Modeling Tetanus Neonatorum case using the regression of negative binomial and zero-inflated negative binomial

NASA Astrophysics Data System (ADS)

Amaliana, Luthfatul; Sa'adah, Umu; Wayan Surya Wardhani, Ni

2017-12-01

Tetanus Neonatorum is an infectious disease that can be prevented by immunization. The number of Tetanus Neonatorum cases in East Java Province is the highest in Indonesia until 2015. Tetanus Neonatorum data contain over dispersion and big enough proportion of zero-inflation. Negative Binomial (NB) regression is an alternative method when over dispersion happens in Poisson regression. However, the data containing over dispersion and zero-inflation are more appropriately analyzed by using Zero-Inflated Negative Binomial (ZINB) regression. The purpose of this study are: (1) to model Tetanus Neonatorum cases in East Java Province with 71.05 percent proportion of zero-inflation by using NB and ZINB regression, (2) to obtain the best model. The result of this study indicates that ZINB is better than NB regression with smaller AIC.

Work performance decrements are associated with Australian working conditions, particularly the demand to work longer hours.

PubMed

Holden, Libby; Scuffham, Paul A; Hilton, Michael F; Vecchio, Nerina N; Whiteford, Harvey A

2010-03-01

To demonstrate the importance of including a range of working conditions in models exploring the association between health- and work-related performance. The Australian Work Outcomes Research Cost-benefit study cross-sectional screening data set was used to explore health-related absenteeism and work performance losses on a sample of approximately 78,000 working Australians, including available demographic and working condition factors. Data collected using the World Health Organization Health and Productivity Questionnaire were analyzed with negative binomial logistic regression and multinomial logistic regressions for absenteeism and work performance, respectively. Hours expected to work, annual wage, and job insecurity play a vital role in the association between health- and work-related performance for both work attendance and self-reported work performance. Australian working conditions are contributing to both absenteeism and low work performance, regardless of health status.

Censored Hurdle Negative Binomial Regression (Case Study: Neonatorum Tetanus Case in Indonesia)

NASA Astrophysics Data System (ADS)

Yuli Rusdiana, Riza; Zain, Ismaini; Wulan Purnami, Santi

2017-06-01

Hurdle negative binomial model regression is a method that can be used for discreate dependent variable, excess zero and under- and overdispersion. It uses two parts approach. The first part estimates zero elements from dependent variable is zero hurdle model and the second part estimates not zero elements (non-negative integer) from dependent variable is called truncated negative binomial models. The discrete dependent variable in such cases is censored for some values. The type of censor that will be studied in this research is right censored. This study aims to obtain the parameter estimator hurdle negative binomial regression for right censored dependent variable. In the assessment of parameter estimation methods used Maximum Likelihood Estimator (MLE). Hurdle negative binomial model regression for right censored dependent variable is applied on the number of neonatorum tetanus cases in Indonesia. The type data is count data which contains zero values in some observations and other variety value. This study also aims to obtain the parameter estimator and test statistic censored hurdle negative binomial model. Based on the regression results, the factors that influence neonatorum tetanus case in Indonesia is the percentage of baby health care coverage and neonatal visits.

[Application of negative binomial regression and modified Poisson regression in the research of risk factors for injury frequency].

PubMed

Cao, Qingqing; Wu, Zhenqiang; Sun, Ying; Wang, Tiezhu; Han, Tengwei; Gu, Chaomei; Sun, Yehuan

2011-11-01

To Eexplore the application of negative binomial regression and modified Poisson regression analysis in analyzing the influential factors for injury frequency and the risk factors leading to the increase of injury frequency. 2917 primary and secondary school students were selected from Hefei by cluster random sampling method and surveyed by questionnaire. The data on the count event-based injuries used to fitted modified Poisson regression and negative binomial regression model. The risk factors incurring the increase of unintentional injury frequency for juvenile students was explored, so as to probe the efficiency of these two models in studying the influential factors for injury frequency. The Poisson model existed over-dispersion (P < 0.0001) based on testing by the Lagrangemultiplier. Therefore, the over-dispersion dispersed data using a modified Poisson regression and negative binomial regression model, was fitted better. respectively. Both showed that male gender, younger age, father working outside of the hometown, the level of the guardian being above junior high school and smoking might be the results of higher injury frequencies. On a tendency of clustered frequency data on injury event, both the modified Poisson regression analysis and negative binomial regression analysis can be used. However, based on our data, the modified Poisson regression fitted better and this model could give a more accurate interpretation of relevant factors affecting the frequency of injury.

Performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data.

PubMed

Yelland, Lisa N; Salter, Amy B; Ryan, Philip

2011-10-15

Modified Poisson regression, which combines a log Poisson regression model with robust variance estimation, is a useful alternative to log binomial regression for estimating relative risks. Previous studies have shown both analytically and by simulation that modified Poisson regression is appropriate for independent prospective data. This method is often applied to clustered prospective data, despite a lack of evidence to support its use in this setting. The purpose of this article is to evaluate the performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data, by using generalized estimating equations to account for clustering. A simulation study is conducted to compare log binomial regression and modified Poisson regression for analyzing clustered data from intervention and observational studies. Both methods generally perform well in terms of bias, type I error, and coverage. Unlike log binomial regression, modified Poisson regression is not prone to convergence problems. The methods are contrasted by using example data sets from 2 large studies. The results presented in this article support the use of modified Poisson regression as an alternative to log binomial regression for analyzing clustered prospective data when clustering is taken into account by using generalized estimating equations.

Marginalized zero-inflated negative binomial regression with application to dental caries

PubMed Central

Preisser, John S.; Das, Kalyan; Long, D. Leann; Divaris, Kimon

2015-01-01

The zero-inflated negative binomial regression model (ZINB) is often employed in diverse fields such as dentistry, health care utilization, highway safety, and medicine to examine relationships between exposures of interest and overdispersed count outcomes exhibiting many zeros. The regression coefficients of ZINB have latent class interpretations for a susceptible subpopulation at risk for the disease/condition under study with counts generated from a negative binomial distribution and for a non-susceptible subpopulation that provides only zero counts. The ZINB parameters, however, are not well-suited for estimating overall exposure effects, specifically, in quantifying the effect of an explanatory variable in the overall mixture population. In this paper, a marginalized zero-inflated negative binomial regression (MZINB) model for independent responses is proposed to model the population marginal mean count directly, providing straightforward inference for overall exposure effects based on maximum likelihood estimation. Through simulation studies, the finite sample performance of MZINB is compared to marginalized zero-inflated Poisson, Poisson, and negative binomial regression. The MZINB model is applied in the evaluation of a school-based fluoride mouthrinse program on dental caries in 677 children. PMID:26568034

Enrollment Management in Medical School Admissions: A Novel Evidence-Based Approach at One Institution.

PubMed

Burkhardt, John C; DesJardins, Stephen L; Teener, Carol A; Gay, Steven E; Santen, Sally A

2016-11-01

In higher education, enrollment management has been developed to accurately predict the likelihood of enrollment of admitted students. This allows evidence to dictate numbers of interviews scheduled, offers of admission, and financial aid package distribution. The applicability of enrollment management techniques for use in medical education was tested through creation of a predictive enrollment model at the University of Michigan Medical School (U-M). U-M and American Medical College Application Service data (2006-2014) were combined to create a database including applicant demographics, academic application scores, institutional financial aid offer, and choice of school attended. Binomial logistic regression and multinomial logistic regression models were estimated in order to study factors related to enrollment at the local institution versus elsewhere and to groupings of competing peer institutions. A predictive analytic "dashboard" was created for practical use. Both models were significant at P < .001 and had similar predictive performance. In the binomial model female, underrepresented minority students, grade point average, Medical College Admission Test score, admissions committee desirability score, and most individual financial aid offers were significant (P < .05). The significant covariates were similar in the multinomial model (excluding female) and provided separate likelihoods of students enrolling at different institutional types. An enrollment-management-based approach would allow medical schools to better manage the number of students they admit and target recruitment efforts to improve their likelihood of success. It also performs a key institutional research function for understanding failed recruitment of highly desirable candidates.

Dental enamel defects, caries experience and oral health-related quality of life: a cohort study.

PubMed

Arrow, P

2017-06-01

The impact of enamel defects of the first permanent molars on caries experience and child oral health-related quality of life was evaluated in a cohort study. Children who participated in a study of enamel defects of the first permanent molars 8 years earlier were invited for a follow-up assessment. Consenting children completed the Child Perception Questionnaire and the faces Modified Child Dental Anxiety Scale, and were examined by two calibrated examiners. ANOVA, Kruskal-Wallis, negative binomial and logistic regression were used for data analyses. One hundred and eleven children returned a completed questionnaire and 91 were clinically examined. Negative binomial regression found that oral health impacts were associated with gender (boys, risk ratio (RR) = 0.73, P = 0.03) and decayed, missing or filled permanent teeth (DMFT) (RR = 1.1, P = 0.04). The mean DMFT of children were sound (0.9, standard deviation (SD) = 1.4), diffuse defects (0.8, SD = 1.7), demarcated defects (1.5, SD = 1.4) and pit defects (1.3, SD = 2.3) (Kruskal-Wallis, P = 0.05). Logistic regression of first permanent molar caries found higher odds of caries experience with baseline primary tooth caries experience (odds ratio (OR) = 1.5, P = 0.01), the number of teeth affected by enamel defects (OR = 1.9, P = 0.05) and lower odds with the presence of diffuse enamel defects (OR = 0.1, P = 0.04). The presence of diffuse enamel defects was associated with lower odds of caries experience. © 2016 Australian Dental Association.

New diagnostic index for sarcopenia in patients with cardiovascular diseases

PubMed Central

Kai, Hisashi; Shibata, Rei; Niiyama, Hiroshi; Nishiyama, Yasuhiro; Murohara, Toyoaki; Yoshida, Noriko; Katoh, Atsushi; Ikeda, Hisao

2017-01-01

Background Sarcopenia is an aging and disease-related syndrome characterized by progressive and generalized loss of skeletal muscle mass and strength, with the risk of frailty and poor quality of life. Sarcopenia is diagnosed by a decrease in skeletal muscle index (SMI) and reduction of either handgrip strength or gait speed. However, measurement of SMI is difficult for general physicians because it requires special equipment for bioelectrical impedance assay or dual-energy X-ray absorptiometry. The purpose of this study was, therefore, to explore a novel, simple diagnostic method of sarcopenia evaluation in patients with cardiovascular diseases (CVD). Methods We retrospectively investigated 132 inpatients with CVD (age: 72±12 years, age range: 27–93 years, males: 61%) Binomial logistic regression and correlation analyses were used to assess the associations of sarcopenia with simple physical data and biomarkers, including muscle-related inflammation makers and nutritional markers. Results Sarcopenia was present in 29.5% of the study population. Serum concentrations of adiponectin and sialic acid were significantly higher in sarcopenic than non-sarcopenic CVD patients. Stepwise multivariate binomial logistic regression analysis revealed that adiponectin, sialic acid, sex, age, and body mass index were independent factors for sarcopenia detection. Sarcopenia index, obtained from the diagnostic regression formula for sarcopenia detection including the five independent factors, indicated a high accuracy in ROC curve analysis (sensitivity 94.9%, specificity 69.9%) and the cutoff value for sarcopenia detection was -1.6134. Sarcopenia index had a significant correlation with the conventional diagnostic parameters of sarcopenia. Conclusions Our new sarcopenia index using simple parameters would be useful for diagnosing sarcopenia in CVD patients. PMID:28542531

Patterns of medicinal plant use: an examination of the Ecuadorian Shuar medicinal flora using contingency table and binomial analyses.

