; Berndt, Sonja I.; Brenner, Hermann; Butterbach, Katja; Caan, Bette J.; Campbell, Peter T.; Carlson, Christopher S.; Casey, Graham; Chang-Claude, Jenny; Chanock, Stephen J.; Cotterchio, Michelle; Duggan, David; Figueiredo, Jane C.; Fuchs, Charles S.; Giovannucci, Edward L.; Gong, Jian; Haile, Robert W.; Harrison, Tabitha A.; Hayes, Richard B.; Hoffmeister, Michael; Hopper, John L.; Hudson, Thomas J.; Jenkins, Mark A.; Jiao, Shuo; Lindor, Noralane M.; Lemire, Mathieu; Le Marchand, Loic; Newcomb, Polly A.; Ogino, Shuji; Pflugeisen, Bethann M.; Potter, John D.; Qu, Conghui; Rosse, Stephanie A.; Rudolph, Anja; Schoen, Robert E.; Schumacher, Fredrick R.; Seminara, Daniela; Slattery, Martha L.; Thibodeau, Stephen N.; Thomas, Fridtjof; Thornquist, Mark; Warnick, Greg S.; Zanke, Brent W.; Gauderman, W. James; Peters, Ulrike; Hsu, Li; Chan, Andrew T.

2015-01-01

Importance Use of aspirin and other non-steroidal anti-inflammatory drugs (NSAIDs) is associated with lower risk of colorectal cancer. Prior studies examining a potential differential relationship of aspirin and NSAIDs with colorectal cancer risk according to genetic factors have been limited to analyses of candidate genes or pathways. Objective To comprehensively identify common genetic markers that characterize individuals who may obtain differential benefit from aspirin and/or NSAID chemoprevention, we tested gene by environment (G X E) interactions between regular use of aspirin and/or NSAIDs and single nucleotide polymorphisms (SNPs) across the genome in relation to risk of colorectal cancer. Design Case-control study using the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) that enrolled cases of colorectal cancer ascertained between 1976 and 2011 and matched controls. Odds ratios (ORs) of colorectal cancer and 95% confidence intervals (95% CIs) were estimated using conventional logistic regression analysis and case-only interaction analysis, after adjusting for age, sex, center, the first three principal components to account for population structure, and known colorectal cancer risk factors. For all genome-wide analyses, a two-sided p-value<5.0×10-8, which yields a genome-wide significance level of 0.05, was considered statistically significant. Setting 10 observational studies (5 case-control and 5 cohort studies) that were initiated between 1976 and 2003 across the U.S., Canada, Australia and Germany. Participants 8,634 colorectal cancer cases and 8,553 controls of European descent. Exposures Genome-wide SNP data generated from genome-wide association scans and imputation to HapMap II, as well as information on regular use of aspirin and/or NSAIDs and other colorectal cancer risk factors collected using in-person interviews and/or structured questionnaires. Main Outcomes and Measures Colorectal cancer Results Regular use of aspirin and/or NSAIDs was associated with lower risk of colorectal cancer (OR=0.69; 95% CI=0.64-0.74; P=6.2×10-28) compared to non-regular use. In the conventional logistic regression analysis, the SNP rs2965667 at chromosome 12p12.3 near the microsomal glutathione S-transferase 1 (MGST1) gene showed a genome-wide significant interaction with aspirin and/or NSAID use (P for interaction=4.6×10-9). Compared to non-regular use, regular use of aspirin and/or NSAIDs was associated with a lower risk of colorectal cancer among individuals with rs2965667-TT genotype (OR=0.66; 95% CI=0.61-0.70; P=7.7×10-33), but a higher risk among those with much less common (4%) TA or AA genotypes (OR=1.89; 95% CI=1.27-2.81; P=0.002). In case-only interaction analysis, the SNP rs16973225 at chromosome 15q25.2 near the interleukin 16 (IL16) gene showed a genome-wide significant interaction with aspirin and/or NSAID use (P for interaction=8.2×10-9). Compared to non-regular use, regular use of aspirin and/or NSAIDs was associated with a lower risk of colorectal cancer among individuals with rs16973225-AA genotype (OR=0.66; 95% CI=0.62-0.71; P=1.9×10-30), but was not associated with risk of colorectal cancer among those with less common (9%) AC or CC genotypes (OR=0.97; 95% CI=0.78-1.20; P=0.76). CONCLUSIONS AND RELEVANCE In this genome-wide investigation of G X E interactions, use of aspirin and/or NSAIDs was associated with lower risk of colorectal cancer, and the association of these medications with colorectal cancer risk differed according to genetic variation at two SNPs at chromosomes 12 and 15. Validation of these findings in additional populations may facilitate targeted colorectal cancer prevention strategies. PMID:25781442
Practical Session: Logistic Regression

NASA Astrophysics Data System (ADS)

Clausel, M.; Grégoire, G.

2014-12-01

An exercise is proposed to illustrate the logistic regression. One investigates the different risk factors in the apparition of coronary heart disease. It has been proposed in Chapter 5 of the book of D.G. Kleinbaum and M. Klein, "Logistic Regression", Statistics for Biology and Health, Springer Science Business Media, LLC (2010) and also by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr341.pdf). This example is based on data given in the file evans.txt coming from http://www.sph.emory.edu/dkleinb/logreg3.htm#data.
Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghazali, Amirul Syafiq Mohd; Ali, Zalila; Noor, Norlida Mohd

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test ofmore » the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. The results indicated that obesity and overweight of students are related to gender, religion, sleep duration, time spent on electronic games, breakfast intake in a week, with whom meals are taken, protein intake, and also, the interaction between breakfast intake in a week with sleep duration, and the interaction between gender and protein intake.« less
Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

NASA Astrophysics Data System (ADS)

Ghazali, Amirul Syafiq Mohd; Ali, Zalila; Noor, Norlida Mohd; Baharum, Adam

2015-10-01

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test of the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. The results indicated that obesity and overweight of students are related to gender, religion, sleep duration, time spent on electronic games, breakfast intake in a week, with whom meals are taken, protein intake, and also, the interaction between breakfast intake in a week with sleep duration, and the interaction between gender and protein intake.
The cross-validated AUC for MCP-logistic regression with high-dimensional data.

PubMed

Jiang, Dingfeng; Huang, Jian; Zhang, Ying

2013-10-01

We propose a cross-validated area under the receiving operator characteristic (ROC) curve (CV-AUC) criterion for tuning parameter selection for penalized methods in sparse, high-dimensional logistic regression models. We use this criterion in combination with the minimax concave penalty (MCP) method for variable selection. The CV-AUC criterion is specifically designed for optimizing the classification performance for binary outcome data. To implement the proposed approach, we derive an efficient coordinate descent algorithm to compute the MCP-logistic regression solution surface. Simulation studies are conducted to evaluate the finite sample performance of the proposed method and its comparison with the existing methods including the Akaike information criterion (AIC), Bayesian information criterion (BIC) or Extended BIC (EBIC). The model selected based on the CV-AUC criterion tends to have a larger predictive AUC and smaller classification error than those with tuning parameters selected using the AIC, BIC or EBIC. We illustrate the application of the MCP-logistic regression with the CV-AUC criterion on three microarray datasets from the studies that attempt to identify genes related to cancers. Our simulation studies and data examples demonstrate that the CV-AUC is an attractive method for tuning parameter selection for penalized methods in high-dimensional logistic regression models.
Screening and managing cannabis use: comparing GP's and nurses' knowledge, beliefs, and behavior.

PubMed

Norberg, Melissa M; Gates, Peter; Dillon, Paul; Kavanagh, David J; Manocha, Ramesh; Copeland, Jan

2012-07-24

General practitioners (GPs) and nurses are ideally placed to address the significant unmet demand for the treatment of cannabis-related problems given the numbers of people who regularly seek their care. The aim of this study was to evaluate differences between GPs and nurses' perceived knowledge, beliefs, and behaviors toward cannabis use and its screening and management. This study involved 161 nurses and 503 GPs who completed a survey distributed via conference satchels to delegates of Healthed seminars focused on topics relevant to women and children's health. Differences between GPs and nurses were analyzed using χ(2)- tests and two-sample t-tests, while logistic regression examined predictors of service provision. GPs were more likely than nurses to have engaged in cannabis-related service provision, but also more frequently reported barriers related to time, interest, and having more important issues to address. Nurses reported less knowledge, skills, and role legitimacy. Perceived screening skills predicted screening and referral to alcohol and other drug (AOD) services, while knowing a regular user increased the likelihood of referrals only. Approaches to increase cannabis-related screening and intervention may be improved by involving nurses, and by leveraging the relationship between nurses and doctors, in primary care.
[Prevalence and factors associated with dental pain that prevents the performance of routine tasks by civil servants in Rio de Janeiro, Brazil].

PubMed

Alexandre, Gisele Caldas; Nadanovsky, Paulo; Lopes, Claudia S; Faerstein, Eduardo

2006-05-01

The aims of this study were to estimate the prevalence of dental pain preventing the performance of routine tasks and to assess its association with socioeconomic factors, minor psychiatric disorders, number of missing teeth, and dental consultation patterns. A cross-sectional study was conducted using a self-completed questionnaire answered by 4,030 administrative employees at a university in Rio de Janeiro, Brazil (the Pró-Saúde Study). Data were analyzed using multiple logistic regression. Prevalence of toothache preventing the performance of routine tasks in the two weeks prior to the interview was 2.9% (95%CI: 2.5-3.6). Men (OR = 1.6; 95%CI: 1.1-2.4), individuals with minor psychiatric disorders (OR = 1.7; 95%CI: 1.2-2.6), individuals with extensive tooth loss (OR = 3.4; 95%CI: 1.5-7.8), and those failing to appear for regular dental checkups (OR = 2.5; 95%CI: 1.8-17.3) showed increased odds of experiencing dental pain. Dental pain was an important problem in this population. Unfavorable living conditions and lack of regular dental checkups increased the odds of dental pain.
Disability correlates in Canadian Armed Forces Regular Force Veterans.

PubMed

Thompson, James M; Pranger, Tina; Sweet, Jill; VanTil, Linda; McColl, Mary Ann; Besemann, Markus; Shubaly, Colleen; Pedlar, David

2015-01-01

This study was undertaken to inform disability mitigation for military veterans by identifying personal, environmental, and health factors associated with activity limitations. A sample of 3154 Canadian Armed Forces Regular Force Veterans who were released during 1998-2007 participated in the 2010 Survey on Transition to Civilian Life. Associations between personal and environmental factors, health conditions and activity limitations were explored using ordinal logistic regression. The prevalence of activity reduction in life domains was higher than the Canadian general population (49% versus 21%), as was needing assistance with at least one activity of daily living (17% versus 5%). Prior to adjusting for health conditions, disability odds were elevated for increased age, females, non-degree post-secondary graduation, low income, junior non-commissioned members, deployment, low social support, low mastery, high life stress, and weak sense of community belonging. Reduced odds were found for private/recruit ranks. Disability odds were highest for chronic pain (10.9), any mental health condition (2.7), and musculoskeletal conditions (2.6), and there was a synergistic additive effect of physical and mental health co-occurrence. Disability, measured as activity limitation, was associated with a range of personal and environmental factors and health conditions, indicating multifactorial and multidisciplinary approaches to disability mitigation.
Health behaviors among Baby Boomer informal caregivers.

PubMed

Hoffman, Geoffrey J; Lee, Jihey; Mendez-Luck, Carolyn A

2012-04-01

This study examines health-risk behaviors among "Baby Boomer" caregivers and non-caregivers. Data from the 2009 California Health Interview Survey of the state's non-institutionalized population provided individual-level, caregiving, and health behavior characteristics for 5,688 informal caregivers and 12,941 non-caregivers. Logistic regression models were estimated separately for four individual health-risk behaviors-smoking, sedentary behavior, and regular soda and fast-food consumption-as well as a global health-risk measure. Controlling for psychological distress and personal characteristics and social resources such as age, gender, income and education, work and marital status, and neighborhood safety, caregivers had greater odds than non-caregivers of overall negative health behavior and of smoking and regular soda and fast-food consumption. We did not observe significant differences in odds of negative behavior related to stress for spousal caregivers and caregivers in the role for longer periods of time or those providing more hours of weekly care compared with other caregivers. Our study found evidence that Baby Boomer caregivers engage in poor health behaviors that are associated with exposure to caregiving. Baby Boomer caregivers may be at risk for certain behavioral factors that are associated with disability and chronic illness.

Effect of Health Literacy on Decision-Making Preferences among Medically Underserved Patients.

PubMed

Seo, Joann; Goodman, Melody S; Politi, Mary; Blanchard, Melvin; Kaphingst, Kimberly A

2016-05-01

Participation in the decision-making process and health literacy may both affect health outcomes; data on how these factors are related among diverse groups are limited. This study examined the relationship between health literacy and decision-making preferences in a medically underserved population. We analyzed a sample of 576 primary care patients. Multivariable logistic regression was used to examine the independent association of health literacy (measured by the Rapid Estimate of Adult Literacy in Medicine-Revised) and patients' decision-making preferences (physician directed or patient involved), controlling for age, race/ethnicity, and gender. We tested whether having a regular doctor modified this association. Adequate health literacy (odds ratio [OR] = 1.7;P= 0.009) was significantly associated with preferring patient-involved decision making, controlling for age, race/ethnicity, and gender. Having a regular doctor did not modify this relationship. Males were significantly less likely to prefer patient-involved decision making (OR = 0.65;P= 0.024). Findings suggest health literacy affects decision-making preferences in medically underserved patients. More research is needed on how factors, such as patient knowledge or confidence, may influence decision-making preferences, particularly for those with limited health literacy. © The Author(s) 2016.
Church Attendance as a Predictor of Number of Sexual Health Topics Discussed among High Risk HIV Negative Black Women

PubMed Central

Williams, Terrinieka T.; Pichon, Latrice C.; Davey-Rothwell, Melissa; Latkin, Carl A.

2015-01-01

Research suggests that sexual health communication is associated with safer sex practices. In this study, we examined the relationship between church attendance and sexual health topics discussed with both friends and sexual partners among a sample of urban Black women. Participants were 434 HIV negative Black women who were at high risk for contracting HIV through heterosexual sex. They were recruited from Baltimore, Maryland using a network-based sampling approach. Data were collected through face-to-face interviews and Audio-Computer-Assisted Self-Interviews (ACASI). Fifty-four percent of the participants attended church once a month or more (regular attendees). Multivariate logistic regression analyses revealed that regular church attendance among high-risk HIV negative Black women was a significant predictor of the number of sexual health topics discussed with both friends (AOR = 1.85, p =.003) and sexual partners (AOR= 1.68, p =.014). Future efforts to reduce HIV incidence among high-risk Black women may benefit from partnerships with churches that equip faith leaders and congregants with the tools to discuss sexual health topics with both their sexual partners and friends. PMID:25966802
Spousal correlations for lifestyle factors and selected diseases in Chinese couples.

PubMed

Jurj, Adriana L; Wen, Wanqing; Li, Hong-Lan; Zheng, Wei; Yang, Gong; Xiang, Yong-Bing; Gao, Yu-Tang; Shu, Xiao-Ou

2006-04-01

Spouses usually are genetically unrelated and share a common living environment. Thus, concordance of diseases in spouses reflects mainly environmental etiologic contributors. The purpose of this study is to investigate spousal associations for selected lifestyle characteristics and common medical conditions. Baseline information from 66,130 married couples participating in the Shanghai Women's Health Study was used in this analysis. Husband-wife associations were evaluated by means of logistic regression, using women's lifestyle and medical conditions as dependent variables. Adjustments were made for women's age, education, occupation, and family income in all models. Women were more than twice as likely to be current or former smokers; be regular consumers of alcohol, tea, and ginseng; and exercise regularly if their husbands had the same habit. A statistically significant husband-wife disease association was found for tuberculosis, chronic bronchitis, asthma, chronic gastritis, chronic hepatitis, ulcerative colitis, cholelithiasis, high blood pressure, coronary heart disease, and stroke. Spouses share common lifestyle habits and health risks. This study supports the hypothesis that the shared marital environment may contribute to similarities in lifestyle and morbidity in spouses and provides a basis for health promotion and prevention strategies that target the spouses of patients.
A simple approach to power and sample size calculations in logistic regression and Cox regression models.

PubMed

Vaeth, Michael; Skovlund, Eva

2004-06-15

For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.
Psychotropic substance abuse among adolescents: a structural equation model on risk and protective factors.

PubMed

Rumpold, Gerhard; Klingseis, Michael; Dornauer, Kurt; Kopp, Martin; Doering, Stephan; Höfer, Stefan; Mumelter, Birgit; Schüssler, Gerhard

2006-01-01

The use of psychotropic substances in adolescents represents a serious public health problem. In this study a representative sample of 485 Austrian students between 14 and 18 years of age were investigated with a semistructured interview about substance-related issues and completed the general health questionnaire. The following rates of regular psychotropic substance use were found: cigarettes 41.4%, alcohol 44.5%, cannabis 10.1%, and other illicit substances 3%. Logistic regression analyses and structural equation modeling revealed the following major risk factors for substance use: peer pressure, negative family atmosphere, school difficulties, and psychopathology. Knowledge about substance use acted as a protective factor. Prevention of adolescent substance use and misuse should aim at these different targets. Information about coping with peer pressure may be a particularly promising route of intervention.
Robust logistic regression to narrow down the winner's curse for rare and recessive susceptibility variants.

PubMed

Kesselmeier, Miriam; Lorenzo Bermejo, Justo

2017-11-01

Logistic regression is the most common technique used for genetic case-control association studies. A disadvantage of standard maximum likelihood estimators of the genotype relative risk (GRR) is their strong dependence on outlier subjects, for example, patients diagnosed at unusually young age. Robust methods are available to constrain outlier influence, but they are scarcely used in genetic studies. This article provides a non-intimidating introduction to robust logistic regression, and investigates its benefits and limitations in genetic association studies. We applied the bounded Huber and extended the R package 'robustbase' with the re-descending Hampel functions to down-weight outlier influence. Computer simulations were carried out to assess the type I error rate, mean squared error (MSE) and statistical power according to major characteristics of the genetic study and investigated markers. Simulations were complemented with the analysis of real data. Both standard and robust estimation controlled type I error rates. Standard logistic regression showed the highest power but standard GRR estimates also showed the largest bias and MSE, in particular for associated rare and recessive variants. For illustration, a recessive variant with a true GRR=6.32 and a minor allele frequency=0.05 investigated in a 1000 case/1000 control study by standard logistic regression resulted in power=0.60 and MSE=16.5. The corresponding figures for Huber-based estimation were power=0.51 and MSE=0.53. Overall, Hampel- and Huber-based GRR estimates did not differ much. Robust logistic regression may represent a valuable alternative to standard maximum likelihood estimation when the focus lies on risk prediction rather than identification of susceptibility variants. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

PubMed

Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T

2016-02-01

The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.
A comparative study on entrepreneurial attitudes modeled with logistic regression and Bayes nets.

PubMed

López Puga, Jorge; García García, Juan

2012-11-01

Entrepreneurship research is receiving increasing attention in our context, as entrepreneurs are key social agents involved in economic development. We compare the success of the dichotomic logistic regression model and the Bayes simple classifier to predict entrepreneurship, after manipulating the percentage of missing data and the level of categorization in predictors. A sample of undergraduate university students (N = 1230) completed five scales (motivation, attitude towards business creation, obstacles, deficiencies, and training needs) and we found that each of them predicted different aspects of the tendency to business creation. Additionally, our results show that the receiver operating characteristic (ROC) curve is affected by the rate of missing data in both techniques, but logistic regression seems to be more vulnerable when faced with missing data, whereas Bayes nets underperform slightly when categorization has been manipulated. Our study sheds light on the potential entrepreneur profile and we propose to use Bayesian networks as an additional alternative to overcome the weaknesses of logistic regression when missing data are present in applied research.
Epidemiologic programs for computers and calculators. A microcomputer program for multiple logistic regression by unconditional and conditional maximum likelihood methods.

PubMed

Campos-Filho, N; Franco, E L

1989-02-01

A frequent procedure in matched case-control studies is to report results from the multivariate unmatched analyses if they do not differ substantially from the ones obtained after conditioning on the matching variables. Although conceptually simple, this rule requires that an extensive series of logistic regression models be evaluated by both the conditional and unconditional maximum likelihood methods. Most computer programs for logistic regression employ only one maximum likelihood method, which requires that the analyses be performed in separate steps. This paper describes a Pascal microcomputer (IBM PC) program that performs multiple logistic regression by both maximum likelihood estimation methods, which obviates the need for switching between programs to obtain relative risk estimates from both matched and unmatched analyses. The program calculates most standard statistics and allows factoring of categorical or continuous variables by two distinct methods of contrast. A built-in, descriptive statistics option allows the user to inspect the distribution of cases and controls across categories of any given variable.
Comparison of cranial sex determination by discriminant analysis and logistic regression.

PubMed

Amores-Ampuero, Anabel; Alemán, Inmaculada

2016-04-05

Various methods have been proposed for estimating dimorphism. The objective of this study was to compare sex determination results from cranial measurements using discriminant analysis or logistic regression. The study sample comprised 130 individuals (70 males) of known sex, age, and cause of death from San José cemetery in Granada (Spain). Measurements of 19 neurocranial dimensions and 11 splanchnocranial dimensions were subjected to discriminant analysis and logistic regression, and the percentages of correct classification were compared between the sex functions obtained with each method. The discriminant capacity of the selected variables was evaluated with a cross-validation procedure. The percentage accuracy with discriminant analysis was 78.2% for the neurocranium (82.4% in females and 74.6% in males) and 73.7% for the splanchnocranium (79.6% in females and 68.8% in males). These percentages were higher with logistic regression analysis: 85.7% for the neurocranium (in both sexes) and 94.1% for the splanchnocranium (100% in females and 91.7% in males).
Stepwise Distributed Open Innovation Contests for Software Development: Acceleration of Genome-Wide Association Analysis

PubMed Central

Hill, Andrew; Loh, Po-Ru; Bharadwaj, Ragu B.; Pons, Pascal; Shang, Jingbo; Guinan, Eva; Lakhani, Karim; Kilty, Iain

2017-01-01

Abstract Background: The association of differing genotypes with disease-related phenotypic traits offers great potential to both help identify new therapeutic targets and support stratification of patients who would gain the greatest benefit from specific drug classes. Development of low-cost genotyping and sequencing has made collecting large-scale genotyping data routine in population and therapeutic intervention studies. In addition, a range of new technologies is being used to capture numerous new and complex phenotypic descriptors. As a result, genotype and phenotype datasets have grown exponentially. Genome-wide association studies associate genotypes and phenotypes using methods such as logistic regression. As existing tools for association analysis limit the efficiency by which value can be extracted from increasing volumes of data, there is a pressing need for new software tools that can accelerate association analyses on large genotype-phenotype datasets. Results: Using open innovation (OI) and contest-based crowdsourcing, the logistic regression analysis in a leading, community-standard genetics software package (PLINK 1.07) was substantially accelerated. OI allowed us to do this in <6 months by providing rapid access to highly skilled programmers with specialized, difficult-to-find skill sets. Through a crowd-based contest a combination of computational, numeric, and algorithmic approaches was identified that accelerated the logistic regression in PLINK 1.07 by 18- to 45-fold. Combining contest-derived logistic regression code with coarse-grained parallelization, multithreading, and associated changes to data initialization code further developed through distributed innovation, we achieved an end-to-end speedup of 591-fold for a data set size of 6678 subjects by 645 863 variants, compared to PLINK 1.07's logistic regression. This represents a reduction in run time from 4.8 hours to 29 seconds. Accelerated logistic regression code developed in this project has been incorporated into the PLINK2 project. Conclusions: Using iterative competition-based OI, we have developed a new, faster implementation of logistic regression for genome-wide association studies analysis. We present lessons learned and recommendations on running a successful OI process for bioinformatics. PMID:28327993
Stepwise Distributed Open Innovation Contests for Software Development: Acceleration of Genome-Wide Association Analysis.

PubMed

Hill, Andrew; Loh, Po-Ru; Bharadwaj, Ragu B; Pons, Pascal; Shang, Jingbo; Guinan, Eva; Lakhani, Karim; Kilty, Iain; Jelinsky, Scott A

2017-05-01

The association of differing genotypes with disease-related phenotypic traits offers great potential to both help identify new therapeutic targets and support stratification of patients who would gain the greatest benefit from specific drug classes. Development of low-cost genotyping and sequencing has made collecting large-scale genotyping data routine in population and therapeutic intervention studies. In addition, a range of new technologies is being used to capture numerous new and complex phenotypic descriptors. As a result, genotype and phenotype datasets have grown exponentially. Genome-wide association studies associate genotypes and phenotypes using methods such as logistic regression. As existing tools for association analysis limit the efficiency by which value can be extracted from increasing volumes of data, there is a pressing need for new software tools that can accelerate association analyses on large genotype-phenotype datasets. Using open innovation (OI) and contest-based crowdsourcing, the logistic regression analysis in a leading, community-standard genetics software package (PLINK 1.07) was substantially accelerated. OI allowed us to do this in <6 months by providing rapid access to highly skilled programmers with specialized, difficult-to-find skill sets. Through a crowd-based contest a combination of computational, numeric, and algorithmic approaches was identified that accelerated the logistic regression in PLINK 1.07 by 18- to 45-fold. Combining contest-derived logistic regression code with coarse-grained parallelization, multithreading, and associated changes to data initialization code further developed through distributed innovation, we achieved an end-to-end speedup of 591-fold for a data set size of 6678 subjects by 645 863 variants, compared to PLINK 1.07's logistic regression. This represents a reduction in run time from 4.8 hours to 29 seconds. Accelerated logistic regression code developed in this project has been incorporated into the PLINK2 project. Using iterative competition-based OI, we have developed a new, faster implementation of logistic regression for genome-wide association studies analysis. We present lessons learned and recommendations on running a successful OI process for bioinformatics. © The Author 2017. Published by Oxford University Press.
Easy and low-cost identification of metabolic syndrome in patients treated with second-generation antipsychotics: artificial neural network and logistic regression models.

PubMed

Lin, Chao-Cheng; Bai, Ya-Mei; Chen, Jen-Yeu; Hwang, Tzung-Jeng; Chen, Tzu-Ting; Chiu, Hung-Wen; Li, Yu-Chuan

2010-03-01

Metabolic syndrome (MetS) is an important side effect of second-generation antipsychotics (SGAs). However, many SGA-treated patients with MetS remain undetected. In this study, we trained and validated artificial neural network (ANN) and multiple logistic regression models without biochemical parameters to rapidly identify MetS in patients with SGA treatment. A total of 383 patients with a diagnosis of schizophrenia or schizoaffective disorder (DSM-IV criteria) with SGA treatment for more than 6 months were investigated to determine whether they met the MetS criteria according to the International Diabetes Federation. The data for these patients were collected between March 2005 and September 2005. The input variables of ANN and logistic regression were limited to demographic and anthropometric data only. All models were trained by randomly selecting two-thirds of the patient data and were internally validated with the remaining one-third of the data. The models were then externally validated with data from 69 patients from another hospital, collected between March 2008 and June 2008. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of all models. Both the final ANN and logistic regression models had high accuracy (88.3% vs 83.6%), sensitivity (93.1% vs 86.2%), and specificity (86.9% vs 83.8%) to identify MetS in the internal validation set. The mean +/- SD AUC was high for both the ANN and logistic regression models (0.934 +/- 0.033 vs 0.922 +/- 0.035, P = .63). During external validation, high AUC was still obtained for both models. Waist circumference and diastolic blood pressure were the common variables that were left in the final ANN and logistic regression models. Our study developed accurate ANN and logistic regression models to detect MetS in patients with SGA treatment. The models are likely to provide a noninvasive tool for large-scale screening of MetS in this group of patients. (c) 2010 Physicians Postgraduate Press, Inc.
Bayesian logistic regression in detection of gene-steroid interaction for cancer at PDLIM5 locus.

PubMed

Wang, Ke-Sheng; Owusu, Daniel; Pan, Yue; Xie, Changchun

2016-06-01

The PDZ and LIM domain 5 (PDLIM5) gene may play a role in cancer, bipolar disorder, major depression, alcohol dependence and schizophrenia; however, little is known about the interaction effect of steroid and PDLIM5 gene on cancer. This study examined 47 single-nucleotide polymorphisms (SNPs) within the PDLIM5 gene in the Marshfield sample with 716 cancer patients (any diagnosed cancer, excluding minor skin cancer) and 2848 noncancer controls. Multiple logistic regression model in PLINK software was used to examine the association of each SNP with cancer. Bayesian logistic regression in PROC GENMOD in SAS statistical software, ver. 9.4 was used to detect gene- steroid interactions influencing cancer. Single marker analysis using PLINK identified 12 SNPs associated with cancer (P< 0.05); especially, SNP rs6532496 revealed the strongest association with cancer (P = 6.84 × 10⁻³); while the next best signal was rs951613 (P = 7.46 × 10⁻³). Classic logistic regression in PROC GENMOD showed that both rs6532496 and rs951613 revealed strong gene-steroid interaction effects (OR=2.18, 95% CI=1.31-3.63 with P = 2.9 × 10⁻³ for rs6532496 and OR=2.07, 95% CI=1.24-3.45 with P = 5.43 × 10⁻³ for rs951613, respectively). Results from Bayesian logistic regression showed stronger interaction effects (OR=2.26, 95% CI=1.2-3.38 for rs6532496 and OR=2.14, 95% CI=1.14-3.2 for rs951613, respectively). All the 12 SNPs associated with cancer revealed significant gene-steroid interaction effects (P < 0.05); whereas 13 SNPs showed gene-steroid interaction effects without main effect on cancer. SNP rs4634230 revealed the strongest gene-steroid interaction effect (OR=2.49, 95% CI=1.5-4.13 with P = 4.0 × 10⁻⁴ based on the classic logistic regression and OR=2.59, 95% CI=1.4-3.97 from Bayesian logistic regression; respectively). This study provides evidence of common genetic variants within the PDLIM5 gene and interactions between PLDIM5 gene polymorphisms and steroid use influencing cancer.
Deletion Diagnostics for Alternating Logistic Regressions

PubMed Central

Preisser, John S.; By, Kunthel; Perin, Jamie; Qaqish, Bahjat F.

2013-01-01

Deletion diagnostics are introduced for the regression analysis of clustered binary outcomes estimated with alternating logistic regressions, an implementation of generalized estimating equations (GEE) that estimates regression coefficients in a marginal mean model and in a model for the intracluster association given by the log odds ratio. The diagnostics are developed within an estimating equations framework that recasts the estimating functions for association parameters based upon conditional residuals into equivalent functions based upon marginal residuals. Extensions of earlier work on GEE diagnostics follow directly, including computational formulae for one-step deletion diagnostics that measure the influence of a cluster of observations on the estimated regression parameters and on the overall marginal mean or association model fit. The diagnostic formulae are evaluated with simulations studies and with an application concerning an assessment of factors associated with health maintenance visits in primary care medical practices. The application and the simulations demonstrate that the proposed cluster-deletion diagnostics for alternating logistic regressions are good approximations of their exact fully iterated counterparts. PMID:22777960
Quantifying predictive capability of electronic health records for the most harmful breast cancer

NASA Astrophysics Data System (ADS)

Wu, Yirong; Fan, Jun; Peissig, Peggy; Berg, Richard; Tafti, Ahmad Pahlavan; Yin, Jie; Yuan, Ming; Page, David; Cox, Jennifer; Burnside, Elizabeth S.

2018-03-01

Improved prediction of the "most harmful" breast cancers that cause the most substantive morbidity and mortality would enable physicians to target more intense screening and preventive measures at those women who have the highest risk; however, such prediction models for the "most harmful" breast cancers have rarely been developed. Electronic health records (EHRs) represent an underused data source that has great research and clinical potential. Our goal was to quantify the value of EHR variables in the "most harmful" breast cancer risk prediction. We identified 794 subjects who had breast cancer with primary non-benign tumors with their earliest diagnosis on or after 1/1/2004 from an existing personalized medicine data repository, including 395 "most harmful" breast cancer cases and 399 "least harmful" breast cancer cases. For these subjects, we collected EHR data comprised of 6 components: demographics, diagnoses, symptoms, procedures, medications, and laboratory results. We developed two regularized prediction models, Ridge Logistic Regression (Ridge-LR) and Lasso Logistic Regression (Lasso-LR), to predict the "most harmful" breast cancer one year in advance. The area under the ROC curve (AUC) was used to assess model performance. We observed that the AUCs of Ridge-LR and Lasso-LR models were 0.818 and 0.839 respectively. For both the Ridge-LR and LassoLR models, the predictive performance of the whole EHR variables was significantly higher than that of each individual component (p<0.001). In conclusion, EHR variables can be used to predict the "most harmful" breast cancer, providing the possibility to personalize care for those women at the highest risk in clinical practice.
Rural-urban difference in the use of annual physical examination among seniors in Shandong, China: a cross-sectional study.

PubMed

Ge, Dandan; Chu, Jie; Zhou, Chengchao; Qian, Yangyang; Zhang, Li; Sun, Long

2017-05-23

Regular physical examination contributes to early detection and timely treatment, which is helpful in promoting healthy behaviors and preventing diseases. The objective of this study is to compare the annual physical examination (APE) use between rural and urban elderly in China. A total of 3,922 participants (60+) were randomly selected from three urban districts and three rural counties in Shandong Province, China, and were interviewed using a standardized questionnaire. We performed unadjusted and adjusted logistic regression models to examine the difference in the utilization of APE between rural and urban elderly. Two adjusted logistic regression models were employed to identify the factors associated with APE use in rural and urban seniors respectively. The utilization rates of APE in rural and urban elderly are 37.4% and 76.2% respectively. Factors including education level, exercise, watching TV, and number of non-communicable chronic conditions, are associated with APE use both in rural and urban elderly. Hospitalization, self-reported economic status, and health insurance are found to be significant (p < 0.05) predictors for APE use in rural elderly. Elderly covered by Urban Resident Basic Medical Insurance (URBMI) (p < 0.05, OR = 1.874) are more likely to use APE in urban areas. There is a big difference in APE utilization between rural and urban elderly. Interventions targeting identified at-risk subgroups, especially for those rural elderly, are essential to reduce such a gap. To improve health literacy might be helpful to increase the utilization rate of APE among the elderly.
Quantifying predictive capability of electronic health records for the most harmful breast cancer.

PubMed

Wu, Yirong; Fan, Jun; Peissig, Peggy; Berg, Richard; Tafti, Ahmad Pahlavan; Yin, Jie; Yuan, Ming; Page, David; Cox, Jennifer; Burnside, Elizabeth S

2018-02-01

Improved prediction of the "most harmful" breast cancers that cause the most substantive morbidity and mortality would enable physicians to target more intense screening and preventive measures at those women who have the highest risk; however, such prediction models for the "most harmful" breast cancers have rarely been developed. Electronic health records (EHRs) represent an underused data source that has great research and clinical potential. Our goal was to quantify the value of EHR variables in the "most harmful" breast cancer risk prediction. We identified 794 subjects who had breast cancer with primary non-benign tumors with their earliest diagnosis on or after 1/1/2004 from an existing personalized medicine data repository, including 395 "most harmful" breast cancer cases and 399 "least harmful" breast cancer cases. For these subjects, we collected EHR data comprised of 6 components: demographics, diagnoses, symptoms, procedures, medications, and laboratory results. We developed two regularized prediction models, Ridge Logistic Regression (Ridge-LR) and Lasso Logistic Regression (Lasso-LR), to predict the "most harmful" breast cancer one year in advance. The area under the ROC curve (AUC) was used to assess model performance. We observed that the AUCs of Ridge-LR and Lasso-LR models were 0.818 and 0.839 respectively. For both the Ridge-LR and Lasso-LR models, the predictive performance of the whole EHR variables was significantly higher than that of each individual component (p<0.001). In conclusion, EHR variables can be used to predict the "most harmful" breast cancer, providing the possibility to personalize care for those women at the highest risk in clinical practice.
Can You Ride a Bicycle? The Ability to Ride a Bicycle Prevents Reduced Social Function in Older Adults With Mobility Limitation

PubMed Central

Sakurai, Ryota; Kawai, Hisashi; Yoshida, Hideyo; Fukaya, Taro; Suzuki, Hiroyuki; Kim, Hunkyung; Hirano, Hirohiko; Ihara, Kazushige; Obuchi, Shuichi; Fujiwara, Yoshinori

2016-01-01

Background The health benefits of bicycling in older adults with mobility limitation (ML) are unclear. We investigated ML and functional capacity of older cyclists by evaluating their instrumental activities of daily living (IADL), intellectual activity, and social function. Methods On the basis of interviews, 614 community-dwelling older adults (after excluding 63 participants who never cycled) were classified as cyclists with ML, cyclists without ML, non-cyclists with ML (who ceased bicycling due to physical difficulties), or non-cyclists without ML (who ceased bicycling for other reasons). A cyclist was defined as a person who cycled at least a few times per month, and ML was defined as difficulty walking 1 km or climbing stairs without using a handrail. Functional capacity and physical ability were evaluated by standardized tests. Results Regular cycling was documented in 399 participants, and 74 of them (18.5%) had ML; among non-cyclists, 49 had ML, and 166 did not. Logistic regression analysis for evaluating the relationship between bicycling and functional capacity revealed that non-cyclists with ML were more likely to have reduced IADL and social function compared to cyclists with ML. However, logistic regression analysis also revealed that the risk of bicycle-related falls was significantly associated with ML among older cyclists. Conclusions The ability and opportunity to bicycle may prevent reduced IADL and social function in older adults with ML, although older adults with ML have a higher risk of falls during bicycling. It is important to develop a safe environment for bicycling for older adults. PMID:26902165
Estimating interaction on an additive scale between continuous determinants in a logistic regression model.

PubMed

Knol, Mirjam J; van der Tweel, Ingeborg; Grobbee, Diederick E; Numans, Mattijs E; Geerlings, Mirjam I

2007-10-01

To determine the presence of interaction in epidemiologic research, typically a product term is added to the regression model. In linear regression, the regression coefficient of the product term reflects interaction as departure from additivity. However, in logistic regression it refers to interaction as departure from multiplicativity. Rothman has argued that interaction estimated as departure from additivity better reflects biologic interaction. So far, literature on estimating interaction on an additive scale using logistic regression only focused on dichotomous determinants. The objective of the present study was to provide the methods to estimate interaction between continuous determinants and to illustrate these methods with a clinical example. and results From the existing literature we derived the formulas to quantify interaction as departure from additivity between one continuous and one dichotomous determinant and between two continuous determinants using logistic regression. Bootstrapping was used to calculate the corresponding confidence intervals. To illustrate the theory with an empirical example, data from the Utrecht Health Project were used, with age and body mass index as risk factors for elevated diastolic blood pressure. The methods and formulas presented in this article are intended to assist epidemiologists to calculate interaction on an additive scale between two variables on a certain outcome. The proposed methods are included in a spreadsheet which is freely available at: http://www.juliuscenter.nl/additive-interaction.xls.

Logits and Tigers and Bears, Oh My! A Brief Look at the Simple Math of Logistic Regression and How It Can Improve Dissemination of Results

ERIC Educational Resources Information Center

Osborne, Jason W.