PubMed

Bennett, Bradley C; Husby, Chad E

2008-03-28

Botanical pharmacopoeias are non-random subsets of floras, with some taxonomic groups over- or under-represented. Moerman [Moerman, D.E., 1979. Symbols and selectivity: a statistical analysis of Native American medical ethnobotany, Journal of Ethnopharmacology 1, 111-119] introduced linear regression/residual analysis to examine these patterns. However, regression, the commonly-employed analysis, suffers from several statistical flaws. We use contingency table and binomial analyses to examine patterns of Shuar medicinal plant use (from Amazonian Ecuador). We first analyzed the Shuar data using Moerman's approach, modified to better meet requirements of linear regression analysis. Second, we assessed the exact randomization contingency table test for goodness of fit. Third, we developed a binomial model to test for non-random selection of plants in individual families. Modified regression models (which accommodated assumptions of linear regression) reduced R(2) to from 0.59 to 0.38, but did not eliminate all problems associated with regression analyses. Contingency table analyses revealed that the entire flora departs from the null model of equal proportions of medicinal plants in all families. In the binomial analysis, only 10 angiosperm families (of 115) differed significantly from the null model. These 10 families are largely responsible for patterns seen at higher taxonomic levels. Contingency table and binomial analyses offer an easy and statistically valid alternative to the regression approach.

Factors associated with dental caries in a group of American Indian children at age 36 months.

PubMed

Warren, John J; Blanchette, Derek; Dawson, Deborah V; Marshall, Teresa A; Phipps, Kathy R; Starr, Delores; Drake, David R

2016-04-01

Early childhood caries (ECC) is rampant among American Indian children, but there has been relatively little study of this problem. This article reports on risk factors for caries for a group of American Indian children at age 36 months as part of a longitudinal study. Pregnant women from a Northern Plains Tribal community were recruited to participate in a longitudinal study of caries and caries risk factors. Standardized dental examinations were completed on children, and questionnaires were completed by mothers at baseline and when children were 4, 8, 12, 16, 22, 28, and 36 months of age. Examinations were surface-specific for dental caries, and the questionnaires collected data on demographic, dietary, and behavioral factors. Nonparametric bivariate tests and logistic regression models were used to identify risk factors for caries at 36 months, and negative binomial regression was used to identify factors related to caries severity (dmf counts). Among the 232 children, and caries prevalence for cavitated lesions was 80%, with an additional 15% having only noncavitated lesions. The mean dmfs was 9.6, and of the total dmfs, nearly 62% of affected surfaces were decayed, 31% were missing, and 7% were filled. Logistic regression identified higher added-sugar beverage consumption, younger maternal age at baseline, higher maternal DMFS at baseline, and greater number of people in the household as significant (P < 0.05) risk factors. Negative binomial regression found that only maternal DMFS was associated with child dmf counts. By the age of 36 months, dental caries is nearly universal in this population of American Indian children. Caries risk factors included sugared beverage consumption, greater household size, and maternal factors, but further analyses are needed to better understand caries in this population. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Analysis of Binary Adherence Data in the Setting of Polypharmacy: A Comparison of Different Approaches

PubMed Central

Esserman, Denise A.; Moore, Charity G.; Roth, Mary T.

2009-01-01

Older community dwelling adults often take multiple medications for numerous chronic diseases. Non-adherence to these medications can have a large public health impact. Therefore, the measurement and modeling of medication adherence in the setting of polypharmacy is an important area of research. We apply a variety of different modeling techniques (standard linear regression; weighted linear regression; adjusted linear regression; naïve logistic regression; beta-binomial (BB) regression; generalized estimating equations (GEE)) to binary medication adherence data from a study in a North Carolina based population of older adults, where each medication an individual was taking was classified as adherent or non-adherent. In addition, through simulation we compare these different methods based on Type I error rates, bias, power, empirical 95% coverage, and goodness of fit. We find that estimation and inference using GEE is robust to a wide variety of scenarios and we recommend using this in the setting of polypharmacy when adherence is dichotomously measured for multiple medications per person. PMID:20414358

Multilevel Models for Binary Data

ERIC Educational Resources Information Center

Powers, Daniel A.

2012-01-01

The methods and models for categorical data analysis cover considerable ground, ranging from regression-type models for binary and binomial data, count data, to ordered and unordered polytomous variables, as well as regression models that mix qualitative and continuous data. This article focuses on methods for binary or binomial data, which are…

Risk Factors for Proximal Junctional Kyphosis Associated With Dual-rod Growing-rod Surgery for Early-onset Scoliosis.

PubMed

Watanabe, Kota; Uno, Koki; Suzuki, Teppei; Kawakami, Noriaki; Tsuji, Taichi; Yanagida, Haruhisa; Ito, Manabu; Hirano, Toru; Yamazaki, Ken; Minami, Shohei; Taneichi, Hiroshi; Imagama, Shiro; Takeshita, Katsushi; Yamamoto, Takuya; Matsumoto, Morio

2016-10-01

A retrospective, multicenter study. To identify risk factors for proximal junctional kyphosis (PJK) when treating early-onset scoliosis (EOS) with dual-rod growing-rod (GR) procedure. The risk factors for PJK associated with GR treatment for EOS have not been adequately studied. We evaluated clinical and radiographic results from 88 patients with EOS who underwent dual-rod GR surgery in 12 spine centers in Japan. The mean age at the time of the initial surgery was 6.5±2.2 years (range, 1.5-9.8 y), and the mean follow-up period was 3.9±2.6 years (range, 2.0-12.0 y). Risk factors for PJK were analyzed by binomial multiple logistic regression analysis. The potential factors analyzed were sex, etiology, age, the number of rod-lengthening procedures, coronal and sagittal parameters on radiographs, the type of foundation (pedicle screws or hooks), the uppermost level of the proximal foundation, and the lowermost level of the distal foundation. PJK developed in 23 patients (26%); in 19 of these, the proximal foundation became dislodged following PJK. Binomial multiple logistic regression analysis identified the following significant independent risk factors for PJK: a lower instrumented vertebra at or cranial to L3 [odds ratio (OR), 3.32], a proximal thoracic scoliosis of ≥40 degrees (OR, 2.95), and a main thoracic kyphosis of ≥60 degrees (OR, 5.08). The significant independent risk factors for PJK during dual-rod GR treatment for EOS were a lower instrumented vertebra at or cranial to L3, a proximal thoracic scoliosis of ≥40 degrees, and a main thoracic kyphosis of ≥60 degrees.

Influence of prehospital airway management on neurological outcome in patients transferred to a heart attack centre following out-of-hospital cardiac arrest.

PubMed

Edwards, Timothy; Williams, Julia; Cottee, Michaela

2018-05-11

To describe the association between prehospital airway management and neurological outcomes in patients transferred by the ambulance service directly to a heart attack centre (HAC) post-return of spontaneous circulation (ROSC). A retrospective observational cohort study in which ambulance records were reviewed to determine prehospital airway management strategy and collect physiological and demographic data. HAC notes were obtained to determine in-hospital management and quantify neurological outcome via the cerebral performance category (CPC) scale. Statistical analyses were performed via χ 2 -test, Mann-Whitney U-test, odds ratios and binomial logistic regression. Two hundred and twenty patients were included between August 2013 and August 2014, with complete outcome data obtained for 209. Median age of patients with complete outcome data was 67 years and 71.3% were male (n = 149). Airway management was provided using a supraglottic airway (SGA) in 72.7% of cases (n = 152) with the remainder undergoing endotracheal intubation (ETI). There was no significant difference in the proportion of patients who had a good neurological outcome (CPC 1 and 2) at discharge between the SGA and ETI groups (P = 0.29). Binomial logistic regression incorporating factors known to influence outcome demonstrated no significant difference in neurological outcomes between the SGA and ETI groups (adjusted OR 0.73, 95% CI 0.34-1.56). In this observational study, there was no significant difference in the proportion of good neurological outcomes in patients managed with SGA versus ETI during cardiac arrest and in the post-ROSC transfer phase. Further research is required to provide more definitive evidence in relation to the optimal airway management strategy in out-of-hospital cardiac arrest. © 2018 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine.

Temporal association between the influenza virus and respiratory syncytial virus (RSV): RSV as a predictor of seasonal influenza.

PubMed

Míguez, A; Iftimi, A; Montes, F

2016-09-01

Epidemiologists agree that there is a prevailing seasonality in the presentation of epidemic waves of respiratory syncytial virus (RSV) infections and influenza. The aim of this study is to quantify the potential relationship between the activity of RSV, with respect to the influenza virus, in order to use the RSV seasonal curve as a predictor of the evolution of an influenza virus epidemic wave. Two statistical tools, logistic regression and time series, are used for predicting the evolution of influenza. Both logistic models and time series of influenza consider RSV information from previous weeks. Data consist of influenza and confirmed RSV cases reported in Comunitat Valenciana (Spain) during the period from week 40 (2010) to week 8 (2014). Binomial logistic regression models used to predict the two states of influenza wave, basal or peak, result in a rate of correct classification higher than 92% with the validation set. When a finer three-states categorization is established, basal, increasing peak and decreasing peak, the multinomial logistic model performs well in 88% of cases of the validation set. The ARMAX model fits well for influenza waves and shows good performance for short-term forecasts up to 3 weeks. The seasonal evolution of influenza virus can be predicted a minimum of 4 weeks in advance using logistic models based on RSV. It would be necessary to study more inter-pandemic seasons to establish a stronger relationship between the epidemic waves of both viruses.

A comparison of methods for the analysis of binomial clustered outcomes in behavioral research.

PubMed

Ferrari, Alberto; Comelli, Mario

2016-12-01

In behavioral research, data consisting of a per-subject proportion of "successes" and "failures" over a finite number of trials often arise. This clustered binary data are usually non-normally distributed, which can distort inference if the usual general linear model is applied and sample size is small. A number of more advanced methods is available, but they are often technically challenging and a comparative assessment of their performances in behavioral setups has not been performed. We studied the performances of some methods applicable to the analysis of proportions; namely linear regression, Poisson regression, beta-binomial regression and Generalized Linear Mixed Models (GLMMs). We report on a simulation study evaluating power and Type I error rate of these models in hypothetical scenarios met by behavioral researchers; plus, we describe results from the application of these methods on data from real experiments. Our results show that, while GLMMs are powerful instruments for the analysis of clustered binary outcomes, beta-binomial regression can outperform them in a range of scenarios. Linear regression gave results consistent with the nominal level of significance, but was overall less powerful. Poisson regression, instead, mostly led to anticonservative inference. GLMMs and beta-binomial regression are generally more powerful than linear regression; yet linear regression is robust to model misspecification in some conditions, whereas Poisson regression suffers heavily from violations of the assumptions when used to model proportion data. We conclude providing directions to behavioral scientists dealing with clustered binary data and small sample sizes. Copyright © 2016 Elsevier B.V. All rights reserved.

Immigrants from Mexico experience serious behavioral and psychiatric problems at far lower rates than US-born Americans.

PubMed

Salas-Wright, Christopher P; Vaughn, Michael G; Goings, Trenette Clark

2017-10-01

To examine the prevalence of self-reported criminal and violent behavior, substance use disorders, and mental disorders among Mexican immigrants vis-à-vis the US born. Study findings are based on national data collected between 2012 and 2013. Binomial logistic regression was employed to examine the relationship between immigrant status and behavioral/psychiatric outcomes. Mexican immigrants report substantially lower levels of criminal and violent behaviors, substance use disorders, and mental disorders compared to US-born individuals. While some immigrants from Mexico have serious behavioral and psychiatric problems, Mexican immigrants in general experience such problems at far lower rates than US-born individuals.

Modeling recall memory for emotional objects in Alzheimer's disease.

PubMed

Sundstrøm, Martin

2011-07-01

To examine whether emotional memory (EM) of objects with self-reference in Alzheimer's disease (AD) can be modeled with binomial logistic regression in a free recall and an object recognition test to predict EM enhancement. Twenty patients with AD and twenty healthy controls were studied. Six objects (three presented as gifts) were shown to each participant. Ten minutes later, a free recall and a recognition test were applied. The recognition test had target-objects mixed with six similar distracter objects. Participants were asked to name any object in the recall test and identify each object in the recognition test as known or unknown. The total of gift objects recalled in AD patients (41.6%) was larger than neutral objects (13.3%) and a significant EM recall effect for gifts was found (Wilcoxon: p < .003). EM was not found for recognition in AD patients due to a ceiling effect. Healthy older adults scored overall higher in recall and recognition but showed no EM enhancement due to a ceiling effect. A logistic regression showed that likelihood of emotional recall memory can be modeled as a function of MMSE score (p < .014) and object status (p < .0001) as gift or non-gift. Recall memory was enhanced in AD patients for emotional objects indicating that EM in mild to moderate AD although impaired can be provoked with strong emotional load. The logistic regression model suggests that EM declines with the progression of AD rather than disrupts and may be a useful tool for evaluating magnitude of emotional load.

Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany

PubMed Central

Wang, Zhu; Shuangge, Ma; Wang, Ching-Yun

2017-01-01

In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized weighted negative binomial models and penalized logistic models via the coordinated descent algorithm. Furthermore, statistical properties including the standard error formulae are provided. A simulation study shows that the new algorithm not only has more accurate or at least comparable estimation, also is more robust than the traditional stepwise variable selection. The proposed methods are applied to analyze the health care demand in Germany using an open-source R package mpath. PMID:26059498

Simulation on Poisson and negative binomial models of count road accident modeling

NASA Astrophysics Data System (ADS)

Sapuan, M. S.; Razali, A. M.; Zamzuri, Z. H.; Ibrahim, K.

2016-11-01

Accident count data have often been shown to have overdispersion. On the other hand, the data might contain zero count (excess zeros). The simulation study was conducted to create a scenarios which an accident happen in T-junction with the assumption the dependent variables of generated data follows certain distribution namely Poisson and negative binomial distribution with different sample size of n=30 to n=500. The study objective was accomplished by fitting Poisson regression, negative binomial regression and Hurdle negative binomial model to the simulated data. The model validation was compared and the simulation result shows for each different sample size, not all model fit the data nicely even though the data generated from its own distribution especially when the sample size is larger. Furthermore, the larger sample size indicates that more zeros accident count in the dataset.

The gap between suicide characteristics in the print media and in the population.

PubMed

Niederkrotenthaler, Thomas; Till, Benedikt; Herberth, Arno; Voracek, Martin; Kapusta, Nestor D; Etzersdorfer, Elmar; Strauss, Markus; Sonneck, Gernot

2009-08-01

Programmes to educate media professionals about suicide are increasingly established, but information about which suicide cases are most likely to be reported in the mass media is sparse. We applied binomial tests to compare frequencies of social characteristics of all domestic suicides in the 13 largest Austrian print media in 2005 with frequencies of suicide characteristics in the population. Additionally, each reported suicide case was linked to its respective entry in the suicide database. We performed a logistic regression analysis, with presence of an article as outcome, and sex of the suicide case, age, religious affiliation, family status, conduction of an autopsy and location of the suicide as explaining variables. Time of the year and federal state where the suicide happened was controlled for. Binomial tests showed that suicides involving murder or murder attempt were over-represented in the media. Reporting on mental disorders was under-represented. In the regression analysis, the likelihood of a report was negatively associated with the age of suicide cases. Foreign citizenship was a further predictor of a suicide report. The methods of drowning, jumping, shooting and rare methods were more likely to be reported than hanging, which is the most frequent suicide method in Austria. Suicide characteristics in the media are not representative of the population. The identified discrepancies provide a basis for tailor-made education of mass media professionals.

Estimating cavity tree and snag abundance using negative binomial regression models and nearest neighbor imputation methods

Treesearch

Bianca N.I. Eskelson; Hailemariam Temesgen; Tara M. Barrett

2009-01-01

Cavity tree and snag abundance data are highly variable and contain many zero observations. We predict cavity tree and snag abundance from variables that are readily available from forest cover maps or remotely sensed data using negative binomial (NB), zero-inflated NB, and zero-altered NB (ZANB) regression models as well as nearest neighbor (NN) imputation methods....

Impact of cigarette smoking on utilization of nursing home services.

PubMed

Warner, Kenneth E; McCammon, Ryan J; Fries, Brant E; Langa, Kenneth M

2013-11-01

Few studies have examined the effects of smoking on nursing home utilization, generally using poor data on smoking status. No previous study has distinguished utilization for recent from long-term quitters. Using the Health and Retirement Study, we assessed nursing home utilization by never-smokers, long-term quitters (quit >3 years), recent quitters (quit ≤3 years), and current smokers. We used logistic regression to evaluate the likelihood of a nursing home admission. For those with an admission, we used negative binomial regression on the number of nursing home nights. Finally, we employed zero-inflated negative binomial regression to estimate nights for the full sample. Controlling for other variables, compared with never-smokers, long-term quitters have an odds ratio (OR) for nursing home admission of 1.18 (95% CI: 1.07-1.2), current smokers 1.39 (1.23-1.57), and recent quitters 1.55 (1.29-1.87). The probability of admission rises rapidly with age and is lower for African Americans and Hispanics, more affluent respondents, respondents with a spouse present in the home, and respondents with a living child. Given admission, smoking status is not associated with length of stay (LOS). LOS is longer for older respondents and women and shorter for more affluent respondents and those with spouses present. Compared with otherwise identical never-smokers, former and current smokers have a significantly increased risk of nursing home admission. That recent quitters are at greatest risk of admission is consistent with evidence that many stop smoking because they are sick, often due to smoking.

Sickness absence and psychosocial job quality: an analysis from a longitudinal survey of working Australians, 2005-2012.

PubMed

Milner, Allison; Butterworth, Peter; Bentley, Rebecca; Kavanagh, Anne M; LaMontagne, Anthony D

2015-05-15

Sickness absence is associated with adverse health, organizational, and societal outcomes. Using data from a longitudinal cohort study of working Australians (the Household, Income and Labour Dynamics in Australia (HILDA) Survey), we examined the relationship between changes in individuals' overall psychosocial job quality and variation in sickness absence. The outcome variables were paid sickness absence (yes/no) and number of days of paid sickness absence in the past year (2005-2012). The main exposure variable was psychosocial job quality, measured using a psychosocial job quality index (levels of job control, demands and complexity, insecurity, and perceptions of unfair pay). Analysis was conducted using longitudinal fixed-effects logistic regression models and negative binomial regression models. There was a dose-response relationship between the number of psychosocial job stressors reported by an individual and the odds of paid sickness absence (1 adversity: odds ratio (OR) = 1.26, 95% confidence interval (CI): 1.09, 1.45 (P = 0.002); 2 adversities: OR = 1.28, 95% CI: 1.09, 1.51 (P = 0.002); ≥3 adversities: OR = 1.58, 95% CI: 1.29, 1.94 (P < 0.001)). The negative binomial regression models also indicated that respondents reported a greater number of days of sickness absence in response to worsening psychosocial job quality. These results suggest that workplace interventions aiming to improve the quality of work could help reduce sickness absence. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Data mining of tree-based models to analyze freeway accident frequency.

PubMed

Chang, Li-Yen; Chen, Wen-Chieh

2005-01-01

Statistical models, such as Poisson or negative binomial regression models, have been employed to analyze vehicle accident frequency for many years. However, these models have their own model assumptions and pre-defined underlying relationship between dependent and independent variables. If these assumptions are violated, the model could lead to erroneous estimation of accident likelihood. Classification and Regression Tree (CART), one of the most widely applied data mining techniques, has been commonly employed in business administration, industry, and engineering. CART does not require any pre-defined underlying relationship between target (dependent) variable and predictors (independent variables) and has been shown to be a powerful tool, particularly for dealing with prediction and classification problems. This study collected the 2001-2002 accident data of National Freeway 1 in Taiwan. A CART model and a negative binomial regression model were developed to establish the empirical relationship between traffic accidents and highway geometric variables, traffic characteristics, and environmental factors. The CART findings indicated that the average daily traffic volume and precipitation variables were the key determinants for freeway accident frequencies. By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies. By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies.

Exploring Audiologists' Language and Hearing Aid Uptake in Initial Rehabilitation Appointments.

PubMed

Sciacca, Anna; Meyer, Carly; Ekberg, Katie; Barr, Caitlin; Hickson, Louise

2017-06-13

The study aimed (a) to profile audiologists' language during the diagnosis and management planning phase of hearing assessment appointments and (b) to explore associations between audiologists' language and patients' decisions to obtain hearing aids. Sixty-two audiologist-patient dyads participated. Patient participants were aged 55 years or older. Hearing assessment appointments were audiovisually recorded and transcribed for analysis. Audiologists' language was profiled using two measures: general language complexity and use of jargon. A binomial, multivariate logistic regression analysis was conducted to investigate the associations between these language measures and hearing aid uptake. The logistic regression model revealed that the Flesch-Kincaid reading grade level of audiologists' language was significantly associated with hearing aid uptake. Patients were less likely to obtain hearing aids when audiologists' language was at a higher reading grade level. No associations were found between audiologists' use of jargon and hearing aid uptake. Audiologists' use of complex language may present a barrier for patients to understand hearing rehabilitation recommendations. Reduced understanding may limit patient participation in the decision-making process and result in patients being less willing to trial hearing aids. Clear, concise language is recommended to facilitate shared decision making.

Mental Health Symptoms Among Student Service Members/Veterans and Civilian College Students.

PubMed

Cleveland, Sandi D; Branscum, Adam J; Bovbjerg, Viktor E; Thorburn, Sheryl

2015-01-01

The aim of this study was to investigate if and to what extent student service members/veterans differ from civilian college students in the prevalence of self-reported symptoms of poor mental health. The Fall 2011 implementation of the American College Health Association-National College Health Assessment included 27,774 respondents from 44 colleges and universities. Participants were matched using propensity scores, and the prevalence of symptoms was compared using logistic regression and zero-inflated negative binomial regression models. The odds of feeling overwhelmed in the last 12 months were significantly lower among student service members/veterans with a history of hazardous duty (odd ratio [OR] = 0.46, adjusted p value <.05) compared with civilian students. Military service, with and without hazardous duty deployment, was not a significant predictor of the total number of symptoms of poor mental health. Current student service members/veterans may not be disproportionately affected by poor psychological functioning.