2012-01-01

Logistic regression is slowly gaining acceptance in the social sciences, and fills an important niche in the researcher's toolkit: being able to predict important outcomes that are not continuous in nature. While OLS regression is a valuable tool, it cannot routinely be used to predict outcomes that are binary or categorical in nature. These…
Regularized matrix regression

PubMed Central

Zhou, Hua; Li, Lexin

2014-01-01

Summary Modern technologies are producing a wealth of data with complex structures. For instance, in two-dimensional digital imaging, flow cytometry and electroencephalography, matrix-type covariates frequently arise when measurements are obtained for each combination of two underlying variables. To address scientific questions arising from those data, new regression methods that take matrices as covariates are needed, and sparsity or other forms of regularization are crucial owing to the ultrahigh dimensionality and complex structure of the matrix data. The popular lasso and related regularization methods hinge on the sparsity of the true signal in terms of the number of its non-zero coefficients. However, for the matrix data, the true signal is often of, or can be well approximated by, a low rank structure. As such, the sparsity is frequently in the form of low rank of the matrix parameters, which may seriously violate the assumption of the classical lasso. We propose a class of regularized matrix regression methods based on spectral regularization. A highly efficient and scalable estimation algorithm is developed, and a degrees-of-freedom formula is derived to facilitate model selection along the regularization path. Superior performance of the method proposed is demonstrated on both synthetic and real examples. PMID:24648830
Intermediate and advanced topics in multilevel logistic regression analysis

PubMed Central

Merlo, Juan

2017-01-01

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher‐level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within‐cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population‐average effect of covariates measured at the subject and cluster level, in contrast to the within‐cluster or cluster‐specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster‐level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:28543517
Intermediate and advanced topics in multilevel logistic regression analysis.

PubMed

Austin, Peter C; Merlo, Juan

2017-09-10

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
Optimizing methods for linking cinematic features to fMRI data.

PubMed

Kauttonen, Janne; Hlushchuk, Yevhen; Tikka, Pia

2015-04-15

One of the challenges of naturalistic neurosciences using movie-viewing experiments is how to interpret observed brain activations in relation to the multiplicity of time-locked stimulus features. As previous studies have shown less inter-subject synchronization across viewers of random video footage than story-driven films, new methods need to be developed for analysis of less story-driven contents. To optimize the linkage between our fMRI data collected during viewing of a deliberately non-narrative silent film 'At Land' by Maya Deren (1944) and its annotated content, we combined the method of elastic-net regularization with the model-driven linear regression and the well-established data-driven independent component analysis (ICA) and inter-subject correlation (ISC) methods. In the linear regression analysis, both IC and region-of-interest (ROI) time-series were fitted with time-series of a total of 36 binary-valued and one real-valued tactile annotation of film features. The elastic-net regularization and cross-validation were applied in the ordinary least-squares linear regression in order to avoid over-fitting due to the multicollinearity of regressors, the results were compared against both the partial least-squares (PLS) regression and the un-regularized full-model regression. Non-parametric permutation testing scheme was applied to evaluate the statistical significance of regression. We found statistically significant correlation between the annotation model and 9 ICs out of 40 ICs. Regression analysis was also repeated for a large set of cubic ROIs covering the grey matter. Both IC- and ROI-based regression analyses revealed activations in parietal and occipital regions, with additional smaller clusters in the frontal lobe. Furthermore, we found elastic-net based regression more sensitive than PLS and un-regularized regression since it detected a larger number of significant ICs and ROIs. Along with the ISC ranking methods, our regression analysis proved a feasible method for ordering the ICs based on their functional relevance to the annotated cinematic features. The novelty of our method is - in comparison to the hypothesis-driven manual pre-selection and observation of some individual regressors biased by choice - in applying data-driven approach to all content features simultaneously. We found especially the combination of regularized regression and ICA useful when analyzing fMRI data obtained using non-narrative movie stimulus with a large set of complex and correlated features. Copyright © 2015. Published by Elsevier Inc.
Clinic Attendance for Antiretroviral Pills Pick-Up among HIV-Positive People in Nepal: Roles of Perceived Family Support and Associated Factors.

PubMed

Ayer, Rakesh; Kikuchi, Kimiyo; Ghimire, Mamata; Shibanuma, Akira; Pant, Madhab Raj; Poudel, Krishna C; Jimba, Masamine

2016-01-01

HIV-positive people's clinic attendance for medication pick-up is critical for successful HIV treatment. However, limited evidence exists on it especially in low-income settings such as Nepal. Moreover, the role of family support in clinic attendance remains under-explored. Therefore, this study was conducted to examine the association between perceived family support and regular clinic attendance and to assess factors associated with regular clinic attendance for antiretroviral pills pick-up among HIV-positive individuals in Nepal. A cross-sectional study was conducted among 423 HIV-positive people in three districts of Nepal. Clinic attendance was assessed retrospectively for the period of 12 months. To assess the factors associated, an interview survey was conducted using a semi-structured questionnaire from July to August, 2015. Multiple logistic regression models were used to assess the factors associated with regular clinic attendance. Of 423 HIV-positive people, only 32.6% attended the clinics regularly. They were more likely to attend them regularly when they received high family support (AOR = 3.98, 95% CI = 2.29, 6.92), participated in support programs (AOR = 1.68, 95% CI = 1.00, 2.82), and had knowledge on the benefits of antiretroviral therapy (AOR = 2.62, 95% CI = 1.15, 5.99). In contrast, they were less likely to attend them regularly when they commuted more than 60 minutes to the clinics (AOR = 0.53, 95% CI = 0.30, 0.93), when they self-rated their health status as being very good (AOR = 0.13, 95% CI = 0.04, 0.44), good (AOR = 0.14, 95% CI = 0.04, 0.46), and fair (AOR = 0.21, 95% CI = 0.06, 0.70). HIV-positive individuals are more likely to attend the clinics regularly when they receive high family support, know the benefits of antiretroviral therapy, and participate in support programs. To improve clinic attendance, family support should be incorporated with HIV care programs in resource limited settings. Service providers should also consider educating them about the benefits of antiretroviral therapy.
Clinic Attendance for Antiretroviral Pills Pick-Up among HIV-Positive People in Nepal: Roles of Perceived Family Support and Associated Factors

PubMed Central

Kikuchi, Kimiyo; Ghimire, Mamata; Shibanuma, Akira; Pant, Madhab Raj; Poudel, Krishna C.; Jimba, Masamine

2016-01-01

Introduction HIV-positive people’s clinic attendance for medication pick-up is critical for successful HIV treatment. However, limited evidence exists on it especially in low-income settings such as Nepal. Moreover, the role of family support in clinic attendance remains under-explored. Therefore, this study was conducted to examine the association between perceived family support and regular clinic attendance and to assess factors associated with regular clinic attendance for antiretroviral pills pick-up among HIV-positive individuals in Nepal. Methods A cross-sectional study was conducted among 423 HIV-positive people in three districts of Nepal. Clinic attendance was assessed retrospectively for the period of 12 months. To assess the factors associated, an interview survey was conducted using a semi-structured questionnaire from July to August, 2015. Multiple logistic regression models were used to assess the factors associated with regular clinic attendance. Results Of 423 HIV-positive people, only 32.6% attended the clinics regularly. They were more likely to attend them regularly when they received high family support (AOR = 3.98, 95% CI = 2.29, 6.92), participated in support programs (AOR = 1.68, 95% CI = 1.00, 2.82), and had knowledge on the benefits of antiretroviral therapy (AOR = 2.62, 95% CI = 1.15, 5.99). In contrast, they were less likely to attend them regularly when they commuted more than 60 minutes to the clinics (AOR = 0.53, 95% CI = 0.30, 0.93), when they self-rated their health status as being very good (AOR = 0.13, 95% CI = 0.04, 0.44), good (AOR = 0.14, 95% CI = 0.04, 0.46), and fair (AOR = 0.21, 95% CI = 0.06, 0.70). Conclusion HIV-positive individuals are more likely to attend the clinics regularly when they receive high family support, know the benefits of antiretroviral therapy, and participate in support programs. To improve clinic attendance, family support should be incorporated with HIV care programs in resource limited settings. Service providers should also consider educating them about the benefits of antiretroviral therapy. PMID:27438024
Factors associated with irregular breakfast consumption among high school students in a Japanese community.

PubMed

Oba, Shino; Oogushi, Kazuhiro; Ogata, Hiromitsu; Nakai, Hiromitsu

2016-01-01

This study evaluated the associations between the characteristics of high school students and irregular breakfast consumption and explored the association with knowledge regarding diet and dietary education in a community in Japan. A cross-sectional survey using a self-administered questionnaire was conducted in 2007 among all the high school students in the second grade in Imari, Saga. Data for 318 male and 292 female students were analyzed. Irregular breakfast consumption was defined as consuming breakfast three times or less in a week. The associations between the characteristics of students and irregular breakfast consumption were assessed using logistic regression with adjustments for sex and school. Among male students, a strong association between the consumption of juice or pop and irregular breakfast consumption was observed (OR comparing ">=2 servings" vs "rarely"=8.97, 95% CI=2.99-26.9). The associations with wake times and bed times were strong among male students, and the association with regular bowel movements was strong among female students. Students who had knowledge of regional agricultural and livestock products were more likely to consume breakfast regularly, and this association was significant among female students (OR=2.89, 95% CI=1.23-6.82). Significant associations were also observed with the consumption of snacks, and traditional greeting before meals. Several characteristics, including specific knowledge, were associated with the irregular consumption of breakfast. The results are of interest to policy makers, nutrition specialists, and educators working to enhance regular breakfast consumption among students.
The demographic, system, and psychosocial origins of mammographic screening disparities: prediction of initiation versus maintenance screening among immigrant and non-immigrant women.

PubMed

Consedine, Nathan S

2012-08-01

Disparities in breast screening are well documented. Less clear are differences within groups of immigrant and non-immigrant minority women or differences in adherence to mammography guidelines over time. A sample of 1,364 immigrant and non-immigrant women (African American, English Caribbean, Haitian, Dominican, Eastern European, and European American) were recruited using a stratified cluster-sampling plan. In addition to measuring established predictors of screening, women reported mammography frequency in the last 10 years and were (per ACS guidelines at the time) categorized as never, sub-optimal (<1 screen/year), or adherent (1+ screens/year) screeners. Multinomial logistic regression showed that while ethnicity infrequently predicted the never versus sub-optimal comparison, English Caribbean, Haitian, and Eastern European women were less likely to screen systematically over time. Demographics did not predict the never versus sub-optimal distinction; only regular physician, annual exam, physician recommendation, and cancer worry showed effects. However, the adherent categorization was predicted by demographics, was less likely among women without insurance, a regular physician, or an annual exam, and more likely among women reporting certain patterns of emotion (low embarrassment and greater worry). Because regular screening is crucial to breast health, there is a clear need to consider patterns of screening among immigrant and non-immigrant women as well as whether the variables predicting the initiation of screening are distinct from those predicting systematic screening over time.
Eating at the university canteen. Associations with socioeconomic status and healthier self-reported eating habits in France.

PubMed

Guagliardo, Valérie; Lions, Caroline; Darmon, Nicole; Verger, Pierre

2011-02-01

French university canteens offer structured meals at a fixed moderate price. We examined whether eating regularly at university canteens was associated with socioeconomic status (SES) or dietary practices. The study data came from a cross-sectional study of a random sample of 1723 students aged 18-24 years, in their first year of university in 2005-2006, enrolled in the universities of southeastern France (response rate=71%). Self-reported dietary practices were collected with a behavioral questionnaire. Adjusted logistic regressions showed that eating regularly at university canteens was less frequent among students with less than € 300 monthly resources and not living with their families (OR=0.68 [95%CI: 0.49-0.94]). It was also positively associated, regardless of SES, with the consumption of at least five servings of fruit/vegetables daily (OR=1.42 [1.05-1.92]) and one serving of meat/fish daily (OR=1.41 [1.13-1.76]) but not with either restricting fatty food (OR=1.04 [0.81-1.33]) or never/rarely adding salt to food (OR=1.06 [0.85-1.32]). Eating regularly at university canteens was less frequent among less well-off students and was positively associated with some healthier self-reported dietary habits. Further research is needed to confirm these results in the overall student population in France and to understand the determinants of university canteen utilization. Copyright © 2010 Elsevier Ltd. All rights reserved.
Dual-stiffness flooring: can it reduce fracture rates associated with falls?

PubMed

Knoefel, Frank; Patrick, Louise; Taylor, Jodie; Goubran, Rafik

2013-04-01

Falls cause significant morbidity and mortality in long term care facilities. Dual-stiffness flooring (DSF) has previously shown promise in reducing such morbidity in experimental models. This study set out to measure the impact of SmartCell flooring on falls-related morbidity in a nursing home. All falls occurring at an Arizona nursing home between July 1, 2008, and December 31, 2010, were reviewed for age, sex, diagnosis of osteoporosis, number of medications, history of previous falls, type of flooring (normal vs DSF), time of day, type of injury, and resulting actions. Fall-related outcomes were compared across room types using chi-square and logistic regression methods. Eighty-two falls on the DSF were compared with 85 falls on the regular floor. There was a tendency for residents falling on DSF to have less bruising and abrasions, while having more redness and cuts. There were 2 fractures on regular flooring (2.4% fracture rate) and none on the DSF flooring (0% fracture rate). The fracture rate of 2.4% of falls on the regular floor is consistent with previous reports in the literature, whereas a 0% rate found on the DSF floor is a clinically significant improvement. This suggests that DSF may be a practical approach for institutions and consumers to reduce fall-related injuries. A larger scale controlled study to confirm these encouraging preliminary findings is warranted. Copyright © 2013 American Medical Directors Association, Inc. Published by Elsevier Inc. All rights reserved.
Barriers associated with frequency of leisure-time physical activity among Brazilian adults of different income strata.

PubMed

Silva, K S; Del Duca, G F; Garcia, L M T; da Silva, J A; Bertuol, C; de Oliveira, E S A; de Barros, M V G; Nahas, M V

2016-02-01

This study aimed to estimate the prevalence of the main perceived barriers to leisure-time physical activity (LTPA) and their associations with the frequency of LTPA in a representative sample of industrial workers from Brazil (n = 47,477), according to their income strata (low income: ≤$US280, middle income: $US281-$US1400, and high income: ≥$US1401). Data were collected between 2006 and 2008 via questionnaires about the main perceived barrier to LTPA and the frequency of LTPA. Multinomial logistic regression was performed to evaluate differences among groups. There was a lower prevalence of regular practice of LTPA in the low- (15.8%) and middle-income strata (18.2%) than among the individuals of the high-income stratum (27.6%). A large proportion of workers who regularly participated in LTPA reported no barriers (low: 43.1%; middle: 46.8%; high: 51.6%). Additional obligations and fatigue were the two most common perceived barriers in all family income strata among participants who engaged in different frequencies of LTPA. The odds for all perceived barriers showed a positive trend related to frequency of LTPA (from regular to no LTPA), with higher values according to income. In summary, the ordering of the main perceived barriers to LTPA differed according to workers' income stratum and frequency of engaging in LTPA. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Desire for more children, contraceptive use and unmet need for family planning in a remote area of Bali, Indonesia.

PubMed

Withers, Mellissa; Kano, Megumi; Pinatih, Gde Ngurah Indraguna

2010-07-01

Exploring fertility preferences in relation to contraceptive use can increase the understanding of future reproductive behaviour and unmet family planning needs. This knowledge can help assist women in meeting their reproductive goals. The influences on the desire for more children and current contraceptive use were examined among 1528 married women of reproductive age in an isolated community in Bali, Indonesia, using multivariate logistic regression analysis. Women who were younger, had fewer living children, had given birth in the past year and had regular access to health services were more likely to desire children. Being older, having fewer living children, not having regular access to health services, having given birth in the past year and having the desire for more children were associated with a lower likelihood of using contraception. Women with regular access to health care are more likely to desire more children, probably because they are confident in their ability to have successful birth outcomes. However, specialized clinics or family planning outreach workers may be required to reduce barriers to service utilization among some groups. The findings of this study identify key target populations for family planning, including older women and postpartum women--groups that may not perceive themselves to be at risk for unintended pregnancy. Meeting unmet need for family planning among these groups could help women meet their fertility goals, as well as reduce maternal morbidity and mortality.
Predicting Social Trust with Binary Logistic Regression

ERIC Educational Resources Information Center

Adwere-Boamah, Joseph; Hufstedler, Shirley

2015-01-01

This study used binary logistic regression to predict social trust with five demographic variables from a national sample of adult individuals who participated in The General Social Survey (GSS) in 2012. The five predictor variables were respondents' highest degree earned, race, sex, general happiness and the importance of personally assisting…
Effect of folic acid on appetite in children: ordinal logistic and fuzzy logistic regressions.

PubMed

Namdari, Mahshid; Abadi, Alireza; Taheri, S Mahmoud; Rezaei, Mansour; Kalantari, Naser; Omidvar, Nasrin

2014-03-01

Reduced appetite and low food intake are often a concern in preschool children, since it can lead to malnutrition, a leading cause of impaired growth and mortality in childhood. It is occasionally considered that folic acid has a positive effect on appetite enhancement and consequently growth in children. The aim of this study was to assess the effect of folic acid on the appetite of preschool children 3 to 6 y old. The study sample included 127 children ages 3 to 6 who were randomly selected from 20 preschools in the city of Tehran in 2011. Since appetite was measured by linguistic terms, a fuzzy logistic regression was applied for modeling. The obtained results were compared with a statistical ordinal logistic model. After controlling for the potential confounders, in a statistical ordinal logistic model, serum folate showed a significantly positive effect on appetite. A small but positive effect of folate was detected by fuzzy logistic regression. Based on fuzzy regression, the risk for poor appetite in preschool children was related to the employment status of their mothers. In this study, a positive association was detected between the levels of serum folate and improved appetite. For further investigation, a randomized controlled, double-blind clinical trial could be helpful to address causality. Copyright © 2014 Elsevier Inc. All rights reserved.
Regular examinations for toxic maculopathy in long-term chloroquine or hydroxychloroquine users.

PubMed

Nika, Melisa; Blachley, Taylor S; Edwards, Paul; Lee, Paul P; Stein, Joshua D

2014-10-01

According to evidence-based, expert recommendations, long-term users of chloroquine or hydroxychloroquine sulfate should undergo regular visits to eye care providers and diagnostic testing to check for maculopathy. To determine whether patients with rheumatoid arthritis (RA) or systemic lupus erythematosus (SLE) taking chloroquine or hydroxychloroquine are regularly visiting eye care providers and being screened for maculopathy. Patients with RA or SLE who were continuously enrolled in a particular managed care network for at least 5 years between January 1, 2001, and December 31, 2011, were studied. Patients' amount of chloroquine or hydroxychloroquine use in the 5 years since the initial RA or SLE diagnosis was calculated, along with their number of eye care visits and diagnostic tests for maculopathy. Those at high risk for maculopathy were identified. Logistic regression was performed to assess potential factors associated with regular eye care visits (annual visits in ≥3 of 5 years) among chloroquine or hydroxychloroquine users, including those at highest risk for maculopathy. Among chloroquine or hydroxychloroquine users and those at high risk for toxic maculopathy, the proportions with regular eye care visits and diagnostic testing, as well as the likelihood of regular eye care visits. Among 18 051 beneficiaries with RA or SLE, 6339 (35.1%) had at least 1 record of chloroquine or hydroxychloroquine use, and 1409 (7.8%) had used chloroquine or hydroxychloroquine for at least 4 years. Among those at high risk for maculopathy, 27.9% lacked regular eye care visits, 6.1% had no visits to eye care providers, and 34.5% had no diagnostic testing for maculopathy during the 5-year period. Among high-risk patients, each additional month of chloroquine or hydroxychloroquine use was associated with a 2.0% increased likelihood of regular eye care (adjusted odds ratio, 1.02; 95% CI, 1.01-1.03). High-risk patients whose SLE or RA was managed by rheumatologists had a 77.4% increased likelihood of regular eye care (adjusted odds ratio, 1.77; 95% CI, 1.27-2.47) relative to other patients. In this insured population, many patients at high risk for maculopathy associated with the use of chloroquine or hydroxychloroquine are not undergoing routine monitoring for this serious adverse effect. Future studies should explore factors contributing to suboptimal adherence to expert guidelines and the potential effect on patients' vision-related outcomes.
Acculturation and cancer screening among Asian Americans: role of health insurance and having a regular physician.

PubMed

Lee, Sunmin; Chen, Lu; Jung, Mary Y; Baezconde-Garbanati, Lourdes; Juon, Hee-Soon

2014-04-01

Cancer is the leading cause of death among Asian Americans, but screening rates are significantly lower in Asians than in non-Hispanic Whites. This study examined associations between acculturation and three types of cancer screening (colorectal, cervical, and breast), focusing on the role of health insurance and having a regular physician. A cross-sectional study of 851 Chinese, Korean, and Vietnamese Americans was conducted in Maryland. Acculturation was measured using an abridged version of the Suinn-Lew Asian Self-Identity Acculturation Scale, acculturation clusters, language preference, length of residency in the US, and age at arrival. Age, health insurance, regular physician, gender, ethnicity, income, marital status, and health status were adjusted in the multivariate analysis. Logistic regression analysis showed that various measures of acculturation were positively associated with the odds of having all cancer screenings. Those lived for more than 20 years in the US were about 2-4 times [odds ratio (OR) and 95 % confidence interval (CI) colorectal: 2.41 (1.52-3.82); cervical: 1.79 (1.07-3.01); and breast: 2.11 (1.25-3.57)] more likely than those who lived for less than 10 years to have had cancer screening. When health insurance and having a regular physician were adjusted, the associations between length of residency and colorectal cancer [OR 1.72 (1.05-2.81)] was reduced and the association between length of residency and cervical and breast cancer became no longer significant. Findings from this study provide a robust and comprehensive picture of AA cancer screening behavior. They will provide helpful information on future target groups for promoting cancer screening.
Fourier analysis algorithm for the posterior corneal keratometric data: clinical usefulness in keratoconus.

PubMed

Sideroudi, Haris; Labiris, Georgios; Georgantzoglou, Kimon; Ntonti, Panagiota; Siganos, Charalambos; Kozobolis, Vassilios

2017-07-01

To develop an algorithm for the Fourier analysis of posterior corneal videokeratographic data and to evaluate the derived parameters in the diagnosis of Subclinical Keratoconus (SKC) and Keratoconus (KC). This was a cross-sectional, observational study that took place in the Eye Institute of Thrace, Democritus University, Greece. Eighty eyes formed the KC group, 55 eyes formed the SKC group while 50 normal eyes populated the control group. A self-developed algorithm in visual basic for Microsoft Excel performed a Fourier series harmonic analysis for the posterior corneal sagittal curvature data. The algorithm decomposed the obtained curvatures into a spherical component, regular astigmatism, asymmetry and higher order irregularities for averaged central 4 mm and for each individual ring separately (1, 2, 3 and 4 mm). The obtained values were evaluated for their diagnostic capacity using receiver operating curves (ROC). Logistic regression was attempted for the identification of a combined diagnostic model. Significant differences were detected in regular astigmatism, asymmetry and higher order irregularities among groups. For the SKC group, the parameters with high diagnostic ability (AUC > 90%) were the higher order irregularities, the asymmetry and the regular astigmatism, mainly in the corneal periphery. Higher predictive accuracy was identified using diagnostic models that combined the asymmetry, regular astigmatism and higher order irregularities in averaged 3and 4 mm area (AUC: 98.4%, Sensitivity: 91.7% and Specificity:100%). Fourier decomposition of posterior Keratometric data provides parameters with high accuracy in differentiating SKC from normal corneas and should be included in the prompt diagnosis of KC. © 2017 The Authors Ophthalmic & Physiological Optics © 2017 The College of Optometrists.
Impact of preoperative regular physical activity on postoperative course after open abdominal aortic aneurysm surgery.

PubMed

Hayashi, Kazuhiro; Hirashiki, Akihiro; Kodama, Akio; Kobayashi, Kiyonori; Yasukawa, Yuto; Shimizu, Miho; Kondo, Takahisa; Komori, Kimihiro; Murohara, Toyoaki

2016-04-01

Early ambulation after open abdominal aortic aneurysm (AAA) surgery is assumed to play a key role in preventing postoperative complications and reducing hospital length of stay. However, the factors predicting early ambulation after open AAA surgery have not yet been sufficiently investigated. Here, we investigated which preoperative and intraoperative variables are associated with start time for ambulation in patients after open AAA surgery. A total of 67 consecutive patients undergoing open AAA surgery were included in the study [male, 62 (92 %); mean age, 68 years (range, 47-82 years), mean AAA diameter, 53 mm (range, 28-80 mm)]. Preoperative physical activity was examined by means of 6-min walk distance (6MWD) and a medical interview. Patients were divided into two groups, according to when independence in walking was attained: early group <3 days (n = 36) and late group ≥3 days (n = 31), and the pre-, intra-, and postoperative recovery data were compared. There were no significant differences in patient baseline characteristics or intraoperative data between the two groups. The number of patients engaging in preoperative regular physical activity and 6MWD were significantly greater (p = 0.042 and p = 0.034, respectively) in the early group than in the late group. In addition, time to hospital discharge was significantly shorter in the early group than in the late group (p = 0.031). Binary logistic regression analysis showed that preoperative regular physical activity was the only independent factor for identifying patients in the early group (odds ratio 2.769, 95 % confidence interval 1.024-7.487, p = 0.045). These results suggest that engaging in regular physical activity is an effective predictor of early ambulation after open AAA surgery.
Predictors of smoking initiation--results from the Global Adult Tobacco Survey (GATS) in Poland 2009-2010.

PubMed

Kaleta, Dorota; Makowiec-Dąbrowska, Teresa; Dziankowska-Zaborszczyk, Elżbieta; Fronczak, Adam

2013-01-01

Improving the access to information on determinants of the smoking epidemic is essential for increasing the effectiveness of tobacco control policies. While the statistics of smoking prevalence in Poland are available, data on smoking initiation and its social correlates are still poorly described. To investigate the association of socio-demographic indicators with regular smoking initiation among adults. Data from the Global Adult Tobacco Survey (GATS) on socio-demographic and smoking-related characteristics of respondents were used. GATS is a nationally representative household survey. GATS provided data on a representative sample of 7,840 adult individuals--2,207 male and 1,321 female ever smokers. Logistic regression analysis was performed and the χ2 test for relevant calculations. Among males, the regular smoking initiation rate was significantly higher compared to females (59.2% vs. 34.2%; p<0.01). Mean age of smoking initiation was lower in men compared to women (18.4±3.6 vs. 20.0± 4.7 p < 0.01). Lack of awareness on smoking health consequences was strongly associated with initiating of regular smoking among both genders (unaware vs. aware respondents: OR = 3.0 CI 2.3-4.0; p < 0.001 in men and OR = 3.07 CI 2.3-3.9; p<0.001 in women). Older age, vocational education and unemployment were associated with regular smoking initiation among men and women. Also, not being religious considerably contributed to increased likelihood of smoking initiation in women (OR = 4.4 CI 2.5-7.7; p<0.001). The results indicate that policies focused on preventing smoking onset among Poles are needed to reduce tobacco epidemic, with the ultimate goal of translating evidence into policy.

Factors influencing participation in colorectal cancer screening programs in Spain.

PubMed

Vanaclocha-Espi, Mercedes; Ibáñez, Josefa; Molina-Barceló, Ana; Pérez, Elena; Nolasco, Andreu; Font, Rebeca; Pérez-Riquelme, Francisco; de la Vega, Mariola; Arana-Arri, Eunate; Oceja, MªElena; Espinàs, Josep Alfons; Portillo, Isabel; Salas, Dolores

2017-12-01

To analyze the sociodemographic and organizational factors influencing participation in population-based colorectal cancer screening programs (CRCSP) in Spain, a retrospective study was conducted in a cohort of people invited to participate in the first 3 screening rounds of 6 CRCSP from 2000 to 2012. Mixed logistic regression models were used to analyze the relationship between sociodemographic and organizational factors, such as the type of fecal occult blood test (FOBT) used and the FOBT delivery type. The analysis was performed separately in groups (Initial screening-first invitation, Subsequent invitation for previous never-responders, Subsequent invitation-regular, Subsequent invitation-irregular intervals). The results showed that, in the Initial screening-first invitation group, participation was higher in women than in men in all age groups (OR 1.05 in persons aged 50-59years and OR 1.12 in those aged 60-69years). Participation was also higher when no action was required to receive the FOBT kit, independently of the type of screening (Initial screening-first invitation [OR 2.24], Subsequent invitation for previous never-responders [OR 2.14], Subsequent invitation-regular [OR 2.03], Subsequent invitation-irregular intervals [OR 9.38]) and when quantitative rather than qualitative immunological FOBT (FIT) was offered (Initial screening-first invitation [OR 0.70], Subsequent invitation for previous never-responders [OR 0.12], Subsequent invitation-regular [OR 0.20]) or guaiac testing (Initial screening-first invitation [OR 0.81], Subsequent invitation for previous never-responders [OR 0.88], Subsequent invitation-regular [OR 0.73]). In conclusion, the results of this study show that screening participation could be enhanced by inclusion of the FOBT kit with the screening invitation and the use of the quantitative FIT. Copyright © 2017 Elsevier Inc. All rights reserved.
Factors Associated With Exercise Behavior in People With Parkinson Disease

PubMed Central

Cavanaugh, James T.; Earhart, Gammon M.; Ford, Matthew P.; Foreman, K. Bo; Fredman, Lisa; Boudreau, Jennifer K.; Dibble, Leland E.

2011-01-01

Background The benefits of exercise for reducing disability in people with Parkinson disease (PD) are becoming more evident. Optimal benefit, however, requires regular and sustained participation. Factors associated with engaging in regular exercise have received little scientific scrutiny in people with PD. Objective The purpose of this study was to explore factors associated with exercise behavior in patients with PD using the International Classification of Functioning, Disability and Health (ICF) as a guiding framework. Design This was a cross-sectional study. Methods The participants in this study were 260 patients with PD from 4 institutions. Participants were designated as “exercisers” or “nonexercisers” based on responses to the Stages of Readiness to Exercise Questionnaire. Exercise status was validated using the Physical Activity Scale for the Elderly and an activity monitor. Factors potentially associated with exercise behavior included measures of body structure and function, activity, participation, environmental factors, and personal factors. Their relative contributions were analyzed using logistic regression and quantified with odds ratios. Results One hundred sixty-four participants (63%) were designated as exercisers. Participants with high self-efficacy were more than twice as likely to engage in regular exercise than those with low self-efficacy (adjusted odds ratio=2.34, 95% confidence interval=1.30–4.23). College educated and older participants also were more likely to exercise. Disabling influences of impairments, activity limitations, and participation restrictions were not associated with exercise behavior. Limitations The cross-sectional nature of the study limited the ability to make causal inferences. Conclusions Self-efficacy, rather than disability, appears to be strongly associated with whether ambulatory, community-dwelling people with PD exercise regularly. The results of this study suggest that physical therapists should include strategies to increase exercise self-efficacy when designing patient intervention programs for patients with PD. PMID:22003171
Clustering performance comparison using K-means and expectation maximization algorithms.

PubMed

Jung, Yong Gyu; Kang, Min Soo; Heo, Jun

2014-11-14

Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are the K -means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and the K -means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results.
Racial/ethnic and educational differences in the estimated odds of recent nitrite use among adult household residents in the United States: an illustration of matching and conditional logistic regression.

PubMed

Delva, J; Spencer, M S; Lin, J K

2000-01-01

This article compares estimates of the relative odds of nitrite use obtained from weighted unconditional logistic regression with estimates obtained from conditional logistic regression after post-stratification and matching of cases with controls by neighborhood of residence. We illustrate these methods by comparing the odds associated with nitrite use among adults of four racial/ethnic groups, with and without a high school education. We used aggregated data from the 1994-B through 1996 National Household Survey on Drug Abuse (NHSDA). Difference between the methods and implications for analysis and inference are discussed.
Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble-based methods?

PubMed Central

Austin, Peter C; Lee, Douglas S; Steyerberg, Ewout W; Tu, Jack V

2012-01-01

In biomedical research, the logistic regression model is the most commonly used method for predicting the probability of a binary outcome. While many clinical researchers have expressed an enthusiasm for regression trees, this method may have limited accuracy for predicting health outcomes. We aimed to evaluate the improvement that is achieved by using ensemble-based methods, including bootstrap aggregation (bagging) of regression trees, random forests, and boosted regression trees. We analyzed 30-day mortality in two large cohorts of patients hospitalized with either acute myocardial infarction (N = 16,230) or congestive heart failure (N = 15,848) in two distinct eras (1999–2001 and 2004–2005). We found that both the in-sample and out-of-sample prediction of ensemble methods offered substantial improvement in predicting cardiovascular mortality compared to conventional regression trees. However, conventional logistic regression models that incorporated restricted cubic smoothing splines had even better performance. We conclude that ensemble methods from the data mining and machine learning literature increase the predictive performance of regression trees, but may not lead to clear advantages over conventional logistic regression models for predicting short-term mortality in population-based samples of subjects with cardiovascular disease. PMID:22777999
Strategies for Testing Statistical and Practical Significance in Detecting DIF with Logistic Regression Models

ERIC Educational Resources Information Center

Fidalgo, Angel M.; Alavi, Seyed Mohammad; Amirian, Seyed Mohammad Reza

2014-01-01

This study examines three controversial aspects in differential item functioning (DIF) detection by logistic regression (LR) models: first, the relative effectiveness of different analytical strategies for detecting DIF; second, the suitability of the Wald statistic for determining the statistical significance of the parameters of interest; and…
Iterative Purification and Effect Size Use with Logistic Regression for Differential Item Functioning Detection

ERIC Educational Resources Information Center

French, Brian F.; Maller, Susan J.

2007-01-01

Two unresolved implementation issues with logistic regression (LR) for differential item functioning (DIF) detection include ability purification and effect size use. Purification is suggested to control inaccuracies in DIF detection as a result of DIF items in the ability estimate. Additionally, effect size use may be beneficial in controlling…
A Note on Three Statistical Tests in the Logistic Regression DIF Procedure

ERIC Educational Resources Information Center

Paek, Insu

2012-01-01

Although logistic regression became one of the well-known methods in detecting differential item functioning (DIF), its three statistical tests, the Wald, likelihood ratio (LR), and score tests, which are readily available under the maximum likelihood, do not seem to be consistently distinguished in DIF literature. This paper provides a clarifying…
"Let Me Count the Ways:" Fostering Reasons for Living among Low-Income, Suicidal, African American Women

ERIC Educational Resources Information Center

West, Lindsey M.; Davis, Telsie A.; Thompson, Martie P.; Kaslow, Nadine J.

2011-01-01

Protective factors for fostering reasons for living were examined among low-income, suicidal, African American women. Bivariate logistic regressions revealed that higher levels of optimism, spiritual well-being, and family social support predicted reasons for living. Multivariate logistic regressions indicated that spiritual well-being showed…
Comparison of Two Approaches for Handling Missing Covariates in Logistic Regression

ERIC Educational Resources Information Center

Peng, Chao-Ying Joanne; Zhu, Jin

2008-01-01

For the past 25 years, methodological advances have been made in missing data treatment. Most published work has focused on missing data in dependent variables under various conditions. The present study seeks to fill the void by comparing two approaches for handling missing data in categorical covariates in logistic regression: the…
Comparison of IRT Likelihood Ratio Test and Logistic Regression DIF Detection Procedures

ERIC Educational Resources Information Center

Atar, Burcu; Kamata, Akihito

2011-01-01

The Type I error rates and the power of IRT likelihood ratio test and cumulative logit ordinal logistic regression procedures in detecting differential item functioning (DIF) for polytomously scored items were investigated in this Monte Carlo simulation study. For this purpose, 54 simulation conditions (combinations of 3 sample sizes, 2 sample…
Multiple Logistic Regression Analysis of Cigarette Use among High School Students

ERIC Educational Resources Information Center

Adwere-Boamah, Joseph

2011-01-01

A binary logistic regression analysis was performed to predict high school students' cigarette smoking behavior from selected predictors from 2009 CDC Youth Risk Behavior Surveillance Survey. The specific target student behavior of interest was frequent cigarette use. Five predictor variables included in the model were: a) race, b) frequency of…
Modeling Polytomous Item Responses Using Simultaneously Estimated Multinomial Logistic Regression Models

ERIC Educational Resources Information Center

Anderson, Carolyn J.; Verkuilen, Jay; Peyton, Buddy L.

2010-01-01

Survey items with multiple response categories and multiple-choice test questions are ubiquitous in psychological and educational research. We illustrate the use of log-multiplicative association (LMA) models that are extensions of the well-known multinomial logistic regression model for multiple dependent outcome variables to reanalyze a set of…
Propensity Score Estimation with Data Mining Techniques: Alternatives to Logistic Regression

ERIC Educational Resources Information Center

Keller, Bryan S. B.; Kim, Jee-Seon; Steiner, Peter M.

2013-01-01

Propensity score analysis (PSA) is a methodological technique which may correct for selection bias in a quasi-experiment by modeling the selection process using observed covariates. Because logistic regression is well understood by researchers in a variety of fields and easy to implement in a number of popular software packages, it has…
Two-factor logistic regression in pediatric liver transplantation

NASA Astrophysics Data System (ADS)

Uzunova, Yordanka; Prodanova, Krasimira; Spasov, Lyubomir

2017-12-01

Using a two-factor logistic regression analysis an estimate is derived for the probability of absence of infections in the early postoperative period after pediatric liver transplantation. The influence of both the bilirubin level and the international normalized ratio of prothrombin time of blood coagulation at the 5th postoperative day is studied.
Predictors of Placement Stability at the State Level: The Use of Logistic Regression to Inform Practice

ERIC Educational Resources Information Center

Courtney, Jon R.; Prophet, Retta

2011-01-01

Placement instability is often associated with a number of negative outcomes for children. To gain state level contextual knowledge of factors associated with placement stability/instability, logistic regression was applied to selected variables from the New Mexico Adoption and Foster Care Administrative Reporting System dataset. Predictors…
Classifying machinery condition using oil samples and binary logistic regression

NASA Astrophysics Data System (ADS)

Phillips, J.; Cripps, E.; Lau, John W.; Hodkiewicz, M. R.

2015-08-01

The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.
Length bias correction in gene ontology enrichment analysis using logistic regression.

PubMed

Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H

2012-01-01

When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.
Matched samples logistic regression in case-control studies with missing values: when to break the matches.

PubMed

Hansson, Lisbeth; Khamis, Harry J

2008-12-01

Simulated data sets are used to evaluate conditional and unconditional maximum likelihood estimation in an individual case-control design with continuous covariates when there are different rates of excluded cases and different levels of other design parameters. The effectiveness of the estimation procedures is measured by method bias, variance of the estimators, root mean square error (RMSE) for logistic regression and the percentage of explained variation. Conditional estimation leads to higher RMSE than unconditional estimation in the presence of missing observations, especially for 1:1 matching. The RMSE is higher for the smaller stratum size, especially for the 1:1 matching. The percentage of explained variation appears to be insensitive to missing data, but is generally higher for the conditional estimation than for the unconditional estimation. It is particularly good for the 1:2 matching design. For minimizing RMSE, a high matching ratio is recommended; in this case, conditional and unconditional logistic regression models yield comparable levels of effectiveness. For maximizing the percentage of explained variation, the 1:2 matching design with the conditional logistic regression model is recommended.
Label-noise resistant logistic regression for functional data classification with an application to Alzheimer's disease study.

PubMed

Lee, Seokho; Shin, Hyejin; Lee, Sang Han

2016-12-01

Alzheimer's disease (AD) is usually diagnosed by clinicians through cognitive and functional performance test with a potential risk of misdiagnosis. Since the progression of AD is known to cause structural changes in the corpus callosum (CC), the CC thickness can be used as a functional covariate in AD classification problem for a diagnosis. However, misclassified class labels negatively impact the classification performance. Motivated by AD-CC association studies, we propose a logistic regression for functional data classification that is robust to misdiagnosis or label noise. Specifically, our logistic regression model is constructed by adopting individual intercepts to functional logistic regression model. This approach enables to indicate which observations are possibly mislabeled and also lead to a robust and efficient classifier. An effective algorithm using MM algorithm provides simple closed-form update formulas. We test our method using synthetic datasets to demonstrate its superiority over an existing method, and apply it to differentiating patients with AD from healthy normals based on CC from MRI. © 2016, The International Biometric Society.