Perceived health status and daily activity participation of older Malaysians.

PubMed

Ng, Sor Tho; Tengku-Aizan, Hamid; Tey, Nai Peng

2011-07-01

This article investigates the influence of perceived health status on the daily activity participation of older Malaysians. Data from the Survey on Perceptions of Needs and Problems of the Elderly, which was conducted in 1999, were used. The negative binomial regression results show that older persons with good perceived health status reported more varieties of daily activity participation, especially among the uneducated and those with below-average self-esteem. The multinomial logistic regression model suggests that older persons with good perceived health status tended to engage daily in paid work only or with leisure activities, whereas those perceived to have poor health were more likely to engage in leisure activities only or leisure and family role activities. Promotion of a healthy lifestyle at a younger age encourages every person to monitor and take responsibility for their own health, which is a necessary strategy to ensure active participation at an older age, and thus improve their well-being.

The 6-min push test is reliable and predicts low fitness in spinal cord injury.

PubMed

Cowan, Rachel E; Callahan, Morgan K; Nash, Mark S

2012-10-01

The objective of this study is to assess 6-min push test (6MPT) reliability, determine whether the 6MPT is sensitive to fitness differences, and assess if 6MPT distance predicts fitness level in persons with spinal cord injury (SCI) or disease. Forty individuals with SCI who could self-propel a manual wheelchair completed an incremental arm crank peak oxygen consumption assessment and two 6MPTs across 3 d (37% tetraplegia (TP), 63% paraplegia (PP), 85% men, 70% white, 63% Hispanic, mean age = 34 ± 10 yr, mean duration of injury = 13 ± 10 yr, and mean body mass index = 24 ± 5 kg.m). Intraclass correlation and Bland-Altman plots assessed 6MPT distance (m) reliability. Mann-Whitney U test compared 6MPT distance (m) of high and low fitness groups for TP and PP. The fitness status prediction was developed using N = 30 and validated in N = 10 (validation group (VG)). A nonstatistical prediction approach, below or above a threshold distance (TP = 445 m and PP = 604 m), was validated statistically by binomial logistic regression. Accuracy, sensitivity, and specificity were computed to evaluate the threshold approach. Intraclass correlation coefficients exceeded 0.90 for the whole sample and the TP/PP subsets. High fitness persons propelled farther than low fitness persons for both TP/PP (both P < 0.05). Binomial logistic regression (P < 0.008) predicted the same fitness levels in the VG as the threshold approach. In the VG, overall accuracy was 70%. Eighty-six percent of low fitness persons were correctly identified (sensitivity), and 33% of high fitness persons were correctly identified (specificity). The 6MPT may be a useful tool for SCI clinicians and researchers. 6MPT distance demonstrates excellent reliability and is sensitive to differences in fitness level. 6MPT distances less than a threshold distance may be an effective approach to identify low fitness in person with SCI.

Logistic quantile regression provides improved estimates for bounded avian counts: A case study of California Spotted Owl fledgling production

USGS Publications Warehouse

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

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 conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of the variance in the fledgling counts as climate, parent age class, and landscape habitat predictors. Our logistic quantile regression model can be used for any discrete response variables with fixed upper and lower bounds.

Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany.

PubMed

Wang, Zhu; Ma, Shuangge; Wang, Ching-Yun

2015-09-01

In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD), and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized weighted negative binomial models and penalized logistic models via the coordinated descent algorithm. Furthermore, statistical properties including the standard error formulae are provided. A simulation study shows that the new algorithm not only has more accurate or at least comparable estimation, but also is more robust than the traditional stepwise variable selection. The proposed methods are applied to analyze the health care demand in Germany using the open-source R package mpath. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Using Financial Ratios to Select Companies for Tax Auditing: A Preliminary Study

NASA Astrophysics Data System (ADS)

Marghescu, Dorina; Kallio, Minna; Back, Barbro

Tax auditing procedures include an investigation of the accounting records of a company and of other sources of information in order to assess whether the taxation has been based on correct and complete information. When there are found discrepancies between the accounting information and the real situation, the taxation should be corrected so that the eventual tax defaults are assessed and debited. The paper analyzes to what extent the financial performance of a company can be used as an indicator of tax defaults. We focus on one type of tax, namely employer's contribution, and four financial ratios. We evaluate the model in a study of Finnish companies by using a binomial logistic regression analysis. The study is exploratory and at a preliminary stage.

Comparison of robustness to outliers between robust poisson models and log-binomial models when estimating relative risks for common binary outcomes: a simulation study.

PubMed

Chen, Wansu; Shi, Jiaxiao; Qian, Lei; Azen, Stanley P

2014-06-26

To estimate relative risks or risk ratios for common binary outcomes, the most popular model-based methods are the robust (also known as modified) Poisson and the log-binomial regression. Of the two methods, it is believed that the log-binomial regression yields more efficient estimators because it is maximum likelihood based, while the robust Poisson model may be less affected by outliers. Evidence to support the robustness of robust Poisson models in comparison with log-binomial models is very limited. In this study a simulation was conducted to evaluate the performance of the two methods in several scenarios where outliers existed. The findings indicate that for data coming from a population where the relationship between the outcome and the covariate was in a simple form (e.g. log-linear), the two models yielded comparable biases and mean square errors. However, if the true relationship contained a higher order term, the robust Poisson models consistently outperformed the log-binomial models even when the level of contamination is low. The robust Poisson models are more robust (or less sensitive) to outliers compared to the log-binomial models when estimating relative risks or risk ratios for common binary outcomes. Users should be aware of the limitations when choosing appropriate models to estimate relative risks or risk ratios.

Investigation of shipping accident injury severity and mortality.

PubMed

Weng, Jinxian; Yang, Dong

2015-03-01

Shipping movements are operated in a complex and high-risk environment. Fatal shipping accidents are the nightmares of seafarers. With ten years' worldwide ship accident data, this study develops a binary logistic regression model and a zero-truncated binomial regression model to predict the probability of fatal shipping accidents and corresponding mortalities. The model results show that both the probability of fatal accidents and mortalities are greater for collision, fire/explosion, contact, grounding, sinking accidents occurred in adverse weather conditions and darkness conditions. Sinking has the largest effects on the increment of fatal accident probability and mortalities. The results also show that the bigger number of mortalities is associated with shipping accidents occurred far away from the coastal area/harbor/port. In addition, cruise ships are found to have more mortalities than non-cruise ships. The results of this study are beneficial for policy-makers in proposing efficient strategies to prevent fatal shipping accidents. Copyright © 2015 Elsevier Ltd. All rights reserved.

Characterizing environmental risk factors for West Nile virus in Quebec, Canada, using clinical data in humans and serology in pet dogs.

PubMed

Rocheleau, J P; Michel, P; Lindsay, L R; Drebot, M; Dibernardo, A; Ogden, N H; Fortin, A; Arsenault, J

2017-10-01

The identification of specific environments sustaining emerging arbovirus amplification and transmission to humans is a key component of public health intervention planning. This study aimed at identifying environmental factors associated with West Nile virus (WNV) infections in southern Quebec, Canada, by modelling and jointly interpreting aggregated clinical data in humans and serological data in pet dogs. Environmental risk factors were estimated in humans by negative binomial regression based on a dataset of 191 human WNV clinical cases reported in the study area between 2011 and 2014. Risk factors for infection in dogs were evaluated by logistic and negative binomial models based on a dataset including WNV serological results from 1442 dogs sampled from the same geographical area in 2013. Forested lands were identified as low-risk environments in humans. Agricultural lands represented higher risk environments for dogs. Environments identified as impacting risk in the current study were somewhat different from those identified in other studies conducted in north-eastern USA, which reported higher risk in suburban environments. In the context of the current study, combining human and animal data allowed a more comprehensive and possibly a more accurate view of environmental WNV risk factors to be obtained than by studying aggregated human data alone.

Multiple imputation for handling missing outcome data when estimating the relative risk.

PubMed

Sullivan, Thomas R; Lee, Katherine J; Ryan, Philip; Salter, Amy B

2017-09-06

Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whether misspecification of the imputation model in this setting could lead to biased parameter estimates. Using simulated data, we evaluated the performance of multiple imputation for handling missing data prior to estimating adjusted relative risks from a correctly specified multivariable log binomial model. We considered an arbitrary pattern of missing data in both outcome and exposure variables, with missing data induced under missing at random mechanisms. Focusing on standard model-based methods of multiple imputation, missing data were imputed using multivariate normal imputation or fully conditional specification with a logistic imputation model for the outcome. Multivariate normal imputation performed poorly in the simulation study, consistently producing estimates of the relative risk that were biased towards the null. Despite outperforming multivariate normal imputation, fully conditional specification also produced somewhat biased estimates, with greater bias observed for higher outcome prevalences and larger relative risks. Deleting imputed outcomes from analysis datasets did not improve the performance of fully conditional specification. Both multivariate normal imputation and fully conditional specification produced biased estimates of the relative risk, presumably since both use a misspecified imputation model. Based on simulation results, we recommend researchers use fully conditional specification rather than multivariate normal imputation and retain imputed outcomes in the analysis when estimating relative risks. However fully conditional specification is not without its shortcomings, and so further research is needed to identify optimal approaches for relative risk estimation within the multiple imputation framework.

Marital status and survival of patients with oral cavity squamous cell carcinoma: a population-based study.

PubMed

Shi, Xiao; Zhang, Ting-Ting; Hu, Wei-Ping; Ji, Qing-Hai

2017-04-25

The relationship between marital status and oral cavity squamous cell carcinoma (OCSCC) survival has not been explored. The objective of our study was to evaluate the impact of marital status on OCSCC survival and investigate the potential mechanisms. Married patients had better 5-year cancer-specific survival (CSS) (66.7% vs 54.9%) and 5-year overall survival (OS) (56.0% vs 41.1%). In multivariate Cox regression models, unmarried patients also showed higher mortality risk for both CSS (Hazard Ratio [HR]: 1.260, 95% confidence interval (CI): 1.187-1.339, P < 0.001) and OS (HR: 1.328, 95% CI: 1.266-1.392, P < 0.001). Multivariate logistic regression showed married patients were more likely to be diagnosed at earlier stage (P < 0.001) and receive surgery (P < 0.001). Married patients still demonstrated better prognosis in the 1:1 matched group analysis (CSS: 62.9% vs 60.8%, OS: 52.3% vs 46.5%). 11022 eligible OCSCC patients were identified from Surveillance, Epidemiology, and End Results (SEER) database, including 5902 married and 5120 unmarried individuals. Kaplan-Meier analysis, Log-rank test and Cox proportional hazards regression model were used to analyze survival and mortality risk. Influence of marital status on stage, age at diagnosis and selection of treatment was determined by binomial and multinomial logistic regression. Propensity score matching method was adopted to perform a 1:1 matched cohort. Marriage has an independently protective effect on OCSCC survival. Earlier diagnosis and more sufficient treatment are possible explanations. Besides, even after 1:1 matching, survival advantage of married group still exists, indicating that spousal support from other aspects may also play an important role.