The Effect of Latent Binary Variables on the Uncertainty of the Prediction of a Dichotomous Outcome Using Logistic Regression Based Propensity Score Matching.

PubMed

Szekér, Szabolcs; Vathy-Fogarassy, Ágnes

2018-01-01

Logistic regression based propensity score matching is a widely used method in case-control studies to select the individuals of the control group. This method creates a suitable control group if all factors affecting the output variable are known. However, if relevant latent variables exist as well, which are not taken into account during the calculations, the quality of the control group is uncertain. In this paper, we present a statistics-based research in which we try to determine the relationship between the accuracy of the logistic regression model and the uncertainty of the dependent variable of the control group defined by propensity score matching. Our analyses show that there is a linear correlation between the fit of the logistic regression model and the uncertainty of the output variable. In certain cases, a latent binary explanatory variable can result in a relative error of up to 70% in the prediction of the outcome variable. The observed phenomenon calls the attention of analysts to an important point, which must be taken into account when deducting conclusions.
Waterpipe tobacco smoking prevalence and illegal underage use in waterpipe-serving premises: a cross-sectional analysis among schoolchildren in Stoke-on-Trent.

PubMed

Jawad, M; McIver, C

2017-05-01

Waterpipe tobacco smoking has received little epidemiological and policy attention in the UK despite reports of increasing prevalence alongside an anecdotally non-compliant industry. This study aimed to determine how waterpipe tobacco smoking is changing among young people in the UK, both in terms of prevalence and sociodemographic correlates of use, and to quantify the extent of illegal underage use in waterpipe-serving premises in the UK. Repeat cross-sectional. A secondary analysis of two cross-sectional surveys (total N = 3376), conducted in 2013 and 2015 among secondary school students aged 11-16 years in Stoke-on-Trent, measured lifetime (both surveys) and regular (at least monthly; 2015 survey only) waterpipe tobacco prevalence and location of usual use. Logistic regression models measured the association between independent variables (age, sex, ethnicity, presence of free school meals, cigarette smoking status) with lifetime and regular waterpipe tobacco use, and with illegal underage use; the latter defined as usually smoking waterpipe tobacco in a waterpipe-serving premise. Lifetime waterpipe tobacco prevalence remained similar in 2013 (13.7%, 95% confidence interval [CI] 12.0-15.4%) and 2015 (14.6%, 95% CI 12.8-16.4%), whereas regular use was measured at 2.9% (95% CI 2.1-3.8%) in 2015. Older, non-white, males who concurrently used cigarettes had higher odds of lifetime waterpipe tobacco use. Illegal underage use was reported among 27.1% of all regular users, correlates of which included increasing age and South Asian ethnicity. The presence of free school meals was not associated with lifetime or regular waterpipe tobacco prevalence, nor illegal underage use. Increased monitoring of waterpipe tobacco prevalence and patterns, including the underage policy compliance of waterpipe-serving premises, is needed to help inform policy decisions to control waterpipe tobacco use. Copyright © 2017 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
Logistic regression for circular data

NASA Astrophysics Data System (ADS)

Al-Daffaie, Kadhem; Khan, Shahjahan

2017-05-01

This paper considers the relationship between a binary response and a circular predictor. It develops the logistic regression model by employing the linear-circular regression approach. The maximum likelihood method is used to estimate the parameters. The Newton-Raphson numerical method is used to find the estimated values of the parameters. A data set from weather records of Toowoomba city is analysed by the proposed methods. Moreover, a simulation study is considered. The R software is used for all computations and simulations.
Naval Research Logistics Quarterly. Volume 28. Number 3,

DTIC Science & Technology

1981-09-01

denotes component-wise maximum. f has antone (isotone) differences on C x D if for cl < c2 and d, < d2, NAVAL RESEARCH LOGISTICS QUARTERLY VOL. 28...or negative correlations and linear or nonlinear regressions. Given are the mo- ments to order two and, for special cases, (he regression function and...data sets. We designate this bnb distribution as G - B - N(a, 0, v). The distribution admits only of positive correlation and linear regressions
Regression approaches in the test-negative study design for assessment of influenza vaccine effectiveness.

PubMed

Bond, H S; Sullivan, S G; Cowling, B J

2016-06-01

Influenza vaccination is the most practical means available for preventing influenza virus infection and is widely used in many countries. Because vaccine components and circulating strains frequently change, it is important to continually monitor vaccine effectiveness (VE). The test-negative design is frequently used to estimate VE. In this design, patients meeting the same clinical case definition are recruited and tested for influenza; those who test positive are the cases and those who test negative form the comparison group. When determining VE in these studies, the typical approach has been to use logistic regression, adjusting for potential confounders. Because vaccine coverage and influenza incidence change throughout the season, time is included among these confounders. While most studies use unconditional logistic regression, adjusting for time, an alternative approach is to use conditional logistic regression, matching on time. Here, we used simulation data to examine the potential for both regression approaches to permit accurate and robust estimates of VE. In situations where vaccine coverage changed during the influenza season, the conditional model and unconditional models adjusting for categorical week and using a spline function for week provided more accurate estimates. We illustrated the two approaches on data from a test-negative study of influenza VE against hospitalization in children in Hong Kong which resulted in the conditional logistic regression model providing the best fit to the data.
Breast ptosis: causes and cure.

PubMed

Rinker, Brian; Veneracion, Melissa; Walsh, Catherine P

2010-05-01

Breast ptosis is one of the most common conditions treated by plastic surgeons, but the causes are not clearly defined. A review was conducted of 132 consecutive patients presenting for breast augmentation or mastopexy. Information was obtained by chart review and telephone interview. Standardized photographs were examined to determine degree of ptosis by the Regnault classification. Of patients who had at least one pregnancy, 85% reported adverse changes in breast shape following pregnancy, 35% reported a reduction in breast size, and 30% reported an increase in size. Upon logistic regression, age, history of significant (>50 lbs) weight loss, higher body mass index, larger bra cup size, number of pregnancies, and smoking history were found to be significant risk factors for breast ptosis (P < 0.05). History of breast-feeding, weight gain during pregnancy, and lack of participation in regular upper body exercise were not found to be significant risk factors for ptosis.
Prevalence and Severity of Dementia in Nursing Home Residents.

PubMed

Helvik, Anne-Sofie; Engedal, Knut; Benth, Jūratė Šaltytė; Selbæk, Geir

2015-01-01

The aim of this study was to compare the presence and severity of dementia in two large cross-sectional samples of nursing home residents from 2004/2005 and 2010/2011. Demographic information as well as data on the type of nursing home unit, length of stay before assessment, physical health, regularly used prescribed drugs and Clinical Dementia Rating scale scores were used in the analyses. Logistic and linear regression models for hierarchical data were estimated. The odds of the occurrence and of a greater severity of dementia were higher in 2010/2011 than in 2004/2005. Independent of the time of study, married men had more severe dementia than single men, and single women had more severe dementia than single men. The findings may reflect the increase in the need for more nursing home beds designed for people with dementia between 2004/2005 and 2010/2011. © 2015 S. Karger AG, Basel.
Employee Health Behaviors, Self-Reported Health Status, and Association With Absenteeism: Comparison With the General Population.

PubMed

Yun, Young Ho; Sim, Jin Ah; Park, Eun-Gee; Park, June Dong; Noh, Dong-Young

2016-09-01

To perform a comparison between health behaviors and health status of employees with those of the general population, to evaluate the association between employee health behaviors, health status, and absenteeism. Cross-sectional study enrolled 2433 employees from 16 Korean companies in 2014, and recruited 1000 general population randomly in 2012. The distribution of employee health behaviors, health status, and association with absenteeism were assessed. Employees had significantly worse health status and low rates of health behaviors maintenance compared with the general population. Multiple logistic regression model revealed that regular exercise, smoking cessation, work life balance, proactive living, religious practice, and good physical health status were associated with lower absenteeism. Maintaining health behaviors and having good health status were associated with less absenteeism. This study suggests investment of multidimensional health approach in workplace health and wellness (WHW) programs.
Predictors of Cervical Cancer Screening for Rarely or Never Screened Rural Appalachian Women

PubMed Central

Hatcher, Jennifer; Studts, Christina R.; Dignan, Mark; Turner, Lisa M.; Schoenberg, Nancy E.

2011-01-01

Background and Purpose Women who have not had a Papanicolaou test in five years or more have increased risk of developing invasive cervical cancer. This study compares Appalachian women whose last screening was more than one year ago but less than five years ago with those not screened for the previous five years or more. Methods Using PRECEDE/PROCEED as a guide, factors related to obtaining Pap tests were examined using cross-sectional data from 345 Appalachian Kentucky women. Bivariate and multivariate analyses were conducted to identify predictors of screening. Results Thirty-four percent of participants were rarely- or never-screened. In multiple logistic regression analyses, several factors increased those odds, including belief that cervical cancer has symptoms, and not having a regular source of medical care. Conclusion The findings from this study may lead to the development of effective intervention and policies that increase cervical cancer screening in this population. PMID:21317514
A ferromagnetic surgical system reduces phrenic nerve injury in redo congenital cardiac surgery.

PubMed

Shinkawa, Takeshi; Holloway, Jessica; Tang, Xinyu; Gossett, Jeffrey M; Imamura, Michiaki

2017-05-01

A ferromagnetic surgical system (FMwand®) is a new type of dissection device expected to reduce the risk of adjacent tissue damage. We reviewed 426 congenital cardiac operations with cardiopulmonary bypass through redo sternotomy to assess if this device prevented phrenic nerve injury. The ferromagnetic surgical system was used in 203 operations (47.7%) with regular electrocautery and scissors. The preoperative and operative details were similar between the operations with or without the ferromagnetic surgical system. The incidence of phrenic nerve injury was significantly lower with the ferromagnetic surgical system (0% vs 2.7%, P = 0.031). A logistic regression model showed that the use of the ferromagnetic surgical system was significantly associated with reduced odds of phrenic nerve injury (P < 0.001). © The Author 2017. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.
How do mothers and fathers influence pediatric injury risk in middle childhood?

PubMed

Schwebel, David C; Brezausek, Carl M

2010-09-01

Parental influences are among the strongest behavioral correlates to unintentional injury outcome in early childhood, but are less well understood as children develop. We implemented a prospective research design to study how parenting style, parent-child relationships, and parental mental health influence injury during middle childhood. We also considered the roles of parent and child gender. Parental influences were assessed from a sample of 584 first graders, plus their mothers and fathers. Injuries requiring medical treatment were assessed regularly over the subsequent 5 years. Logistic regression models examined how maternal and paternal parenting factors predicted injury among all children, just boys, and just girls. Fathers who reported more positive relationships with their children had children protected from injury. This was particularly true of father-son relationships. No maternal traits predicted injury. A positive father-child, and especially a positive father-son relationship, may protect children from injury during middle childhood.
Job stress and mental health among nonregular workers in Korea: What dimensions of job stress are associated with mental health?

PubMed

Park, Soo Kyung; Rhee, Min-Kyoung; Barak, Michàlle Mor

2016-01-01

Although nonregular workers experience higher job stress, poorer mental health, and different job stress dimensions relative to regular workers, little is known about which job stress dimensions are associated with poor mental health among nonregular workers. This study investigated the association between job stress dimensions and mental health among Korean nonregular workers. Data were collected from 333 nonregular workers in Seoul and Gyeonggi Province, and logistic regression analysis was conducted. Results of the study indicated that high job insecurity and lack of rewards had stronger associations with poor mental health than other dimensions of job stress when controlling for sociodemographic and psychosocial variables. It is important for the government and organizations to improve job security and reward systems to reduce job stress among nonregular workers and ultimately alleviate their mental health issues.
Athletic participation and seatbelt omission among u.s. High school students.

PubMed

Melnick, Merrill J; Miller, Kathleen E; Sabo, Donald F; Barnes, Grace M; Farrell, Michael P

2010-02-01

Although seatbelts save lives, adolescents may be disproportionately likely to omit their use. Using data from the 1997 Youth Risk Behavior Survey, a national survey of more than 16,000 U.S. public and private high school students, the authors employed a series of logistic regression analyses to examine cross-sectional associations between past year athletic participation and regular seatbelt omission. Controlling for the effects of gender, age, race, parental education, and school urbanicity, student athletes were significantly less likely than nonathletes to report seatbelt omission. Separate gender-specific analyses showed that this effect was significant for girls but only marginally significant for boys; in addition, the effect was strongest for adolescents who participated on three or more school or community sports teams. Possible explanations for the relationship between athletic participation and seatbelt omission, including Jessor's problem behavior syndrome, prosocial sport subcultures, and sensation seeking, are considered.
Visual attention based bag-of-words model for image classification

NASA Astrophysics Data System (ADS)

Wang, Qiwei; Wan, Shouhong; Yue, Lihua; Wang, Che

2014-04-01

Bag-of-words is a classical method for image classification. The core problem is how to count the frequency of the visual words and what visual words to select. In this paper, we propose a visual attention based bag-of-words model (VABOW model) for image classification task. The VABOW model utilizes visual attention method to generate a saliency map, and uses the saliency map as a weighted matrix to instruct the statistic process for the frequency of the visual words. On the other hand, the VABOW model combines shape, color and texture cues and uses L1 regularization logistic regression method to select the most relevant and most efficient features. We compare our approach with traditional bag-of-words based method on two datasets, and the result shows that our VABOW model outperforms the state-of-the-art method for image classification.
A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network

PubMed Central

Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

2012-01-01

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins. PMID:27418910
A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network.

PubMed

Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

2012-01-01

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins.
Differential item functioning analysis with ordinal logistic regression techniques. DIFdetect and difwithpar.

PubMed

Crane, Paul K; Gibbons, Laura E; Jolley, Lance; van Belle, Gerald

2006-11-01

We present an ordinal logistic regression model for identification of items with differential item functioning (DIF) and apply this model to a Mini-Mental State Examination (MMSE) dataset. We employ item response theory ability estimation in our models. Three nested ordinal logistic regression models are applied to each item. Model testing begins with examination of the statistical significance of the interaction term between ability and the group indicator, consistent with nonuniform DIF. Then we turn our attention to the coefficient of the ability term in models with and without the group term. If including the group term has a marked effect on that coefficient, we declare that it has uniform DIF. We examined DIF related to language of test administration in addition to self-reported race, Hispanic ethnicity, age, years of education, and sex. We used PARSCALE for IRT analyses and STATA for ordinal logistic regression approaches. We used an iterative technique for adjusting IRT ability estimates on the basis of DIF findings. Five items were found to have DIF related to language. These same items also had DIF related to other covariates. The ordinal logistic regression approach to DIF detection, when combined with IRT ability estimates, provides a reasonable alternative for DIF detection. There appear to be several items with significant DIF related to language of test administration in the MMSE. More attention needs to be paid to the specific criteria used to determine whether an item has DIF, not just the technique used to identify DIF.
Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis.

PubMed

Armstrong, Ben G; Gasparrini, Antonio; Tobias, Aurelio

2014-11-24

The time stratified case cross-over approach is a popular alternative to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes. These are almost always analyzed using conditional logistic regression on data expanded to case-control (case crossover) format, but this has some limitations. In particular adjusting for overdispersion and auto-correlation in the counts is not possible. It has been established that a Poisson model for counts with stratum indicators gives identical estimates to those from conditional logistic regression and does not have these limitations, but it is little used, probably because of the overheads in estimating many stratum parameters. The conditional Poisson model avoids estimating stratum parameters by conditioning on the total event count in each stratum, thus simplifying the computing and increasing the number of strata for which fitting is feasible compared with the standard unconditional Poisson model. Unlike the conditional logistic model, the conditional Poisson model does not require expanding the data, and can adjust for overdispersion and auto-correlation. It is available in Stata, R, and other packages. By applying to some real data and using simulations, we demonstrate that conditional Poisson models were simpler to code and shorter to run than are conditional logistic analyses and can be fitted to larger data sets than possible with standard Poisson models. Allowing for overdispersion or autocorrelation was possible with the conditional Poisson model but when not required this model gave identical estimates to those from conditional logistic regression. Conditional Poisson regression models provide an alternative to case crossover analysis of stratified time series data with some advantages. The conditional Poisson model can also be used in other contexts in which primary control for confounding is by fine stratification.
Use of generalized ordered logistic regression for the analysis of multidrug resistance data.

PubMed

Agga, Getahun E; Scott, H Morgan

2015-10-01

Statistical analysis of antimicrobial resistance data largely focuses on individual antimicrobial's binary outcome (susceptible or resistant). However, bacteria are becoming increasingly multidrug resistant (MDR). Statistical analysis of MDR data is mostly descriptive often with tabular or graphical presentations. Here we report the applicability of generalized ordinal logistic regression model for the analysis of MDR data. A total of 1,152 Escherichia coli, isolated from the feces of weaned pigs experimentally supplemented with chlortetracycline (CTC) and copper, were tested for susceptibilities against 15 antimicrobials and were binary classified into resistant or susceptible. The 15 antimicrobial agents tested were grouped into eight different antimicrobial classes. We defined MDR as the number of antimicrobial classes to which E. coli isolates were resistant ranging from 0 to 8. Proportionality of the odds assumption of the ordinal logistic regression model was violated only for the effect of treatment period (pre-treatment, during-treatment and post-treatment); but not for the effect of CTC or copper supplementation. Subsequently, a partially constrained generalized ordinal logistic model was built that allows for the effect of treatment period to vary while constraining the effects of treatment (CTC and copper supplementation) to be constant across the levels of MDR classes. Copper (Proportional Odds Ratio [Prop OR]=1.03; 95% CI=0.73-1.47) and CTC (Prop OR=1.1; 95% CI=0.78-1.56) supplementation were not significantly associated with the level of MDR adjusted for the effect of treatment period. MDR generally declined over the trial period. In conclusion, generalized ordered logistic regression can be used for the analysis of ordinal data such as MDR data when the proportionality assumptions for ordered logistic regression are violated. Published by Elsevier B.V.
Prevalence and correlates of sexual risk among male and female sex workers in Tijuana, Mexico.

PubMed

Katsulis, Yasmina; Durfee, Alesha

2012-01-01

We investigated prevalence and correlates of sexual risk behaviours among male and female sex workers in Tijuana, Mexico, the busiest border crossing area on the US - Mexico border, analysing survey data from a purposive, cross-sectional sample of male and female sex workers who worked in a range of indoor and outdoor settings. Logistic regression was used to determine factors that were associated with sexual risk-taking, defined as failing to use a condom with last client. In bivariate regression models, gender, work setting (e.g., indoor vs. outdoor), poverty, engaging in survival sex, marital status and perceived drug addiction were correlated with sexual risk. When controlling for work location, housing insecurity, poverty, survival sex, marital status and perceived drug addiction, male sex workers were still 10 times more likely than female sex workers (FSW) to engage in sex without a condom during their last encounter with a client. And, although FSW were significantly more likely than males to have used a condom with a client, they were significantly less likely than males to have used a condom with their regular partner. Future research should further examine how gender shapes sexual risk activities in both commercial and non-commercial relationships.

Descriptive Epidemiology of Factors Associated with HIV Infections Among Men and Transgender Women Who Have Sex with Men in South India.

PubMed

Shaw, Souradet Y; Lorway, Robert; Bhattacharjee, Parinita; Reza-Paul, Sushena; du Plessis, Elsabé; McKinnon, Lyle; Thompson, Laura H; Isac, Shajy; Ramesh, Banadakoppa M; Washington, Reynold; Moses, Stephen; Blanchard, James F

2016-08-01

Men and transgender women who have sex with men (MTWSM) continue to be an at-risk population for human immunodeficiency virus (HIV) infection in India. Identification of risk factors and determinants of HIV infection is urgently needed to inform prevention and intervention programming. Data were collected from cross-sectional biological and behavioral surveys from four districts in Karnataka, India. Multivariable logistic regression models were constructed to examine factors related to HIV infection. Sociodemographic, sexual history, sex work history, condom practices, and substance use covariates were included in regression models. A total of 456 participants were included; HIV prevalence was 12.4%, with the highest prevalence (26%) among MTWSM from Bellary District. In bivariate analyses, district (P = 0.002), lack of a current regular female partner (P = 0.022), and reported consumption of an alcoholic drink in the last month (P = 0.004) were associated with HIV infection. In multivariable models, only alcohol use remained statistically significant (adjusted odds ratios: 2.6, 95% confidence intervals: 1.2-5.8; P = 0.02). The prevalence of HIV continues to be high among MTWSM, with the highest prevalence found in Bellary district.
Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis.

PubMed

Fei, Y; Hu, J; Li, W-Q; Wang, W; Zong, G-Q

2017-03-01

Essentials Predicting the occurrence of portosplenomesenteric vein thrombosis (PSMVT) is difficult. We studied 72 patients with acute pancreatitis. Artificial neural networks modeling was more accurate than logistic regression in predicting PSMVT. Additional predictive factors may be incorporated into artificial neural networks. Objective To construct and validate artificial neural networks (ANNs) for predicting the occurrence of portosplenomesenteric venous thrombosis (PSMVT) and compare the predictive ability of the ANNs with that of logistic regression. Methods The ANNs and logistic regression modeling were constructed using simple clinical and laboratory data of 72 acute pancreatitis (AP) patients. The ANNs and logistic modeling were first trained on 48 randomly chosen patients and validated on the remaining 24 patients. The accuracy and the performance characteristics were compared between these two approaches by SPSS17.0 software. Results The training set and validation set did not differ on any of the 11 variables. After training, the back propagation network training error converged to 1 × 10 -20 , and it retained excellent pattern recognition ability. When the ANNs model was applied to the validation set, it revealed a sensitivity of 80%, specificity of 85.7%, a positive predictive value of 77.6% and negative predictive value of 90.7%. The accuracy was 83.3%. Differences could be found between ANNs modeling and logistic regression modeling in these parameters (10.0% [95% CI, -14.3 to 34.3%], 14.3% [95% CI, -8.6 to 37.2%], 15.7% [95% CI, -9.9 to 41.3%], 11.8% [95% CI, -8.2 to 31.8%], 22.6% [95% CI, -1.9 to 47.1%], respectively). When ANNs modeling was used to identify PSMVT, the area under receiver operating characteristic curve was 0.849 (95% CI, 0.807-0.901), which demonstrated better overall properties than logistic regression modeling (AUC = 0.716) (95% CI, 0.679-0.761). Conclusions ANNs modeling was a more accurate tool than logistic regression in predicting the occurrence of PSMVT following AP. More clinical factors or biomarkers may be incorporated into ANNs modeling to improve its predictive ability. © 2016 International Society on Thrombosis and Haemostasis.
PREDICTION OF MALIGNANT BREAST LESIONS FROM MRI FEATURES: A COMPARISON OF ARTIFICIAL NEURAL NETWORK AND LOGISTIC REGRESSION TECHNIQUES

PubMed Central

McLaren, Christine E.; Chen, Wen-Pin; Nie, Ke; Su, Min-Ying

2009-01-01

Rationale and Objectives Dynamic contrast enhanced MRI (DCE-MRI) is a clinical imaging modality for detection and diagnosis of breast lesions. Analytical methods were compared for diagnostic feature selection and performance of lesion classification to differentiate between malignant and benign lesions in patients. Materials and Methods The study included 43 malignant and 28 benign histologically-proven lesions. Eight morphological parameters, ten gray level co-occurrence matrices (GLCM) texture features, and fourteen Laws’ texture features were obtained using automated lesion segmentation and quantitative feature extraction. Artificial neural network (ANN) and logistic regression analysis were compared for selection of the best predictors of malignant lesions among the normalized features. Results Using ANN, the final four selected features were compactness, energy, homogeneity, and Law_LS, with area under the receiver operating characteristic curve (AUC) = 0.82, and accuracy = 0.76. The diagnostic performance of these 4-features computed on the basis of logistic regression yielded AUC = 0.80 (95% CI, 0.688 to 0.905), similar to that of ANN. The analysis also shows that the odds of a malignant lesion decreased by 48% (95% CI, 25% to 92%) for every increase of 1 SD in the Law_LS feature, adjusted for differences in compactness, energy, and homogeneity. Using logistic regression with z-score transformation, a model comprised of compactness, NRL entropy, and gray level sum average was selected, and it had the highest overall accuracy of 0.75 among all models, with AUC = 0.77 (95% CI, 0.660 to 0.880). When logistic modeling of transformations using the Box-Cox method was performed, the most parsimonious model with predictors, compactness and Law_LS, had an AUC of 0.79 (95% CI, 0.672 to 0.898). Conclusion The diagnostic performance of models selected by ANN and logistic regression was similar. The analytic methods were found to be roughly equivalent in terms of predictive ability when a small number of variables were chosen. The robust ANN methodology utilizes a sophisticated non-linear model, while logistic regression analysis provides insightful information to enhance interpretation of the model features. PMID:19409817
Logistic regression analysis of factors associated with avascular necrosis of the femoral head following femoral neck fractures in middle-aged and elderly patients.

PubMed

Ai, Zi-Sheng; Gao, You-Shui; Sun, Yuan; Liu, Yue; Zhang, Chang-Qing; Jiang, Cheng-Hua

2013-03-01

Risk factors for femoral neck fracture-induced avascular necrosis of the femoral head have not been elucidated clearly in middle-aged and elderly patients. Moreover, the high incidence of screw removal in China and its effect on the fate of the involved femoral head require statistical methods to reflect their intrinsic relationship. Ninety-nine patients older than 45 years with femoral neck fracture were treated by internal fixation between May 1999 and April 2004. Descriptive analysis, interaction analysis between associated factors, single factor logistic regression, multivariate logistic regression, and detailed interaction analysis were employed to explore potential relationships among associated factors. Avascular necrosis of the femoral head was found in 15 cases (15.2 %). Age × the status of implants (removal vs. maintenance) and gender × the timing of reduction were interactive according to two-factor interactive analysis. Age, the displacement of fractures, the quality of reduction, and the status of implants were found to be significant factors in single factor logistic regression analysis. Age, age × the status of implants, and the quality of reduction were found to be significant factors in multivariate logistic regression analysis. In fine interaction analysis after multivariate logistic regression analysis, implant removal was the most important risk factor for avascular necrosis in 56-to-85-year-old patients, with a risk ratio of 26.00 (95 % CI = 3.076-219.747). The middle-aged and elderly have less incidence of avascular necrosis of the femoral head following femoral neck fractures treated by cannulated screws. The removal of cannulated screws can induce a significantly high incidence of avascular necrosis of the femoral head in elderly patients, while a high-quality reduction is helpful to reduce avascular necrosis.
Multivariate logistic regression analysis of postoperative complications and risk model establishment of gastrectomy for gastric cancer: A single-center cohort report.

PubMed

Zhou, Jinzhe; Zhou, Yanbing; Cao, Shougen; Li, Shikuan; Wang, Hao; Niu, Zhaojian; Chen, Dong; Wang, Dongsheng; Lv, Liang; Zhang, Jian; Li, Yu; Jiao, Xuelong; Tan, Xiaojie; Zhang, Jianli; Wang, Haibo; Zhang, Bingyuan; Lu, Yun; Sun, Zhenqing

2016-01-01

Reporting of surgical complications is common, but few provide information about the severity and estimate risk factors of complications. If have, but lack of specificity. We retrospectively analyzed data on 2795 gastric cancer patients underwent surgical procedure at the Affiliated Hospital of Qingdao University between June 2007 and June 2012, established multivariate logistic regression model to predictive risk factors related to the postoperative complications according to the Clavien-Dindo classification system. Twenty-four out of 86 variables were identified statistically significant in univariate logistic regression analysis, 11 significant variables entered multivariate analysis were employed to produce the risk model. Liver cirrhosis, diabetes mellitus, Child classification, invasion of neighboring organs, combined resection, introperative transfusion, Billroth II anastomosis of reconstruction, malnutrition, surgical volume of surgeons, operating time and age were independent risk factors for postoperative complications after gastrectomy. Based on logistic regression equation, p=Exp∑BiXi / (1+Exp∑BiXi), multivariate logistic regression predictive model that calculated the risk of postoperative morbidity was developed, p = 1/(1 + e((4.810-1.287X1-0.504X2-0.500X3-0.474X4-0.405X5-0.318X6-0.316X7-0.305X8-0.278X9-0.255X10-0.138X11))). The accuracy, sensitivity and specificity of the model to predict the postoperative complications were 86.7%, 76.2% and 88.6%, respectively. This risk model based on Clavien-Dindo grading severity of complications system and logistic regression analysis can predict severe morbidity specific to an individual patient's risk factors, estimate patients' risks and benefits of gastric surgery as an accurate decision-making tool and may serve as a template for the development of risk models for other surgical groups.
Detecting DIF in Polytomous Items Using MACS, IRT and Ordinal Logistic Regression

ERIC Educational Resources Information Center

Elosua, Paula; Wells, Craig

2013-01-01

The purpose of the present study was to compare the Type I error rate and power of two model-based procedures, the mean and covariance structure model (MACS) and the item response theory (IRT), and an observed-score based procedure, ordinal logistic regression, for detecting differential item functioning (DIF) in polytomous items. A simulation…
Accuracy of Bayes and Logistic Regression Subscale Probabilities for Educational and Certification Tests

ERIC Educational Resources Information Center

Rudner, Lawrence

2016-01-01

In the machine learning literature, it is commonly accepted as fact that as calibration sample sizes increase, Naïve Bayes classifiers initially outperform Logistic Regression classifiers in terms of classification accuracy. Applied to subtests from an on-line final examination and from a highly regarded certification examination, this study shows…
Comparing Linear Discriminant Function with Logistic Regression for the Two-Group Classification Problem.

ERIC Educational Resources Information Center

Fan, Xitao; Wang, Lin

The Monte Carlo study compared the performance of predictive discriminant analysis (PDA) and that of logistic regression (LR) for the two-group classification problem. Prior probabilities were used for classification, but the cost of misclassification was assumed to be equal. The study used a fully crossed three-factor experimental design (with…
Effects of Social Class and School Conditions on Educational Enrollment and Achievement of Boys and Girls in Rural Viet Nam

ERIC Educational Resources Information Center

Nguyen, Phuong L.

2006-01-01

This study examines the effects of parental SES, school quality, and community factors on children's enrollment and achievement in rural areas in Viet Nam, using logistic regression and ordered logistic regression. Multivariate analysis reveals significant differences in educational enrollment and outcomes by level of household expenditures and…
School Exits in the Milwaukee Parental Choice Program: Evidence of a Marketplace?

ERIC Educational Resources Information Center

Ford, Michael

2011-01-01

This article examines whether the large number of school exits from the Milwaukee school voucher program is evidence of a marketplace. Two logistic regression and multinomial logistic regression models tested the relation between the inability to draw large numbers of voucher students and the ability for a private school to remain viable. Data on…
Hierarchical Bayesian Logistic Regression to forecast metabolic control in type 2 DM patients.

PubMed

Dagliati, Arianna; Malovini, Alberto; Decata, Pasquale; Cogni, Giulia; Teliti, Marsida; Sacchi, Lucia; Cerra, Carlo; Chiovato, Luca; Bellazzi, Riccardo

2016-01-01

In this work we present our efforts in building a model able to forecast patients' changes in clinical conditions when repeated measurements are available. In this case the available risk calculators are typically not applicable. We propose a Hierarchical Bayesian Logistic Regression model, which allows taking into account individual and population variability in model parameters estimate. The model is used to predict metabolic control and its variation in type 2 diabetes mellitus. In particular we have analyzed a population of more than 1000 Italian type 2 diabetic patients, collected within the European project Mosaic. The results obtained in terms of Matthews Correlation Coefficient are significantly better than the ones gathered with standard logistic regression model, based on data pooling.
An empirical study of statistical properties of variance partition coefficients for multi-level logistic regression models

USGS Publications Warehouse

Li, Ji; Gray, B.R.; Bates, D.M.

2008-01-01

Partitioning the variance of a response by design levels is challenging for binomial and other discrete outcomes. Goldstein (2003) proposed four definitions for variance partitioning coefficients (VPC) under a two-level logistic regression model. In this study, we explicitly derived formulae for multi-level logistic regression model and subsequently studied the distributional properties of the calculated VPCs. Using simulations and a vegetation dataset, we demonstrated associations between different VPC definitions, the importance of methods for estimating VPCs (by comparing VPC obtained using Laplace and penalized quasilikehood methods), and bivariate dependence between VPCs calculated at different levels. Such an empirical study lends an immediate support to wider applications of VPC in scientific data analysis.
Model building strategy for logistic regression: purposeful selection.

PubMed

Zhang, Zhongheng

2016-03-01

Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood ratio test to see whether deleting a variable will have significant impact on model fit. A deleted variable should also be checked for whether it is an important adjustment of remaining covariates. Interaction should be checked to disentangle complex relationship between covariates and their synergistic effect on response variable. Model should be checked for the goodness-of-fit (GOF). In other words, how the fitted model reflects the real data. Hosmer-Lemeshow GOF test is the most widely used for logistic regression model.
Multipurpose Cargo Transfer Bag

NASA Technical Reports Server (NTRS)

Broyan, James; Baccus, Shelley

2014-01-01

The Logistics Reduction (LR) project within the Advanced Exploration Systems (AES) program is tasked with reducing logistical mass and repurposing logistical items. Multipurpose Cargo Transfer Bags (MCTB) have been designed such that they can serve the same purpose as a Cargo Transfer Bag, the suitcase-shaped common logistics carrying bag for Shuttle and the International Space Station. After use as a cargo carrier, a regular CTB becomes trash, whereas the MCTB can be unzipped, unsnapped, and unfolded to be reused. Reuse ideas that have been investigated include partitions, crew quarters, solar radiation storm shelters, acoustic blankets, and forward osmosis water processing.
Exploring the association of dental care utilization with oral impacts on daily performances (OIDP) - a prospective study of ageing people in Norway and Sweden.

PubMed

Gülcan, Ferda; Ekbäck, Gunnar; Ordell, Sven; Klock, Kristin S; Lie, Stein Atle; Åstrøm, Anne Nordrehaug

2018-01-01

To explore the association of dental health care utilization with oral impacts on daily performances (OIDP) across time focusing ageing Norwegian and Swedish adults adjusting for predisposing, enabling, and need related-factors as defined by Andersen's model. Data were based on Norwegian and Swedish 1942 birth-cohorts conducted in 2007 (age 65) and 2012 (age 70). In Norway, the response rates ranged from 54% to 58%. Corresponding figures in Sweden were from 72% to 73%. Self-administered questionnaires assessed OIDP, dental care utilization and predisposing, enabling and need related factors. Logistic regression with robust variance estimation was used to adjust for clustering in repeated data. Significant covariates of OIDP were satisfaction with dental services, dental care avoidance due to financial constraints, frightening experience with dental care during childhood and patient initiated dental visiting. Frequency and regularity of dental attendance were associated with OIDP in the Swedish cohort, only. In spite of country differences in the public co-financing of dental care, dental care utilization indicators were associated with OIDP across time in both cohorts. Encouraging regular and dentist initiated visiting patterns and strengthening beliefs in keeping own teeth could be useful in attempts to reduce poor oral health related quality of life in ageing people.
Health Behaviors Among Baby Boomer Informal Caregivers

PubMed Central

Hoffman, Geoffrey J.; Lee, Jihey; Mendez-Luck, Carolyn A.

2012-01-01

Purpose of the Study: This study examines health-risk behaviors among “Baby Boomer” caregivers and non-caregivers. Design and Methods: Data from the 2009 California Health Interview Survey of the state’s non-institutionalized population provided individual-level, caregiving, and health behavior characteristics for 5,688 informal caregivers and 12,941 non-caregivers. Logistic regression models were estimated separately for four individual health-risk behaviors—smoking, sedentary behavior, and regular soda and fast-food consumption—as well as a global health-risk measure. Results: Controlling for psychological distress and personal characteristics and social resources such as age, gender, income and education, work and marital status, and neighborhood safety, caregivers had greater odds than non-caregivers of overall negative health behavior and of smoking and regular soda and fast-food consumption. We did not observe significant differences in odds of negative behavior related to stress for spousal caregivers and caregivers in the role for longer periods of time or those providing more hours of weekly care compared with other caregivers. Implications: Our study found evidence that Baby Boomer caregivers engage in poor health behaviors that are associated with exposure to caregiving. Baby Boomer caregivers may be at risk for certain behavioral factors that are associated with disability and chronic illness. PMID:22391873
Screening and managing cannabis use: comparing GP’s and nurses’ knowledge, beliefs, and behavior

PubMed Central

2012-01-01

Background General practitioners (GPs) and nurses are ideally placed to address the significant unmet demand for the treatment of cannabis-related problems given the numbers of people who regularly seek their care. The aim of this study was to evaluate differences between GPs and nurses’ perceived knowledge, beliefs, and behaviors toward cannabis use and its screening and management. Methods This study involved 161 nurses and 503 GPs who completed a survey distributed via conference satchels to delegates of Healthed seminars focused on topics relevant to women and children’s health. Differences between GPs and nurses were analyzed using χ2- tests and two-sample t-tests, while logistic regression examined predictors of service provision. Results GPs were more likely than nurses to have engaged in cannabis-related service provision, but also more frequently reported barriers related to time, interest, and having more important issues to address. Nurses reported less knowledge, skills, and role legitimacy. Perceived screening skills predicted screening and referral to alcohol and other drug (AOD) services, while knowing a regular user increased the likelihood of referrals only. Conclusions Approaches to increase cannabis-related screening and intervention may be improved by involving nurses, and by leveraging the relationship between nurses and doctors, in primary care. PMID:22827931
Consuming energy drinks at the age of 14 predicted legal and illegal substance use at 16.

PubMed

Barrense-Dias, Yara; Berchtold, André; Akre, Christina; Surís, Joan-Carles

2016-11-01

This study examined whether consuming energy drinks at the age of 14 predicted substance use at 16. We followed 621 youths from an area of Switzerland who completed a longitudinal online survey in both 2012 and 2014 when they were 14 and 16 years of age. At 14, participants, who were divided into nonenergy drink users (n = 262), occasional users (n = 183) and regular users (n = 176), reported demographic, health-related and substance use data. Substance use at 16 was assessed through logistic regression using nonusers as the reference group and controlling for significant variables at 14. At the bivariate level, energy drink consumption was associated with substance use at both 14 and 16. Energy drink consumers were also more likely to be male, older, less academic, sleep less on schooldays and live in an urban area. In the multivariate analysis, smokers, alcohol misusers and cannabis users at the age of 16 were significantly more likely to have been regular energy drink users at the age of 14. Consuming energy drinks at 14 years of age predicted using legal and illegal substances at 16. Health providers should screen young adolescents for energy drink use and closely monitor weekly users. ©2016 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.
Factors Associated with Dental Pain in Mexican Schoolchildren Aged 6 to 12 Years

PubMed Central

Escoffié-Ramirez, Mauricio; Ávila-Burgos, Leticia; Baena-Santillan, Elena Saraí; Aguilar-Ayala, Fernando; Lara-Carrillo, Edith; Minaya-Sánchez, Mirna; Mendoza-Rodríguez, Martha; Márquez-Corona, María de Lourdes

2017-01-01

Objective To identify dental pain prevalence and associated factors in Mexican schoolchildren. Methods This cross-sectional study included 1,404 schoolchildren aged 6 to 12 years from public schools in the city of Pachuca de Soto, Hidalgo, Mexico. Data were collected through a questionnaire that addressed sociodemographic and socioeconomic factors, eating and dental hygiene habits, and behavior variables. The dependent variable was self-reported dental pain in the 12 months prior to the survey. Data were analyzed using nonparametric statistics and a binary logistical regression model. Results Dental pain prevalence among the studied children was 49.9%. The variables associated in the final model (p < 0.05) were younger mother's age, higher socioeconomic level, absence of an automobile in the home, fried food, fruit intake, lower tooth brushing frequency, never having used mouthwash or not knowing about it, and parents/guardians with regular to high levels of knowledge about oral health and a regular or good/very good perception of their child's oral health. Conclusions One in two children in the study had experienced dental pain in the twelve months prior to the survey. The association of socioeconomic variables with dental pain suggested inequalities among the children in terms of oral health. PMID:28685149
Predictors of sustainability for community-managed handpumps in sub-Saharan Africa: evidence from Liberia, Sierra Leone, and Uganda.