Marital status and survival of patients with oral cavity squamous cell carcinoma: a population-based study

PubMed Central

Shi, Xiao; Zhang, Ting-ting; Hu, Wei-ping; Ji, Qing-hai

2017-01-01

Background The relationship between marital status and oral cavity squamous cell carcinoma (OCSCC) survival has not been explored. The objective of our study was to evaluate the impact of marital status on OCSCC survival and investigate the potential mechanisms. Results Married patients had better 5-year cancer-specific survival (CSS) (66.7% vs 54.9%) and 5-year overall survival (OS) (56.0% vs 41.1%). In multivariate Cox regression models, unmarried patients also showed higher mortality risk for both CSS (Hazard Ratio [HR]: 1.260, 95% confidence interval (CI): 1.187–1.339, P < 0.001) and OS (HR: 1.328, 95% CI: 1.266–1.392, P < 0.001). Multivariate logistic regression showed married patients were more likely to be diagnosed at earlier stage (P < 0.001) and receive surgery (P < 0.001). Married patients still demonstrated better prognosis in the 1:1 matched group analysis (CSS: 62.9% vs 60.8%, OS: 52.3% vs 46.5%). Materials and Methods 11022 eligible OCSCC patients were identified from Surveillance, Epidemiology, and End Results (SEER) database, including 5902 married and 5120 unmarried individuals. Kaplan-Meier analysis, Log-rank test and Cox proportional hazards regression model were used to analyze survival and mortality risk. Influence of marital status on stage, age at diagnosis and selection of treatment was determined by binomial and multinomial logistic regression. Propensity score matching method was adopted to perform a 1:1 matched cohort. Conclusions Marriage has an independently protective effect on OCSCC survival. Earlier diagnosis and more sufficient treatment are possible explanations. Besides, even after 1:1 matching, survival advantage of married group still exists, indicating that spousal support from other aspects may also play an important role. PMID:28415710

Prevalence and Geographic Variations of Polypharmacy Among West Virginia Medicaid Beneficiaries.

PubMed

Feng, Xue; Tan, Xi; Riley, Brittany; Zheng, Tianyu; Bias, Thomas K; Becker, James B; Sambamoorthi, Usha

2017-11-01

West Virginia (WV) residents are at high risk for polypharmacy given its considerable chronic disease burdens. To evaluate the prevalence, correlates, outcomes, and geographic variations of polypharmacy among WV Medicaid beneficiaries. In this cross-sectional study, we analyzed 2009-2010 WV Medicaid fee-for-service (FFS) claims data for adults aged 18-64 (N=37,570). We defined polypharmacy as simultaneous use of drugs from five or more different drug classes on a daily basis for at least 60 consecutive days in one year. Multilevel logistic regression was used to explore the individual- and county-level factors associated with polypharmacy. Its relationship with healthcare utilization was assessed using negative binomial regression and logistic regression. The univariate local indicators of spatial association method was applied to explore spatial patterns of polypharmacy in WV. The prevalence of polypharmacy among WV Medicaid beneficiaries was 44.6%. High-high clusters of polypharmacy were identified in southern WV, indicating counties with above-average prevalence surrounded by counties with above-average prevalence. Polypharmacy was associated with being older, female, eligible for Medicaid due to cash assistance or medical eligibility, having any chronic conditions or more chronic conditions, and living in a county with lower levels of education. Polypharmacy was associated with more hospitalizations, emergency department visits, and outpatient visits, as well as higher non-drug medical expenditures. Polypharmacy was prevalent among WV Medicaid beneficiaries and was associated with substantial healthcare utilization and expenditures. The clustering of high prevalence of polypharmacy in southern WV may suggest targeted strategies to reduce polypharmacy burden in these areas.

Morbidity and Health Risk Factors Among New Mexico Miners: A Comparison Across Mining Sectors.

PubMed

Shumate, Alice M; Yeoman, Kristin; Victoroff, Tristan; Evans, Kandace; Karr, Roger; Sanchez, Tami; Sood, Akshay; Laney, Anthony Scott

2017-08-01

This study examines differences in chronic health outcomes between coal, uranium, metal, and nonmetal miners. In a cross-sectional study using data from a health screening program for current and former New Mexico miners, log-binomial logistic regression models were used to estimate relative risks of respiratory and heart disease, cancer, osteoarthritis, and back pain associated with mining in each sector as compared with coal, adjusting for other relevant risk factors. Differential risks in angina, pulmonary symptoms, asthma, cancer, osteoarthritis, and back pain between mining sectors were found. New Mexico miners experience different chronic health challenges across sectors. These results demonstrate the importance of using comparable data to understand how health risks differ across mining sectors. Further investigation among a broader geographic population of miners will help identify the health priorities and needs in each sector.

Causes and implications of the unforeseen 2016 extreme yield loss in the breadbasket of France.

PubMed

Ben-Ari, Tamara; Boé, Julien; Ciais, Philippe; Lecerf, Remi; Van der Velde, Marijn; Makowski, David

2018-04-24

In 2016, France, one of the leading wheat-producing and wheat-exporting regions in the world suffered its most extreme yield loss in over half a century. Yet, yield forecasting systems failed to anticipate this event. We show that this unprecedented event is a new type of compound extreme with a conjunction of abnormally warm temperatures in late autumn and abnormally wet conditions in the following spring. A binomial logistic regression accounting for fall and spring conditions is able to capture key yield loss events since 1959. Based on climate projections, we show that the conditions that led to the 2016 wheat yield loss are projected to become more frequent in the future. The increased likelihood of such compound extreme events poses a challenge: farming systems and yield forecasting systems, which often support them, must adapt.

Functional limitations as potential mediators of the effects of self-reported vision status on fall risk of older adults.

PubMed

Steinman, Bernard A; Allen, Susan M; Chen, Jie; Pynoos, Jon

2015-02-01

To test whether limitations in mobility and large-muscle functioning mediate self-reported vision status to increase fall risk among respondents age 65 and above. This study used two waves from the Health and Retirement Study. We conducted binary logistic and negative binomial regression analyses to test indirect paths leading from self-reported vision status to falls, via indices of mobility and large-muscle functioning. Limited evidence was found for a mediating effect among women; however, large-muscle groups were implicated as partially mediating risk factors for falls among men with fair self-reported vision status. Implications of these findings are discussed including the need for prioritizing improved muscle strength of older men and women with poor vision as a preventive measure against falls. © The Author(s) 2014.

The Social Acceptance of Community Solar: A Portland Case Study

NASA Astrophysics Data System (ADS)

Weaver, Anne

Community solar is a renewable energy practice that's been adopted by multiple U.S. states and is being considered by many more, including the state of Oregon. A recent senate bill in Oregon, called the "Clean Electricity and Coal Transition Plan", includes a provision that directs the Oregon Public Utility Commission to establish a community solar program for investor-owned utilities by late 2017. Thus, energy consumers in Portland will be offered participation in community solar projects in the near future. Community solar is a mechanism that allows ratepayers to experience both the costs and benefits of solar energy while also helping to offset the proportion of fossil-fuel generated electricity in utility grids, thus aiding climate change mitigation. For community solar to achieve market success in the residential sector of Portland, ratepayers of investor-owned utilities must socially accept this energy practice. The aim of this study was to forecast the potential social acceptance of community solar among Portland residents by measuring willingness to participate in these projects. Additionally, consumer characteristics, attitudes, awareness, and knowledge were captured to assess the influence of these factors on intent to enroll in community solar. The theory of planned behavior, as well as the social acceptance, diffusion of innovation, and dual-interest theories were frameworks used to inform the analysis of community solar adoption. These research objectives were addressed through a mixed-mode survey of Portland residents, using a stratified random sample of Portland neighborhoods to acquire a gradient of demographics. 330 questionnaires were completed, yielding a 34.2% response rate. Descriptive statistics, binomial logistic regression models, and mean willingness to pay were the analyses conducted to measure the influence of project factors and demographic characteristics on likelihood of community solar participation. Roughly 60% of respondents exhibited interest in community solar enrollment. The logistic regression model revealed the percent change in utility bill (essentially the rate of return on the community solar investment) as a dramatically influential variable predicting willingness to participate. Community solar project scenarios also had a strong influence on willingness to participate: larger, cheaper, and distant projects were preferred over small and expensive local projects. Results indicate that community solar project features that accentuate affordability are most important to energy consumers. Additionally, demographic characteristics that were strongly correlated with willingness to enroll were politically liberal ideologies, higher incomes, current enrollment in green utility programs, and membership in an environmental organization. Thus, the market acceptance of community solar in Portland will potentially be broadened by emphasizing affordability over other features, such as community and locality. Additionally, I explored attitudinal influences on interest in community solar by conducting exploratory factor analysis on attitudes towards energy, climate change, and solar barriers and subsequently conducting binomial logistic regression models. Results found that perceiving renewable energy as environmentally beneficial was positively correlated with intent to enroll in community solar, which supported the notion that environmental attitudes will lead to environmental behaviors. The logistic regression model also revealed a negative correlation between community solar interest and negative attitudes towards renewable energy. Perceptions of solar barriers were mild, indicating that lack of an enabling mechanism may be the reason solar continues to be underutilized in this region.

A Model Comparison for Count Data with a Positively Skewed Distribution with an Application to the Number of University Mathematics Courses Completed

ERIC Educational Resources Information Center

Liou, Pey-Yan

2009-01-01

The current study examines three regression models: OLS (ordinary least square) linear regression, Poisson regression, and negative binomial regression for analyzing count data. Simulation results show that the OLS regression model performed better than the others, since it did not produce more false statistically significant relationships than…

Population heterogeneity in the salience of multiple risk factors for adolescent delinquency.

PubMed

Lanza, Stephanie T; Cooper, Brittany R; Bray, Bethany C

2014-03-01

To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors. We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered. Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents. Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Should the poor have no medicines to cure? A study on the association between social class and social security among the rural migrant workers in urban China.

PubMed

Guan, Ming

2017-11-07

The rampant urbanization and medical marketization in China have resulted in increased vulnerabilities to health and socioeconomic disparities among the rural migrant workers in urban China. In the Chinese context, the socioeconomic characteristics of rural migrant workers have attracted considerable research attention in the recent past years. However, to date, no previous studies have explored the association between the socioeconomic factors and social security among the rural migrant workers in urban China. This study aims to explore the association between socioeconomic inequity and social security inequity and the subsequent associations with medical inequity and reimbursement rejection. Data from a regionally representative sample of 2009 Survey of Migrant Workers in Pearl River Delta in China were used for analyses. Multiple logistic regressions were used to analyze the impacts of socioeconomic factors on the eight dimensions of social security (sick pay, paid leave, maternity pay, medical insurance, pension insurance, occupational injury insurance, unemployment insurance, and maternity insurance) and the impacts of social security on medical reimbursement rejection. The zero-inflated negative binomial regression model (ZINB regression) was adopted to explore the relationship between socioeconomic factors and hospital visits among the rural migrant workers with social security. The study population consisted of 848 rural migrant workers with high income who were young and middle-aged, low-educated, and covered by social security. Reimbursement rejection and abusive supervision for the rural migrant workers were observed. Logistic regression analysis showed that there were significant associations between socioeconomic factors and social security. ZINB regression showed that there were significant associations between socioeconomic factors and hospital visits among the rural migrant workers. Also, several dimensions of social security had significant associations with reimbursement rejections. This study showed that social security inequity, medical inequity, and reimbursement inequity happened to the rural migrant workers simultaneously. Future policy should strengthen health justice and enterprises' medical responsibilities to the employed rural migrant workers.

A Study of Global Health Elective Outcomes

PubMed Central

Russ, Christiana M.; Tran, Tony; Silverman, Melanie; Palfrey, Judith

2017-01-01

Background and Objectives: To identify the effects of global health electives over a decade in a pediatric residency program. Methods: This was an anonymous email survey of the Boston Combined Residency alumni funded for global health electives from 2002 to 2011. A test for trend in binomial proportions and logistic regression were used to document associations between elective and participant characteristics and the effects of the electives. Qualitative data were also analyzed. Results: Of the 104 alumni with available email addresses, 69 (66%) responded, describing 94 electives. Elective products included 27 curricula developed, 11 conference presentations, and 7 academic publications. Thirty-two (46%) alumni continued global health work. Previous experience, previous travel to the site, number of global electives, and cumulative global elective time were associated with postresidency work in global health or with the underserved. Conclusions: Resident global electives resulted in significant scholarship and teaching and contributed to long-term career trajectories. PMID:28229096

[Parenting styles and their relationship with hyperactivity].

PubMed

Raya Trenas, Antonio Félix; Herreruzo Cabrera, Javier; Pino Osuna, María José

2008-11-01

The present study aims to determine the relationship among factors that make up the parenting styles according to the PCRI (Parent-Child Relationship Inventory) and hyperactivity reported by parents through the BASC (Behaviour Assessment System for Children). We selected a sample of 32 children between 3 and 14 years old (23 male and 9 female) with risk scores in hyperactivity and another similar group with low scores in hyperactivity. After administering both instruments to the parents, we carried out a binomial logistic regression analysis which resulted in a prediction model for 84.4% of the sample, made up of the PCRI factors: fathers' involvement, communication and role orientation, mothers' parental support, and both parents' limit-setting and autonomy. Moreover, our analysis of the variance produced significant differences in the support perceived by the fathers and mothers of both groups. Lastly, the utility of results to propose intervention strategies within the family based on an authoritative style is discussed.

Motorcycle dependency index at household level: case of Yogyakarta urbanized area

NASA Astrophysics Data System (ADS)

Herwangi, Y.; Putri, S. P.; Ronita, P. S.

2018-05-01

Dependency on private vehicles has become a prevalent phenomenon in big cities experiencing urban sprawl. Related to that, there are still many unknown factors affecting the dependence on motorcycles. Various factors are suspected to influence this, ranging from spatial factors to aspatial factors. This research was conducted in Yogyakarta Urbanized Area (YUA) by taking 175 samples. Binomial Logistic Regression is used in order to find the factors that affect motorcycle dependency. The results showed that the index of dependency in YUA can be quite high. Motorcycle usage, bicycle ownership, and perception about the increase of fuel price are the factors that have a significant influence on motorcycle dependence in YUA. Even though the correlation between spatial factors and motorcycle dependency was weak, it cannot be said to have no effect. These factors are most likely to be influential if other indicators are included with more suitable proxies.

Adult Children's Education and Parents' Functional Limitations in Mexico.

PubMed

Yahirun, Jenjira J; Sheehan, Connor M; Hayward, Mark D

2016-04-01

This article asks how adult children's education influences older parents' physical health in Mexico, a context where older adults often lack access to institutional resources and rely on kin, primarily children, as a main source of support. Using logistic and negative binomial regression models and data from the first wave of the Mexican Health and Aging Study (N = 9,661), we find that parents whose children all completed high school are less likely to report any functional limitations as well as fewer limitations compared to parents with no children who completed high school. This association remains significant even after accounting for parent and offspring-level characteristics, including parents' income that accounts for children's financial transfers to parents. Future research should aim to understand the mechanisms that explain the association between adult children's education and changes to parents' health over time. © The Author(s) 2015.

A Nationwide Study of Discrimination and Chronic Health Conditions Among Asian Americans

PubMed Central

Gee, Gilbert C.; Spencer, Michael S.; Chen, Juan; Takeuchi, David

2007-01-01

Objectives. We examined whether self-reported everyday discrimination was associated with chronic health conditions among a nationally representative sample of Asian Americans. Methods. Data were from the Asian American subsample (n = 2095) of the National Latino and Asian American Study conducted in 2002 and 2003. Regression techniques (negative binomial and logistic) were used to examine the association between discrimination and chronic health conditions. Analyses were conducted for the entire sample and 3 Asian subgroups (Chinese, Vietnamese, and Filipino). Results. Reports of everyday discrimination were associated with many chronic conditions, after we controlled for age, gender, region, per capita income, education, employment, and social desirability bias. Discrimination was also associated with indicators of heart disease, pain, and respiratory illnesses. There were some differences by Asian subgroup. Conclusions. Everyday discrimination may contribute to stress experienced by racial/ethnic minorities and could lead to chronic illness. PMID:17538055

Study of the uses of Information and Communication Technologies by Pain Treatment Unit Physicians.

PubMed

Muriel Fernandez, Jorge; Sánchez Ledesma, María José; López Millan, Manuel; García Cenador, María Begoña

2017-05-01

Adequate use of Information and Communication Technologies (ICTs) in health has been shown to save the patient and caregiver time, improve access to the health system, improve diagnosis and control of disease or treatment. All this results in cost savings, and more importantly, they help improve the quality of service and the lives of patients. The purpose of this study is to analyse the differences in the uses of this ICTs between those physicians that belong to Pain Treatment Units (PU) and other physicians that work in pain not linked to these PUs. An online survey, generated by Netquest online survey tool, was sent to both groups of professionals and the data collected was statistical analysed through a logistic regression methodology which is the Logit binomial model. Our results show that those physicians that belong to PUs use ICTs more frequently and consider it more relevant to their clinical practice.

Clinical reasoning in feline epilepsy: Which combination of clinical information is useful?

PubMed

Stanciu, Gabriela-Dumitrita; Packer, Rowena Mary Anne; Pakozdy, Akos; Solcan, Gheorghe; Volk, Holger Andreas

2017-07-01

We sought to identify the association between clinical risk factors and the diagnosis of idiopathic epilepsy (IE) or structural epilepsy (SE) in cats, using statistical models to identify combinations of discrete parameters from the patient signalment, history and neurological examination findings that could suggest the most likely diagnosis. Data for 138 cats with recurrent seizures were reviewed, of which 110 were valid for inclusion. Seizure aetiology was classified as IE in 57% and SE in 43% of cats. Binomial logistic regression analyses demonstrated that pedigree status, older age at seizure onset (particularly >7years old), abnormal neurological examinations, and ictal vocalisation were associated with a diagnosis of SE compared to IE, and that ictal salivation was more likely to be associated with a diagnosis of IE than SE. These findings support the importance of considering inter-ictal neurological deficits and seizure history in clinical reasoning. Copyright © 2017 Elsevier Ltd. All rights reserved.

Adult Children’s Education and Parents’ Functional Limitations in Mexico

PubMed Central

Yahirun, Jenjira J.; Sheehan, Connor M.; Hayward, Mark D.

2016-01-01

This article asks how adult children’s education influences older parents’ physical health in Mexico, a context where older adults often lack access to institutional resources and rely on kin, primarily children, as a main source of support. Using logistic and negative binomial regression models and data from the first wave of the Mexican Health and Aging Study (N = 9,661), we find that parents whose children all completed high school are less likely to report any functional limitations as well as fewer limitations compared to parents with no children who completed high school. This association remains significant even after accounting for parent and offspring-level characteristics, including parents’ income that accounts for children’s financial transfers to parents. Future research should aim to understand the mechanisms that explain the association between adult children’s education and changes to parents’ health over time. PMID:26966254

Trace element analysis of rough diamond by LA-ICP-MS: a case of source discrimination?

PubMed

Dalpé, Claude; Hudon, Pierre; Ballantyne, David J; Williams, Darrell; Marcotte, Denis

2010-11-01

Current profiling of rough diamond source is performed using different physical and/or morphological techniques that require strong knowledge and experience in the field. More recently, chemical impurities have been used to discriminate diamond source and with the advance of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) empirical profiling of rough diamonds is possible to some extent. In this study, we present a LA-ICP-MS methodology that we developed for analyzing ultra-trace element impurities in rough diamond for origin determination ("profiling"). Diamonds from two sources were analyzed by LA-ICP-MS and were statistically classified by accepted methods. For the two diamond populations analyzed in this study, binomial logistic regression produced a better overall correct classification than linear discriminant analysis. The results suggest that an anticipated matrix match reference material would improve the robustness of our methodology for forensic applications. © 2010 American Academy of Forensic Sciences.

Does the Organized Sexual Murderer Better Delay and Avoid Detection?

PubMed

Beauregard, Eric; Martineau, Melissa

2016-01-01

According to the organized-disorganized model, organized sexual murderers adopt specific behaviors during the commission of their crimes that contribute to avoiding police detection. The current study examines the effect of sexual murderers' organized behaviors on their ability to both delay and/or avoid police detection. Using a combination of negative binomial and logistic regression analyses on a sample of 350 sexual murder cases, findings showed that although both measures of delaying and avoiding detection are positively correlated, different behavioral patterns were observed. For instance, offenders who moved the victim's body were more likely to avoid detection but the victim's body was likely to be recovered faster. Moreover, victim characteristics have an impact on both measures; however, this effect disappears for the measure of delaying detection once the organized behaviors are introduced. Implications of the findings are discussed. © The Author(s) 2014.

Does a birthday predispose to vascular events?

PubMed

Saposnik, Gustavo; Baibergenova, Akerke; Dang, Jason; Hachinski, Vladimir

2006-07-25

To examine the influence of birthdays on the onset and course of vascular events such as stroke, TIA, and acute myocardial infarction (AMI). This population-based study included all emergency department (ED) admissions due to ischemic stroke, TIA, or AMI from April 2002 to March 2004 in Ontario, Canada. All cases were identified through the National Ambulatory Care Reporting System. Calculations of daily and weekly numbers of events were centered on the patient's birthday and the week of the birthday. Statistical analyses include binomial tests and logistic regression. During the study period, there were 24,315 ED admissions with acute stroke, 16,088 with TIAs, and 29,090 with AMI. The observed number of vascular events during the birthday was higher than the expected daily number of visits for stroke (87 vs 67; p = 0.009), TIA (58 vs 44; p = 0.02), and AMI (97 vs 80; p = 0.027) but not for selected control conditions (asthma, appendicitis, head trauma). Vascular events were more likely to occur on birthday (242 vs 191; odds ratio [OR] = 1.27). No significant differences were observed during the birthday week for any of the conditions. Multivariate logistic regression showed that birthday vascular events were more likely to occur in patients with a history of hypertension (OR = 1.88; 95% CI 1.09 to 3.24). Sensitivity analyses with alternative definitions of birthday week did not alter the results. Stress associated with birthdays may trigger vascular events in patients with predisposing conditions.

Self-declared stock ownership and association with positive trial outcome in randomized controlled trials with binary outcomes published in general medical journals: a cross-sectional study.