PubMed

Foster, Tim

2013-01-01

Rural water supply sustainability has remained an enduring policy challenge in sub-Saharan Africa for decades. Drawing on the largest data set assembled on rural water points in sub-Saharan Africa to date, this paper employs logistic regression analyses to identify operational, technical, institutional, financial, and environmental predictors of functionality for over 25 000 community-managed handpumps in Liberia, Sierra Leone, and Uganda. Risk factors significantly associated with nonfunctionality across all three countries were (a) system age, (b) distance from district/county capital, and (c) absence of user fee collection. In at least one of the three countries, other variables found to have significant multivariable adjusted associations with functionality status included well type, handpump type, funding organization, implementing organization, spare parts proximity, availability of a handpump mechanic, regular servicing, regular water committee meetings, women in key water committee positions, rainfall season, and perceived water quality. While the findings reinforce views that a multifaceted range of conditions is critical for the sustainability of community-managed handpumps, they also demonstrate that these factors remain absent from a high proportion of cases. Governments and development partners must significantly strengthen postconstruction support for operation and maintenance systems, and greater efforts are needed to test and evaluate alternative models for managing handpump water supplies.

Reported contraceptive use, risk behaviours and STIs among military conscripts in Estonian defence forces.

PubMed

David Parker, R; Regier, Michael D; Widmeyer, Joseph; Honaker, John; Rüütel, Kristi

2015-10-01

Limited research exists on sexually transmitted infection (STI) and risk behaviour among military personnel. Published research on condom use and types of contraceptives used yield mixed results, yet, the perception that military members are at higher risk for STIs remains. The objectives of this cross-sectional study were to measure factors such as condom use, contraceptive methods, and risky behaviours (i.e. drug use and sex with commercial sex workers) and investigate differences between ethnic groups, where culture could influence behaviour. Data were collected from a recruited population of 584 male, military conscripts in northeastern Europe. Using multinomial logistic regression models, statistically significant findings include an interaction between the use of contraceptive methods of Russians with casual partners and ethnicity, with higher odds of effective methods used among Estonians with regular partners (OR = 8.13) or casual partners (OR = 11.58) and Russians with regular partners (OR = 4.98). Effective contraceptive methods used less frequently with casual partners by ethnic Russians is important in providing education and risk reduction services to young, male conscripts. These findings may be used as a baseline to inform health education and STI prevention programmes tailored to military members in Eastern Europe in the absence of other published studies. © The Author(s) 2015.
Identification of post traumatic stress disorder and risk factors in military first responders 6 months after Wen Chuan earthquake in China.

PubMed

Wang, Huanlin; Jin, Hua; Nunnink, Sarah E; Guo, Wei; Sun, Jian; Shi, Jianan; Zhao, Bin; Bi, Yinhau; Yan, Tongjun; Yu, Haiying; Wang, Guangjian; Gao, Zhiqing; Zhao, Hanqing; Ou, Yanghui; Song, Zixiagn; Chen, Fangbin; Lohr, James B; Baker, Dewleen G

2011-04-01

Military personnel commonly serve as first responders to natural disasters. Our aim is to identify Post-Traumatic Stress Disorder (PTSD) and determine risk in military responders to the Wen Chuan earthquake. Analyses were carried out on 1056 of the 1125 soldiers enrolled. In addition to social demographic characteristics, the Davidson Trauma Scale (DTS) and an Earthquake exposure screening scale were administered. PTSD prevalence was 6.53% (69 cases). Logistic regression indicated that intensity of traumatic exposure (odds ratio 6.46, 95% CI 4.47-9.32, p<0.001), not having received psychological counseling (odds ratio 3.28, 95% CI 1.31-8.20, p<0.02) and regular drinking (odds ratio 2.42, 95% CI 1.04-5.62, p<0.05) were significant predictors of PTSD. Being a single-child, not being raised by both parents and regular smoking also independently predicted PTSD if intensity of earthquake traumatic exposure was not included in the model. The self-rated DTS was used to classify PTSD in this study and psychiatric co-morbidity outside of PTSD was not assessed in this sample. PTSD is a concern for Military disaster responders; to identify those with high risk of developing PTSD would be important and beneficial. Published by Elsevier B.V.
Price elasticity of demand for malt liquor beer: findings from a US pilot study.

PubMed

French, Michael Thomas; Browntaylor, Didra; Bluthenthal, Ricky Neville

2006-05-01

Our objective is to estimate the relative price elasticity of demand for malt liquor beer (MLB), regular beer, hard liquor, and a combined group of all other alcoholic beverages. Three hundred and twenty-nine alcohol consumers (mostly male) in South-Central Los Angeles answered a series of questions pertaining to expected consumption responses to hypothetical price increases. We found that based on a 10% price increase, the mean price elasticity of demand (% change in quantity demanded / % change in price) was -0.79 for MLB drinkers, -1.14 for regular beer drinkers, -1.11 for hard liquor drinkers, and -1.69 for the combined group of all other drinkers. Logistic regression analysis revealed that the personal characteristics significantly related to being a MLB drinker were older age, not working, being homeless, and a daily drinker. Daily (or nearly daily) drinkers were more likely to be married, earning lower incomes, and hard liquor drinkers. This study is the first to investigate the price elasticity of demand for MLB drinkers and other heavy alcohol consumers in poor urban neighborhoods of the US. Future research can use the methods from this pilot study to more rigorously examine and compare the price sensitivity among heavy drinking groups.
Risk factors for lower extremity injuries among half marathon and marathon runners of the Lage Landen Marathon Eindhoven 2012: A prospective cohort study in the Netherlands.

PubMed

van Poppel, D; de Koning, J; Verhagen, A P; Scholten-Peeters, G G M

2016-02-01

To determine risk factors for running injuries during the Lage Landen Marathon Eindhoven 2012. Prospective cohort study. Population-based study. This study included 943 runners. Running injuries after the Lage Landen Marathon. Sociodemographic and training-related factors as well as lifestyle factors were considered as potential risk factors and assessed in a questionnaire 1 month before the running event. The association between potential risk factors and injuries was determined, per running distance separately, using univariate and multivariate logistic regression analysis. In total, 154 respondents sustained a running injury. Among the marathon runners, in the univariate model, body mass index ≥ 26 kg/m(2), ≤ 5 years of running experience, and often performing interval training, were significantly associated with running injuries, whereas in the multivariate model only ≤ 5 years of running experience and not performing interval training on a regular basis were significantly associated with running injuries. Among marathon runners, no multivariate model could be created because of the low number of injuries and participants. This study indicates that interval training on a regular basis may be recommended to marathon runners to reduce the risk of injury. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Yoga practice is associated with attenuated weight gain in healthy, middle-aged men and women.

PubMed

Kristal, Alan R; Littman, Alyson J; Benitez, Denise; White, Emily

2005-01-01

Yoga is promoted or weight maintenance, but there is little evidence of its efficacy. To examine whether yoga practice is associated with lower mean 10-year weight gain after age 45. Participants included 15,550 adults, aged 53 to 57 years, recruited to the Vitamin and Lifestyle (VITAL) cohort study between 2000 and 2002. Physical activity (including yoga) during the past 10 years, diet, height, and weight at recruitment and at ages 30 and 45. All measures were based on self-reporting, and past weight was retrospectively ascertained. Multiple regression analyses were used to examined covariate-adjusted associations between yoga practice and weight change from age 45 to recruitment, and polychotomous logistic regression was used to examine associations of yoga practice with the relative odds of weight maintenance (within 5%) and weight loss (> 5%) compared to weight gain. Yoga practice for four or more years was associated with a 3.1-lb lower weight gain among normal weight (BMI < 25) participants [9.5 lbs versus 12.6 Ibs] and an 18.5-lb lower weight gain among overweight participants [-5.0 lbs versus 13.5 Ibs] (both P for trend <.001). Among overweight individuals, 4+ years of yoga practice was associated with a relative odds of 1.85 (95% confidence interval [CI] 0.63-5.42) for weight maintenance (within 5%) and 3.88 (95% Cl 1.30-9.88) for weight loss (> 5%) compared to weight gain (P for trend .026 and .003, respectively). Regular yoga practice was associated with attenuated weight gain, most strongly among individuals who were overweight. Although causal inference from this observational study is not possible, results are consistent with the hypothesis that regular yoga practice can benefit individuals who wish to maintain or lose weight.
Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach.

PubMed

Hettige, Nuwan C; Nguyen, Thai Binh; Yuan, Chen; Rajakulendran, Thanara; Baddour, Jermeen; Bhagwat, Nikhil; Bani-Fatemi, Ali; Voineskos, Aristotle N; Mallar Chakravarty, M; De Luca, Vincenzo

2017-07-01

Suicide is a major concern for those afflicted by schizophrenia. Identifying patients at the highest risk for future suicide attempts remains a complex problem for psychiatric interventions. Machine learning models allow for the integration of many risk factors in order to build an algorithm that predicts which patients are likely to attempt suicide. Currently it is unclear how to integrate previously identified risk factors into a clinically relevant predictive tool to estimate the probability of a patient with schizophrenia for attempting suicide. We conducted a cross-sectional assessment on a sample of 345 participants diagnosed with schizophrenia spectrum disorders. Suicide attempters and non-attempters were clearly identified using the Columbia Suicide Severity Rating Scale (C-SSRS) and the Beck Suicide Ideation Scale (BSS). We developed four classification algorithms using a regularized regression, random forest, elastic net and support vector machine models with sociocultural and clinical variables as features to train the models. All classification models performed similarly in identifying suicide attempters and non-attempters. Our regularized logistic regression model demonstrated an accuracy of 67% and an area under the curve (AUC) of 0.71, while the random forest model demonstrated 66% accuracy and an AUC of 0.67. Support vector classifier (SVC) model demonstrated an accuracy of 67% and an AUC of 0.70, and the elastic net model demonstrated and accuracy of 65% and an AUC of 0.71. Machine learning algorithms offer a relatively successful method for incorporating many clinical features to predict individuals at risk for future suicide attempts. Increased performance of these models using clinically relevant variables offers the potential to facilitate early treatment and intervention to prevent future suicide attempts. Copyright © 2017 Elsevier Inc. All rights reserved.
Tsetse Fly (G.f. fuscipes) Distribution in the Lake Victoria Basin of Uganda

PubMed Central

Albert, Mugenyi; Wardrop, Nicola A; Atkinson, Peter M; Torr, Steve J; Welburn, Susan C

2015-01-01

Tsetse flies transmit trypanosomes, the causative agent of human and animal African trypanosomiasis. The tsetse vector is extensively distributed across sub-Saharan Africa. Trypanosomiasis maintenance is determined by the interrelationship of three elements: vertebrate host, parasite and the vector responsible for transmission. Mapping the distribution and abundance of tsetse flies assists in predicting trypanosomiasis distributions and developing rational strategies for disease and vector control. Given scarce resources to carry out regular full scale field tsetse surveys to up-date existing tsetse maps, there is a need to devise inexpensive means for regularly obtaining dependable area-wide tsetse data to guide control activities. In this study we used spatial epidemiological modelling techniques (logistic regression) involving 5000 field-based tsetse-data (G. f. fuscipes) points over an area of 40,000 km2, with satellite-derived environmental surrogates composed of precipitation, temperature, land cover, normalised difference vegetation index (NDVI) and elevation at the sub-national level. We used these extensive tsetse data to analyse the relationships between presence of tsetse (G. f. fuscipes) and environmental variables. The strength of the results was enhanced through the application of a spatial autologistic regression model (SARM). Using the SARM we showed that the probability of tsetse presence increased with proportion of forest cover and riverine vegetation. The key outputs are a predictive tsetse distribution map for the Lake Victoria basin of Uganda and an improved understanding of the association between tsetse presence and environmental variables. The predicted spatial distribution of tsetse in the Lake Victoria basin of Uganda will provide significant new information to assist with the spatial targeting of tsetse and trypanosomiasis control. PMID:25875201
Is chloroquine chemoprophylaxis still effective to prevent low birth weight? Results of a study in Benin

PubMed Central

Denoeud, Lise; Fievet, Nadine; Aubouy, Agnès; Ayemonna, Paul; Kiniffo, Richard; Massougbodji, Achille; Cot, Michel

2007-01-01

Background In areas of stable transmission, malaria during pregnancy is associated with severe maternal and foetal outcomes, especially low birth weight (LBW). To prevent these complications, weekly chloroquine (CQ) chemoprophylaxis is now being replaced by intermittent preventive treatment with sulfadoxine-pyrimethamine in West Africa. The prevalence of placental malaria and its burden on LBW were assessed in Benin to evaluate the efficacy of weekly CQ chemoprophylaxis, prior to its replacement by intermittent preventive treatment. Methods In two maternity clinics in Ouidah, an observational study was conducted between April 2004 and April 2005. At each delivery, placental blood smears were examined for malaria infection and women were interviewed on their pregnancy history including CQ intake and dosage. CQ was measured in the urine of a sub-sample (n = 166). Multiple logistic and linear regression were used to assess factors associated with LBW and placental malaria. Results Among 1090 singleton live births, prevalence of placental malaria and LBW were 16% and 17% respectively. After adjustment, there was a non-significant association between placental malaria and LBW (adjusted OR = 1.43; P = 0.10). Multiple linear regression showed a positive association between placental malaria and decreased birth weight in primigravidae. More than 98% of the women reported regular chemoprophylaxis and CQ was detectable in 99% of urine samples. Protection from LBW was high in women reporting regular CQ prophylaxis, with a strong duration-effect relationship (test for linear trend: P < 0,001). Conclusion Despite high parasite resistance and limited effect on placental malaria, a CQ chemoprophylaxis taken at adequate doses showed to be still effective in reducing LBW in Benin. PMID:17341298
Overweight dogs exercise less frequently and for shorter periods: results of a large online survey of dog owners from the UK.

PubMed

German, Alexander J; Blackwell, Emily; Evans, Mark; Westgarth, Carri

2017-01-01

Canine obesity is now the number one health concern in dogs worldwide. Regular physical activity can improve health, and owners are advised to exercise their dogs on a regular basis. However, limited information exists about associations between overweight status of dogs and walking activity. An online survey was conducted between June and August in 2014, coinciding with the broadcast of a national UK television programme, exploring dog behaviour. Information gathered included signalment, overweight status, and owner-reported information on duration and frequency of dog walking. The University of Liverpool Ethics Committee approved the project, and owners consented to data use. Simple and multiple logistic regression analyses were used to determine associations between overweight status and dog walking activity. Data were available from 11 154 adult dogs, and 1801 (16·1 %) of these were reported as overweight by their owners. Dogs reported to be overweight dogs were more likely to be neutered ( P < 0·0001) and older ( P < 0·0001). Various breeds were over-represented including beagle, Cavalier King Charles spaniel, golden retriever, Labrador retriever and pug ( P < 0·0001 for all). Both frequency and duration of walking were negatively associated with overweight status ( P < 0·0001 for both). On multiple regression analysis, duration and frequency were independently and negatively associated with the odds of being overweight, along with a range of other factors including age, neuter status and breed. This study has identified associations between overweight status and exercise. In the future, studies should determine the reason for this association, and whether changes in walking activity can influence weight status.
Assessment of U.S. and Coalition Efforts to Develop the Logistics and Maintenance Sustainment Capability of the Afghan National Police

DTIC Science & Technology

2015-01-30

Border Police ( ABP ) facilities officer reported he had not received the budget allocation he required to maintain over 135 buildings and checkpoints in...visited the Kandahar and Herat RLCs. The team also spoke with key ANP logistics personnel in the MoI, Afghan Border Police ( ABP ), Afghan Uniformed...AUP, ABP , and ANCOP units regularly bypassed the RLC and conducted logistics / supply business directly with the NLC. This occurred for various
The prevalence of halitosis (oral malodor) and associated factors among dental students and interns, Lahore, Pakistan.

PubMed

Nazir, Muhammad Ashraf; Almas, Khalid; Majeed, Muhammad Irfan

2017-01-01

To evaluate the prevalence of halitosis and the factors associated with it among dental students and interns in Lahore, Pakistan. A cross-sectional study design was chosen, and a sample of dental students and interns was collected from seven dental colleges in Lahore, Pakistan. A total of 833 participants were approached in person as convenient sample population. A self-reported questionnaire was administered and informed consent was obtained. The associations between oral malodor and different variables of the study were explored using analytical statistics (Chi-square test and logistic regression analysis). Statistical significance was determined using a 95% confidence interval (CI). Six hundred and fifteen participants (aged 19-27 years) completed the survey with a response rate of 73.8%. The prevalence of self-reported halitosis was 75.1%. More female (51.4%) than male students (23.7%) reported oral malodor, and most participants (61%) reported early morning halitosis. Thirteen percent of respondents had examination for oral malodor by a dentist and 37.6% treated the condition with self-medication. Binary logistic regression model showed that male gender (odds ratio [OR] =0.44, CI = 0.22-0.87), daily use of dental floss (OR = 0.28, CI = 0.13-0.58), and drinking tea with mint (OR = 0.44, CI = 0.22-0.89) were significantly associated with oral malodor. The participants with tongue coating had higher odds (OR = 2.75, CI = 1.13-6.69) of having oral malodor than those without tongue coating, and the association was statistically significant. The study identified high prevalence of oral malodor among dental students and interns. They should receive appropriate diagnosis and management of the condition from dentist. The regular use of dental floss and removal of tongue coating can significantly reduce halitosis.
Prevalence of Enterobius vermicularis amongst kindergartens and preschool children in Mazandaran Province, North of Iran.

PubMed

Afrakhteh, Narges; Marhaba, Zahra; Mahdavi, Seif Ali; Garoosian, Sahar; Mirnezhad, Reyhaneh; Vakili, Mahsa Eshkevar; Shahraj, Haniye Ahmadi; Javadian, Behzad; Rezaei, Rozita; Moosazadeh, Mahmood

2016-12-01

Enterobiasis (oxyuriasis) is probably the most common helminth, which infects humans. Amongst different age groups, prevalence of Enterobius vermicularis in children is high compared to adults. Oxyuriasis is one of the most significant parasitic diseases of children. This nematode in children can result in loss of appetite, insomnia, grinding of the teeth, restlessness, endometritis, abdominal cramps, diarrhea and etc. Due to important complications of this parasite, the objective of the current study was to determine the prevalence of enterobiasis in kindergarten and preschool children of Amol, Mazandaran Province, North of Iran. A total number of 462 children from 32 kindergartens of Amol were examined for the prevalence of E. vermicularis infection, 2013. Adhesive cello-tape anal swab method was trained to parents for sampling. In addition, a questionnaire was designed and filled out to collect demographic information for each individual. Data were analyzed using Chi square test and multivariate logistic regression for each risk factor. The overall prevalence of E. vermicularis infection was 7.1 % (33). Although infection with E. vermicularis in girls 7.9 % was higher compared to boys 6.3 %, there was no significant difference between gender and age ( p > 0.05) whereas binary logistic regression showed significant difference between enterobiasis and age ( p < 0.05). The findings indicated that the prevalence of E. vermicularis in kindergarten and preschool children is relatively high and still is an important health problem and should not be underestimated due to being highly contagious infection. Therefore, educational programs and mass treatment should be carried out in order to reduce infection incidence in this area and regular parasitological test and attention to personal hygiene in kindergarten and preschool is of great importance.
Prevalence and determinants of susceptibility to cigarette smoking among school students in Pakistan: secondary analysis of Global Youth Tobacco Survey.

PubMed

Aslam, Syeda Kanwal; Zaheer, Sidra; Rao, Saadiyah; Shafique, Kashif

2014-02-21

Susceptibility to smoke has been recognized as a strong predictor of smoking experimentation and taking up regular smoking habit. The identification of smoking susceptible individuals and its determinants is important in the efforts to reduce future smoking prevalence. The aims of this study are to estimate prevalence of susceptibility to smoke among adolescents, and identify factors associated with it. Cross sectional data was obtained from Global Youth Tobacco Survey conducted in three cities of Pakistan in year 2004. Study population consisted of students in grades, 8th, 9th, and 10th; aged 13 to 15 years. Secondary analysis using univariate and multivariate logistic regression analyses were performed to estimate the associations between smoking susceptibility and co-variates. Descriptive statistics were reported in proportions, and adjusted odds ratios with 95% confidence interval were used to report logistic regression analyses. Approximately 12% of nonsmoking students were found susceptible to smoking. Students, who were females (OR = 1.53, 95% CI [1.24-1.89]); whose parents (OR = 1.64, 95% CI [1.35-1.99]); or close friend smoked (OR = 2.77, 95% CI [2.27- 3.40]) were more susceptible to cigarette smoking. Students who had good knowledge about harmful effects of smoking (OR = 0.54, 95% CI [0.43-0.69]); and had access to anti-smoking media (OR = 0.73, 95% CI [0.59-0.89]) were less likely to be susceptible to smoking. Students who were females, had smoking parents, friends or exposure to newspaper/magazines cigarette marketing, were more susceptible to cigarette smoking among Pakistani adolescents. While knowledge of harmful effects of smoking and access to anti-smoking media served as protective factors against susceptibility to smoking.
Insomnia, psychiatric disorders and suicidal ideation in a National Representative Sample of active Canadian Forces members.

PubMed

Richardson, J D; Thompson, A; King, L; Corbett, B; Shnaider, P; St Cyr, K; Nelson, C; Sareen, J; Elhai, J; Zamorski, M

2017-06-06

Past research on the association between insomnia and suicidal ideation (SI) has produced mixed findings. The current study explored the relationship between insomnia, SI, and past-year mental health status among a large Canadian Forces (CF) sample. Data was obtained from the 2013 Canadian Forces Mental Health Survey (CFMHS), and included a large representative sample of Canadian Regular Forces personnel (N = 6700). A series of univariate logistic regressions were conducted to test individual associations between past-year mental health status, insomnia, and potential confounds and SI. Mental health status included three groups: 0, 1, or two or more probable diagnoses of posttraumatic stress disorder (PTSD), major depressive disorder (MDD), generalized anxiety disorder (GAD), panic disorder (PD) and alcohol abuse/dependence. Stepwise multivariate logistic regression was used to assess the relationship between insomnia and SI with mental health status as a moderator. 40.8% of respondents reported experiencing insomnia. Both insomnia and number of mental health conditions incrementally increased the risk of SI. However, past-year mental health status was a significant moderator of this relationship, such that for CF personnel with either no (AOR = 1.61, 1.37-1.89) or only one past-year mental health condition (AOR = 1.39, 1.12-1.73), an incremental increase in insomnia was associated with an increased likelihood of SI. However, in personnel with two or more past-year mental health disorders, insomnia was no longer significantly associated with SI (AOR = 1.04, 0.81-1.33). Insomnia significantly increased the odds of SI, but only among individuals with no or one mental health condition. Findings highlight the importance of assessing insomnia among CF members in order to further suicide prevention efforts.
The prevalence and risk factors of visual impairment among the elderly in Eastern Taiwan.

PubMed

Wang, Wen-Li; Chen, Nancy; Sheu, Min-Muh; Wang, Jen-Hung; Hsu, Wen-Lin; Hu, Yih-Jin

2016-09-01

Visual impairment is associated with disability and poor quality of life. This study aimed to investigate the prevalence and associated risk factors of visual impairment among the suburban elderly in Eastern Taiwan. The cross-sectional research was conducted from April 2012 to August 2012. The ocular condition examination took place in suburban areas of Hualien County. Medical records from local infirmaries and questionnaires were utilized to collect demographic data and systemic disease status. Logistic regression models were used for the simultaneous analysis of the association between the prevalence of visual impairment and risk factors. Six hundred and eighty-one residents participated in this project. The mean age of the participants was 71.4±7.3 years. The prevalence of vision impairment (better eye<6/18) was 11.0%. Refractive error and cataract were the main causes of vision impairment. Logistic regression analysis showed that people aged 65-75 years had a 3.8 times higher risk of developing visual impairment (p=0.021), while the odds ratio of people aged > 75 years was 10.0 (p<0.001). In addition, patients with diabetic retinopathy had a 3.7 times higher risk of developing visual impairment (p=0.002), while the odds ratio of refractive error was 0.36 (p<0.001). The prevalence of visual impairment was relatively high compared with previous studies. Diabetic retinopathy was an important risk factor of visual impairment; by contrast, refractive error was beneficial to resist visual impairment. Therefore, regular screening of ocular condition and early intervention might aid in the prevention of avoidable vision loss. Copyright © 2016. Published by Elsevier Taiwan.
Pharmacokinetic-Pharmacodynamic Modeling of Unboosted Atazanavir in a Cohort of Stable HIV-Infected Patients

PubMed Central

Baudry, Thomas; Gagnieu, Marie-Claude; Boibieux, André; Livrozet, Jean-Michel; Peyramond, Dominique; Tod, Michel; Ferry, Tristan

2013-01-01

Limited data on the pharmacokinetics and pharmacodynamics (PK/PD) of unboosted atazanavir (uATV) in treatment-experienced patients are available. The aim of this work was to study the PK/PD of unboosted atazanavir in a cohort of HIV-infected patients. Data were available for 58 HIV-infected patients (69 uATV-based regimens). Atazanavir concentrations were analyzed by using a population approach, and the relationship between atazanavir PK and clinical outcome was examined using logistic regression. The final PK model was a linear one-compartment model with a mixture absorption model to account for two subgroups of absorbers. The mean (interindividual variability) of population PK parameters were as follows: clearance, 13.4 liters/h (40.7%), volume of distribution, 71.1 liters (29.7%), and fraction of regular absorbers, 0.49. Seven subjects experienced virological failure after switch to uATV. All of them were identified as low absorbers in the PK modeling. The absorption rate constant (0.38 ± 0.20 versus 0.75 ± 0.28 h−1; P = 0.002) and ATV exposure (area under the concentration-time curve from 0 to 24 h [AUC0–24], 10.3 ± 2.1 versus 22.4 ± 11.2 mg · h · liter−1; P = 0.001) were significantly lower in patients with virological failure than in patients without failure. In the logistic regression analysis, both the absorption rate constant and ATV trough concentration significantly influenced the probability of virological failure. A significant relationship between ATV pharmacokinetics and virological response was observed in a cohort of HIV patients who were administered unboosted atazanavir. This study also suggests that twice-daily administration of uATV may optimize drug therapy. PMID:23147727
Menstrual cycle phase and single tablet antiretroviral medication adherence in women with HIV.

PubMed

Hessol, Nancy A; Holman, Susan; Minkoff, Howard; Cohen, Mardge H; Golub, Elizabeth T; Kassaye, Seble; Karim, Roksana; Sosanya, Oluwakemi; Shaheen, Christopher; Merhi, Zaher

2016-01-01

Suboptimal adherence to antiretroviral (ARV) therapy among HIV-infected individuals is associated with increased risk of progression to AIDS and the development of HIV resistance to ARV medications. To examine whether the luteal phase of the menstrual cycle is independently associated with suboptimal adherence to single tablet regimen (STR) ARV medication, data were analyzed from a multicenter cohort study of HIV-infected women who reported regular menstrual cycles and were taking an STR. In a cross-sectional analysis, suboptimal adherence to an STR among women in their follicular phase was compared with suboptimal adherence among women in their luteal phase. In two-way crossover analyses, whereby the same woman was assessed for STR medication adherence in both her follicular and luteal phases, the estimated exact conditional odds of non-adherence to an STR was measured. In adjusted logistic regression analysis of the cross-sectional data (N=327), women with ≤12 years of education were more than three times more likely to have suboptimal adherence (OR=3.6, p=.04) compared to those with >12 years of education. Additionally, women with Center for Epidemiological Studies Depression Scale (CES-D) scores ≥23 were 2.5-times more likely to have suboptimal adherence (OR=2.6, p=.02) compared to those with CES-D scores <23. In conditional logistic regression analyses of the crossover data (N=184), having childcare responsibilities was associated with greater odds of ≤95% adherence. Menstrual cycle phase was not associated with STR adherence in either the cross-sectional or crossover analyses. The lack of association between phase of the menstrual cycle and adherence to an STR in HIV-infected women means attention can be given to other more important risk factors for suboptimal adherence, such as depression, level of education, and childcare responsibilities.
Power, community mobilization, and condom use practices among female sex workers in Andhra Pradesh, India.

PubMed

Blankenship, Kim M; West, Brooke S; Kershaw, Trace S; Biradavolu, Monica R

2008-12-01

We used a structural interventions framework to analyse the associations between power and condom use among a sample of female sex workers (FSW), and how exposure to a local community mobilization intervention (CMI) affects these associations. Data came from a cross-sectional survey of 812 FSW in the East Godavari district of Andhra Pradesh, India, recruited through respondent-driven sampling. We identified three types of power - collective power, control over work, and economic power, and three dimensions of collective power - collective identity, efficacy, and agency. Multivariate logistic regression analysis was used to analyse the relationship of these three types of power and exposure to a CMI with consistent condom use with clients. A total of 803 respondents exchanged sex with an occasional or regular client in the 7 days before the interview. Multivariate logistic regression shows that control over both the type of sex [adjusted odds ratio (AOR) 1.70, 95% confidence interval (CI) 1.23-2.34] and the amount charged (AOR 1.56, 95% CI 1.12-2.16), and economic dependence (AOR 0.54, 95% CI 0.35-0.83) are associated with consistent condom use as is programme exposure (AOR 2.09, 95% CI 1.48-2.94). The interaction between programme exposure and collective agency was also significant (chi-square 6.62, P = 0.01). Among respondents who reported both programme exposure and high levels of collective agency, the odds ratio of consistent condom use was 2.5 times that of other FSW. A structural interventions framework is useful for understanding HIV risk among FSW. More needs to be done to promote FSW control over work and access to economic resources.
Sleep architecture and obstructive sleep apnea in obese children with and without metabolic syndrome: a case control study.

PubMed

Jalilolghadr, Shabnam; Yazdi, Zohreh; Mahram, Manoochehr; Babaei, Farkhondeh; Esmailzadehha, Neda; Nozari, Hoormehr; Saffari, Fatemeh

2016-05-01

Obesity and biochemical parameters of metabolic disorders are both closely related to obstructive sleep apnea (OSA). The aim of this study was to compare sleep architecture and OSA in obese children with and without metabolic syndrome. Forty-two children with metabolic syndrome were selected as case group and 38 children without metabolic syndrome were matched for age, sex, and BMI as control group. The standardized Persian version of bedtime problems, excessive daytime sleepiness, awakenings during the night, regularity and duration of sleep, snoring (BEARS) and Children's Sleep Habits Questionnaires were completed, and polysomnography (PSG) was performed for all study subjects. Scoring was performed using the manual of American Academy of Sleep Medicine for children. Data were analyzed using chi-square test, T test, Mann-Whitney U test, and logistic regression analysis. Non-rapid eye movement (NREM) sleep and N1 stage in the case group were significantly longer than the control group, while REM sleep was significantly shorter. Waking after sleep onset (WASO) was significantly different between two groups. Severe OSA was more frequent in the control group. Multivariate logistic regression analysis showed that severe OSA (OR 21.478, 95 % CI 2.160-213.600; P = 0.009) and REM sleep (OR 0.856, 95 % CI 0.737-0.994; P = 0.041) had independent association with metabolic syndrome. Obese children with metabolic syndrome had increased WASO, N1 sleep stage, and severe OSA. But the results regarding sleep architecture are most likely a direct result of OSA severity. More longitudinal studies are needed to confirm the association of metabolic syndrome and OSA.
Long and irregular menstrual cycles, polycystic ovary syndrome, and ovarian cancer risk in a population-based case-control study.

PubMed

Harris, H R; Titus, L J; Cramer, D W; Terry, K L

2017-01-15

Long and irregular menstrual cycles, a hallmark of polycystic ovary syndrome (PCOS), have been associated with higher androgen and lower sex hormone binding globulin levels and this altered hormonal environment may increase the risk of specific histologic subtypes of ovarian cancer. We investigated whether menstrual cycle characteristics and self-reported PCOS were associated with ovarian cancer risk among 2,041 women with epithelial ovarian cancer and 2,100 controls in the New England Case-Control Study (1992-2008). Menstrual cycle irregularity, menstrual cycle length, and PCOS were collected through in-person interview. Unconditional logistic regression models were used to calculate odds ratios (OR) and 95% confidence intervals (95% CIs) for ovarian cancer risk overall, and polytomous logistic regression to evaluate whether risk differed between histologic subtypes. Overall, we observed no elevation in ovarian cancer risk for women who reported periods that were never regular or for those reporting a menstrual cycle length of >35 days with ORs of 0.87 (95% CI = 0.69-1.10) and 0.83 (95% CI = 0.44-1.54), respectively. We observed no overall association between self-reported PCOS and ovarian cancer (OR = 0.97; 95% CI = 0.61-1.56). However, we observed significant differences in the association with menstrual cycle irregularity and risk of ovarian cancer subtypes (p heterogeneity = 0.03) as well as by BMI and OC use (p interaction < 0.01). Most notable, menstrual cycle irregularity was associated with a decreased risk of high grade serous tumors but an increased risk of serous borderline tumors among women who had never used OCs and those who were overweight. Future research in a large collaborative consortium may help clarify these associations. © 2016 UICC.

Correlates of Nocturia and Relationships of Nocturia With Sleep Quality and Glycemic Control in Women With Type 2 Diabetes.

PubMed

Chang, Chun-Jen; Pei, Dee; Wu, Chien-Chih; Palmer, Mary H; Su, Ching-Chieh; Kuo, Shu-Fen; Liao, Yuan-Mei

2017-07-01

To explore correlates of nocturia, compare sleep quality and glycemic control for women with and without nocturia, and examine relationships of nocturia with sleep quality and glycemic control in women with diabetes. This study was a cross-sectional, correlational study with data collected from 275 women with type 2 diabetes. Data were collected using a structured questionnaire. Multivariate logistic regression analyses were used to identify correlates. Chi-squared tests were used to identify candidate variables for the first logistic regression model. A one-way analysis of variance was used to compare sleep quality and glycemic control for women with and those without nocturia. Pearson correlations were used to examine the relationships of nocturia with sleep quality and glycemic control. Of the 275 participants, 124 (45.1%) had experienced nocturia (at least two voids per night). Waist circumference, parity, time since diagnosis of diabetes, sleep quality, and increased daytime urinary frequency were correlated with nocturia after adjusting for age. Compared to women without nocturia, women who had nocturia reported poorer sleep quality. A significant correlation was found between the number of nocturnal episodes and sleep quality. Nocturia and poor sleep are common among women with diabetes. The multifactorial nature of nocturia supports the delivered management and treatments being targeted to underlying etiologies in order to optimize women's symptom management. Interventions aimed at modifiable correlates may include maintaining a normal body weight and regular physical exercise for maintaining a normal waist circumference, and decreasing caffeine consumption, implementing feasible modifications in sleeping environments and maintaining sleep hygiene to improve sleep quality. Healthcare professionals should screen for nocturia and poor sleep and offer appropriate nonpharmacological lifestyle management, behavioral interventions, or pharmacotherapy for women with diabetes. © 2017 Sigma Theta Tau International.
Socio-demographic predictors and average annual rates of caesarean section in Bangladesh between 2004 and 2014.

PubMed

Khan, Md Nuruzzaman; Islam, M Mofizul; Shariff, Asma Ahmad; Alam, Md Mahmudul; Rahman, Md Mostafizur

2017-01-01

Globally the rates of caesarean section (CS) have steadily increased in recent decades. This rise is not fully accounted for by increases in clinical factors which indicate the need for CS. We investigated the socio-demographic predictors of CS and the average annual rates of CS in Bangladesh between 2004 and 2014. Data were derived from four waves of nationally representative Bangladesh Demographic and Health Survey (BDHS) conducted between 2004 and 2014. Rate of change analysis was used to calculate the average annual rate of increase in CS from 2004 to 2014, by socio-demographic categories. Multi-level logistic regression was used to identify the socio-demographic predictors of CS in a cross-sectional analysis of the 2014 BDHS data. CS rates increased from 3.5% in 2004 to 23% in 2014. The average annual rate of increase in CS was higher among women of advanced maternal age (≥35 years), urban areas, and relatively high socio-economic status; with higher education, and who regularly accessed antenatal services. The multi-level logistic regression model indicated that lower (≤19) and advanced maternal age (≥35), urban location, relatively high socio-economic status, higher education, birth of few children (≤2), antenatal healthcare visits, overweight or obese were the key factors associated with increased utilization of CS. Underweight was a protective factor for CS. The use of CS has increased considerably in Bangladesh over the survey years. This rising trend and the risk of having CS vary significantly across regions and socio-economic status. Very high use of CS among women of relatively high socio-economic status and substantial urban-rural difference call for public awareness and practice guideline enforcement aimed at optimizing the use of CS.
Socio-demographic predictors and average annual rates of caesarean section in Bangladesh between 2004 and 2014

PubMed Central

Khan, Md. Nuruzzaman; Islam, M. Mofizul; Shariff, Asma Ahmad; Alam, Md. Mahmudul; Rahman, Md. Mostafizur

2017-01-01

Background Globally the rates of caesarean section (CS) have steadily increased in recent decades. This rise is not fully accounted for by increases in clinical factors which indicate the need for CS. We investigated the socio-demographic predictors of CS and the average annual rates of CS in Bangladesh between 2004 and 2014. Methods Data were derived from four waves of nationally representative Bangladesh Demographic and Health Survey (BDHS) conducted between 2004 and 2014. Rate of change analysis was used to calculate the average annual rate of increase in CS from 2004 to 2014, by socio-demographic categories. Multi-level logistic regression was used to identify the socio-demographic predictors of CS in a cross-sectional analysis of the 2014 BDHS data. Result CS rates increased from 3.5% in 2004 to 23% in 2014. The average annual rate of increase in CS was higher among women of advanced maternal age (≥35 years), urban areas, and relatively high socio-economic status; with higher education, and who regularly accessed antenatal services. The multi-level logistic regression model indicated that lower (≤19) and advanced maternal age (≥35), urban location, relatively high socio-economic status, higher education, birth of few children (≤2), antenatal healthcare visits, overweight or obese were the key factors associated with increased utilization of CS. Underweight was a protective factor for CS. Conclusion The use of CS has increased considerably in Bangladesh over the survey years. This rising trend and the risk of having CS vary significantly across regions and socio-economic status. Very high use of CS among women of relatively high socio-economic status and substantial urban-rural difference call for public awareness and practice guideline enforcement aimed at optimizing the use of CS. PMID:28493956
The association of lifetime physical inactivity with bladder and renal cancer risk: A hospital-based case-control analysis.