PubMed

Falk Delgado, Alberto; Falk Delgado, Anna

2017-07-26

Describe the prevalence and types of conflicts of interest (COI) in published randomized controlled trials (RCTs) in general medical journals with a binary primary outcome and assess the association between conflicts of interest and favorable outcome. Parallel-group RCTs with a binary primary outcome published in three general medical journals during 2013-2015 were identified. COI type, funding source, and outcome were extracted. Binomial logistic regression model was performed to assess association between COI and funding source with outcome. A total of 509 consecutive parallel-group RCTs were included in the study. COI was reported in 74% in mixed funded RCTs and in 99% in for-profit funded RCTs. Stock ownership was reported in none of the non-profit RCTs, in 7% of mixed funded RCTs, and in 50% of for-profit funded RCTs. Mixed-funded RCTs had employees from the funding company in 11% and for-profit RCTs in 76%. Multivariable logistic regression revealed that stock ownership in the funding company among any of the authors was associated with a favorable outcome (odds ratio = 3.53; 95% confidence interval = 1.59-7.86; p < 0.01). COI in for-profit funded RCTs is extensive, because the factors related to COI are not fully independent, a multivariable analysis should be cautiously interpreted. However, after multivariable adjustment only stock ownership from the funding company among authors is associated with a favorable outcome.

Health-related productivity losses increase when the health condition is co-morbid with psychological distress: findings from a large cross-sectional sample of working Australians.

PubMed

Holden, Libby; Scuffham, Paul A; Hilton, Michael F; Ware, Robert S; Vecchio, Nerina; Whiteford, Harvey A

2011-05-31

The health condition of workers is known to impact on productivity outcomes. The relationship between health and productivity is of increasing interest amid the need to increase productivity to meet global financial challenges. Prevalence of psychological distress is also of growing concern in Australia with a two-fold increase in the prevalence of psychological distress in Australia from 1997-2005. We used the cross-sectional data set from the Australian Work Outcomes Research Cost-benefit (WORC) study to explore the impacts of health conditions with and without co-morbid psychological distress, compared to those with neither condition, in a sample of approximately 78,000 working Australians. The World Health Organisation Health and Performance Questionnaire was used which provided data on demographic characteristics, health condition and working conditions. Data were analysed using negative binomial logistic regression and multinomial logistic regression models for absenteeism and presenteeism respectively. For both absenteeism and presenteeism productivity measures there was a greater risk of productivity loss associated when health conditions were co-morbid with psychological distress. For some conditions this risk was much greater for those with co-morbid psychological distress compared to those without. Co-morbid psychological distress demonstrates an increased risk of productivity loss for a range of health conditions. These findings highlight the need for further research to determine whether co-morbid psychological distress potentially exacerbates lost productivity.

Applied Prevalence Ratio estimation with different Regression models: An example from a cross-national study on substance use research.

PubMed

Espelt, Albert; Marí-Dell'Olmo, Marc; Penelo, Eva; Bosque-Prous, Marina

2016-06-14

To examine the differences between Prevalence Ratio (PR) and Odds Ratio (OR) in a cross-sectional study and to provide tools to calculate PR using two statistical packages widely used in substance use research (STATA and R). We used cross-sectional data from 41,263 participants of 16 European countries participating in the Survey on Health, Ageing and Retirement in Europe (SHARE). The dependent variable, hazardous drinking, was calculated using the Alcohol Use Disorders Identification Test - Consumption (AUDIT-C). The main independent variable was gender. Other variables used were: age, educational level and country of residence. PR of hazardous drinking in men with relation to women was estimated using Mantel-Haenszel method, log-binomial regression models and poisson regression models with robust variance. These estimations were compared to the OR calculated using logistic regression models. Prevalence of hazardous drinkers varied among countries. Generally, men have higher prevalence of hazardous drinking than women [PR=1.43 (1.38-1.47)]. Estimated PR was identical independently of the method and the statistical package used. However, OR overestimated PR, depending on the prevalence of hazardous drinking in the country. In cross-sectional studies, where comparisons between countries with differences in the prevalence of the disease or condition are made, it is advisable to use PR instead of OR.

Serum Vitamin D Levels and Markers of Severity of Childhood Asthma in Costa Rica

PubMed Central

Brehm, John M.; Celedón, Juan C.; Soto-Quiros, Manuel E.; Avila, Lydiana; Hunninghake, Gary M.; Forno, Erick; Laskey, Daniel; Sylvia, Jody S.; Hollis, Bruce W.; Weiss, Scott T.; Litonjua, Augusto A.

2009-01-01

Rationale: Maternal vitamin D intake during pregnancy has been inversely associated with asthma symptoms in early childhood. However, no study has examined the relationship between measured vitamin D levels and markers of asthma severity in childhood. Objectives: To determine the relationship between measured vitamin D levels and both markers of asthma severity and allergy in childhood. Methods: We examined the relation between 25-hydroxyvitamin D levels (the major circulating form of vitamin D) and markers of allergy and asthma severity in a cross-sectional study of 616 Costa Rican children between the ages of 6 and 14 years. Linear, logistic, and negative binomial regressions were used for the univariate and multivariate analyses. Measurements and Main Results: Of the 616 children with asthma, 175 (28%) had insufficient levels of vitamin D (<30 ng/ml). In multivariate linear regression models, vitamin D levels were significantly and inversely associated with total IgE and eosinophil count. In multivariate logistic regression models, a log10 unit increase in vitamin D levels was associated with reduced odds of any hospitalization in the previous year (odds ratio [OR], 0.05; 95% confidence interval [CI], 0.004–0.71; P = 0.03), any use of antiinflammatory medications in the previous year (OR, 0.18; 95% CI, 0.05–0.67; P = 0.01), and increased airway responsiveness (a ≤8.58-μmol provocative dose of methacholine producing a 20% fall in baseline FEV1 [OR, 0.15; 95% CI, 0.024–0.97; P = 0.05]). Conclusions: Our results suggest that vitamin D insufficiency is relatively frequent in an equatorial population of children with asthma. In these children, lower vitamin D levels are associated with increased markers of allergy and asthma severity. PMID:19179486

JROTC as a Substitute for PE: Really?

PubMed Central

Lounsbery, Monica A. F.; Holt, Kathryn A.; Monnat, Shannon A.; McKenzie, Thomas L.; Funk, Brian

2014-01-01

Purpose Even though physical education (PE) is an evidence-based strategy for providing and promoting physical activity, alternative programs such as Junior Reserve Officer Training Corps (JROTC) are commonly substituted for PE in many states. The purpose of this study was to compare student physical activity and lesson contexts during high school PE and JROTC sessions. Method SOFIT (System for Observing Fitness Instruction Time) was used to assess PE and JROTC sessions (N=38 each) in 4 high schools that provided both programs. Data were analyzed using t-tests, negative binomial regression, and logistic regression. Results Students engaged in significantly more moderate to vigorous physical activity during PE than JROTC sessions and they were significantly less sedentary. Significant differences between the two program types were also found among lesson contexts. Conclusions PE and JROTC provide substantially different content and contexts and students in them engage in substantially different amounts of moderate to vigorous physical activity. Students in JROTC, and perhaps other alternative programs, are less likely to accrue health-supporting physical activity and engage in fewer opportunities to be physically fit and motorically skilled. Policies and practices for providing substitutions for PE should be carefully examined. PMID:25141093

Resistant to the recession: low-income adults' maintenance of cooking and away-from-home eating behaviors during times of economic turbulence.

PubMed

Smith, Lindsey P; Ng, Shu Wen; Popkin, Barry M

2014-05-01

We examined the effects of state-level unemployment rates during the recession of 2008 on patterns of home food preparation and away-from-home (AFH) eating among low-income and minority populations. We analyzed pooled cross-sectional data on 118 635 adults aged 18 years or older who took part in the American Time Use Study. Multinomial logistic regression models stratified by gender were used to evaluate the associations between state-level unemployment, poverty, race/ethnicity, and time spent cooking, and log binomial regression was used to assess respondents' AFH consumption patterns. High state-level unemployment was associated with only trivial increases in respondents' cooking patterns and virtually no change in their AFH eating patterns. Low-income and racial/ethnic minority groups were not disproportionately affected by the recession. Even during a major economic downturn, US adults are resistant to food-related behavior change. More work is needed to understand whether this reluctance to change is attributable to time limits, lack of knowledge or skill related to food preparation, or lack of access to fresh produce and raw ingredients.

Resistant to the Recession: Low-Income Adults’ Maintenance of Cooking and Away-From-Home Eating Behaviors During Times of Economic Turbulence

PubMed Central

Smith, Lindsey P.; Ng, Shu Wen

2014-01-01

Objectives. We examined the effects of state-level unemployment rates during the recession of 2008 on patterns of home food preparation and away-from-home (AFH) eating among low-income and minority populations. Methods. We analyzed pooled cross-sectional data on 118 635 adults aged 18 years or older who took part in the American Time Use Study. Multinomial logistic regression models stratified by gender were used to evaluate the associations between state-level unemployment, poverty, race/ethnicity, and time spent cooking, and log binomial regression was used to assess respondents’ AFH consumption patterns. Results. High state-level unemployment was associated with only trivial increases in respondents’ cooking patterns and virtually no change in their AFH eating patterns. Low-income and racial/ethnic minority groups were not disproportionately affected by the recession. Conclusions. Even during a major economic downturn, US adults are resistant to food-related behavior change. More work is needed to understand whether this reluctance to change is attributable to time limits, lack of knowledge or skill related to food preparation, or lack of access to fresh produce and raw ingredients. PMID:24625145

A comparison of different ways of including baseline counts in negative binomial models for data from falls prevention trials.

PubMed

Zheng, Han; Kimber, Alan; Goodwin, Victoria A; Pickering, Ruth M

2018-01-01

A common design for a falls prevention trial is to assess falling at baseline, randomize participants into an intervention or control group, and ask them to record the number of falls they experience during a follow-up period of time. This paper addresses how best to include the baseline count in the analysis of the follow-up count of falls in negative binomial (NB) regression. We examine the performance of various approaches in simulated datasets where both counts are generated from a mixed Poisson distribution with shared random subject effect. Including the baseline count after log-transformation as a regressor in NB regression (NB-logged) or as an offset (NB-offset) resulted in greater power than including the untransformed baseline count (NB-unlogged). Cook and Wei's conditional negative binomial (CNB) model replicates the underlying process generating the data. In our motivating dataset, a statistically significant intervention effect resulted from the NB-logged, NB-offset, and CNB models, but not from NB-unlogged, and large, outlying baseline counts were overly influential in NB-unlogged but not in NB-logged. We conclude that there is little to lose by including the log-transformed baseline count in standard NB regression compared to CNB for moderate to larger sized datasets. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Experiences of Racism and Breastfeeding Initiation and Duration Among First-Time Mothers of the Black Women's Health Study.