PubMed

Cannioto, Rikki; Etter, John Lewis; Guterman, Lauren Beryl; Joseph, Janine M; Gulati, Nicholas R; Schmitt, Kristina L; LaMonte, Michael J; Nagy, Ryan; Minlikeeva, Albina; Szender, James Brian; Moysich, Kirsten B

2017-08-01

Recreational physical inactivity has been gaining recognition as an independent epidemiological exposure of interest in relation to cancer endpoints due to evidence suggesting that it may associate with cancer independent of obesity. In the current analyses, we examined the associations of lifetime recreational physical inactivity with renal and bladder cancer risk. In this hospital-based case-control study, we identified N=160 renal cancer patients, N=208 bladder cancer patients, and N=766 age frequency-matched controls without cancer. Participants self-reporting never participating in any regular/weekly recreational physical activity throughout their lifetime were classified as physically inactive. Utilizing unconditional multivariable logistic regression analyses, we estimated odds ratios and 95% confidence intervals to represent the associations between lifetime physical inactivity and renal and bladder cancer risk. In multivariable logistic regression models, we observed significant positive associations between lifetime recreational physical inactivity and renal cancer and bladder cancer risk: odds ratio=1.77 (95% CI: 1.10-2.85) and odds ratio=1.73 (95% CI: 1.13-2.63), respectively. Similar associations also persisted among individuals who were not obese for both renal and bladder cancer: odds ratio=1.75 (95% CI: 1.03-2.98) and odds ratio=1.70 (95% CI: 1.08-2.69), respectively. In this case-control study, we observed evidence of a positive association between renal and bladder cancer with lifetime recreational physical inactivity. These data add to the growing body of evidence suggesting that physical inactivity may be an important independent risk factor for cancer. However, additional studies using a larger sample and prospectively collected data are needed to substantiate the current findings. Copyright © 2017 Elsevier Ltd. All rights reserved.
Satisfaction with dental care and its role in dental health-related behaviour among lithuanian university employees.

PubMed

Sakalauskienė, Zana; Machiulskiene, Vita; Murtomaa, Heikki; Vehkalahti, Miira M

2015-01-01

To assess factors related to satisfaction with dental care and its role in dental health-related behaviour among Lithuanian university employees. Our cross-sectional survey collected data on respondents' satisfaction with dental care using 24 statements. The self-administered questionnaire also inquired about dental attendance, dental health-related behaviour and attitudes, self-assessed dental status and background details. All 35- to 44-year-old employees (n = 862) of four universities in Lithuania were invited to participate; 64% (n = 553) responded, 78% of them were women. Statements on satisfaction with technical, personal and organisational dimensions of the dental surgery were assessed using a five-point scale, ranging from entirely agree to entirely disagree, with higher scores indicating stronger agreement. Overall satisfaction scores were summed and subjects divided into tertiles to evaluate dental health-related behaviour. For the logistic regression model, subjects were divided into two groups of satisfaction level (below and above the mean of the sum score). Subjects were highly satisfied with dental care, with the mean sum score being 99.5 (SD = 12.62, range 59-120). Stronger satisfaction was reported by those visiting private practices (p < 0.001) and the same dentist longer (p = 0.006) and by those who entirely agreed with the statements on dental health-related attitudes (p ≤ 0.001). The logistic regression model showed that higher satisfaction with dental care level was more likely for those who indicated check-up-based regular dental attendance (OR = 1.7) and brushing their teeth at least twice daily (OR = 1.6). Satisfaction with dental care is positively related to individuals' dental health-related attitudes and behaviour among highly-educated subjects in particular.
Sex differences regarding the impact of physical activity on left ventricular systolic function in elderly patients with an acute coronary event.

PubMed

Aggelopoulos, Panagiotis; Chrysohoou, Christina; Pitsavos, Christos; Panagiotakos, Demosthenes B; Vaina, Sophia; Brili, Stella; Lazaros, George; Vavouranakis, Manolis; Stefanadis, Christodoulos

2014-01-01

Regular physical activity has been associated with less severity of an acute coronary syndrome (ACS), lower in-hospital mortality rates, and an improved short term prognosis. This study evaluated the relationship between physical activity status and the development of left ventricular systolic dysfunction (LVSD) according to inflammation and sex in elderly patients who had had an ACS. We analyzed prospectively collected data from 355 male (age 74 ± 6 years) and 137 female (76 ± 6 years) patients who were hospitalized with an ACS. LVSD was evaluated by echocardiography on the 5th day of hospitalization and physical activity status was assessed by a self-reported questionnaire. Inflammatory response was evaluated by measuring C-reactive protein levels. Logistic regression models were applied to evaluate the effect of physical activity status on the development of LVSD and inflammatory response at entry. Physical inactivity had a higher prevalence in women who developed LVSD than in the female patients with preserved systolic function (46% vs. 20%, p=0.02). There was a significant positive association between physical activity levels and ejection fraction in women (p=0.06), but not in men (p=0.30). Multiadjusted logistic regression showed that women who were physically active had 76% lower odds (95%CI: 1-94%) of developing LVSD compared to their sedentary counterparts. Furthermore, physical activity was inversely associated with C-reactive protein levels in both sexes (p=0.08). Long-term involvement in a physically active lifestyle seems to confer further cardio-protection by reducing the inflammatory response and preserving left ventricular systolic function in elderly female, but not male patients with an ACS.
Classification of Urban Aerial Data Based on Pixel Labelling with Deep Convolutional Neural Networks and Logistic Regression

NASA Astrophysics Data System (ADS)

Yao, W.; Poleswki, P.; Krzystek, P.

2016-06-01

The recent success of deep convolutional neural networks (CNN) on a large number of applications can be attributed to large amounts of available training data and increasing computing power. In this paper, a semantic pixel labelling scheme for urban areas using multi-resolution CNN and hand-crafted spatial-spectral features of airborne remotely sensed data is presented. Both CNN and hand-crafted features are applied to image/DSM patches to produce per-pixel class probabilities with a L1-norm regularized logistical regression classifier. The evidence theory infers a degree of belief for pixel labelling from different sources to smooth regions by handling the conflicts present in the both classifiers while reducing the uncertainty. The aerial data used in this study were provided by ISPRS as benchmark datasets for 2D semantic labelling tasks in urban areas, which consists of two data sources from LiDAR and color infrared camera. The test sites are parts of a city in Germany which is assumed to consist of typical object classes including impervious surfaces, trees, buildings, low vegetation, vehicles and clutter. The evaluation is based on the computation of pixel-based confusion matrices by random sampling. The performance of the strategy with respect to scene characteristics and method combination strategies is analyzed and discussed. The competitive classification accuracy could be not only explained by the nature of input data sources: e.g. the above-ground height of nDSM highlight the vertical dimension of houses, trees even cars and the nearinfrared spectrum indicates vegetation, but also attributed to decision-level fusion of CNN's texture-based approach with multichannel spatial-spectral hand-crafted features based on the evidence combination theory.
Toothbrushing frequency among 4–6-year-old Iranian children and associated maternal attitude and sociobehavioral factors

PubMed Central

Soltani, Raheleh; Eslami, Ahmad Ali; Akhlaghi, Najmeh; Sharifirad, Gholamreza; Alipoor, Mikaeil; Mahaki, Behzad

2017-01-01

Background: Toothbrushing is an important aspect of children's oral health self-care. This study aimed to explore toothbrushing frequency among 4–6-year-old Iranian children and associated maternal attitude and sociobehavioral factors. Materials and Methods: This cross-sectional study was conducted on 407 mother–child (aged 4–6 years) pairs through stratified random sampling in Tabriz, Iran. Data were collected using self-reported questionnaires including demographic characteristic, maternal attitude, and toothbrushing frequency of both mothers and children. Logistic regression was used to determine the predicators of children's toothbrushing. Statistical significance was set at P < 0.05 for all tests. Results: The mean ages were 32.6 ± 4.8 and 5.3 ± 1.1 years for mothers and children respectively. Twice-daily toothbrushing was observed at a relative frequency of 12.8% in children and 18.4% in mothers. About 43.7% of children brushed their teeth once daily. Nearly 38.7% of children started toothbrushing behavior regularly at 4 years of age, and 41% had dental visits. Multiple logistic regression analysis indicated that children's toothbrushing (once daily or more) was associated with maternal brushing frequency (odds ratio [OR] =2.0, 95% confidence interval [CI] =1.53–2.86), maternal attitude toward oral health (OR = 1.15, CI = 1.08–1.22), and children's age (OR = 1.21, 95% CI = 1.02–1.77). Conclusion: The descriptive results indicated that maternal and children toothbrushing behaviors are unfavorable. Furthermore, maternal toothbrushing behavior is a strong predicator of children's brushing behavior. Health promotional activities seem necessary for mothers to enhance oral health behavior of their children. PMID:28348618
High prevalence of anemia in 10-month-old Japanese infants with breastfeeding.

PubMed

Kimura, Masahiko; Kurozawa, Youichi; Saito, Yumi; Watanabe, Hiroshi; Kobayashi, Ayame; Taketani, Takeshi

2018-05-05

Anemia in infancy is still prevalent in developing countries. Commercial iron-fortified complementary foods or iron drops are not available in Japan and breastfed infants have a higher risk of anemia. We studied anemia screening in infants in 10-month old infants and evaluated whether breastfeeding is a risk factor for anemia. Anemia screening was performed during a regular health check of 10-month children at four local pediatric clinics in Shimane prefecture, Japan. Venous blood was obtained for complete blood count. The clinical characteristics of each child were obtained through a questionnaire. Anemia was defined as a hemoglobin level < 11.0 g/dL. Children were categorized into anemia and no-anemia and univariate analyses were conducted to compare with clinical variables. Multivariate logistic regression analyses for anemia were performed to adjust for several clinical variables. We analyzed data in 325 children. In the univariate analyses, anemia was associated with breastfeeding, monthly body weight gain and gestational week. Multivariate logistic regression analyses revealed that anemia was associated with feeding type and gestational week, where the odds ratio (OR) of partial breastfeeding and formula feeding was 0.446 (95% confidential interval [CI], 0.208-0.957) and 0.223 ([CI], 0.075-0.660) respectively, compared to exclusive breastfeeding, in which the OR was taken as 1.0 and the OR of gestational week was 0.753 ([CI], 0583-0.972). Breastfeeding was an important factor for anemia in 10-month-old Japanese infants. Breastfed infants after 6 months of age may need sufficient iron sources such as iron supplements or iron fortified complimentary foods. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Low back pain among school teachers in Botswana, prevalence and risk factors.

PubMed

Erick, Patience N; Smith, Derek R

2014-10-30

Although low back pain (LBP) represents a common occupational problem, few epidemiological studies have investigated the prevalence and risk factors for LBP among school teachers, particularly in Africa. School teachers are known to represent an occupational group among which there appears to be a high prevalence of LBP. The objective of this study was, therefore, to conduct one of the first epidemiological investigations of LBP among teachers in Botswana. A cross-sectional study was conducted among teachers in Botswana using self-administered questionnaires which were distributed to 3100 randomly selected school teachers and collected over a five-month period between July and November 2012. The questionnaire included low back pain information, demographic data, lifestyle, work-related characteristics and psychosocial factors. Data were analysed using Chi-squared and logistic regression models. The 12 month prevalence and LBP disability and associated risk factors were also analysed. A total of 1747 teachers returned completed questionnaires, yielding a response rate of 56.3%. The 12-month prevalence of LBP was 55.7%, with 67.1% of them reporting minimal disability. The results of logistic regression analysis revealed that female gender [OR: 1.51, 95% CI: 1.14-2.00] and previous back injury [OR: 9.67, 95% CI: 4.94-18.93] were positively correlated to LBP. Awkward arm position [OR: 1.81, 95% CI: 1.24-2.62] and high psychological job demands [OR: 1.40, 95% CI: 1.02-1.93] were also significantly associated with LBP. Regular physical exercise was negatively associated with LBP [OR: 0.63, 95% CI: 0.43-0.93]. Female gender [OR: 2.67, 95% CI: 1.52-3.99] and previous back injury [OR: 3.01, 95% CI: 1.92-4.74] were also positively associated with LBP disability. The prevalence of LBP appears to be high among school teachers in Botswana. A wide variety of LBP risk factors were identified in this study. Female gender and previous injury were both associated with LBP presence and disability. The complex nature of LBP risk factors found in this study suggests than no single specific preventative or intervention strategy will help in reducing these conditions. As such, to help reduce the prevalence, progression and burden of LBP among Botswana teachers, a greater emphasis should now be placed on ergonomics education, regular physical exercise and occupational stress.
Anxiety symptoms among Chinese nurses and the associated factors: a cross sectional study.

PubMed

Gao, Yu-Qin; Pan, Bo-Chen; Sun, Wei; Wu, Hui; Wang, Jia-Na; Wang, Lie

2012-09-14

Nurses are an indispensable component of the work force in the health care system. However, many of them are known to work in a stressful environment which may affect their mental well-being; the situation could be worse in rapidly transforming societies such as China. The purpose of this study was to investigate anxiety symptoms and the associated factors in Chinese nurses working in public city hospitals. A cross-sectional survey was performed for Chinese nurses in public city hospitals of Liaoning Province, northeast China. Seven hospitals in different areas of the province were randomly selected for the study. The Zung Self-Rating Anxiety Scale was used to measure anxiety symptoms. Effort-reward imbalance questionnaire and Job Content Questionnaire were used to assess the work stressors. Univariate analysis and stepwise multivariate logistic regression analysis were used to identify the factors associated with anxiety symptoms. All registered nurses in the seven city hospitals, totaling 1807 registered nurses were surveyed. Of the returned questionnaires, 1437 were valid (79.5%) for analysis. Utilizing the total raw score ≥ 40 as the cut-off point, the prevalence of anxiety symptoms in these nurses was 43.4%. Demographic factors (education, chronic disease and life event), lifestyle factors (regular meals and physical exercise), work conditions (hospital grade, job rank, monthly salary, nurse-patient relationships, job satisfaction and intention of leaving), job content (social support and decision latitude), effort-reward imbalance and overcommitment were all significantly related to the anxiety symptoms. Multivariate logistic regression analysis showed main factors associated with anxiety symptoms were lower job rank (OR 2.501), overcommitment (OR 2.018), chronic diseases (OR 1.541), worse nurse-patient relationship (OR 1.434), higher social support (OR 0.573), lower hospital grade (OR 0.629), taking regular meals (OR 0.719) and higher level of job satisfaction (OR 0.722). A large proportion of Chinese nurses working in public city hospitals had anxiety symptoms, which warrants immediate investigation and intervention from the hospital administrators. Meanwhile, results of the study suggest that proper counseling, promotion of healthy lifestyle behavior and improvements to the social environment in the work place may be helpful toward reducing or preventing the anxiety symptoms.
Anxiety symptoms among Chinese nurses and the associated factors: a cross sectional study

PubMed Central

2012-01-01

Background Nurses are an indispensable component of the work force in the health care system. However, many of them are known to work in a stressful environment which may affect their mental well-being; the situation could be worse in rapidly transforming societies such as China. The purpose of this study was to investigate anxiety symptoms and the associated factors in Chinese nurses working in public city hospitals. Methods A cross-sectional survey was performed for Chinese nurses in public city hospitals of Liaoning Province, northeast China. Seven hospitals in different areas of the province were randomly selected for the study. The Zung Self-Rating Anxiety Scale was used to measure anxiety symptoms. Effort-reward imbalance questionnaire and Job Content Questionnaire were used to assess the work stressors. Univariate analysis and stepwise multivariate logistic regression analysis were used to identify the factors associated with anxiety symptoms. Results All registered nurses in the seven city hospitals, totaling 1807 registered nurses were surveyed. Of the returned questionnaires, 1437 were valid (79.5%) for analysis. Utilizing the total raw score ≥ 40 as the cut-off point, the prevalence of anxiety symptoms in these nurses was 43.4%. Demographic factors (education, chronic disease and life event), lifestyle factors (regular meals and physical exercise), work conditions (hospital grade, job rank, monthly salary, nurse-patient relationships, job satisfaction and intention of leaving), job content (social support and decision latitude), effort-reward imbalance and overcommitment were all significantly related to the anxiety symptoms. Multivariate logistic regression analysis showed main factors associated with anxiety symptoms were lower job rank (OR 2.501), overcommitment (OR 2.018), chronic diseases (OR 1.541), worse nurse-patient relationship (OR 1.434), higher social support (OR 0.573), lower hospital grade (OR 0.629), taking regular meals (OR 0.719) and higher level of job satisfaction (OR 0.722). Conclusions A large proportion of Chinese nurses working in public city hospitals had anxiety symptoms, which warrants immediate investigation and intervention from the hospital administrators. Meanwhile, results of the study suggest that proper counseling, promotion of healthy lifestyle behavior and improvements to the social environment in the work place may be helpful toward reducing or preventing the anxiety symptoms. PMID:22978466
Factors associated with the use of cognitive aids in operating room crises: a cross-sectional study of US hospitals and ambulatory surgical centers.

PubMed

Alidina, Shehnaz; Goldhaber-Fiebert, Sara N; Hannenberg, Alexander A; Hepner, David L; Singer, Sara J; Neville, Bridget A; Sachetta, James R; Lipsitz, Stuart R; Berry, William R

2018-03-26

Operating room (OR) crises are high-acuity events requiring rapid, coordinated management. Medical judgment and decision-making can be compromised in stressful situations, and clinicians may not experience a crisis for many years. A cognitive aid (e.g., checklist) for the most common types of crises in the OR may improve management during unexpected and rare events. While implementation strategies for innovations such as cognitive aids for routine use are becoming better understood, cognitive aids that are rarely used are not yet well understood. We examined organizational context and implementation process factors influencing the use of cognitive aids for OR crises. We conducted a cross-sectional study using a Web-based survey of individuals who had downloaded OR cognitive aids from the websites of Ariadne Labs or Stanford University between January 2013 and January 2016. In this paper, we report on the experience of 368 respondents from US hospitals and ambulatory surgical centers. We analyzed the relationship of more successful implementation (measured as reported regular cognitive aid use during applicable clinical events) with organizational context and with participation in a multi-step implementation process. We used multivariable logistic regression to identify significant predictors of reported, regular OR cognitive aid use during OR crises. In the multivariable logistic regression, small facility size was associated with a fourfold increase in the odds of a facility reporting more successful implementation (p = 0.0092). Completing more implementation steps was also significantly associated with more successful implementation; each implementation step completed was associated with just over 50% higher odds of more successful implementation (p ≤ 0.0001). More successful implementation was associated with leadership support (p < 0.0001) and dedicated time to train staff (p = 0.0189). Less successful implementation was associated with resistance among clinical providers to using cognitive aids (p < 0.0001), absence of an implementation champion (p = 0.0126), and unsatisfactory content or design of the cognitive aid (p = 0.0112). Successful implementation of cognitive aids in ORs was associated with a supportive organizational context and following a multi-step implementation process. Building strong organizational support and following a well-planned multi-step implementation process will likely increase the use of OR cognitive aids during intraoperative crises, which may improve patient outcomes.
Assessing landslide susceptibility by statistical data analysis and GIS: the case of Daunia (Apulian Apennines, Italy)

NASA Astrophysics Data System (ADS)

Ceppi, C.; Mancini, F.; Ritrovato, G.

2009-04-01

This study aim at the landslide susceptibility mapping within an area of the Daunia (Apulian Apennines, Italy) by a multivariate statistical method and data manipulation in a Geographical Information System (GIS) environment. Among the variety of existing statistical data analysis techniques, the logistic regression was chosen to produce a susceptibility map all over an area where small settlements are historically threatened by landslide phenomena. By logistic regression a best fitting between the presence or absence of landslide (dependent variable) and the set of independent variables is performed on the basis of a maximum likelihood criterion, bringing to the estimation of regression coefficients. The reliability of such analysis is therefore due to the ability to quantify the proneness to landslide occurrences by the probability level produced by the analysis. The inventory of dependent and independent variables were managed in a GIS, where geometric properties and attributes have been translated into raster cells in order to proceed with the logistic regression by means of SPSS (Statistical Package for the Social Sciences) package. A landslide inventory was used to produce the bivariate dependent variable whereas the independent set of variable concerned with slope, aspect, elevation, curvature, drained area, lithology and land use after their reductions to dummy variables. The effect of independent parameters on landslide occurrence was assessed by the corresponding coefficient in the logistic regression function, highlighting a major role played by the land use variable in determining occurrence and distribution of phenomena. Once the outcomes of the logistic regression are determined, data are re-introduced in the GIS to produce a map reporting the proneness to landslide as predicted level of probability. As validation of results and regression model a cell-by-cell comparison between the susceptibility map and the initial inventory of landslide events was performed and an agreement at 75% level achieved.
Electronic cigarettes, quit attempts and smoking cessation: a 6-month follow-up.

PubMed

Pasquereau, Anne; Guignard, Romain; Andler, Raphaël; Nguyen-Thanh, Viêt

2017-09-01

There is conflicting evidence that use of e-cigarettes promotes cessation in regular smokers, but contrasting findings may be due to differing definitions of vaping. The aim was to assess whether regular use of e-cigarettes while smoking is associated with subsequent smoking cessation. Baseline internet survey with outcomes measured at 6-month follow-up. All French metropolitan territory. A total of 2057 smokers aged 15-85 years were recruited through an access panel and responded to a 6-month follow-up: 1805 exclusive tobacco smokers and 252 dual users (tobacco plus regular e-cigarette users) at baseline. The three outcomes assessed at 6 months were: a minimum 50% reduction in the number of cigarettes smoked per day, quit attempts of at least 7 days and smoking cessation of at least 7 days at the time of follow-up. Logistic regressions were performed to model the three outcomes according to regular e-cigarette use at baseline, adjusted for socio-economic variables and smoking behaviours. Baseline dual users were more likely than baseline exclusive tobacco smokers to have halved cigarette consumption [25.9 versus 11.2%, P < 0.001, adjusted odds ratio (aOR) = 2.6, confidence interval (CI) = 1.8-3.8]. Dual users at baseline were also more likely to have made a quit attempt of at least 7 days (22.8 versus 10.9%, P < 0.001, aOR = 1.8, CI = 1.2-2.6). No significant difference was found for 7-day cessation rates at 6 months (12.5 versus 9.5%, P = 0.18, aOR = 1.2, CI = 0.8-1.9). Among people who smoke, those also using an e-cigarette regularly are more likely to try to quit smoking and reduce their cigarette consumption during the next 6 months. It remains unclear whether regular e-cigarette users are also more likely to stop smoking. © 2017 Society for the Study of Addiction.
Determination of riverbank erosion probability using Locally Weighted Logistic Regression

NASA Astrophysics Data System (ADS)

Ioannidou, Elena; Flori, Aikaterini; Varouchakis, Emmanouil A.; Giannakis, Georgios; Vozinaki, Anthi Eirini K.; Karatzas, George P.; Nikolaidis, Nikolaos

2015-04-01

Riverbank erosion is a natural geomorphologic process that affects the fluvial environment. The most important issue concerning riverbank erosion is the identification of the vulnerable locations. An alternative to the usual hydrodynamic models to predict vulnerable locations is to quantify the probability of erosion occurrence. This can be achieved by identifying the underlying relations between riverbank erosion and the geomorphological or hydrological variables that prevent or stimulate erosion. Thus, riverbank erosion can be determined by a regression model using independent variables that are considered to affect the erosion process. The impact of such variables may vary spatially, therefore, a non-stationary regression model is preferred instead of a stationary equivalent. Locally Weighted Regression (LWR) is proposed as a suitable choice. This method can be extended to predict the binary presence or absence of erosion based on a series of independent local variables by using the logistic regression model. It is referred to as Locally Weighted Logistic Regression (LWLR). Logistic regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable (e.g. binary response) based on one or more predictor variables. The method can be combined with LWR to assign weights to local independent variables of the dependent one. LWR allows model parameters to vary over space in order to reflect spatial heterogeneity. The probabilities of the possible outcomes are modelled as a function of the independent variables using a logistic function. Logistic regression measures the relationship between a categorical dependent variable and, usually, one or several continuous independent variables by converting the dependent variable to probability scores. Then, a logistic regression is formed, which predicts success or failure of a given binary variable (e.g. erosion presence or absence) for any value of the independent variables. The erosion occurrence probability can be calculated in conjunction with the model deviance regarding the independent variables tested. The most straightforward measure for goodness of fit is the G statistic. It is a simple and effective way to study and evaluate the Logistic Regression model efficiency and the reliability of each independent variable. The developed statistical model is applied to the Koiliaris River Basin on the island of Crete, Greece. Two datasets of river bank slope, river cross-section width and indications of erosion were available for the analysis (12 and 8 locations). Two different types of spatial dependence functions, exponential and tricubic, were examined to determine the local spatial dependence of the independent variables at the measurement locations. The results show a significant improvement when the tricubic function is applied as the erosion probability is accurately predicted at all eight validation locations. Results for the model deviance show that cross-section width is more important than bank slope in the estimation of erosion probability along the Koiliaris riverbanks. The proposed statistical model is a useful tool that quantifies the erosion probability along the riverbanks and can be used to assist managing erosion and flooding events. Acknowledgements This work is part of an on-going THALES project (CYBERSENSORS - High Frequency Monitoring System for Integrated Water Resources Management of Rivers). The project has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES. Investing in knowledge society through the European Social Fund.
Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)

NASA Astrophysics Data System (ADS)

Yilmaz, Işık

2009-06-01

The purpose of this study is to compare the landslide susceptibility mapping methods of frequency ratio (FR), logistic regression and artificial neural networks (ANN) applied in the Kat County (Tokat—Turkey). Digital elevation model (DEM) was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index (TWI) and stream power index (SPI) were used in the landslide susceptibility analyses. Landslide susceptibility maps were produced from the frequency ratio, logistic regression and neural networks models, and they were then compared by means of their validations. The higher accuracies of the susceptibility maps for all three models were obtained from the comparison of the landslide susceptibility maps with the known landslide locations. However, respective area under curve (AUC) values of 0.826, 0.842 and 0.852 for frequency ratio, logistic regression and artificial neural networks showed that the map obtained from ANN model is more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results obtained in this study also showed that the frequency ratio model can be used as a simple tool in assessment of landslide susceptibility when a sufficient number of data were obtained. Input process, calculations and output process are very simple and can be readily understood in the frequency ratio model, however logistic regression and neural networks require the conversion of data to ASCII or other formats. Moreover, it is also very hard to process the large amount of data in the statistical package.
A Comparison of Logistic Regression, Neural Networks, and Classification Trees Predicting Success of Actuarial Students

ERIC Educational Resources Information Center

Schumacher, Phyllis; Olinsky, Alan; Quinn, John; Smith, Richard

2010-01-01

The authors extended previous research by 2 of the authors who conducted a study designed to predict the successful completion of students enrolled in an actuarial program. They used logistic regression to determine the probability of an actuarial student graduating in the major or dropping out. They compared the results of this study with those…
Logistic regression accuracy across different spatial and temporal scales for a wide-ranging species, the marbled murrelet

Treesearch

Carolyn B. Meyer; Sherri L. Miller; C. John Ralph

2004-01-01

The scale at which habitat variables are measured affects the accuracy of resource selection functions in predicting animal use of sites. We used logistic regression models for a wide-ranging species, the marbled murrelet, (Brachyramphus marmoratus) in a large region in California to address how much changing the spatial or temporal scale of...
Odds Ratio, Delta, ETS Classification, and Standardization Measures of DIF Magnitude for Binary Logistic Regression

ERIC Educational Resources Information Center

Monahan, Patrick O.; McHorney, Colleen A.; Stump, Timothy E.; Perkins, Anthony J.

2007-01-01

Previous methodological and applied studies that used binary logistic regression (LR) for detection of differential item functioning (DIF) in dichotomously scored items either did not report an effect size or did not employ several useful measures of DIF magnitude derived from the LR model. Equations are provided for these effect size indices.…

A Generalized Logistic Regression Procedure to Detect Differential Item Functioning among Multiple Groups

ERIC Educational Resources Information Center

Magis, David; Raiche, Gilles; Beland, Sebastien; Gerard, Paul

2011-01-01

We present an extension of the logistic regression procedure to identify dichotomous differential item functioning (DIF) in the presence of more than two groups of respondents. Starting from the usual framework of a single focal group, we propose a general approach to estimate the item response functions in each group and to test for the presence…
Risk Factors of Falls in Community-Dwelling Older Adults: Logistic Regression Tree Analysis

ERIC Educational Resources Information Center

Yamashita, Takashi; Noe, Douglas A.; Bailer, A. John

2012-01-01

Purpose of the Study: A novel logistic regression tree-based method was applied to identify fall risk factors and possible interaction effects of those risk factors. Design and Methods: A nationally representative sample of American older adults aged 65 years and older (N = 9,592) in the Health and Retirement Study 2004 and 2006 modules was used.…
Estimation of Logistic Regression Models in Small Samples. A Simulation Study Using a Weakly Informative Default Prior Distribution

ERIC Educational Resources Information Center

Gordovil-Merino, Amalia; Guardia-Olmos, Joan; Pero-Cebollero, Maribel

2012-01-01

In this paper, we used simulations to compare the performance of classical and Bayesian estimations in logistic regression models using small samples. In the performed simulations, conditions were varied, including the type of relationship between independent and dependent variable values (i.e., unrelated and related values), the type of variable…
Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA

USGS Publications Warehouse

Ohlmacher, G.C.; Davis, J.C.

2003-01-01

Landslides in the hilly terrain along the Kansas and Missouri rivers in northeastern Kansas have caused millions of dollars in property damage during the last decade. To address this problem, a statistical method called multiple logistic regression has been used to create a landslide-hazard map for Atchison, Kansas, and surrounding areas. Data included digitized geology, slopes, and landslides, manipulated using ArcView GIS. Logistic regression relates predictor variables to the occurrence or nonoccurrence of landslides within geographic cells and uses the relationship to produce a map showing the probability of future landslides, given local slopes and geologic units. Results indicated that slope is the most important variable for estimating landslide hazard in the study area. Geologic units consisting mostly of shale, siltstone, and sandstone were most susceptible to landslides. Soil type and aspect ratio were considered but excluded from the final analysis because these variables did not significantly add to the predictive power of the logistic regression. Soil types were highly correlated with the geologic units, and no significant relationships existed between landslides and slope aspect. ?? 2003 Elsevier Science B.V. All rights reserved.
A Method for Calculating the Probability of Successfully Completing a Rocket Propulsion Ground Test

NASA Technical Reports Server (NTRS)

Messer, Bradley

2007-01-01

Propulsion ground test facilities face the daily challenge of scheduling multiple customers into limited facility space and successfully completing their propulsion test projects. Over the last decade NASA s propulsion test facilities have performed hundreds of tests, collected thousands of seconds of test data, and exceeded the capabilities of numerous test facility and test article components. A logistic regression mathematical modeling technique has been developed to predict the probability of successfully completing a rocket propulsion test. A logistic regression model is a mathematical modeling approach that can be used to describe the relationship of several independent predictor variables X(sub 1), X(sub 2),.., X(sub k) to a binary or dichotomous dependent variable Y, where Y can only be one of two possible outcomes, in this case Success or Failure of accomplishing a full duration test. The use of logistic regression modeling is not new; however, modeling propulsion ground test facilities using logistic regression is both a new and unique application of the statistical technique. Results from this type of model provide project managers with insight and confidence into the effectiveness of rocket propulsion ground testing.
Predicting risk for portal vein thrombosis in acute pancreatitis patients: A comparison of radical basis function artificial neural network and logistic regression models.

PubMed

Fei, Yang; Hu, Jian; Gao, Kun; Tu, Jianfeng; Li, Wei-Qin; Wang, Wei

2017-06-01

To construct a radical basis function (RBF) artificial neural networks (ANNs) model to predict the incidence of acute pancreatitis (AP)-induced portal vein thrombosis. The analysis included 353 patients with AP who had admitted between January 2011 and December 2015. RBF ANNs model and logistic regression model were constructed based on eleven factors relevant to AP respectively. Statistical indexes were used to evaluate the value of the prediction in two models. The predict sensitivity, specificity, positive predictive value, negative predictive value and accuracy by RBF ANNs model for PVT were 73.3%, 91.4%, 68.8%, 93.0% and 87.7%, respectively. There were significant differences between the RBF ANNs and logistic regression models in these parameters (P<0.05). In addition, a comparison of the area under receiver operating characteristic curves of the two models showed a statistically significant difference (P<0.05). The RBF ANNs model is more likely to predict the occurrence of PVT induced by AP than logistic regression model. D-dimer, AMY, Hct and PT were important prediction factors of approval for AP-induced PVT. Copyright © 2017 Elsevier Inc. All rights reserved.
Coexistence of risk behaviors for being overweight among Brazilian adolescents.

PubMed

Ferreira, Nathália Luíza; Claro, Rafael Moreira; Mingoti, Sueli Aparecida; Lopes, Aline Cristine Souza

2017-07-01

This study aimed to evaluate the magnitude of and the factors associated with the coexistence of risk behaviors for being overweight among Brazilian adolescents. This is a cross-sectional study with a representative sample of adolescents (mostly aged 13-15years) enrolled from public and private schools of Brazil in 2012. The co-occurring sedentary behavior and inadequate food consumption (regular intake of sugary and fried foods, and irregular consumption of fruits and vegetables-FV) was estimated using a Venn diagram. Sociodemographic, familial, and behavioral factors associated with the number of risk behaviors for being overweight were identified using an ordinal logistic regression analysis. Sedentary behavior was observed in 62.0% of adolescents. Regular intake of sugary or fried food was observed in 55.3% and 23.5% of adolescents, respectively, with 51.9% having an inadequate intake of FV. At least one risk behavior was reported in >90.0% of adolescents; 6.1% reported all 4. Being female, having a higher maternal education level, attending private school, not having breakfast or meals with parents regularly, eat watching television, and not practicing weekly leisure time physical activity were associated with an increased chance of having multiple risk behaviors. This study observed a high prevalence of coexisting of risk behaviors, which was associated with sociodemographic, familial, and behavioral factors. These findings may contribute to a clearer understanding of the associations between different behaviors among adolescents, and may be used to improve public health surveillance and to develop strategies that address multiple behaviors, in order to prevent overweight among adolescents. Copyright © 2017 Elsevier Inc. All rights reserved.
Factors associated with recent HIV testing among younger gay and bisexual men in New Zealand, 2006-2011.

PubMed

Lachowsky, Nathan J; Saxton, Peter J W; Dickson, Nigel P; Hughes, Anthony J; Summerlee, Alastair J S; Dewey, Cate E

2014-03-31

Understanding HIV testing behaviour is vital to developing evidence-based policy and programming that supports optimal HIV care, support, and prevention. This has not been investigated among younger gay, bisexual, and other men who have sex with men (YMSM, aged 16-29) in New Zealand. National HIV sociobehavioural surveillance data from 2006, 2008, and 2011 was pooled to determine the prevalence of recent HIV testing (in the last 12 months) among YMSM. Factors associated with recent testing were determined using manual backward stepwise multivariate logistic regression. Of 3,352 eligible YMSM, 1,338 (39.9%) reported a recent HIV test. In the final adjusted model, the odds of having a recent HIV test were higher for YMSM who were older, spent more time with other gay men, reported multiple sex partners, had a regular partner for 6-12 months, reported high condom use with casual partners, and disagreed that HIV is a less serious threat nowadays and that an HIV-positive man would disclose before sex. The odds of having a recent HIV test were lower for YMSM who were bisexual, recruited online, reported Pacific Islander or Asian ethnicities, reported no regular partner or one for >3 years, were insertive-only during anal intercourse with a regular partner, and who had less HIV-related knowledge. A priority for HIV management should be connecting YMSM at risk of infection, but unlikely to test with appropriate testing services. New generations of YMSM require targeted, culturally relevant health promotion that provides accurate understandings about HIV transmission and prevention.
Associations between food consumption habits with meal intake behaviour in Spanish adults.

PubMed

Keller, Kristin; Rodríguez López, Santiago; Carmenate Moreno, M Margarita; Acevedo Cantero, Paula

2014-12-01

The aim of the present study is to explore the contribution of different types of meal intake behaviour on a healthy diet and seeks to find associations with food consumption habits. A cross-sectional survey with data from 1332 Spanish adults aged between 20 and 79 years was conducted. The survey was carried out during the cardiovascular health event 'Semanas del Corazon 2008' in four Spanish cities. Several food consumption habits such as the recommended intake of fruits, vegetables, milk and dairy products, as well as the regular consumption of fatty and salty food and ready-made meals, were used as dependent variables in logistic regression. We evaluated different meal intake behaviour such as the type of meals, snacking, and drinks taken with a meal. Our survey revealed that snacking is positively associated with the regular consumption of salty and fatty food, and having sugary drinks with meals was positively associated with the regular consumption of ready-made meals. Having a forenoon meal is positively associated with the consumption of two or more portions of milk and dairy products and vegetables, and taking an afternoon meal with the recommended intake of milk and dairy products and fruits. Drinking water during a meal increases the probability of consuming two or more portions of fruits and vegetables. Our results enhance the understanding of the contribution that meal intake behaviour makes to a healthy diet based on food consumption habits. This work provides an insight into eating behaviour and would make a useful contribution to interventions aimed at promoting healthier eating habits. Copyright © 2014 Elsevier Ltd. All rights reserved.
Doctor communication quality and Friends' attitudes influence complementary medicine use in inflammatory bowel disease.

PubMed

Mountifield, Réme; Andrews, Jane M; Mikocka-Walus, Antonina; Bampton, Peter

2015-03-28

To examine the frequency of regular complementary and alternative therapy (CAM) use in three Australian cohorts of contrasting care setting and geography, and identify independent attitudinal and psychological predictors of CAM use across all cohorts. A cross sectional questionnaire was administered to inflammatory bowel disease (IBD) patients in 3 separate cohorts which differed by geographical region and care setting. Demographics and frequency of regular CAM use were assessed, along with attitudes towards IBD medication and psychological parameters such as anxiety, depression, personality traits and quality of life (QOL), and compared across cohorts. Independent attitudinal and psychological predictors of CAM use were determined using binary logistic regression analysis. In 473 respondents (mean age 50.3 years, 60.2% female) regular CAM use was reported by 45.4%, and did not vary between cohorts. Only 54.1% of users disclosed CAM use to their doctor. Independent predictors of CAM use which confirm those reported previously were: covert conventional medication dose reduction (P < 0.001), seeking psychological treatment (P < 0.001), adverse effects of conventional medication (P = 0.043), and higher QOL (P < 0.001). Newly identified predictors were CAM use by family or friends (P < 0.001), dissatisfaction with patient-doctor communication (P < 0.001), and lower depression scores (P < 0.001). In addition to previously identified predictors of CAM use, these data show that physician attention to communication and the patient-doctor relationship is important as these factors influence CAM use. Patient reluctance to discuss CAM with physicians may promote greater reliance on social contacts to influence CAM decisions.
The relationship between subjective wellbeing, low income and substance use among schoolchildren in the north west of England: a cross-sectional study.

PubMed

Farmer, Siobhan; Hanratty, Barbara

2012-12-01

The consumption of tobacco, alcohol and illegal drugs by young people is a public health concern. This study aimed to explore the associations between subjective wellbeing, living in a low-income household and substance use by schoolchildren. Data were analysed from a nationally representative cross-sectional survey of schoolchildren in England (Tellus4, 2009). Participants were 3903 children aged 10 and 15 years from two local authorities in the North West. Eligibility for free school meals provided a proxy for living in a low-income household. Multiple logistic regression was conducted with the main outcome measure, a composite indicator of self-reported regular substance use. More boys than girls had experimented with drugs or alcohol, but in the fourth year of secondary education, girls were significantly more likely than boys to have been drunk (P ≤ 0.001). In the multivariate analysis, older age was the most important factor associated with the consumption of substances. Living in a low-income household was associated with substance use, adjusting for age and subjective wellbeing (adj. OR = 1.78, 95% CI = 1.36-2.34). Respondents who reported being happy (adj. OR = 0.67, 95% CI = 0.52-0.86) or able to communicate with their family (adj. OR = 0.51, 95% CI = 0.39-0.65), were less likely to be regular users. Interventions to prevent regular substance use should be carefully targeted by age. Policies aimed at social determinants may be an important adjunct to individual-level interventions to reduce some inequalities in health associated with substance misuse.
Differences of smoking knowledge, attitudes, and behaviors between medical and non-medical students.

PubMed

Han, Min-Yan; Chen, Wei-Qing; Wen, Xiao-Zhong; Liang, Cai-Hua; Ling, Wen-Hua

2012-03-01

Previous studies in the world reported inconsistent results about the relationship of medical professional education with medical students' smoking behaviors, and no similar research had been published in China. This paper aims to explore whether the differences of smoking-related knowledge, attitudes, and behaviors existed between medical and non-medical undergraduate students. Eight thousand one hundred thirty-eight undergraduate students sampled from a university in Guangzhou were investigated with a self-administered structured questionnaire about their smoking-related knowledge, attitude and behaviors, and other relevant factors. General linear model and multinomial logistic regression were conducted to test the differences in smoking-related knowledge, attitude, and behaviors between medical and non-medical students while controlling for potential confounding variables. There was no difference in smoking-related knowledge scores between medical and non-medical freshmen, but medical sophomores and juniors had higher scores of smoking-related knowledge than their non-medical counterparts. The medical sophomores had higher mean score of attitudes towards smoking than non-medical ones. Before entering university, the difference in the prevalence of experimental and regular smoking between medical and non-medical college students was not significant. After entering university, in contrast, the overall prevalence of regular smoking was significantly higher among male non-medical college students than among male medical students. Stratified by current academic year, this difference was significant only among male sophomores. Medical students have higher smoking-related knowledge, stronger anti-smoking attitude, and lower prevalence of regular smoking than non-medical college students of similar age, which may be associated with medical professional education.
Health care-associated infection prevention in Japan: the role of safety culture.

PubMed

Sakamoto, Fumie; Sakihama, Tomoko; Saint, Sanjay; Greene, M Todd; Ratz, David; Tokuda, Yasuharu

2014-08-01

Limited data exist on the use of infection prevention practices in Japan. We conducted a nationwide survey to examine the use of recommended infection prevention strategies and factors affecting their use in Japanese hospitals. Between April 1, 2012, and January 31, 2013, we surveyed 971 hospitals in Japan. The survey instrument assessed general hospital and infection prevention program characteristics and use of infection prevention practices, including practices specific to preventing catheter-associated urinary tract infection (CAUTI), central line-associated bloodstream infection (CLABSI), and ventilator-associated pneumonia (VAP). Logistic regression models were used to examine multivariable associations between hospital characteristics and the use of the various prevention practices. A total of 685 hospitals (71%) responded to the survey. Maintaining aseptic technique during catheter insertion and maintenance, avoiding routine central line changes, and using maximum sterile barrier precautions and semirecumbent positioning were the only practices regularly used by more than one-half of the hospitals to prevent CAUTI, CLABSI, and VAP, respectively. Higher safety-centeredness was associated with regular use of prevention practices across all infection types. Although certain practices were used commonly, the rate of regular use of many evidence-based prevention practices was low in Japanese hospitals. Our findings highlight the importance of fostering an organization-wide atmosphere that prioritizes patient safety. Such a commitment to patient safety should in turn promote the use of effective measures to reduce health care-associated infections in Japan. Copyright © 2014 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Mosby, Inc. All rights reserved.
The associations of personality traits and parental education with smoking behaviour among adolescents.

PubMed

Yáñez, Aina M; Leiva, Alfonso; Estela, Andreu; Čukić, Iva

2017-01-01

We examined whether personality traits and parental education are associated with smoking initiation in a sample of Spanish secondary school students. Participants, taken from the ITACA study (842 adolescents aged 14-15 years), completed a questionnaire assessing personality traits of the Five Factor Model, smoking behaviours and parental education. Multinomial logistic regression models controlling for age and sex were used to determine the independent associations and interactions of personality traits and parental education with risk of ever trying smoking, as well as with being a regular smoker in adolescence. Higher conscientiousness was related to a lower chance of trying smoking at least once (OR = 0.57, 95% CIs = 0.46, 0.71) as well as being a regular smoker (OR = 0.39, 95% CIs = 0.27, 0.55). Higher emotional instability (neuroticism) was associated with higher risk of being in either smoking category (OR = 1.33, 95% CIs = 1.10, 1.60 and OR = 1.76, 95% CIs = 1.31, 2.35, respectively). Higher extraversion was also associated with a higher risk of both types of smoking behaviour (OR = 1.38, 95% CIs = 1.12, 1.70 and OR = 2.43 (1.67, 3.55, respectively). Higher parental education was significantly related to lower risk of being a regular smoker (OR = 0.70, 95% CIs = 0.54, 0.89), but not with trying smoking in the past. Finally, we found no evidence of the interactions between adolescents' personality and parental education in predicting adolescent smoking behaviours. We conclude that personality factors and parental education are important and independent factors associated with smoking behaviour in adolescents.
Child Abuse Experiences and Perceived Need for Care and Mental Health Service Use among Members of the Canadian Armed Forces

PubMed Central

Turner, Sarah; Taillieu, Tamara; Cheung, Kristene; Zamorski, Mark; Boulos, David; Sareen, Jitender

2017-01-01

Objective: Child abuse is associated with poor mental health outcomes in adulthood. However, little is known about how a history of child abuse may be related to perceived need for care (PNC) and mental health service use (MHSU) among Canadian military personnel. The objectives of this study were to determine 1) the relationship between child abuse history and PNC and 2) the relationship between child abuse history and MHSU in the Canadian military. Method: Data were drawn from the 2013 Canadian Forces Mental Health Survey (n = 6692 Regular Force personnel between the ages of 18 and 60 years). Logistic regression was used to examine the relationships between individual child abuse types and PNC and MHSU while adjusting for sociodemographic variables, the presence of mental disorders, deployment-related variables, and other types of child abuse. Population attributable fractions (PAFs) were calculated to estimate the proportion of PNC and MHSU that may be attributable to child abuse. Results: Each individual child abuse type was associated with increased odds of PNC and MHSU after adjusting for all covariates (adjusted odds ratio ranging from 1.26 to 1.80). PAFs showed that if any child abuse did not occur, PNC and MHSU among Regular Force personnel may be reduced by approximately 14.3% and 11.3%, respectively. Conclusions: This study highlights that preenlistment factors, such as a history of child abuse, have an independent association with PNC and MHSU and hence need to be considered when assessing the mental health service needs of the Canadian Regular Force personnel. PMID:28562093
Effectiveness in Regular Practice of Collaborative Care for Depression Among Adolescents: A Retrospective Cohort Study.

PubMed

Shippee, Nathan D; Mattson, Angela; Brennan, RoxAnne; Huxsahl, John; Billings, Marcie L; Williams, Mark D

2018-05-01

Depression is common among adolescents, but many lack ready access to mental health services. Integrated models of care for depression are needed, along with evidence to support their use in regular practice. The authors examined the effectiveness of an ongoing collaborative care program for depressed adolescents embedded in a busy primary care practice. This retrospective cohort study assessed EMERALD (Early Management and Evidence-based Recognition of Adolescents Living with Depression), a collaborative care program. All patients ages 12-17 and age 18 and still in high school with a score of ≥10 on the nine-item Patient Health Questionnaire for Adolescents (PHQ-9A) and without a diagnosis of bipolar disorder were eligible. The sample included 162 EMERALD participants and 499 similarly eligible non-EMERALD patients. Outcomes were six-month remission of depression (score <5) and six-month treatment response (>50% reduction from baseline) as measured by the PHQ-9A. Analyses included logistic regression and propensity score matching to adjust for differences in demographic factors and number of contacts-observations. After propensity score matching, EMERALD patients had better adjusted rates of depression remission (11 percentage points higher, p=.035) and treatment response (14 percentage points higher, p<.001) than comparison patients. Results from primary analyses were as conservative as or more conservative than results from all sensitivity analyses tested. Collaborative care for adolescents in regular practice led to better remission and treatment response than usual care. Future studies could examine which groups might benefit most and flexible payment models to support these services.
Child Abuse Experiences and Perceived Need for Care and Mental Health Service Use among Members of the Canadian Armed Forces.

PubMed

Turner, Sarah; Taillieu, Tamara; Cheung, Kristene; Zamorski, Mark; Boulos, David; Sareen, Jitender; Afifi, Tracie O

2017-06-01

Child abuse is associated with poor mental health outcomes in adulthood. However, little is known about how a history of child abuse may be related to perceived need for care (PNC) and mental health service use (MHSU) among Canadian military personnel. The objectives of this study were to determine 1) the relationship between child abuse history and PNC and 2) the relationship between child abuse history and MHSU in the Canadian military. Data were drawn from the 2013 Canadian Forces Mental Health Survey ( n = 6692 Regular Force personnel between the ages of 18 and 60 years). Logistic regression was used to examine the relationships between individual child abuse types and PNC and MHSU while adjusting for sociodemographic variables, the presence of mental disorders, deployment-related variables, and other types of child abuse. Population attributable fractions (PAFs) were calculated to estimate the proportion of PNC and MHSU that may be attributable to child abuse. Each individual child abuse type was associated with increased odds of PNC and MHSU after adjusting for all covariates (adjusted odds ratio ranging from 1.26 to 1.80). PAFs showed that if any child abuse did not occur, PNC and MHSU among Regular Force personnel may be reduced by approximately 14.3% and 11.3%, respectively. This study highlights that preenlistment factors, such as a history of child abuse, have an independent association with PNC and MHSU and hence need to be considered when assessing the mental health service needs of the Canadian Regular Force personnel.
Patterns of Alcohol Use Among Canadian Military Personnel and Their Associations With Health and Well-Being.

PubMed

Richer, Isabelle; Lee, Jennifer E C; Born, Jennifer

2016-04-07

Heavy drinking increases the risk of injury, adverse physical and mental health outcomes, and loss of productivity. Nonetheless, patterns of alcohol use and related symptomatology among military personnel remain poorly understood. A latent class analysis (LCA) was used to explore the presence of subgroups of alcohol users among Canadian Armed Forces (CAF) Regular Forces members. Correlates of empirically derived subgroups were further explored. Analyses were performed on a subsample of alcohol users who participated in a 2008/09 cross-sectional survey of a stratified random sample of currently serving CAF Regular Force members (N = 1980). Multinomial logistic regression models were conducted to verify physical and mental health differences across subgroups of alcohol users. All analyses were adjusted for complex survey design. A 4-class solution was considered the best fit for the data. Subgroups were labeled as follows: Class 1 - Infrequent drinkers (27.2%); Class 2 - Moderate drinkers (41.5%); Class 3 - Regular binge drinkers with minimal problems (14.8%); and Class 4 - Problem drinkers (16.6%). Significant differences by age, sex, marital status, element, rank, recent serious injuries, chronic conditions, psychological distress, posttraumatic stress disorder, and depression symptoms were found across the subgroups. Problem drinkers demonstrated the most degraded physical and mental health. Findings highlight the heterogeneity of alcohol users and heavy drinkers among CAF members and the need for tailored interventions addressing high-risk alcohol use. Results have the potential to inform prevention strategies and screening efforts. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
Transactional Sex With Regular and Casual Partners Among Young Men Who Have Sex With Men in the Detroit Metro Area.

PubMed

Bauermeister, José A; Eaton, Lisa; Meanley, Steven; Pingel, Emily S

2017-05-01

Transactional sex refers to the commodification of the body in exchange for shelter, food, and other goods and needs. Transactional sex has been associated with negative health outcomes including HIV infection, psychological distress, and substance use and abuse. Compared with the body of research examining transactional sex among women, less is known about the prevalence and correlates of transactional sex among men. Using data from a cross-sectional survey of young men who have sex with men (ages 18-29) living in the Detroit Metro Area ( N = 357; 9% HIV infected; 49% Black, 26% White, 16% Latino, 9% Other race), multivariate logistic regression analyses examined the association between transactional sex with regular and casual partners and key psychosocial factors (e.g., race/ethnicity, education, poverty, relationship status, HIV status, prior sexually transmitted infections [STIs], mental health, substance use, and residential instability) previously identified in the transactional sex literature. Forty-four percent of the current sample reported engaging in transactional sex. Transactional sex was associated with age, employment status, relationship status, and anxiety symptoms. When stratified, transactional sex with a regular partner was associated with age, educational attainment, employment status, relationship status, anxiety, and alcohol use. Transactional sex with a casual partner was associated with homelessness, race/ethnicity, employment status, and hard drug use. The implications of these findings for HIV/STI prevention are discussed, including the notion that efforts to address HIV/STIs among young men who have sex with men may require interventions to consider experiences of transactional sex and the psychosocial contexts that may increase its likelihood.
Doctor communication quality and Friends' attitudes influence complementary medicine use in inflammatory bowel disease

PubMed Central

Mountifield, Réme; Andrews, Jane M; Mikocka-Walus, Antonina; Bampton, Peter

2015-01-01

AIM: To examine the frequency of regular complementary and alternative therapy (CAM) use in three Australian cohorts of contrasting care setting and geography, and identify independent attitudinal and psychological predictors of CAM use across all cohorts. METHODS: A cross sectional questionnaire was administered to inflammatory bowel disease (IBD) patients in 3 separate cohorts which differed by geographical region and care setting. Demographics and frequency of regular CAM use were assessed, along with attitudes towards IBD medication and psychological parameters such as anxiety, depression, personality traits and quality of life (QOL), and compared across cohorts. Independent attitudinal and psychological predictors of CAM use were determined using binary logistic regression analysis. RESULTS: In 473 respondents (mean age 50.3 years, 60.2% female) regular CAM use was reported by 45.4%, and did not vary between cohorts. Only 54.1% of users disclosed CAM use to their doctor. Independent predictors of CAM use which confirm those reported previously were: covert conventional medication dose reduction (P < 0.001), seeking psychological treatment (P < 0.001), adverse effects of conventional medication (P = 0.043), and higher QOL (P < 0.001). Newly identified predictors were CAM use by family or friends (P < 0.001), dissatisfaction with patient-doctor communication (P < 0.001), and lower depression scores (P < 0.001). CONCLUSION: In addition to previously identified predictors of CAM use, these data show that physician attention to communication and the patient-doctor relationship is important as these factors influence CAM use. Patient reluctance to discuss CAM with physicians may promote greater reliance on social contacts to influence CAM decisions. PMID:25834335

EXpectation Propagation LOgistic REgRession (EXPLORER): Distributed Privacy-Preserving Online Model Learning

PubMed Central

Wang, Shuang; Jiang, Xiaoqian; Wu, Yuan; Cui, Lijuan; Cheng, Samuel; Ohno-Machado, Lucila

2013-01-01

We developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection etc.) as the traditional frequentist Logistic Regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications. PMID:23562651
Dental caries and beverage consumption in young children.

PubMed

Marshall, Teresa A; Levy, Steven M; Broffitt, Barbara; Warren, John J; Eichenberger-Gilmore, Julie M; Burns, Trudy L; Stumbo, Phyllis J

2003-09-01

Dental caries is a common, chronic disease of childhood. The impact of contemporary changes in beverage patterns, specifically decreased milk intakes and increased 100% juice and soda pop intakes, on dental caries in young children is unknown. We describe associations among caries experience and intakes of dairy foods, sugared beverages, and nutrients and overall diet quality in young children. Subjects (n = 642) are members of the Iowa Fluoride Study, a cohort followed from birth. Food and nutrient intakes were obtained from 3-day diet records analyzed at 1 (n = 636), 2 (n = 525), 3 (n = 441), 4 (n = 410), and 5 (n = 417) years and cumulatively for 1 through 5 (n = 396) years of age. Diet quality was defined by nutrient adequacy ratios (NARs) and calculated as the ratio of nutrient intake to Recommended Dietary Allowance/Adequate Intake. Caries were identified during dental examinations by 2 trained and calibrated dentists at 4 to 7 years of age. Examinations were visual, but a dental explorer was used to confirm questionable findings. Caries experience was assessed at both the tooth and the surface levels. Data were analyzed using SAS. The Wilcoxon rank sum test was used to compare food intakes, nutrient intakes, and NARs of subjects with and without caries experience. Logistic and Tobit regression analyses were used to identify associations among diet variables and caries experience and to develop models to predict caries experience. Not all relationships between food intakes and NARs and caries experience were linear; therefore, categorical variables were used to develop models to predict caries experience. Food and beverage intakes were categorized as none, low, and high intakes, and NARs were categorized as inadequate, low adequate, and high adequate. Subjects with caries had lower median intakes of milk at 2 and 3 years of age than subjects without caries. Subjects with caries had higher median intakes of regular (sugared) soda pop at 2, 3, 4, and 5 years and for 1 through 5 years; regular beverages from powder at 1, 4, and 5 years and for 1 through 5 years; and total sugared beverages at 4 and 5 years than subjects without caries. Logistic regression models were developed for exposure variables at 1, 2, 3, 4, and 5 years and for 1 through 5 years to predict any caries experience at 4 to 7 years of age. Age at dental examination was retained in models at all ages. Children with 0 intake (vs low and high intakes) of regular beverages from powder at 1 year, regular soda pop at 2 and 3 years, and sugar-free beverages from powder at 5 years had a decreased risk of caries experience. High intakes of regular beverages from powder at 4 and 5 years and for 1 through 5 years and regular soda pop at 5 years and for 1 through 5 years were associated with significantly increased odds of caries experience relative to subjects with none or low intakes. Low (vs none or high) intakes of 100% juice at 5 years were associated with decreased caries experience. In general, inadequate intakes (vs low adequate or high adequate intakes) of nutrients (eg, riboflavin, copper, vitamin D, vitamin B(12)) were associated with increased caries experience and low adequate intakes (vs inadequate or high adequate intakes) of nutrients (eg, vitamin B(12), vitamin C) were associated with decreased caries experience. An exception was vitamin E; either low or high adequate intakes were associated with increased caries experience at various ages. Multivariable Tobit regression models were developed for 1- through 5-year exposure variables to predict the number of tooth surfaces with caries experience at 4 to 7 years of age. Age at dental examination showed a significant positive association and fluoride exposure showed a significant negative association with the number of tooth surfaces with caries experience in the final model. Low intakes of nonmilk dairy foods (vs high intakes; all subjects had some nonmilk dairy intakes) and high adequate intakes of vitamin C (vs inadequate and low adequate intakes) were associated with fewer tooth surfaces having caries experience. High intakes of regular soda pop (vs none and low intakes) were associated with more tooth surfaces having caries experience. Results of our study suggest that contemporary changes in beverage patterns, particularly the increase in soda pop consumption, have the potential to increase dental caries rates in children. Consumption of regular soda pop, regular powdered beverages, and, to a lesser extent, 100% juice was associated with increased caries risk. Milk had a neutral association with caries. Associations between different types of sugared beverages and caries experience were not equivalent, which could be attributable to the different sugar compositions of the beverages or different roles in the diet. Our data support contemporary dietary guidelines for children: consume 2 or more servings of dairy foods daily, limit intake of 100% juice to 4 to 6 oz daily, and restrict other sugared beverages to occasional use. Pediatricians, pediatric nurse practitioners, and dietitians are in a position to support pediatric dentists in providing preventive guidance to parents of young children.
Dietary consumption patterns and laryngeal cancer risk.

PubMed

Vlastarakos, Petros V; Vassileiou, Andrianna; Delicha, Evie; Kikidis, Dimitrios; Protopapas, Dimosthenis; Nikolopoulos, Thomas P

2016-06-01

We conducted a case-control study to investigate the effect of diet on laryngeal carcinogenesis. Our study population was made up of 140 participants-70 patients with laryngeal cancer (LC) and 70 controls with a non-neoplastic condition that was unrelated to diet, smoking, or alcohol. A food-frequency questionnaire determined the mean consumption of 113 different items during the 3 years prior to symptom onset. Total energy intake and cooking mode were also noted. The relative risk, odds ratio (OR), and 95% confidence interval (CI) were estimated by multiple logistic regression analysis. We found that the total energy intake was significantly higher in the LC group (p < 0.001), and that the difference remained statistically significant after logistic regression analysis (p < 0.001; OR: 118.70). Notably, meat consumption was higher in the LC group (p < 0.001), and the difference remained significant after logistic regression analysis (p = 0.029; OR: 1.16). LC patients also consumed significantly more fried food (p = 0.036); this difference also remained significant in the logistic regression model (p = 0.026; OR: 5.45). The LC group also consumed significantly more seafood (p = 0.012); the difference persisted after logistic regression analysis (p = 0.009; OR: 2.48), with the consumption of shrimp proving detrimental (p = 0.049; OR: 2.18). Finally, the intake of zinc was significantly higher in the LC group before and after logistic regression analysis (p = 0.034 and p = 0.011; OR: 30.15, respectively). Cereal consumption (including pastas) was also higher among the LC patients (p = 0.043), with logistic regression analysis showing that their negative effect was possibly associated with the sauces and dressings that traditionally accompany pasta dishes (p = 0.006; OR: 4.78). Conversely, a higher consumption of dairy products was found in controls (p < 0.05); logistic regression analysis showed that calcium appeared to be protective at the micronutrient level (p < 0.001; OR: 0.27). We found no difference in the overall consumption of fruits and vegetables between the LC patients and controls; however, the LC patients did have a greater consumption of cooked tomatoes and cooked root vegetables (p = 0.039 for both), and the controls had more consumption of leeks (p = 0.042) and, among controls younger than 65 years, cooked beans (p = 0.037). Lemon (p = 0.037), squeezed fruit juice (p = 0.032), and watermelon (p = 0.018) were also more frequently consumed by the controls. Other differences at the micronutrient level included greater consumption by the LC patients of retinol (p = 0.044), polyunsaturated fats (p = 0.041), and linoleic acid (p = 0.008); LC patients younger than 65 years also had greater intake of riboflavin (p = 0.045). We conclude that the differences in dietary consumption patterns between LC patients and controls indicate a possible role for lifestyle modifications involving nutritional factors as a means of decreasing the risk of laryngeal cancer.
Vitamin E and regression of hypercholesterolemia-induced oxidative stress in kidney.

PubMed

Prasad, Kailash

2014-01-01

Hypercholesterolemia (HC) is an independent risk factor for the onset and progression of renal disease. HC induces oxidative stress (OS) in the kidney; Vitamin E (Vit.E), an antioxidant, slows the progression of OS in the kidney. This study was to investigate if Vit.E regresses the HC-induced OS, and the regression is associated with an increase in the antioxidant reserve (AR). The studies were carried out in four groups of rabbits. The kidneys were removed under anesthesia. OS and AR in the renal tissue were assessed by measuring malondialdetyde (MDA) and chemiluminescent (CL) activity, respectively. High-cholesterol diet elevated the serum total cholesterol (TC), and the regular diet with or without Vit.E following a high-cholesterol diet reduced the serum TC to control levels. HC increased the MDA levels of kidney by 5.54-fold compared to control. The MDA contents of the kidneys in groups on regular diet with or without Vit.E were, respectively, 56 and 53 % lower than the control group. The CL activity in the control group was 12.15 ± 0.73 × 10(6) RLU/mg protein. The CL activity in HC group was 45.26 % lower than that in control, indicating an increase in AR. The regular diet with or without Vit.E following high-cholesterol diet normalized the CL activity/AR. In conclusion, HC increases OS in the kidney; reduction of serum cholesterol by regular diet regresses the renal OS but Vit.E does not regress HC-induced OS in kidney.
Does the utilization of dental services associate with masticatory performance in a Japanese urban population?: the Suita study

PubMed Central

Kikui, Miki; Kida, Momoyo; Kosaka, Takayuki; Yamamoto, Masaaki; Yoshimuta, Yoko; Yasui, Sakae; Nokubi, Takashi; Maeda, Yoshinobu; Kokubo, Yoshihiro; Watanabe, Makoto; Miyamoto, Yoshihiro

2015-01-01

Abstract There are numerous reports on the relationship between regular utilization of dental care services and oral health, but most are based on questionnaires and subjective evaluation. Few have objectively evaluated masticatory performance and its relationship to utilization of dental care services. The purpose of this study was to identify the effect of regular utilization of dental services on masticatory performance. The subjects consisted of 1804 general residents of Suita City, Osaka Prefecture (760 men and 1044 women, mean age 66.5 ± 7.9 years). Regular utilization of dental services and oral hygiene habits (frequency of toothbrushing and use of interdental aids) was surveyed, and periodontal status, occlusal support, and masticatory performance were measured. Masticatory performance was evaluated by a chewing test using gummy jelly. The correlation between age, sex, regular dental utilization, oral hygiene habits, periodontal status or occlusal support, and masticatory performance was analyzed using Spearman's correlation test and t‐test. In addition, multiple linear regression analysis was carried out to investigate the relationship of regular dental utilization with masticatory performance after controlling for other factors. Masticatory performance was significantly correlated to age when using Spearman's correlation test, and to regular dental utilization, periodontal status, or occlusal support with t‐test. Multiple linear regression analysis showed that regular utilization of dental services was significantly related to masticatory performance even after adjusting for age, sex, oral hygiene habits, periodontal status, and occlusal support (standardized partial regression coefficient β = 0.055). These findings suggested that the regular utilization of dental care services is an important factor influencing masticatory performance in a Japanese urban population. PMID:29744141
Does the utilization of dental services associate with masticatory performance in a Japanese urban population?: the Suita study.

PubMed

Kikui, Miki; Ono, Takahiro; Kida, Momoyo; Kosaka, Takayuki; Yamamoto, Masaaki; Yoshimuta, Yoko; Yasui, Sakae; Nokubi, Takashi; Maeda, Yoshinobu; Kokubo, Yoshihiro; Watanabe, Makoto; Miyamoto, Yoshihiro

2015-12-01

There are numerous reports on the relationship between regular utilization of dental care services and oral health, but most are based on questionnaires and subjective evaluation. Few have objectively evaluated masticatory performance and its relationship to utilization of dental care services. The purpose of this study was to identify the effect of regular utilization of dental services on masticatory performance. The subjects consisted of 1804 general residents of Suita City, Osaka Prefecture (760 men and 1044 women, mean age 66.5 ± 7.9 years). Regular utilization of dental services and oral hygiene habits (frequency of toothbrushing and use of interdental aids) was surveyed, and periodontal status, occlusal support, and masticatory performance were measured. Masticatory performance was evaluated by a chewing test using gummy jelly. The correlation between age, sex, regular dental utilization, oral hygiene habits, periodontal status or occlusal support, and masticatory performance was analyzed using Spearman's correlation test and t -test. In addition, multiple linear regression analysis was carried out to investigate the relationship of regular dental utilization with masticatory performance after controlling for other factors. Masticatory performance was significantly correlated to age when using Spearman's correlation test, and to regular dental utilization, periodontal status, or occlusal support with t -test. Multiple linear regression analysis showed that regular utilization of dental services was significantly related to masticatory performance even after adjusting for age, sex, oral hygiene habits, periodontal status, and occlusal support (standardized partial regression coefficient β = 0.055). These findings suggested that the regular utilization of dental care services is an important factor influencing masticatory performance in a Japanese urban population.
Are Long-Term Chloroquine or Hydroxychloroquine Users Being Checked Regularly for Toxic Maculopathy?

PubMed Central

Nika, Melisa; Blachley, Taylor S.; Edwards, Paul; Lee, Paul P.; Stein, Joshua D.

2014-01-01

Importance According to evidence-based, expert recommendations, long-term users of chloroquine (CQ) or hydroxychloroquine (HCQ) should undergo regular visits to eye-care providers and diagnostic testing to check for maculopathy. Objective To determine whether patients with rheumatoid arthritis (RA) or systemic lupus erythematosus (SLE) taking CQ or HCQ are regularly visiting eye-care providers and being screened for maculopathy. Setting, Design and Participants Patients with RA or SLE who were continuously enrolled in a particular managed-care network for ≥5 years during 2001-2011 were studied. Patients' amount of CQ/HCQ use in the 5 years since initial RA/SLE diagnosis was calculated, along with their number of eye-care visits and diagnostic tests for maculopathy. Those at high risk for maculopathy were identified. Visits to eye providers and diagnostic testing for maculopathy were assessed for each enrollee over the study period. Logistic regression was performed to assess potential factors associated with regular eye-care-provider visits (≥3 in 5 years) among CQ/HCQ users, including those at greatest risk for maculopathy. Main Outcome Measures Among CQ/HCQ users and those at high risk for toxic maculopathy, the proportions with regular eye-care visits and diagnostic testing, and the likelihood of regular eye-care visits (odds ratios [ORs] with 95% confidence intervals [CI]). Results Among 18,051 beneficiaries with RA or SLE, 6,339 (35.1%) had ≥1 record of HCQ/CQ use and 1,409 (7.8%) used HCQ/CQ for ≥4 years. Among those at high risk for maculopathy, 27.9% lacked regular eye-provider visits, 6.1% had no visits to eye providers, and 34.5% had no diagnostic testing for maculopathy during the 5-year period. Among high-risk patients, each additional month of HCQ/CQ use was associated with a 2.0%-increased likelihood of regular eye care (adjusted OR=1.02, CI=1.01-1.03). High-risk patients whose SLE/RA were managed by rheumatologists had a 77%-increased likelihood of regular eye care (adjusted OR=1.77, CI=1.27-2.47), relative to other patients. Conclusions and Relevance In this insured population, many patients at high risk for HCQ/CQ-associated maculopathy are not undergoing routine monitoring for this serious side effect. Future studies should explore factors contributing to suboptimal adherence to expert guidelines and the potential impact on patients' vision-related outcomes. PMID:24970348
A Comparison of the Logistic Regression and Contingency Table Methods for Simultaneous Detection of Uniform and Nonuniform DIF

ERIC Educational Resources Information Center

Guler, Nese; Penfield, Randall D.

2009-01-01

In this study, we investigate the logistic regression (LR), Mantel-Haenszel (MH), and Breslow-Day (BD) procedures for the simultaneous detection of both uniform and nonuniform differential item functioning (DIF). A simulation study was used to assess and compare the Type I error rate and power of a combined decision rule (CDR), which assesses DIF…
The Overall Odds Ratio as an Intuitive Effect Size Index for Multiple Logistic Regression: Examination of Further Refinements

ERIC Educational Resources Information Center

Le, Huy; Marcus, Justin

2012-01-01

This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model…
Predicting Student Success on the Texas Chemistry STAAR Test: A Logistic Regression Analysis

ERIC Educational Resources Information Center

Johnson, William L.; Johnson, Annabel M.; Johnson, Jared

2012-01-01

Background: The context is the new Texas STAAR end-of-course testing program. Purpose: The authors developed a logistic regression model to predict who would pass-or-fail the new Texas chemistry STAAR end-of-course exam. Setting: Robert E. Lee High School (5A) with an enrollment of 2700 students, Tyler, Texas. Date of the study was the 2011-2012…
Using ROC curves to compare neural networks and logistic regression for modeling individual noncatastrophic tree mortality

Treesearch

Susan L. King

2003-01-01

The performance of two classifiers, logistic regression and neural networks, are compared for modeling noncatastrophic individual tree mortality for 21 species of trees in West Virginia. The output of the classifier is usually a continuous number between 0 and 1. A threshold is selected between 0 and 1 and all of the trees below the threshold are classified as...
Logistic regression trees for initial selection of interesting loci in case-control studies

PubMed Central

Nickolov, Radoslav Z; Milanov, Valentin B

2007-01-01

Modern genetic epidemiology faces the challenge of dealing with hundreds of thousands of genetic markers. The selection of a small initial subset of interesting markers for further investigation can greatly facilitate genetic studies. In this contribution we suggest the use of a logistic regression tree algorithm known as logistic tree with unbiased selection. Using the simulated data provided for Genetic Analysis Workshop 15, we show how this algorithm, with incorporation of multifactor dimensionality reduction method, can reduce an initial large pool of markers to a small set that includes the interesting markers with high probability. PMID:18466557
Using Logistic Regression to Predict the Probability of Debris Flows in Areas Burned by Wildfires, Southern California, 2003-2006

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.; Michael, John A.; Helsel, Dennis R.

2008-01-01

Logistic regression was used to develop statistical models that can be used to predict the probability of debris flows in areas recently burned by wildfires by using data from 14 wildfires that burned in southern California during 2003-2006. Twenty-eight independent variables describing the basin morphology, burn severity, rainfall, and soil properties of 306 drainage basins located within those burned areas were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows soon after the 2003 to 2006 fires were delineated from data in the National Elevation Dataset using a geographic information system; (2) Data describing the basin morphology, burn severity, rainfall, and soil properties were compiled for each basin. These data were then input to a statistics software package for analysis using logistic regression; and (3) Relations between the occurrence or absence of debris flows and the basin morphology, burn severity, rainfall, and soil properties were evaluated, and five multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combinations produced the most effective models, and the multivariate models that best predicted the occurrence of debris flows were identified. Percentage of high burn severity and 3-hour peak rainfall intensity were significant variables in all models. Soil organic matter content and soil clay content were significant variables in all models except Model 5. Soil slope was a significant variable in all models except Model 4. The most suitable model can be selected from these five models on the basis of the availability of independent variables in the particular area of interest and field checking of probability maps. The multivariate logistic regression models can be entered into a geographic information system, and maps showing the probability of debris flows can be constructed in recently burned areas of southern California. This study demonstrates that logistic regression is a valuable tool for developing models that predict the probability of debris flows occurring in recently burned landscapes.
Polymorphism Thr160Thr in SRD5A1, involved in the progesterone metabolism, modifies postmenopausal breast cancer risk associated with menopausal hormone therapy.

PubMed

Hein, R; Abbas, S; Seibold, P; Salazar, R; Flesch-Janys, D; Chang-Claude, J

2012-01-01

Menopausal hormone therapy (MHT) is associated with an increased breast cancer risk in postmenopausal women, with combined estrogen-progestagen therapy posing a greater risk than estrogen monotherapy. However, few studies focused on potential effect modification of MHT-associated breast cancer risk by genetic polymorphisms in the progesterone metabolism. We assessed effect modification of MHT use by five coding single nucleotide polymorphisms (SNPs) in the progesterone metabolizing enzymes AKR1C3 (rs7741), AKR1C4 (rs3829125, rs17134592), and SRD5A1 (rs248793, rs3736316) using a two-center population-based case-control study from Germany with 2,502 postmenopausal breast cancer patients and 4,833 matched controls. An empirical-Bayes procedure that tests for interaction using a weighted combination of the prospective and the retrospective case-control estimators as well as standard prospective logistic regression were applied to assess multiplicative statistical interaction between polymorphisms and duration of MHT use with regard to breast cancer risk assuming a log-additive mode of inheritance. No genetic marginal effects were observed. Breast cancer risk associated with duration of combined therapy was significantly modified by SRD5A1_rs3736316, showing a reduced risk elevation in carriers of the minor allele (p (interaction,empirical-Bayes) = 0.006 using the empirical-Bayes method, p (interaction,logistic regression) = 0.013 using logistic regression). The risk associated with duration of use of monotherapy was increased by AKR1C3_rs7741 in minor allele carriers (p (interaction,empirical-Bayes) = 0.083, p (interaction,logistic regression) = 0.029) and decreased in minor allele carriers of two SNPs in AKR1C4 (rs3829125: p (interaction,empirical-Bayes) = 0.07, p (interaction,logistic regression) = 0.021; rs17134592: p (interaction,empirical-Bayes) = 0.101, p (interaction,logistic regression) = 0.038). After Bonferroni correction for multiple testing only SRD5A1_rs3736316 assessed using the empirical-Bayes method remained significant. Postmenopausal breast cancer risk associated with combined therapy may be modified by genetic variation in SRD5A1. Further well-powered studies are, however, required to replicate our finding.
Examining the association between possessing a regular source of healthcare and adherence with cancer screenings among Haitian households in Little Haiti, Miami-Dade County, Florida

PubMed Central

Pang, Hauchie; Cataldi, Mariel; Allseits, Emmanuelle; Ward-Peterson, Melissa; de la Vega, Pura Rodríguez; Castro, Grettel; Acuña, Juan Manuel

2017-01-01

Abstract Immigrant minorities regularly experience higher incidence and mortality rates of cancer. Frequently, a variety of social determinants create obstacles for those individuals to get the screenings they need. This is especially true for Haitian immigrants, a particularly vulnerable immigrant population in South Florida, who have been identified as having low cancer screening rates. While Haitian immigrants have some of the lowest cancer screening rates in the country, there is little existing literature that addresses barriers to cancer screenings among the population of Little Haiti in Miami-Dade County, Florida. The objective of this study was to evaluate the association between having a regular source of healthcare and adherence to recommended cancer screenings in the Little Haiti population of Miami. This secondary analysis utilized data collected from a random-sample, population-based household survey conducted from November 2011 to December 2012 among a geographic area approximating Little Haiti in Miami-Dade County, Florida. A total of 421 households identified as Haitian. The main exposure of interest was whether households possessed a regular source of care. Three separate outcomes were considered: adherence with colorectal cancer screening, mammogram adherence, and Pap smear adherence. Analysis was limited to households who met the age criteria for each outcome of interest. Bivariate associations were examined using the chi square test and Fisher exact test. Binary logistic regression was used to estimate unadjusted and adjusted odds ratios (ORs) with 95% confidence intervals (CIs). After adjusting for the head of household's education and household insurance status, households without a regular source of care were significantly less likely to adhere with colorectal cancer screening (OR = 0.33; 95% CI: 0.14–0.80) or mammograms (OR = 0.28; 95% CI: 0.11–0.75). Households with insurance coverage gaps were significantly less likely to adhere with mammograms (OR = 0.40; 95% CI: 0.17–0.97) or Pap smears (OR = 0.28; 95% CI: 0.13–0.58). Our study explored adherence with multiple cancer screenings. We found a strong association between possessing a regular source of care and adherence with colorectal cancer screening and mammogram adherence. Targeted approaches to improving access to regular care may improve adherence to cancer screening adherence among this unique immigrant population. PMID:28796056
Applications of statistics to medical science, III. Correlation and regression.

PubMed

Watanabe, Hiroshi

2012-01-01

In this third part of a series surveying medical statistics, the concepts of correlation and regression are reviewed. In particular, methods of linear regression and logistic regression are discussed. Arguments related to survival analysis will be made in a subsequent paper.
Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression.

PubMed

Schell, Greggory J; Lavieri, Mariel S; Stein, Joshua D; Musch, David C

2013-12-21

Open-angle glaucoma (OAG) is a prevalent, degenerate ocular disease which can lead to blindness without proper clinical management. The tests used to assess disease progression are susceptible to process and measurement noise. The aim of this study was to develop a methodology which accounts for the inherent noise in the data and improve significant disease progression identification. Longitudinal observations from the Collaborative Initial Glaucoma Treatment Study (CIGTS) were used to parameterize and validate a Kalman filter model and logistic regression function. The Kalman filter estimates the true value of biomarkers associated with OAG and forecasts future values of these variables. We develop two logistic regression models via generalized estimating equations (GEE) for calculating the probability of experiencing significant OAG progression: one model based on the raw measurements from CIGTS and another model based on the Kalman filter estimates of the CIGTS data. Receiver operating characteristic (ROC) curves and associated area under the ROC curve (AUC) estimates are calculated using cross-fold validation. The logistic regression model developed using Kalman filter estimates as data input achieves higher sensitivity and specificity than the model developed using raw measurements. The mean AUC for the Kalman filter-based model is 0.961 while the mean AUC for the raw measurements model is 0.889. Hence, using the probability function generated via Kalman filter estimates and GEE for logistic regression, we are able to more accurately classify patients and instances as experiencing significant OAG progression. A Kalman filter approach for estimating the true value of OAG biomarkers resulted in data input which improved the accuracy of a logistic regression classification model compared to a model using raw measurements as input. This methodology accounts for process and measurement noise to enable improved discrimination between progression and nonprogression in chronic diseases.
Computing group cardinality constraint solutions for logistic regression problems.

PubMed

Zhang, Yong; Kwon, Dongjin; Pohl, Kilian M

2017-01-01

We derive an algorithm to directly solve logistic regression based on cardinality constraint, group sparsity and use it to classify intra-subject MRI sequences (e.g. cine MRIs) of healthy from diseased subjects. Group cardinality constraint models are often applied to medical images in order to avoid overfitting of the classifier to the training data. Solutions within these models are generally determined by relaxing the cardinality constraint to a weighted feature selection scheme. However, these solutions relate to the original sparse problem only under specific assumptions, which generally do not hold for medical image applications. In addition, inferring clinical meaning from features weighted by a classifier is an ongoing topic of discussion. Avoiding weighing features, we propose to directly solve the group cardinality constraint logistic regression problem by generalizing the Penalty Decomposition method. To do so, we assume that an intra-subject series of images represents repeated samples of the same disease patterns. We model this assumption by combining series of measurements created by a feature across time into a single group. Our algorithm then derives a solution within that model by decoupling the minimization of the logistic regression function from enforcing the group sparsity constraint. The minimum to the smooth and convex logistic regression problem is determined via gradient descent while we derive a closed form solution for finding a sparse approximation of that minimum. We apply our method to cine MRI of 38 healthy controls and 44 adult patients that received reconstructive surgery of Tetralogy of Fallot (TOF) during infancy. Our method correctly identifies regions impacted by TOF and generally obtains statistically significant higher classification accuracy than alternative solutions to this model, i.e., ones relaxing group cardinality constraints. Copyright © 2016 Elsevier B.V. All rights reserved.
Influential factors of red-light running at signalized intersection and prediction using a rare events logistic regression model.

PubMed

Ren, Yilong; Wang, Yunpeng; Wu, Xinkai; Yu, Guizhen; Ding, Chuan

2016-10-01

Red light running (RLR) has become a major safety concern at signalized intersection. To prevent RLR related crashes, it is critical to identify the factors that significantly impact the drivers' behaviors of RLR, and to predict potential RLR in real time. In this research, 9-month's RLR events extracted from high-resolution traffic data collected by loop detectors from three signalized intersections were applied to identify the factors that significantly affect RLR behaviors. The data analysis indicated that occupancy time, time gap, used yellow time, time left to yellow start, whether the preceding vehicle runs through the intersection during yellow, and whether there is a vehicle passing through the intersection on the adjacent lane were significantly factors for RLR behaviors. Furthermore, due to the rare events nature of RLR, a modified rare events logistic regression model was developed for RLR prediction. The rare events logistic regression method has been applied in many fields for rare events studies and shows impressive performance, but so far none of previous research has applied this method to study RLR. The results showed that the rare events logistic regression model performed significantly better than the standard logistic regression model. More importantly, the proposed RLR prediction method is purely based on loop detector data collected from a single advance loop detector located 400 feet away from stop-bar. This brings great potential for future field applications of the proposed method since loops have been widely implemented in many intersections and can collect data in real time. This research is expected to contribute to the improvement of intersection safety significantly. Copyright © 2016 Elsevier Ltd. All rights reserved.
Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery.

PubMed

Engoren, Milo; Habib, Robert H; Dooner, John J; Schwann, Thomas A

2013-08-01

As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction group and 216 (8.0 %) of the 2,711 patient Validation group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p < .001). Artificial neural nets were less accurate with AU ROC = 0.597 ± .001 in the Construction group. Predictive accuracy of all three techniques fell in the Validation group. However, the accuracy of genetic programming (AU ROC = .654 ± .001) was still trivially but statistically non-significantly better than that of the logistic regression (AU ROC = .644 ± .020, p = .61). Genetic programming and logistic regression provide alternative methods to predict readmission that are similarly accurate.

Artificial neural network, genetic algorithm, and logistic regression applications for predicting renal colic in emergency settings.

PubMed

Eken, Cenker; Bilge, Ugur; Kartal, Mutlu; Eray, Oktay

2009-06-03

Logistic regression is the most common statistical model for processing multivariate data in the medical literature. Artificial intelligence models like an artificial neural network (ANN) and genetic algorithm (GA) may also be useful to interpret medical data. The purpose of this study was to perform artificial intelligence models on a medical data sheet and compare to logistic regression. ANN, GA, and logistic regression analysis were carried out on a data sheet of a previously published article regarding patients presenting to an emergency department with flank pain suspicious for renal colic. The study population was composed of 227 patients: 176 patients had a diagnosis of urinary stone, while 51 ultimately had no calculus. The GA found two decision rules in predicting urinary stones. Rule 1 consisted of being male, pain not spreading to back, and no fever. In rule 2, pelvicaliceal dilatation on bedside ultrasonography replaced no fever. ANN, GA rule 1, GA rule 2, and logistic regression had a sensitivity of 94.9, 67.6, 56.8, and 95.5%, a specificity of 78.4, 76.47, 86.3, and 47.1%, a positive likelihood ratio of 4.4, 2.9, 4.1, and 1.8, and a negative likelihood ratio of 0.06, 0.42, 0.5, and 0.09, respectively. The area under the curve was found to be 0.867, 0.720, 0.715, and 0.713 for all applications, respectively. Data mining techniques such as ANN and GA can be used for predicting renal colic in emergency settings and to constitute clinical decision rules. They may be an alternative to conventional multivariate analysis applications used in biostatistics.
Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey

NASA Astrophysics Data System (ADS)

Duman, T. Y.; Can, T.; Gokceoglu, C.; Nefeslioglu, H. A.; Sonmez, H.

2006-11-01

As a result of industrialization, throughout the world, cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul, the population of which is over 10 million. Due to rapid urbanization, new areas suitable for settlement and engineering structures are necessary. The Cekmece area located west of the Istanbul metropolitan area is studied, because the landslide activity is extensive in this area. The purpose of this study is to develop a model that can be used to characterize landslide susceptibility in map form using logistic regression analysis of an extensive landslide database. A database of landslide activity was constructed using both aerial-photography and field studies. About 19.2% of the selected study area is covered by deep-seated landslides. The landslides that occur in the area are primarily located in sandstones with interbedded permeable and impermeable layers such as claystone, siltstone and mudstone. About 31.95% of the total landslide area is located at this unit. To apply logistic regression analyses, a data matrix including 37 variables was constructed. The variables used in the forwards stepwise analyses are different measures of slope, aspect, elevation, stream power index (SPI), plan curvature, profile curvature, geology, geomorphology and relative permeability of lithological units. A total of 25 variables were identified as exerting strong influence on landslide occurrence, and included by the logistic regression equation. Wald statistics values indicate that lithology, SPI and slope are more important than the other parameters in the equation. Beta coefficients of the 25 variables included the logistic regression equation provide a model for landslide susceptibility in the Cekmece area. This model is used to generate a landslide susceptibility map that correctly classified 83.8% of the landslide-prone areas.
New robust statistical procedures for the polytomous logistic regression models.

PubMed

Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro

2018-05-17

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.
Updated logistic regression equations for the calculation of post-fire debris-flow likelihood in the western United States

USGS Publications Warehouse

Staley, Dennis M.; Negri, Jacquelyn A.; Kean, Jason W.; Laber, Jayme L.; Tillery, Anne C.; Youberg, Ann M.

2016-06-30

Wildfire can significantly alter the hydrologic response of a watershed to the extent that even modest rainstorms can generate dangerous flash floods and debris flows. To reduce public exposure to hazard, the U.S. Geological Survey produces post-fire debris-flow hazard assessments for select fires in the western United States. We use publicly available geospatial data describing basin morphology, burn severity, soil properties, and rainfall characteristics to estimate the statistical likelihood that debris flows will occur in response to a storm of a given rainfall intensity. Using an empirical database and refined geospatial analysis methods, we defined new equations for the prediction of debris-flow likelihood using logistic regression methods. We showed that the new logistic regression model outperformed previous models used to predict debris-flow likelihood.
Nowcasting of Low-Visibility Procedure States with Ordered Logistic Regression at Vienna International Airport

NASA Astrophysics Data System (ADS)

Kneringer, Philipp; Dietz, Sebastian; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Low-visibility conditions have a large impact on aviation safety and economic efficiency of airports and airlines. To support decision makers, we develop a statistical probabilistic nowcasting tool for the occurrence of capacity-reducing operations related to low visibility. The probabilities of four different low visibility classes are predicted with an ordered logistic regression model based on time series of meteorological point measurements. Potential predictor variables for the statistical models are visibility, humidity, temperature and wind measurements at several measurement sites. A stepwise variable selection method indicates that visibility and humidity measurements are the most important model inputs. The forecasts are tested with a 30 minute forecast interval up to two hours, which is a sufficient time span for tactical planning at Vienna Airport. The ordered logistic regression models outperform persistence and are competitive with human forecasters.
EXpectation Propagation LOgistic REgRession (EXPLORER): distributed privacy-preserving online model learning.

PubMed

Wang, Shuang; Jiang, Xiaoqian; Wu, Yuan; Cui, Lijuan; Cheng, Samuel; Ohno-Machado, Lucila

2013-06-01

We developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection, etc.) as the traditional frequentist logistic regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications. Copyright © 2013 Elsevier Inc. All rights reserved.
A computational approach to compare regression modelling strategies in prediction research.

PubMed

Pajouheshnia, Romin; Pestman, Wiebe R; Teerenstra, Steven; Groenwold, Rolf H H

2016-08-25

It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots. The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set. The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.
Cytopathologic differential diagnosis of low-grade urothelial carcinoma and reactive urothelial proliferation in bladder washings: a logistic regression analysis.

PubMed

Cakir, Ebru; Kucuk, Ulku; Pala, Emel Ebru; Sezer, Ozlem; Ekin, Rahmi Gokhan; Cakmak, Ozgur

2017-05-01

Conventional cytomorphologic assessment is the first step to establish an accurate diagnosis in urinary cytology. In cytologic preparations, the separation of low-grade urothelial carcinoma (LGUC) from reactive urothelial proliferation (RUP) can be exceedingly difficult. The bladder washing cytologies of 32 LGUC and 29 RUP were reviewed. The cytologic slides were examined for the presence or absence of the 28 cytologic features. The cytologic criteria showing statistical significance in LGUC were increased numbers of monotonous single (non-umbrella) cells, three-dimensional cellular papillary clusters without fibrovascular cores, irregular bordered clusters, atypical single cells, irregular nuclear overlap, cytoplasmic homogeneity, increased N/C ratio, pleomorphism, nuclear border irregularity, nuclear eccentricity, elongated nuclei, and hyperchromasia (p ˂ 0.05), and the cytologic criteria showing statistical significance in RUP were inflammatory background, mixture of small and large urothelial cells, loose monolayer aggregates, and vacuolated cytoplasm (p ˂ 0.05). When these variables were subjected to a stepwise logistic regression analysis, four features were selected to distinguish LGUC from RUP: increased numbers of monotonous single (non-umbrella) cells, increased nuclear cytoplasmic ratio, hyperchromasia, and presence of small and large urothelial cells (p = 0.0001). By this logistic model of the 32 cases with proven LGUC, the stepwise logistic regression analysis correctly predicted 31 (96.9%) patients with this diagnosis, and of the 29 patients with RUP, the logistic model correctly predicted 26 (89.7%) patients as having this disease. There are several cytologic features to separate LGUC from RUP. Stepwise logistic regression analysis is a valuable tool for determining the most useful cytologic criteria to distinguish these entities. © 2017 APMIS. Published by John Wiley & Sons Ltd.
Science of Test Research Consortium: Year Two Final Report

DTIC Science & Technology

2012-10-02

July 2012. Analysis of an Intervention for Small Unmanned Aerial System ( SUAS ) Accidents, submitted to Quality Engineering, LQEN-2012-0056. Stone... Systems Engineering. Wolf, S. E., R. R. Hill, and J. J. Pignatiello. June 2012. Using Neural Networks and Logistic Regression to Model Small Unmanned ...Human Retina. 6. Wolf, S. E. March 2012. Modeling Small Unmanned Aerial System Mishaps using Logistic Regression and Artificial Neural Networks. 7
Binary Logistic Regression Analysis for Detecting Differential Item Functioning: Effectiveness of R[superscript 2] and Delta Log Odds Ratio Effect Size Measures

ERIC Educational Resources Information Center

Hidalgo, Mª Dolores; Gómez-Benito, Juana; Zumbo, Bruno D.

2014-01-01

The authors analyze the effectiveness of the R[superscript 2] and delta log odds ratio effect size measures when using logistic regression analysis to detect differential item functioning (DIF) in dichotomous items. A simulation study was carried out, and the Type I error rate and power estimates under conditions in which only statistical testing…
Logistic quantile regression provides improved estimates for bounded avian counts: a case study of California Spotted Owl fledgling production

Treesearch

Brian S. Cade; Barry R. Noon; Rick D. Scherer; John J. Keane

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical...
Comparison of four methods for deriving hospital standardised mortality ratios from a single hierarchical logistic regression model.

PubMed

Mohammed, Mohammed A; Manktelow, Bradley N; Hofer, Timothy P

2016-04-01

There is interest in deriving case-mix adjusted standardised mortality ratios so that comparisons between healthcare providers, such as hospitals, can be undertaken in the controversial belief that variability in standardised mortality ratios reflects quality of care. Typically standardised mortality ratios are derived using a fixed effects logistic regression model, without a hospital term in the model. This fails to account for the hierarchical structure of the data - patients nested within hospitals - and so a hierarchical logistic regression model is more appropriate. However, four methods have been advocated for deriving standardised mortality ratios from a hierarchical logistic regression model, but their agreement is not known and neither do we know which is to be preferred. We found significant differences between the four types of standardised mortality ratios because they reflect a range of underlying conceptual issues. The most subtle issue is the distinction between asking how an average patient fares in different hospitals versus how patients at a given hospital fare at an average hospital. Since the answers to these questions are not the same and since the choice between these two approaches is not obvious, the extent to which profiling hospitals on mortality can be undertaken safely and reliably, without resolving these methodological issues, remains questionable. © The Author(s) 2012.
Three methods to construct predictive models using logistic regression and likelihood ratios to facilitate adjustment for pretest probability give similar results.

PubMed

Chan, Siew Foong; Deeks, Jonathan J; Macaskill, Petra; Irwig, Les

2008-01-01

To compare three predictive models based on logistic regression to estimate adjusted likelihood ratios allowing for interdependency between diagnostic variables (tests). This study was a review of the theoretical basis, assumptions, and limitations of published models; and a statistical extension of methods and application to a case study of the diagnosis of obstructive airways disease based on history and clinical examination. Albert's method includes an offset term to estimate an adjusted likelihood ratio for combinations of tests. Spiegelhalter and Knill-Jones method uses the unadjusted likelihood ratio for each test as a predictor and computes shrinkage factors to allow for interdependence. Knottnerus' method differs from the other methods because it requires sequencing of tests, which limits its application to situations where there are few tests and substantial data. Although parameter estimates differed between the models, predicted "posttest" probabilities were generally similar. Construction of predictive models using logistic regression is preferred to the independence Bayes' approach when it is important to adjust for dependency of tests errors. Methods to estimate adjusted likelihood ratios from predictive models should be considered in preference to a standard logistic regression model to facilitate ease of interpretation and application. Albert's method provides the most straightforward approach.
A comparison of three methods of assessing differential item functioning (DIF) in the Hospital Anxiety Depression Scale: ordinal logistic regression, Rasch analysis and the Mantel chi-square procedure.

PubMed

Cameron, Isobel M; Scott, Neil W; Adler, Mats; Reid, Ian C

2014-12-01

It is important for clinical practice and research that measurement scales of well-being and quality of life exhibit only minimal differential item functioning (DIF). DIF occurs where different groups of people endorse items in a scale to different extents after being matched by the intended scale attribute. We investigate the equivalence or otherwise of common methods of assessing DIF. Three methods of measuring age- and sex-related DIF (ordinal logistic regression, Rasch analysis and Mantel χ(2) procedure) were applied to Hospital Anxiety Depression Scale (HADS) data pertaining to a sample of 1,068 patients consulting primary care practitioners. Three items were flagged by all three approaches as having either age- or sex-related DIF with a consistent direction of effect; a further three items identified did not meet stricter criteria for important DIF using at least one method. When applying strict criteria for significant DIF, ordinal logistic regression was slightly less sensitive. Ordinal logistic regression, Rasch analysis and contingency table methods yielded consistent results when identifying DIF in the HADS depression and HADS anxiety scales. Regardless of methods applied, investigators should use a combination of statistical significance, magnitude of the DIF effect and investigator judgement when interpreting the results.
Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Cao, Faxian; Yang, Zhijing; Ren, Jinchang; Ling, Wing-Kuen; Zhao, Huimin; Marshall, Stephen

2017-12-01

Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.
Latin hypercube approach to estimate uncertainty in ground water vulnerability

USGS Publications Warehouse

Gurdak, J.J.; McCray, J.E.; Thyne, G.; Qi, S.L.

2007-01-01

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability. ?? 2007 National Ground Water Association.
Adherence of older women with strength training and aerobic exercise

PubMed Central

Picorelli, Alexandra Miranda Assumpção; Pereira, Daniele Sirineu; Felício, Diogo Carvalho; Dos Anjos, Daniela Maria; Pereira, Danielle Aparecida Gomes; Dias, Rosângela Corrêa; Assis, Marcella Guimarães; Pereira, Leani Souza Máximo

2014-01-01

Background Participation of older people in a program of regular exercise is an effective strategy to minimize the physical decline associated with age. The purpose of this study was to assess adherence rates in older women enrolled in two different exercise programs (one aerobic exercise and one strength training) and identify any associated clinical or functional factors. Methods This was an exploratory observational study in a sample of 231 elderly women of mean age 70.5 years. We used a structured questionnaire with standardized tests to evaluate the relevant clinical and functional measures. A specific adherence questionnaire was developed by the researchers to determine motivators and barriers to exercise adherence. Results The adherence rate was 49.70% in the aerobic exercise group and 56.20% in the strength training group. Multiple logistic regression models for motivation were significant (P=0.003) for the muscle strengthening group (R2=0.310) and also significant (P=0.008) for the aerobic exercise group (R2=0.154). A third regression model for barriers to exercise was significant (P=0.003) only for the muscle strengthening group (R2=0.236). The present study shows no direct relationship between worsening health status and poor adherence. Conclusion Factors related to adherence with exercise in the elderly are multifactorial. PMID:24600212
Dental attendance in preschool children - a prospective study.

PubMed

Leroy, Roos; Bogaerts, Kris; Hoppenbrouwers, Karel; Martens, Luc C; Declerck, Dominique

2013-03-01

At present, our understanding of the use of dental care services is incomplete, certainly where preschool children are concerned. To investigate what proportion of 3- and 5-year-olds living in Flanders (Belgium) have already visited the dentist, to describe parents' experience about their child's dental visit, and to explore factors that may have an impact on children's early dental visit. Data were collected from 1057 children; validated questionnaires were completed, and children were examined by trained dentist at ages 3 and 5. Logistic regression analyses were performed to explain dental attendance. At the age of 3, 62% and by 5 years, 21% had never visited the dentist. The first dental visit was considered a pleasant experience for the majority of children. Multivariable regression analyses revealed that children who were not first born, whose mothers had a higher educational level and whose parents had recently visited the dentist, had significantly higher odds for having visited the dentist at young age. Parents of young children need to be informed about and motivated for an early dental visit. Promotion campaigns should focus on firstborn children, children from less educated parents, and parents who do not regularly see a dentist. © 2012 The Authors. International Journal of Paediatric Dentistry © 2012 BSPD, IAPD and Blackwell Publishing Ltd.
Beyond logistic regression: structural equations modelling for binary variables and its application to investigating unobserved confounders.

PubMed

Kupek, Emil

2006-03-15

Structural equation modelling (SEM) has been increasingly used in medical statistics for solving a system of related regression equations. However, a great obstacle for its wider use has been its difficulty in handling categorical variables within the framework of generalised linear models. A large data set with a known structure among two related outcomes and three independent variables was generated to investigate the use of Yule's transformation of odds ratio (OR) into Q-metric by (OR-1)/(OR+1) to approximate Pearson's correlation coefficients between binary variables whose covariance structure can be further analysed by SEM. Percent of correctly classified events and non-events was compared with the classification obtained by logistic regression. The performance of SEM based on Q-metric was also checked on a small (N = 100) random sample of the data generated and on a real data set. SEM successfully recovered the generated model structure. SEM of real data suggested a significant influence of a latent confounding variable which would have not been detectable by standard logistic regression. SEM classification performance was broadly similar to that of the logistic regression. The analysis of binary data can be greatly enhanced by Yule's transformation of odds ratios into estimated correlation matrix that can be further analysed by SEM. The interpretation of results is aided by expressing them as odds ratios which are the most frequently used measure of effect in medical statistics.
Personality patterns and Smoking behavior among students in Tabriz, Iran

PubMed Central

Fakharri, Ali; Jahani, Ali; Sadeghi-Bazargani, Homayoun; Farahbakhsh, Mostafa; Asl, Asghar Mohammadpour

2017-01-01

Introduction Psychological factors have always been considered for their role on risk taking behavior such as substance abuse, risky driving and smoking. The aim of this study was to determine the association between smoking behavior and potential personality patterns among high school students in Tabriz, Iran. Methods Through a multistage sampling in a cross-sectional study, 1000 students were enrolled to represent the final grade high school student population of Tabriz, Iran in 2013. The personality patterns along with smoking status and some background information were collected through standard questionnaires along with Millon Clinical Multiaxial Inventory-III (MCMI-III). Fourteen personality patterns and ten clinical syndromes. ANOVA and Kruskal Wallis tests were used to compare numeric scales among the study participants, with respect to their smoking status. Stata version 13 statistical software package was used to analyze the data. Multivariate logistic regression was used to predict likelihood of smoking by personality status. Results Two logistic models were developed in both of whom male sex was identified as a determinant of regular smoking (1st model) and ever-smoking (2nd model). Depressive personality increased the likelihood of being a regular smoker by 2.8 times (OR=2.8, 95% CI: 1.3–6.1). The second personality disorder included in the model was sadistic personality with an odds ratio of 7.9 (96% CI: 1.2–53%). Histrionic personality increased the likelihood of experiencing smoking by 2.2 times (OR=2.2, 95% CI: 1.6–3.1) followed by borderline personality (OR=2.8, 95% CI: 0.97–8.1). Conclusion Histrionic and depressive personalities could be considered as strong associates of smoking, followed by borderline and sadistic personalities. A causal relationship couldn’t be assumed unless well controlled longitudinal studies reached the same findings using psychiatric interviews. PMID:28461869

Predictors of postoperative outcomes of cubital tunnel syndrome treatments using multiple logistic regression analysis.

PubMed

Suzuki, Taku; Iwamoto, Takuji; Shizu, Kanae; Suzuki, Katsuji; Yamada, Harumoto; Sato, Kazuki

2017-05-01

This retrospective study was designed to investigate prognostic factors for postoperative outcomes for cubital tunnel syndrome (CubTS) using multiple logistic regression analysis with a large number of patients. Eighty-three patients with CubTS who underwent surgeries were enrolled. The following potential prognostic factors for disease severity were selected according to previous reports: sex, age, type of surgery, disease duration, body mass index, cervical lesion, presence of diabetes mellitus, Workers' Compensation status, preoperative severity, and preoperative electrodiagnostic testing. Postoperative severity of disease was assessed 2 years after surgery by Messina's criteria which is an outcome measure specifically for CubTS. Bivariate analysis was performed to select candidate prognostic factors for multiple linear regression analyses. Multiple logistic regression analysis was conducted to identify the association between postoperative severity and selected prognostic factors. Both bivariate and multiple linear regression analysis revealed only preoperative severity as an independent risk factor for poor prognosis, while other factors did not show any significant association. Although conflicting results exist regarding prognosis of CubTS, this study supports evidence from previous studies and concludes early surgical intervention portends the most favorable prognosis. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.
Occupational factors associated with obesity and leisure-time physical activity among nurses: A cross sectional study

PubMed Central

Chin, Dal Lae; Nam, Soohyun; Lee, Soo-Jeong

2016-01-01

Background and objective Adverse working conditions contribute to obesity and physical inactivity. The purpose of this study was to examine the associations of occupational factors with obesity and leisure-time physical activity among nurses. Methods This study used cross-sectional data of 394 nurses (mean age 48 years, 91% females, 61% white) randomly selected from the California Board of Registered Nursing list. Data on demographic and employment characteristics, musculoskeletal symptom comorbidity, physical and psychosocial occupational factors, body mass index (BMI), and physical activity were collected using postal and on-line surveys from January to July in 2013. Results Of the participants, 31% were overweight and 18% were obese; 41% engaged in regular aerobic physical activity (≥150 min/week) and 57% performed regular muscle-strengthening activity (≥2 days/week). In multivariable logistic regression models, overweight/obesity (BMI ≥ 25 kg/m2) was significantly more common among nurse managers/supervisors (OR = 2.54, 95% CI: 1.16–5.59) and nurses who worked full-time (OR = 2.18, 95% CI: 1.29–3.70) or worked ≥40 h per week (OR = 2.53, 95% CI: 1.58–4.05). Regular aerobic physical activity was significantly associated with high job demand (OR = 1.63, 95% CI: 1.06–2.51). Nurses with passive jobs (low job demand combined with low job control) were significantly less likely to perform aerobic physical activity (OR = 0.49, 95% CI: 0.26–0.93). Regular muscle-strengthening physical activity was significantly less common among nurses working on non-day shifts (OR = 0.55, 95% CI: 0.34–0.89). Physical workload was not associated with obesity and physical activity. Conclusions Our study findings suggest that occupational factors significantly contribute to obesity and physical inactivity among nurses. Occupational characteristics in the work environment should be considered in designing effective workplace health promotion programs targeting physical activity and obesity among nurses. PMID:27045565
Habitual condom use across partner type and sexual position among younger gay and bisexual men: findings from New Zealand HIV behavioural surveillance 2006-2011.

PubMed

Lachowsky, N J; Dewey, C E; Dickson, N P; Saxton, P J W; Hughes, A J; Milhausen, R R; Summerlee, A J S

2015-09-01

Our objectives were to investigate demographic and behavioural factors associated with condom use and to examine how habitual condom use was across partner types and sexual positions among younger men who have sex with men (YMSM), aged 16-29, surveyed in New Zealand. We analysed the 2006-2011 national HIV behavioural surveillance data from YMSM who reported anal intercourse in four scenarios of partner type and sexual position: casual insertive, casual receptive, regular insertive and regular receptive. For each, respondents' condom use was classified as frequent (always/almost always) or otherwise, with associated factors identified with multivariate mixed-effect logistic regression. Habitual condom use across scenarios was examined using a latent variable technique that estimated the intraclass correlation coefficient (ICC). Frequent condom use was reported for 63.6% of 5153 scenarios reported from 2412 YMSM. Frequent use increased from boyfriend to fuckbuddy to casual partners. Infrequent use was associated with online recruitment, Pacific ethnicity, less education, HIV positivity, sex with women, having ≥20 sexual partners versus 1 and reporting insertive and receptive sexual positions. Frequent condom use was associated with having two to five sexual partners versus one and shorter regular partnerships. The ICC=0.865 indicated highly habitual patterns of use; habitual infrequent condom use was most prevalent with regular partners (53.3%) and habitual frequent condom use was most prevalent with casual partners (70.2%) and for either sexual position (50.5% and 49.1%). Habitual condom use among YMSM highlights the value of early, engaging and sustained condom promotion. Public health should provide better and more compelling condom education, training and promotion for YMSM. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
Prevalence and associated factors of sugar-sweetened beverages intake among schoolchildren aged 9 to 17 years from Bogotá, Colombia: the FUPRECOL Study

PubMed

Ramírez-Vélez, Robinson; Fuerte-Celis, Juan Camilo; Martínez-Torres, Javier; Correa-Bautista, Jorge Enrique

2017-03-30

Objective: The aim of the present study was to describe the intake of sugar-sweetened beverages and to examine of associated factors among schoolchildren from Bogotá, Colombia. Methods: From a total of 8,136 schoolchildren and adolescents (age 9-17.9 years) taking part in the FUPRECOL Study. Sugar-sweetened beverages intake was based on intake from “regular soda”, “drink tea” and/or “concentrated juices”. Body weigth, heigth, body mass index (BMI), waist circumference, and percentage body fat by electrical bioimpedance analysis were measured such as adiposity markers. Associated factors (sex, age, abdominal obesity, BMI classification, mothers’ and fathers’ educational level and nutritional status by “Krece plus” questionnaire), were collected by structured questionnaire. Associations were established through a binary logistic regression. Results: Of the subjects, 58.4% were women. According to sex, boys response highest intake of “regular soda” daily/weekly frequency of the 70.9% and 21.0%, respectively, followed by “concentrated juices” (64.4% weekly vs.11.3% daily). In both gender, the prevalence of abdominal obesity was higher in schoolchildren that responded to intake “regular soda” (23.3%), “concentrated juices” (13.2%) and “drink tea” daily (9.7%). Age [OR 1.15 (95%CI 1.03 to 1.28)], mothers’ [OR 1.30 (95%CI 1.03 to 1.65)], and fathers’ [OR 1.34 (95%CI 1.01 to 1.79) low educational level and nutritional status [OR 2.60 (95%CI 2.09 to 3.25)], were associated with daily intake of “regular soda”. Conclusion: Age, parental education level and dietary patterns were associated with sugar-sweetened beverages in schoolchildren in Bogotá, Colombia. We recommended comprehensive interventions which are involved nutritional and educational component among children and adolescents from Bogotá, Colombia.
Patients' perspectives on management and barriers of regular antiepileptic drug intake.

PubMed

May, Theodor W; Berkenfeld, Ralf; Dennig, Dieter; Scheid, Brigitte; Hausfeld, Heiko; Walther, Sonja; Specht, Ulrich

2018-02-01

The aim of our study was to assess the management of drug intake and potential barriers to adherence reported by two different patient groups. The study was performed in cooperation with the Regional Chamber of Pharmacists of Rhineland-Palatinate and three neurologists in private practice specialized in epileptology. In total, 108 patients surveyed in 43 pharmacies (Group P) and 118 patients treated by the specialized neurologists (Group N) completed anonymously a questionnaire on intake of antiepileptic drugs (AEDs). The statistical evaluation was performed using nonparametric tests and logistic regression analyses. Group N more often used adherence aids, compared with Group P (68.6% vs. 46.3%, p<0.01), and the number of doses per day was significantly lower in Group N (Mann-Whitney test, p=0.046), but the percentage of patients who reported problems with the regular intake of their medication did not differ significantly between groups (Group N vs. P: 47.0% vs. 40.0%). If patients noticed that they missed a dose, 45.3% completely skipped the missed dose (Group N vs. P: 43.0% vs. 48.1%, n.s.). In a multivariate analysis, significant risk factors of problems with regular drug intake were age<25yrs. (p<0.01) and patient-reported adverse effect of AED (p<0.01), followed by the number of AED doses per day (p<0.05), while gender, intake habits, usage of adherence aids, and patient-rated efficacy of AEDs were not significant. Patients treated by neurologists specialized in epileptology did not report less problems with adherence than patients surveyed in pharmacies. Since barriers for a regular intake are diverse, the use of a short questionnaire on management of drug intake may lead to an individually tailored counseling of patients to improve adherence. Copyright © 2017 Elsevier Inc. All rights reserved.
Trends in reports of driving following illicit drug consumption among regular drug users in Australia, 2007-2013: Has random roadside drug testing had a deterrent effect?

PubMed

Horyniak, Danielle; Dietze, Paul; Lenton, Simon; Alati, Rosa; Bruno, Raimondo; Matthews, Allison; Breen, Courtney; Burns, Lucy

2017-07-01

Driving following illicit drug consumption ('drug-driving') is a potential road safety risk. Roadside drug testing (RDT) is conducted across Australia with the dual aims of prosecuting drivers with drugs in their system and deterring drug-driving. We examined trends over time in self-reported past six-month drug-driving among sentinel samples of regular drug users and assessed the impact of experiences of RDT on drug-driving among these participants. Data from 1913 people who inject drugs (PWID) and 3140 regular psychostimulant users (RPU) who were first-time participants in a series of repeat cross-sectional sentinel studies conducted in Australian capital cities from 2007 to 2013 and reported driving in the past six months were analysed. Trends over time were assessed using the χ 2 test for trend. Multivariable logistic regressions assessed the relationship between experiences of RDT and recent drug-driving, adjusting for survey year, jurisdiction of residence and socio-demographic and drug use characteristics. The percentage of participants reporting recent (past six months) drug-driving decreased significantly over time among both samples (PWID: 83% [2007] vs. 74% [2013], p<0.001; RPU: 72% vs. 56%, p<0.001), but drug-driving remained prevalent. Lifetime experience of RDT increased significantly over time (PWID: 6% [2007] vs. 32% [2013], p<0.001; RPU: 2% vs. 11%, p<0.001). There were no significant associations between experiencing RDT and drug-driving among either PWID or RPU. Although there is some evidence that drug-driving among key risk groups of regular drug users is declining in Australia, possibly reflecting a general deterrent effect of RDT, experiencing RDT appears to have no specific deterrent effect on drug-driving. Further intervention, with a particular focus on changing attitudes towards drug-driving, may be needed to further reduce this practice among these groups. Copyright © 2017 Elsevier Ltd. All rights reserved.
[Impacts of antiretroviral treatment on drug use and high risk sexual behaviors among HIV-positive MMT clients].

PubMed

Qian, Xiaoai; Cao, Xiaobin; Zhao, Yan; Wang, Changhe; Luo, Wei; Rou, Keming; Zhang, Bo; Min, Xiangdong; Duan, Song; Tang, Renhai; Wu, Zunyou

2015-06-01

To explore the impacts of antiretroviral treatment on drug use and high risk sexual behaviors among HIV-positive MMT clients. A cross-sectional study was conducted in patients undergoing ART (ART-experienced) and patients not undergoing ART (ART-naive) attending MMT in 5 clinics in Yunnan Honghe and Dehong prefectures in 2014. A questionnaire was designed to collect socio-demographic characteristics, ART and MMT information and sexual and drug use behaviors within 3 months before the investigation was conducted. Logistic regression analysis was conducted to identify the predictors for drug use and risky sexual behaviors. A total of 328 cases were included in the analysis, among which 202 were ART-experienced and 126 were ART-naÏve. Among 152 respondents who were sexually active, 61 (40.1%) reported having unprotected sex (UPS) with their regular partners in the prior 3 months. A total of 57.6% (189/328) of the respondents used drugs in the prior 3 months. Multiple logistic regression analysis revealed that younger than 35 years old (OR = 3.57, 95% CI: 1.23-10.37), fertility desire (OR = 4.47, 95% CI: 1.49-13.41), partner being HIV-positive (OR = 4.62, 95% CI: 1.80-11.86), length of MMT attendance less than 5 years (OR = 2.92, 95% CI: 1.14-7.53), agreed that it was necessary to use condom no matter the viral load is high or low (OR = 0.14, 95% CI: 0.04-0.51) were protective factors of UPS in the prior 3 months. Multiple logistic regression analysis revealed that being Han (OR = 0.46, 95% CI: 0.24-0.89), feeling having good health status (OR = 0.39, 95% CI: 0.18-0.85), being enrolled in ART (OR = 0.32, 95% CI: 0.17-0.60) were protective factors for drug use in the prior three months, having contact with drug using friends (OR = 4.41, 95% CI: 2.31-8.29), having experience of missing an MMT dose (OR = 3.47, 95% CI: 1.92-6.29), and not satisfied with current MMT dose (OR = 13.92, 95% CI: 3.24-59.93) were risk factors for drug use during the prior three months. ART was not associated with risky sexual behavior and drug use in the prior 3 months in this population. Future interventions should promote ART among this population, and provide education at the same time to prevent the emergence of cross infections and drug-resistant strains.
Factors Associated with Intention to Donate Blood: Sociodemographic and Past Experience Variables

PubMed Central

Pule, Pule Ishmael; Rachaba, Boitshwarelo; Magafu, Mgaywa Gilbert Mjungu Damas; Habte, Dereje

2014-01-01

Background and Objectives. This study was conducted to assess the level of intention of the general public towards blood donation and the factors associated with it. Methods. A descriptive cross-sectional study was conducted in South-East Botswana amongst participants aged 21–65 years. An interviewer-administered questionnaire was completed for 384 participants. Results. Of the 384 participants, 104 (27.1%) reported that they had donated blood in the past and 269 (70.1%) stated that they were willing to donate blood in the future. Thirteen out of the 104 past donors (12.5%) reported that they had donated blood in the 12 months preceding the survey and only 10 (9.6%) participants reported that they have been regular donors. In the backward logistic regression analysis, the variables that remained significant predictors of the intention to donate blood were secondary education (adjusted odds ratio (AOR) (95% confidence interval (CI)): 2.92 (1.48, 5.77)), tertiary education (AOR (95% CI): 3.83 (1.52, 9.62)), and knowing a family member who had ever donated blood (AOR (95% CI): 2.84 (1.58, 5.12)). Conclusion. Being informed about blood transfusion and its life-saving benefits through either the education system or the experience made people more likely to intend to donate blood. Evidence-based interventions to retain blood donors as regular donors are recommended. PMID:25431742
Factors associated with intention to donate blood: sociodemographic and past experience variables.

PubMed

Pule, Pule Ishmael; Rachaba, Boitshwarelo; Magafu, Mgaywa Gilbert Mjungu Damas; Habte, Dereje

2014-01-01

Background and Objectives. This study was conducted to assess the level of intention of the general public towards blood donation and the factors associated with it. Methods. A descriptive cross-sectional study was conducted in South-East Botswana amongst participants aged 21-65 years. An interviewer-administered questionnaire was completed for 384 participants. Results. Of the 384 participants, 104 (27.1%) reported that they had donated blood in the past and 269 (70.1%) stated that they were willing to donate blood in the future. Thirteen out of the 104 past donors (12.5%) reported that they had donated blood in the 12 months preceding the survey and only 10 (9.6%) participants reported that they have been regular donors. In the backward logistic regression analysis, the variables that remained significant predictors of the intention to donate blood were secondary education (adjusted odds ratio (AOR) (95% confidence interval (CI)): 2.92 (1.48, 5.77)), tertiary education (AOR (95% CI): 3.83 (1.52, 9.62)), and knowing a family member who had ever donated blood (AOR (95% CI): 2.84 (1.58, 5.12)). Conclusion. Being informed about blood transfusion and its life-saving benefits through either the education system or the experience made people more likely to intend to donate blood. Evidence-based interventions to retain blood donors as regular donors are recommended.
Dietary recommendations for infants and toddlers among pediatric dentists in North Carolina.

PubMed

Sim, Chien J; Iida, Hiroko; Vann, William F; Quinonez, Rocio B; Steiner, Michael J

2014-01-01

The purposes of this study were to: describe practice patterns, knowledge, and attitudes of pediatric dentists in North Carolina (N.C.) in delivering dietary recommendations to the parents/caregivers of infants and toddlers; and identify barriers that limit the implementation of related recommendations. Our survey instrument included 30 questions covering eight domains of barriers to guideline adherence. Surveys were mailed to 150 practicing pediatric dentists in N.C. Descriptive and bivariate analyses were performed. Exploratory factor analysis was used to identify subscales and inform the multivariable model. The response rate was 57 percent (86/150), 80 percent of whom reported providing infant and toddler feeding recommendations routinely. Knowledge of and agreement with the recommendation regarding breast-feeding duration was lower than that for bottle-feeding recommendations. Stepwise logistic regression analysis indicated that survey respondents were less likely to provide dietary recommendations regularly to the parents/caregivers of infants and toddlers when they have practice constraints and the respondents disagree with American Academy of Pediatrics (AAP) and American Academy of Pediatric Dentistry (AAPD) recommendations on bottle and juice consumption. Most respondents routinely provide dietary recommendations to the parents/caregivers of infants and toddlers. Disagreement with AAP and AAPD recommendations on bottle, and juice consumption as well as practice constraints impedes practitioners from providing dietary recommendations regularly to the parents/caregivers of infants and toddlers.
Exercising alone versus with others and associations with subjective health status in older Japanese: The JAGES Cohort Study

PubMed Central

Kanamori, Satoru; Takamiya, Tomoko; Inoue, Shigeru; Kai, Yuko; Kawachi, Ichiro; Kondo, Katsunori

2016-01-01

Although exercising with others may have extra health benefits compared to exercising alone, few studies have examined the differences. We sought to examine whether the association of regular exercise to subjective health status differs according to whether people exercise alone and/or with others, adjusting for frequency of exercise. The study was based on the Japan Gerontological Evaluation Study (JAGES) Cohort Study data. Participants were 21,684 subjects aged 65 or older. Multivariable logistic regression models were used to examine the association. The adjusted odds ratios (ORs) for poor self-rated health were significantly lower for people who exercised compared to non-exercisers. In analyses restricted to regular exercisers the ORs for poor health were 0.69 (95% confidence intervals: 0.60–0.79) for individuals exercising alone more often than with others, 0.74 (0.64–0.84) for people who were equally likely to exercise alone as with others, 0.57 (0.43–0.75) for individuals exercising with others more frequently than alone, and 0.79 (0.64–0.97) for individuals only exercising with others compared to individuals only exercising alone. Although exercising alone and exercising with others both seem to have health benefits, increased frequency of exercise with others has important health benefits regardless of the total frequency of exercise. PMID:27974855
Exercising alone versus with others and associations with subjective health status in older Japanese: The JAGES Cohort Study.

PubMed

Kanamori, Satoru; Takamiya, Tomoko; Inoue, Shigeru; Kai, Yuko; Kawachi, Ichiro; Kondo, Katsunori

2016-12-15

Although exercising with others may have extra health benefits compared to exercising alone, few studies have examined the differences. We sought to examine whether the association of regular exercise to subjective health status differs according to whether people exercise alone and/or with others, adjusting for frequency of exercise. The study was based on the Japan Gerontological Evaluation Study (JAGES) Cohort Study data. Participants were 21,684 subjects aged 65 or older. Multivariable logistic regression models were used to examine the association. The adjusted odds ratios (ORs) for poor self-rated health were significantly lower for people who exercised compared to non-exercisers. In analyses restricted to regular exercisers the ORs for poor health were 0.69 (95% confidence intervals: 0.60-0.79) for individuals exercising alone more often than with others, 0.74 (0.64-0.84) for people who were equally likely to exercise alone as with others, 0.57 (0.43-0.75) for individuals exercising with others more frequently than alone, and 0.79 (0.64-0.97) for individuals only exercising with others compared to individuals only exercising alone. Although exercising alone and exercising with others both seem to have health benefits, increased frequency of exercise with others has important health benefits regardless of the total frequency of exercise.
TVs in the bedrooms of children: does it impact health and behavior?

PubMed

Sisson, Susan B; Broyles, Stephanie T; Newton, Robert L; Baker, Birgitta L; Chernausek, Steven D

2011-02-01

To (1) determine socio-demographic characteristics associated with a TV in the bedroom (BTV) and (2) examine relationship of BTV, independent of total viewing time, with social and behavioral characteristics. Children 6-17 years from the 2007 US National Survey of Children's Health were included (n=48,687). BTV, daily TV viewing time, demographic, behavioral and social outcomes (community involvement, social skills, health habits and status, and family) were examined using logistic regression, and adjusted for total viewing time. Overall prevalence of BTV was 49.3% in American children. Older age, non-Hispanic Black (71.3%), Hispanics (56.3%), higher level of poverty (>56.2%), non two-parent biological family structure (>62.6%), Midwest (47.1%), Northeast (46.7%), South Atlantic (56.4%) and South Central (59.8%) region of the country were associated with higher odds of BTV. Female gender (52.7%) and residence in Alaska (33.0%) were associated with lower prevalence of BTV. BTV was associated with higher prevalence of exhibiting problematic social behaviors (29%) and overweight status (44%). BTV was significantly associated with lower prevalence of regular family meals (13%), engagement in school (16%), participation in extracurricular activities (31%), regularly sleeping enough (20%), and participation in community service (25%) after adjustment for total viewing time. BTV appears associated with more social and behavioral indices than previously reported, in addition to total viewing time. Copyright © 2010 Elsevier Inc. All rights reserved.
Welding, a risk factor of lung cancer: the ICARE study.

PubMed

Matrat, Mireille; Guida, Florence; Mattei, Francesca; Cénée, Sylvie; Cyr, Diane; Févotte, Joëlle; Sanchez, Marie; Menvielle, Gwenn; Radoï, Loredana; Schmaus, Annie; Woronoff, Anne-Sophie; Luce, Danièle; Stücker, Isabelle

2016-04-01

We investigated the relationship between lung cancer and occupational exposure to welding activity in ICARE, a population-based case-control study. Analyses were restricted to men (2276 cases, 2780 controls). Welding exposure was assessed through detailed questionnaires, including lifelong occupational history. ORs were computed using unconditional logistic regression, adjusted for lifelong cigarette smoking and occupational exposure to asbestos. Among the regular welders, welding was associated with a risk of lung cancer (OR=1.7, 95% CI 1.1 to 2.5), which increased with the duration (OR=2.0, 95% CI 1.0 to 3.9 when duration >10 years), and was maximum 10-20 years since last welding. The risk was more pronounced in case of gas welding (OR=2.0, 95% CI 1.2 to 3.3), when the workpiece was covered by paint, grease, or other substances (OR=2.0, 95% CI 1.2 to 3.4) and when it was cleaned with chemical substances before welding. No statistically significant increase in lung cancer risk was observed among occasional welders. Although these results should be confirmed, we showed that type of welding and mode of workpiece preparation are important determinants of the lung cancer risk in regular welders. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
Global Evidence on the Association between POS Advertising Bans and Youth Smoking Participation.

PubMed

Shang, Ce; Huang, Jidong; Cheng, Kai-Wen; Li, Qing; Chaloupka, Frank J

2016-03-09

Point-of-sale (POS) tobacco advertising has been linked to youth smoking susceptibility and experimental smoking. However, there is limited evidence of the association between POS advertising bans and youth smoking participation. This study aims to examine how such bans are associated with current smoking, daily smoking, and regular smoking (≥ 1 cigarettes per day) participation among youth. one to two waves (primarily one wave) of the Global Youth Tobacco Survey were conducted in 130 countries between 2007 and 2011. These surveys were linked to the WHO "MPOWER" data using country and year identifiers to analyze the association between POS advertising bans (a dichotomous measure of the existence of such bans) and smoking participation in the past month. Weighted logistic regressions were employed to analyze this association while controlling for age, gender, parents' smoking status, 6 MPOWER policy scores, and GDP per capita. We find that in countries with POS advertising bans, current smoking (OR = 0.73, p ≤ 0.1), daily smoking (OR = 0.70, p ≤ 0.1), and regular smoking (OR = 0.75, p ≤ 0.05) participation in the past month is significantly lower, suggesting that POS promotion bans can potentially reduce youth smoking. This study provides evidence to support the implementation of POS promotion regulations by the US FDA and implementation of the WHO FCTC guidelines regarding restrictions on tobacco POS promotion.
Health Habits of Employees in a Large Medical Center: Time Trends and Impact of a Worksite Wellness Facility.

PubMed

Abu Dabrh, Abd Moain; Gorty, Archana; Jenkins, Sarah M; Murad, Mohammad Hassan; Hensrud, Donald D

2016-02-11

Worksite health interventions are not novel but their effect remains subject of debate. We examined employer-based wellness program to determine health habits trends, and compare prevalence estimates to national data. We conducted serial surveys (1996 and 2007-10) to employees of a large medical center that included questions measuring outcomes, including obesity, regular exercise, cardiovascular activity, and smoking status. Logistic regression models were estimated to compare data by membership across years, considering p-values ≤ 0.01 as statistically significant. 3,206 employees responded (Response rates 59-68%). Obesity prevalence increased over time in members and nonmembers of the wellness facility, consistent with national trends. Members had a lower prevalence of cigarette smoking compared to nonmembers (overall year-adjusted odds ratio 0.66, P < 0.001). Further, employees had a lower prevalence of cigarette smoking (9.7 vs. 17.3% in 2010, P < 0.001) compared with national data. Wellness facility membership was associated with increased regular exercise and cardiovascular exercise (P < 0.001) compared to nonmembers. In summary, working in a medical center was associated with a decreased prevalence of cigarette smoking, but not with lower prevalence of obesity. Worksite wellness facility membership was associated with increased exercise and decreased cigarette smoking. Employer-based interventions may be effective in improving some health behaviors.
Health Habits of Employees in a Large Medical Center: Time Trends and Impact of a Worksite Wellness Facility

PubMed Central

Abu Dabrh, Abd Moain; Gorty, Archana; Jenkins, Sarah M.; Murad, Mohammad Hassan; Hensrud, Donald D.

2016-01-01

Worksite health interventions are not novel but their effect remains subject of debate. We examined employer-based wellness program to determine health habits trends, and compare prevalence estimates to national data. We conducted serial surveys (1996 and 2007–10) to employees of a large medical center that included questions measuring outcomes, including obesity, regular exercise, cardiovascular activity, and smoking status. Logistic regression models were estimated to compare data by membership across years, considering p-values ≤ 0.01 as statistically significant. 3,206 employees responded (Response rates 59–68%). Obesity prevalence increased over time in members and nonmembers of the wellness facility, consistent with national trends. Members had a lower prevalence of cigarette smoking compared to nonmembers (overall year-adjusted odds ratio 0.66, P < 0.001). Further, employees had a lower prevalence of cigarette smoking (9.7 vs. 17.3% in 2010, P < 0.001) compared with national data. Wellness facility membership was associated with increased regular exercise and cardiovascular exercise (P < 0.001) compared to nonmembers. In summary, working in a medical center was associated with a decreased prevalence of cigarette smoking, but not with lower prevalence of obesity. Worksite wellness facility membership was associated with increased exercise and decreased cigarette smoking. Employer-based interventions may be effective in improving some health behaviors. PMID:26864205
Educational career and predictors of type of education in young adults with spina bifida.

PubMed

Barf, H A; Verhoef, M; Post, M W M; Jennekens-Schinkel, A; Gooskens, R H J M; Mullaart, R A; Prevo, A J H

2004-03-01

Children with spina bifida (SB) often require special education. To date, little information is available about the educational career of these children. This study focuses on educational career and predictors of attending special education of young adults with SB, using a cross-sectional study including 178 young Dutch adults with SB aged from 16-25. The main outcome was attending regular versus special education. For searching predictive power we selected age, gender, type of SB, level of lesion, hydrocephalus (HC), number of surgical interventions, ambulation, continence and cognitive functioning. Chi-square tests and binary logistic regression were used in the data analysis. Participants with HC attended special primary education more often (59%) than participants without HC (17%). For those participants with HC, the necessity of special primary education was associated with below average intelligence (75% versus 35%), wheelchair dependence (82% versus 39%) and surgical interventions (74% versus 44%). Only half of the participants with HC followed regular secondary education, whereas for participants with SB without HC, the outcome in secondary education was similar to that of the general population (92%). Intelligence was the main predictor of attending special secondary education (odds 5.1:1), but HC (odds 4.3:1) and wheelchair dependence (odds 2.6:1) were also a significant. Other variables were not significant predictors of special secondary education.
Analgesic and anti-inflammatory drug use and risk of bladder cancer: a population based case control study

PubMed Central

Fortuny, Joan; Kogevinas, Manolis; Zens, Michael S; Schned, Alan; Andrew, Angeline S; Heaney, John; Kelsey, Karl T; Karagas, Margaret R

2007-01-01

Background Use of phenacetin and other analgesic and non-steroidal anti-inflammatory drugs (NSAIDs) potentially influences bladder cancer incidence, but epidemiologic evidence is limited. Methods We analyzed data from 376 incident bladder cancer cases and 463 controls from a population-based case-control study in New Hampshire on whom regular use of analgesic drugs and NSAIDs was obtained. Odds ratios and 95% confidence intervals were computed using logistic regression with adjustment for potentially confounding factors. Separate models by tumor stage, grade and TP53 status were conducted. Results We found an elevated odds ratio (OR) associated with reported use of phenacetin-containing medications, especially with longer duration of use (OR >8 years = 3.00, 95% confidence interval (CI) = 1.4–6.5). In contrast, use of paracetamol did not relate overall to risk of bladder cancer. We also found that regular use of any NSAID was associated with a statistically significant decrease in bladder cancer risk (OR = 0.6, 95% CI = 0.4–0.9), and specifically use of aspirin. Further, the association with NSAID use was largely among invasive, high grade and TP53 positive tumors. Conclusion While these agents have been investigated in several studies, a number of questions remain regarding the effects of analgesic and NSAID use on risk of bladder cancer. PMID:17692123
Prevalence, correlates and attitudes towards sexting among young people in Melbourne, Australia.

PubMed

Yeung, Timothy H; Horyniak, Danielle R; Vella, Alyce M; Hellard, Margaret E; Lim, Megan S C

2014-09-01

Background 'Sexting' is the exchange of sexually explicit material via communication technologies. Despite significant media attention, there has been little examination of sexting in the Australian setting. This study aimed to provide insight into sexting behaviours and attitudes among young Australians. A cross-sectional survey was conducted with a convenience sample of people aged 16-29 years attending a music festival (n=1372). Correlates of lifetime sexting were determined using multivariate logistic regression. Attitudes towards and perceived consequences of sexting were explored in focus group discussions (FGDs) with 39 young people. Forty percent of survey participants reported that they had ever sent or received a sext (48% of males, 36% of females), most commonly with a regular partner. Lower levels of education, greater recreational spending, greater number of sexual partners, inconsistent condom use with a regular partner, identifying as being nonheterosexual and risky alcohol consumption were all independent correlates of sexting. FGD participants made a clear distinction between consensual creating, sending and possessing of sexts, and nonconsensual sharing of sexts. Positive outcomes of consensual sexting included flirting and sexual experimentation, with sexting perceived as a normalised aspect of sexual interaction. Sexting is a common and normalised practice among young Australians. Our findings highlight the distinction in young people's minds between consensual sexting and the nonconsensual sharing or circulation of sexts, which is not currently well recognised in sexuality education, the media or the law.

Factors related to falls among community dwelling elderly.

PubMed

Kuhirunyaratn, Piyathida; Prasomrak, Prasert; Jindawong, Bangonsri

2013-09-01

Falls among the elderly can lead to disability, hospitalization and premature death. This study aimed to determine the factors related to falls among community dwelling elderly. This case-control study was conducted at the Samlium Primary Care Unit (SPCU), Khon Kaen, Thailand. Cases were elderly individuals who had fallen within the previous six months and controls were elderly who had not fallen during that same time period. Subjects were taken from elderly persons registered at the SPCU. The sample size was calculated to be 111 cases and 222 controls. Face to face interviews were conducted with subjects between May and June, 2011. The response rate was 100%. On bivariate analysis, the statistically significant factors related to falls were: regular medication use, co-morbidities, mobility, depression, cluttered rooms, slippery floors, unsupported toilets (without a hand rail), sufficient exercise, rapid posture change and wearing slippers. When controlling for others significant factors, multiple logistic regression revealed significant factors were: regular medication use (AOR: 2.22; 95%CI: 1.19 - 4.12), depression (AOR: 1.76, 95% CI: 1.03 - 2.99), sufficient exercise (AOR: 0.34; 95% CI: 0.19 - 0.58) and wearing slippery shoes (AOR: 2.31; 95% CI: 1.24 - 4.29). Interventions need to be considered to modify these significant factors associated with falls and education should be provided to these at risk.
Breakfast and Other Meal Consumption in Adolescents from Southern Poland

PubMed Central

Ostachowska-Gasior, Agnieszka; Piwowar, Monika; Kwiatkowski, Jacek; Kasperczyk, Janusz; Skop-Lewandowska, Agata

2016-01-01

The aim of the study was to evaluate the frequency of breakfast and other meal consumption by adolescents and to assess the relationship between the first and the last meal consumption and sex, body mass index (BMI), and middle school and high school students’ education level. The study was conducted in 2013–2014 among 3009 students (1658 girls and 1351 boys) from middle s and high schools in Krakow and Silesia (Poland). The data was obtained from questionnaires that were analyzed with a logistic regression model for measurable and dichotomous variables. Breakfast consumers were seen to eat other meals (second breakfast, lunch, dessert, supper) significantly more often than breakfast skippers. The main meal consumption habits depend on sex and change as adolescents age. Being a girl and a high school student predisposed participants to skip breakfast and supper more often. The BMI of breakfast consumers does not differ significantly from the BMI of breakfast skippers, so BMI might thus not be a sufficient marker of breakfast consumption regularity and dietary habits in an adolescent group. The importance of regularly eaten meals, especially breakfast, together with adequate daily dietary energy intake are beneficial for physical and psychological development and cannot be overestimated in nutritional education and it is necessary to promote healthy eating behavior for well-being in later adult life. PMID:27136572
Adherence to physical activity in adults with chronic diseases: ELSA-Brasil.

PubMed

Forechi, Ludimila; Mill, José Geraldo; Griep, Rosane Härter; Santos, Itamar; Pitanga, Francisco; Molina, Maria Del Carmen Bisi

2018-04-09

The objective of this study is to investigate the adherence and the factors that influence adherence to physical activity in adults with dyslipidemia, hypertension, or diabetes. The analyses were based on data collected at the baseline of the 14,521 participants from the study ELSA-Brasil aged between 35 and 74 years. The level of leisure time physical activity was determined using the International Physical Activity Questionnaire. Logistic regression analyses were performed to examine the influence of the demographic data, socioeconomic conditions, perceived health status, and access to exercise facilities in the neighborhood on adherence to physical activity. Men with hypertension and dyslipidemia were more active than women. The results show that 17.8%, 15.1%, and 13.9% of the subjects who reported dyslipidemia, hypertension, and diabetes, respectively, adhere to the physical activity recommendations. The factors positively associated with adherence were higher education and income. Older individuals who reported poor perceived health, were overweight and obese, regularly smoked, and had fewer opportunities to exercise in the neighborhood presented lower adherence. The number of adults with dyslipidemia, hypertension, and diabetes who adhere to the physical activity recommendations is very low. Higher education and income are positively associated with adherence, while age, excess body weight, negative perceived health, regular smoking, and lack of opportunity to exercise in the neighborhood were considered barriers to physical activity.
General health influences episodes of xerostomia: a prospective population-based study.

PubMed

da Silva, Luciana; Kupek, Emil; Peres, Karen G

2017-04-01

The aim of this study was to investigate the associated factors of changes in symptoms of xerostomia (SOX) in adults aged 20-59. A prospective population-based study was conducted in 2009 (n = 1720) and 2012 (n = 1222) in the urban area of Florianópolis, SC, Brazil. Information on SOX was collected in both years together with age, family income, years of schooling, smoking habit, alcohol consumption, changes in the body mass index (BMI; kg/m²), medicine use, self-reported diagnosis of chronic diseases, change in hypertension status and in the use and need for dentures, and number of remaining teeth. Associated factors with changes in SOX were investigated using multinomial logistic regression, considering those who had never reported this symptom as the reference. Prevalence of regular SOX was equal to 3.8% (95% CI: 2.9-5.1) and irregular (one period only) equal to 12.2% (95% CI: 10.2-14.5). Age, smoking habit, medicine use, self-reported diagnosis of depression, and weight gain increased the probability of regular SOX, whereas highest schooling level was associated with lower probability of this symptom. General and psychosocial health influenced the number of episodes of xerostomia symptoms, calling for multidisciplinary actions to prevent common risk behaviors for oral and general diseases. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Does Gender Discrimination Impact Regular Mammography Screening? Findings from the Race Differences in Screening Mammography Study

PubMed Central

DAILEY, AMY B.; KASL, STANISLAV V.; JONES, BETH A.

2011-01-01

Objective To determine if gender discrimination, conceptualized as a negative life stressor, is a deterrent to adherence to mammography screening guidelines. Methods African American and white women (1451) aged 40–79 years who obtained an index screening mammogram at one of five urban hospitals in Connecticut between October 1996 and January 1998 were enrolled in this study. This logistic regression analysis includes the 1229 women who completed telephone interviews at baseline and follow-up (average 29.4 months later) and for whom the study outcome, nonadherence to age-specific mammography screening guidelines, was determined. Gender discrimination was measured as lifetime experience in seven possible situations. Results Gender discrimination, reported by nearly 38% of the study population, was significantly associated with nonadherence to mammography guidelines in women with annual family incomes of ≥$50,000 (OR 1.99, 95% CI 1.33, 2.98) and did not differ across racial/ethnic group. Conclusions Our findings suggest that gender discrimination can adversely influence regular mammography screening in some women. With nearly half of women nonadherent to screening mammography guidelines in this study and with decreasing mammography rates nationwide, it is important to address the complexity of nonadherence across subgroups of women. Life stressors, such as experiences of gender discrimination, may have considerable consequences, potentially influencing health prevention prioritization in women. PMID:18321171
Does gender discrimination impact regular mammography screening? Findings from the race differences in screening mammography study.

PubMed

Dailey, Amy B; Kasl, Stanislav V; Jones, Beth A

2008-03-01

ABSTRACT Objective: To determine if gender discrimination, conceptualized as a negative life stressor, is a deterrent to adherence to mammography screening guidelines. African American and white women (1451) aged 40-79 years who obtained an index screening mammogram at one of five urban hospitals in Connecticut between October 1996 and January 1998 were enrolled in this study. This logistic regression analysis includes the 1229 women who completed telephone interviews at baseline and follow-up (average 29.4 months later) and for whom the study outcome, nonadherence to age-specific mammography screening guidelines, was determined. Gender discrimination was measured as lifetime experience in seven possible situations. Gender discrimination, reported by nearly 38% of the study population, was significantly associated with nonadherence to mammography guidelines in women with annual family incomes of > or =$50,000 (OR 1.99, 95% CI 1.33, 2.98) and did not differ across racial/ethnic group. Our findings suggest that gender discrimination can adversely influence regular mammography screening in some women. With nearly half of women nonadherent to screening mammography guidelines in this study and with decreasing mammography rates nationwide, it is important to address the complexity of nonadherence across subgroups of women. Life stressors, such as experiences of gender discrimination, may have considerable consequences, potentially influencing health prevention prioritization in women.
A Logistic Regression Analysis of Turkey's 15-Year-Olds' Scoring above the OECD Average on the PISA'09 Reading Assessment

ERIC Educational Resources Information Center

Kasapoglu, Koray

2014-01-01

This study aims to investigate which factors are associated with Turkey's 15-year-olds' scoring above the OECD average (493) on the PISA'09 reading assessment. Collected from a total of 4,996 15-year-old students from Turkey, data were analyzed by logistic regression analysis in order to model the data of students who were split into two: (1)…
Upgrade Summer Severe Weather Tool

NASA Technical Reports Server (NTRS)

Watson, Leela

2011-01-01

The goal of this task was to upgrade to the existing severe weather database by adding observations from the 2010 warm season, update the verification dataset with results from the 2010 warm season, use statistical logistic regression analysis on the database and develop a new forecast tool. The AMU analyzed 7 stability parameters that showed the possibility of providing guidance in forecasting severe weather, calculated verification statistics for the Total Threat Score (TTS), and calculated warm season verification statistics for the 2010 season. The AMU also performed statistical logistic regression analysis on the 22-year severe weather database. The results indicated that the logistic regression equation did not show an increase in skill over the previously developed TTS. The equation showed less accuracy than TTS at predicting severe weather, little ability to distinguish between severe and non-severe weather days, and worse standard categorical accuracy measures and skill scores over TTS.
Estimating the Probability of Rare Events Occurring Using a Local Model Averaging.

PubMed

Chen, Jin-Hua; Chen, Chun-Shu; Huang, Meng-Fan; Lin, Hung-Chih

2016-10-01

In statistical applications, logistic regression is a popular method for analyzing binary data accompanied by explanatory variables. But when one of the two outcomes is rare, the estimation of model parameters has been shown to be severely biased and hence estimating the probability of rare events occurring based on a logistic regression model would be inaccurate. In this article, we focus on estimating the probability of rare events occurring based on logistic regression models. Instead of selecting a best model, we propose a local model averaging procedure based on a data perturbation technique applied to different information criteria to obtain different probability estimates of rare events occurring. Then an approximately unbiased estimator of Kullback-Leibler loss is used to choose the best one among them. We design complete simulations to show the effectiveness of our approach. For illustration, a necrotizing enterocolitis (NEC) data set is analyzed. © 2016 Society for Risk Analysis.
Evaluating the perennial stream using logistic regression in central Taiwan

NASA Astrophysics Data System (ADS)

Ruljigaljig, T.; Cheng, Y. S.; Lin, H. I.; Lee, C. H.; Yu, T. T.

2014-12-01

This study produces a perennial stream head potential map, based on a logistic regression method with a Geographic Information System (GIS). Perennial stream initiation locations, indicates the location of the groundwater and surface contact, were identified in the study area from field survey. The perennial stream potential map in central Taiwan was constructed using the relationship between perennial stream and their causative factors, such as Catchment area, slope gradient, aspect, elevation, groundwater recharge and precipitation. Here, the field surveys of 272 streams were determined in the study area. The areas under the curve for logistic regression methods were calculated as 0.87. The results illustrate the importance of catchment area and groundwater recharge as key factors within the model. The results obtained from the model within the GIS were then used to produce a map of perennial stream and estimate the location of perennial stream head.
The use of logistic regression to enhance risk assessment and decision making by mental health administrators.

PubMed

Menditto, Anthony A; Linhorst, Donald M; Coleman, James C; Beck, Niels C

2006-04-01

Development of policies and procedures to contend with the risks presented by elopement, aggression, and suicidal behaviors are long-standing challenges for mental health administrators. Guidance in making such judgments can be obtained through the use of a multivariate statistical technique known as logistic regression. This procedure can be used to develop a predictive equation that is mathematically formulated to use the best combination of predictors, rather than considering just one factor at a time. This paper presents an overview of logistic regression and its utility in mental health administrative decision making. A case example of its application is presented using data on elopements from Missouri's long-term state psychiatric hospitals. Ultimately, the use of statistical prediction analyses tempered with differential qualitative weighting of classification errors can augment decision-making processes in a manner that provides guidance and flexibility while wrestling with the complex problem of risk assessment and decision making.
An application in identifying high-risk populations in alternative tobacco product use utilizing logistic regression and CART: a heuristic comparison.

PubMed

Lei, Yang; Nollen, Nikki; Ahluwahlia, Jasjit S; Yu, Qing; Mayo, Matthew S

2015-04-09

Other forms of tobacco use are increasing in prevalence, yet most tobacco control efforts are aimed at cigarettes. In light of this, it is important to identify individuals who are using both cigarettes and alternative tobacco products (ATPs). Most previous studies have used regression models. We conducted a traditional logistic regression model and a classification and regression tree (CART) model to illustrate and discuss the added advantages of using CART in the setting of identifying high-risk subgroups of ATP users among cigarettes smokers. The data were collected from an online cross-sectional survey administered by Survey Sampling International between July 5, 2012 and August 15, 2012. Eligible participants self-identified as current smokers, African American, White, or Latino (of any race), were English-speaking, and were at least 25 years old. The study sample included 2,376 participants and was divided into independent training and validation samples for a hold out validation. Logistic regression and CART models were used to examine the important predictors of cigarettes + ATP users. The logistic regression model identified nine important factors: gender, age, race, nicotine dependence, buying cigarettes or borrowing, whether the price of cigarettes influences the brand purchased, whether the participants set limits on cigarettes per day, alcohol use scores, and discrimination frequencies. The C-index of the logistic regression model was 0.74, indicating good discriminatory capability. The model performed well in the validation cohort also with good discrimination (c-index = 0.73) and excellent calibration (R-square = 0.96 in the calibration regression). The parsimonious CART model identified gender, age, alcohol use score, race, and discrimination frequencies to be the most important factors. It also revealed interesting partial interactions. The c-index is 0.70 for the training sample and 0.69 for the validation sample. The misclassification rate was 0.342 for the training sample and 0.346 for the validation sample. The CART model was easier to interpret and discovered target populations that possess clinical significance. This study suggests that the non-parametric CART model is parsimonious, potentially easier to interpret, and provides additional information in identifying the subgroups at high risk of ATP use among cigarette smokers.
Determination of osteoporosis risk factors using a multiple logistic regression model in postmenopausal Turkish women.

PubMed

Akkus, Zeki; Camdeviren, Handan; Celik, Fatma; Gur, Ali; Nas, Kemal

2005-09-01

To determine the risk factors of osteoporosis using a multiple binary logistic regression method and to assess the risk variables for osteoporosis, which is a major and growing health problem in many countries. We presented a case-control study, consisting of 126 postmenopausal healthy women as control group and 225 postmenopausal osteoporotic women as the case group. The study was carried out in the Department of Physical Medicine and Rehabilitation, Dicle University, Diyarbakir, Turkey between 1999-2002. The data from the 351 participants were collected using a standard questionnaire that contains 43 variables. A multiple logistic regression model was then used to evaluate the data and to find the best regression model. We classified 80.1% (281/351) of the participants using the regression model. Furthermore, the specificity value of the model was 67% (84/126) of the control group while the sensitivity value was 88% (197/225) of the case group. We found the distribution of residual values standardized for final model to be exponential using the Kolmogorow-Smirnow test (p=0.193). The receiver operating characteristic curve was found successful to predict patients with risk for osteoporosis. This study suggests that low levels of dietary calcium intake, physical activity, education, and longer duration of menopause are independent predictors of the risk of low bone density in our population. Adequate dietary calcium intake in combination with maintaining a daily physical activity, increasing educational level, decreasing birth rate, and duration of breast-feeding may contribute to healthy bones and play a role in practical prevention of osteoporosis in Southeast Anatolia. In addition, the findings of the present study indicate that the use of multivariate statistical method as a multiple logistic regression in osteoporosis, which maybe influenced by many variables, is better than univariate statistical evaluation.
Classification and regression tree analysis of acute-on-chronic hepatitis B liver failure: Seeing the forest for the trees.

PubMed

Shi, K-Q; Zhou, Y-Y; Yan, H-D; Li, H; Wu, F-L; Xie, Y-Y; Braddock, M; Lin, X-Y; Zheng, M-H

2017-02-01

At present, there is no ideal model for predicting the short-term outcome of patients with acute-on-chronic hepatitis B liver failure (ACHBLF). This study aimed to establish and validate a prognostic model by using the classification and regression tree (CART) analysis. A total of 1047 patients from two separate medical centres with suspected ACHBLF were screened in the study, which were recognized as derivation cohort and validation cohort, respectively. CART analysis was applied to predict the 3-month mortality of patients with ACHBLF. The accuracy of the CART model was tested using the area under the receiver operating characteristic curve, which was compared with the model for end-stage liver disease (MELD) score and a new logistic regression model. CART analysis identified four variables as prognostic factors of ACHBLF: total bilirubin, age, serum sodium and INR, and three distinct risk groups: low risk (4.2%), intermediate risk (30.2%-53.2%) and high risk (81.4%-96.9%). The new logistic regression model was constructed with four independent factors, including age, total bilirubin, serum sodium and prothrombin activity by multivariate logistic regression analysis. The performances of the CART model (0.896), similar to the logistic regression model (0.914, P=.382), exceeded that of MELD score (0.667, P<.001). The results were confirmed in the validation cohort. We have developed and validated a novel CART model superior to MELD for predicting three-month mortality of patients with ACHBLF. Thus, the CART model could facilitate medical decision-making and provide clinicians with a validated practical bedside tool for ACHBLF risk stratification. © 2016 John Wiley & Sons Ltd.
Identification of immune correlates of protection in Shigella infection by application of machine learning.

PubMed

Arevalillo, Jorge M; Sztein, Marcelo B; Kotloff, Karen L; Levine, Myron M; Simon, Jakub K

2017-10-01

Immunologic correlates of protection are important in vaccine development because they give insight into mechanisms of protection, assist in the identification of promising vaccine candidates, and serve as endpoints in bridging clinical vaccine studies. Our goal is the development of a methodology to identify immunologic correlates of protection using the Shigella challenge as a model. The proposed methodology utilizes the Random Forests (RF) machine learning algorithm as well as Classification and Regression Trees (CART) to detect immune markers that predict protection, identify interactions between variables, and define optimal cutoffs. Logistic regression modeling is applied to estimate the probability of protection and the confidence interval (CI) for such a probability is computed by bootstrapping the logistic regression models. The results demonstrate that the combination of Classification and Regression Trees and Random Forests complements the standard logistic regression and uncovers subtle immune interactions. Specific levels of immunoglobulin IgG antibody in blood on the day of challenge predicted protection in 75% (95% CI 67-86). Of those subjects that did not have blood IgG at or above a defined threshold, 100% were protected if they had IgA antibody secreting cells above a defined threshold. Comparison with the results obtained by applying only logistic regression modeling with standard Akaike Information Criterion for model selection shows the usefulness of the proposed method. Given the complexity of the immune system, the use of machine learning methods may enhance traditional statistical approaches. When applied together, they offer a novel way to quantify important immune correlates of protection that may help the development of vaccines. Copyright © 2017 Elsevier Inc. All rights reserved.
The quest for conditional independence in prospectivity modeling: weights-of-evidence, boost weights-of-evidence, and logistic regression

NASA Astrophysics Data System (ADS)

Schaeben, Helmut; Semmler, Georg

2016-09-01

The objective of prospectivity modeling is prediction of the conditional probability of the presence T = 1 or absence T = 0 of a target T given favorable or prohibitive predictors B, or construction of a two classes 0,1 classification of T. A special case of logistic regression called weights-of-evidence (WofE) is geologists' favorite method of prospectivity modeling due to its apparent simplicity. However, the numerical simplicity is deceiving as it is implied by the severe mathematical modeling assumption of joint conditional independence of all predictors given the target. General weights of evidence are explicitly introduced which are as simple to estimate as conventional weights, i.e., by counting, but do not require conditional independence. Complementary to the regression view is the classification view on prospectivity modeling. Boosting is the construction of a strong classifier from a set of weak classifiers. From the regression point of view it is closely related to logistic regression. Boost weights-of-evidence (BoostWofE) was introduced into prospectivity modeling to counterbalance violations of the assumption of conditional independence even though relaxation of modeling assumptions with respect to weak classifiers was not the (initial) purpose of boosting. In the original publication of BoostWofE a fabricated dataset was used to "validate" this approach. Using the same fabricated dataset it is shown that BoostWofE cannot generally compensate lacking conditional independence whatever the consecutively processing order of predictors. Thus the alleged features of BoostWofE are disproved by way of counterexamples, while theoretical findings are confirmed that logistic regression including interaction terms can exactly compensate violations of joint conditional independence if the predictors are indicators.
Breast and cervical cancer-screening uptake among females in Ardabil, northwest Iran: a community-based study.

PubMed

Farzaneh, Esmaeil; Heydari, Heshmatolah; Shekarchi, Ali Akbar; Kamran, Aziz

2017-01-01

Breast and cervical cancers are the most commonly diagnosed type of cancer and cause of cancer-related deaths in Iranian females. In contrast to previous studies, this study was carried out with a large sample size for assessment of breast self-examination (BSE)-, clinical breast examination (CBE)-, mammography-, and Pap smear-uptake rates and determination of associations among these screening behaviors with sociodemographic and cognitive variables in Azeri females. This was a cross-sectional, community-based study that was carried out among 1,134 females 20-60 years old during March-June 2016. Data-collection variables included sociodemographic questions, screening behaviors for breast and cervical cancer, self-efficacy, beliefs, and barriers to breast and cervical cancer screening. Collected data were analyzed by SPSS version 13 using χ 2 , Mann-Whitney U , and logistic regression tests. Among the 1,134 participants, 53.9%, 9.8%, and 28.1% had done BSE, CBE, and Pap smear tests, respectively, and among the 625 females aged >40 years, 187 (29.9%) had done the mammography test. Moreover, 416 (36.7%), 103 (16.5%), and 64 (5.6%) females had done BSE, mammography, and CBE regularly, respectively. Beliefs, barriers, income, health insurance, number of children, and age were all important factors for BSE and regular BSE and mammography. Females who had high belief scores were more likely to undertake mammography (odds ratio [OR]: 1.2, 95% confidence interval [CI]: 1.03-1.5), regular mammography (OR: 4.2, 95% CI: 1.9-9.3), regular CBE (OR: 1.25, 95% CI: 1.2-1.3), and Pap smears (OR: 1.2, 95% CI: 1.1-1.4). Also, females who had high self-efficacy scores were more likely to perform regular BSE (OR: 1.8, 95% CI: 1.4-2.5) and mammography (OR: 2.5, 95% CI: 1.4-4.6) than females with lower self-efficacy scores. The frequency of breast and cervical cancer screening was low in our study. The findings of this study indicated that beliefs, self-efficacy, and barriers were important predictive factors of cancer-screening behavior among the females studied.
The Association Between Developmental Assets and Sexual Enjoyment among Emerging Adults in the United States

PubMed Central

Galinsky, Adena M.; Sonenstein, Freya L.

2010-01-01

Purpose To examine the associations between three key developmental assets and an aspect of sexual health, sexual enjoyment, which has rarely been studied in young adults, although its importance is stressed in all recent sexual health policy statements. Methods Using data from Wave III (2001 – 2002) of the National Longitudinal Study of Adolescent Health, and multiple logistic and ordered logistic regression, we explored the associations between sexual pleasure and autonomy, self-esteem and empathy among 3,237 respondents ages 18–26 in heterosexual relationships of three or more month duration. We also examined the distribution of sexual pleasure across various socio-demographic groups. Results Compared to young women, young men reported more regular orgasms and more enjoyment of two kinds of partnered sexual behavior. Sexual enjoyment was not associated with age, race/ethnicity or socioeconomic status. Among women, autonomy, self-esteem, and empathy co-varied positively with all three sexual enjoyment measures. Among men, all associations were in the same direction, but not all were statistically significant. Conclusions A substantial gender difference in enjoyment of partnered sexual behavior exists among emerging adults in the United States. This study is the first to use a representative population sample to find a relationship between developmental assets and a positive aspect of sexual health - sexual pleasure. PMID:21575822
A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data.

PubMed

Pandey, Gaurav; Pandey, Om P; Rogers, Angela J; Ahsen, Mehmet E; Hoffman, Gabriel E; Raby, Benjamin A; Weiss, Scott T; Schadt, Eric E; Bunyavanich, Supinda

2018-06-11

Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal samples. A machine learning-based pipeline identified an asthma classifier consisting of 90 genes interpreted via an L2-regularized logistic regression classification model. This classifier performed with strong predictive value and sensitivity across eight test sets, including (1) a test set of independent asthmatic and control subjects profiled by RNA sequencing (positive and negative predictive values of 1.00 and 0.96, respectively; AUC of 0.994), (2) two independent case-control cohorts of asthma profiled by microarray, and (3) five cohorts with other respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, smoking), where the classifier had a low to zero misclassification rate. Following validation in large, prospective cohorts, this classifier could be developed into a nasal biomarker of asthma.
Social influence and motivation to change health behaviors among Mexican origin adults: Implications for diet and physical activity

PubMed Central

Ashida, Sato; Wilkinson, Anna V.; Koehly, Laura M.

2011-01-01

Purpose To evaluate whether influence from social network members is associated with motivation to change dietary and physical activity behaviors. Design Baseline assessment followed by mailing of family health history-based personalized messages (2 weeks) and follow-up assessment (3 months). Setting Families from an ongoing population-based cohort in Houston, TX. Subjects 475 adults from 161 Mexican origin families. Out of 347 households contacted, 162 (47%) participated. Measures Family health history, social networks, and motivation to change behaviors. Analysis Two-level logistic regression modeling. Results Having at least one network member who encourages one to eat more fruits and vegetables (p=.010) and to engage in regular physical activity (p=.046) was associated with motivation to change the relevant behavior. About 40% of the participants did not have encouragers for these behaviors. Conclusions Identification of new encouragers within networks and targeting natural encouragers (e.g., children, spouses) may increase the efficacy of interventions to motivate behavioral changes among Mexican origin adults. PMID:22208416

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