PubMed

Griswold, Michele K; Crawford, Sybil L; Perry, Donna J; Person, Sharina D; Rosenberg, Lynn; Cozier, Yvette C; Palmer, Julie R

2018-02-12

Breastfeeding rates are lower for black women in the USA compared with other groups. Breastfeeding and lactation are sensitive time points in the life course, centering breastfeeding as a health equity issue. In the USA, experiences of racism have been linked to poor health outcomes but racism relative to breastfeeding has not been extensively investigated. This study aims to investigate the association between experiences of racism, neighborhood segregation, and nativity with breastfeeding initiation and duration. This is a prospective secondary analysis of the Black Women's Health Study, based on data collected from 1995 through 2005. Daily and institutional (job, housing, police) racism, nativity, and neighborhood segregation in relation to breastfeeding were examined. Odds ratios and 95% confidence intervals were calculated using binomial logistic regression for the initiation outcomes (N = 2705) and multinomial logistic regression for the duration outcomes (N = 2172). Racism in the job setting was associated with lower odds of breastfeeding duration at 3-5 months. Racism with the police was associated with higher odds of breastfeeding initiation and duration at 3-5 and 6 months. Being born in the USA or having a parent born in the USA predicted lower odds of breastfeeding initiation and duration. Living in a segregated neighborhood (primarily black residents) as a child was associated with decreased breastfeeding initiation and duration relative to growing up in a predominantly white neighborhood. Experiences of institutionalized racism influenced breastfeeding initiation and duration. Structural-level interventions are critical to close the gap of racial inequity in breastfeeding rates in the USA.

Analysis of vaccination status of preschool children in Teresina (PI), Brazil.

PubMed

Fernandes, Ana Catharina Nunes; Gomes, Keila Rejane Oliveira; de Araújo, Telma Maria Evangelista; Moreira-Araújo, Regilda Saraiva dos Reis

2015-01-01

Immunization is a priority action of the Ministry of Health for contributing to reducing child mortality; however, studies show increased vaccination delays and non-vaccination. This study aims to analyze the immunization status of preschool children in Teresina - PI. Cross-sectional study involving 542 children, aged 2-6 years, enrolled in local public schools in four Municipal Childhood Education Centers selected at random, following the proportional division by regions of the city. Data were collected through a pre-coded and pre-tested form, in addition to scanning the children's vaccination card. For univariate descriptive statistical analysis, Pearson's χ2 Test and Fisher's Exact Test were used, and for multivariate analysis, multiple logistic regression was conducted using SPSS version 17.0. The study complied with the ethical aspects in accordance with current legislation. The frequency of delayed vaccination/non-vaccination was 24.9%. The average of non-administered vaccines was 1.7 (SD ± 1.2) and of delayed vaccines was 3.3 (SD ± 1.6). The binomial logistic regression model showed a significant association (p < 0.05) between young caregivers (under 24 years) and low frequency in childcare consultations with delayed vaccination/non-vaccination. There was no association with the variables related to the experience of children in the vaccination room and with the implementation of the Family Health Strategy. Ensuring and strengthening primary healthcare actions are essential tools to reduce non-vaccination and vaccine delays. Professionals who care for children in vaccination rooms need to sensitize themselves to guide and encourage parents/caregivers to meet the vaccination schedules without delays or errors.

Variability in results from negative binomial models for Lyme disease measured at different spatial scales.

PubMed

Tran, Phoebe; Waller, Lance

2015-01-01

Lyme disease has been the subject of many studies due to increasing incidence rates year after year and the severe complications that can arise in later stages of the disease. Negative binomial models have been used to model Lyme disease in the past with some success. However, there has been little focus on the reliability and consistency of these models when they are used to study Lyme disease at multiple spatial scales. This study seeks to explore how sensitive/consistent negative binomial models are when they are used to study Lyme disease at different spatial scales (at the regional and sub-regional levels). The study area includes the thirteen states in the Northeastern United States with the highest Lyme disease incidence during the 2002-2006 period. Lyme disease incidence at county level for the period of 2002-2006 was linked with several previously identified key landscape and climatic variables in a negative binomial regression model for the Northeastern region and two smaller sub-regions (the New England sub-region and the Mid-Atlantic sub-region). This study found that negative binomial models, indeed, were sensitive/inconsistent when used at different spatial scales. We discuss various plausible explanations for such behavior of negative binomial models. Further investigation of the inconsistency and sensitivity of negative binomial models when used at different spatial scales is important for not only future Lyme disease studies and Lyme disease risk assessment/management but any study that requires use of this model type in a spatial context. Copyright © 2014 Elsevier Inc. All rights reserved.

Leavers, Movers, and Stayers: The Role of Workplace Conditions in Teacher Mobility Decisions

ERIC Educational Resources Information Center

Kukla-Acevedo, Sharon

2009-01-01

The author explored whether 3 workplace conditions were related to teacher mobility decisions. The modeling strategy incorporated a series of binomial and multinomial logistic models to estimate the effects of administrative support, classroom control, and behavioral climate on teachers' decisions to quit teaching or switch schools. The results…

Clinicians' adherence to clinical practice guidelines for cardiac function monitoring during antipsychotic treatment: a retrospective report on 434 patients with severe mental illness.

PubMed

Manchia, Mirko; Firinu, Giorgio; Carpiniello, Bernardo; Pinna, Federica

2017-03-31

Severe mental illness (SMI) has considerable excess morbidity and mortality, a proportion of which is explained by cardiovascular diseases, caused in part by antipsychotic (AP) induced QT-related arrhythmias and sudden death by Torsade de Point (TdP). The implementation of evidence-based recommendations for cardiac function monitoring might reduce the incidence of these AP-related adverse events. To investigate clinicians' adherence to cardiac function monitoring before and after starting AP, we performed a retrospective assessment of 434 AP-treated SMI patients longitudinally followed-up for 5 years at an academic community mental health center. We classified antipsychotics according to their risk of inducing QT-related arrhythmias and TdP (Center for Research on Therapeutics, University of Arizona). We used univariate tests and multinomial or binary logistic regression model for data analysis. Univariate and multinomial regression analysis showed that psychiatrists were more likely to perform pre-treatment electrocardiogram (ECG) and electrolyte testing with AP carrying higher cardiovascular risk, but not on the basis of AP pharmacological class. Univariate and binomial regression analysis showed that cardiac function parameters (ECG and electrolyte balance) were more frequently monitored during treatment with second generation AP than with first generation AP. Our data show the presence of weaknesses in the cardiac function monitoring of AP-treated SMI patients, and might guide future interventions to tackle them.

Categorical Data Analysis Using a Skewed Weibull Regression Model

NASA Astrophysics Data System (ADS)

Caron, Renault; Sinha, Debajyoti; Dey, Dipak; Polpo, Adriano

2018-03-01

In this paper, we present a Weibull link (skewed) model for categorical response data arising from binomial as well as multinomial model. We show that, for such types of categorical data, the most commonly used models (logit, probit and complementary log-log) can be obtained as limiting cases. We further compare the proposed model with some other asymmetrical models. The Bayesian as well as frequentist estimation procedures for binomial and multinomial data responses are presented in details. The analysis of two data sets to show the efficiency of the proposed model is performed.

Indicators of Terrorism Vulnerability in Africa

DTIC Science & Technology

2015-03-26

the terror threat and vulnerabilities across Africa. Key words: Terrorism, Africa, Negative Binomial Regression, Classification Tree iv I would like...31 Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Log -likelihood...70 viii Page 5.3 Classification Tree Description

Maternal employment and Mexican school-age children overweight in 2012: the importance of households features.

PubMed

Espinosa, Alejandro Martínez

2018-01-01

International evidence regarding the relationship between maternal employment and school-age children overweight and obesity shows divergent results. In Mexico, this relationship has not been confirmed by national data sets analysis. Consequently, the objective of this article was to evaluate the role of the mothers' participation in labor force related to excess body weight in Mexican school-age children (aged 5-11 years). A cross-sectional study was conducted on a sample of 17,418 individuals from the National Health and Nutrition Survey 2012, applying binomial logistic regression models. After controlling for individual, maternal and contextual features, the mothers' participation in labor force was associated with children body composition. However, when the household features (living arrangements, household ethnicity, size, food security and socioeconomic status) were incorporated, maternal employment was no longer statically significant. Household features are crucial factors for understanding the overweight and obesity prevalence levels in Mexican school-age children, despite the mother having a paid job. Copyright: © 2018 Permanyer.

Cell Phone Use While Driving: Prospective Association with Emerging Adult Use.

PubMed

Trivedi, Neha; Haynie, Denise; Bible, Joe; Liu, Danping; Simons-Morton, Bruce

2017-09-01

Secondary task engagement such as cell phone use while driving is a common behavior among adolescents and emerging adults. Texting and other distracting cell phone use in this population contributes to the high rate of fatal car crashes. Peer engagement in similar risky driving behaviors, such as texting, could socially influence driver phone use behavior. The present study investigates the prospective association between peer and emerging adult texting while driving the first year after high school. Surveys were conducted with a national sample of emerging adults and their nominated peers. Binomial logistic regression analyses, adjusting for gender, race/ethnicity, parental education, and family affluence, showed that participants (n=212) with peers (n=675) who reported frequently texting while driving, were significantly more likely to text while driving the following year (odds ratio, 3.01; 95% CI, 1.19-7.59; P=0.05). The findings are consistent with the idea that peer texting behavior influences the prevalence of texting while driving among emerging adults. Copyright © 2017 Elsevier Ltd. All rights reserved.

Self-reported discrimination and health-related quality of life among Whites, Blacks, Mexicans and Central Americans.

PubMed

Otiniano, Angie Denisse; Gee, Gilbert C

2012-04-01

This study investigates whether self-reported racial discrimination is related to poor health-related quality of life (HRQoL). Analyses focused on Whites (n = 52,571), Blacks (n = 4,343), Mexicans (n = 12,336), Central Americans (n = 1,504), Multi-ethnic Latinos (n = 1,102), and Other Latinos (n = 1,828) who participated in the 2003 and 2005 California Health Interview survey. Logistic and negative binomial regression was used to examine the association between HRQoL (assessed with the CDC unhealthy days measures) and self-reported racial discrimination. Discrimination was reported by 10% of Whites, 57% of Blacks, and 24-31% of the Latino groups. These reports were associated with increased number of unhealthy days, disability days, and poor self-rated health, even after, controlling for education and other factors. This association did not consistently vary by race/ethnicity. Racial discrimination may be a risk factor for poor HRQoL among diverse groups. Future research should examine the factors that may reduce potential exposure to racial discrimination.

Poverty is the main environmental factor for obesity in a Mexican-border city.

PubMed

Jiménez-Cruz, Arturo; Castañeda-Gonzalez, Lidia M; Bacardí-Gascón, Montserrat

2013-05-01

Obesity is a pandemic in Mexico. The purpose of this study was to assess the environmental factors that have the strongest association with obesity and abdominal obesity among adults in Tijuana. Four neighborhoods differing in socioeconomic status were chosen. A questionnaire for weekly walking, social cohesion, satisfaction with their community, weekly income, and convenience store, education, family income, crime safety, pedestrian safety, street connectivity, walking/cycling facilities, and sociodemographic characteristics was administered. Weight, height, and waist circumference were measured. Univariate and multivariate binomial logistic regressions were conducted. Three hundred and twenty-two (322) individuals, 70% females with a mean age of 39 years, were assessed. The prevalence of obesity and abdominal obesity was 27% and 43.5% respectively. The odds ratio for obesity and abdominal obesity among those living in the lowest-income neighborhood was 2.4 and 7.8 respectively, compared with those living in a middle-class neighborhood. Residence in a low-income neighborhood was a predictor for obesity.

Interdependence in Health and Functioning Among Older Spousal Caregivers and Care Recipients.

PubMed

Hoffman, Geoffrey J; Burgard, Sarah; Mendez-Luck, Carolyn A; Gaugler, Joseph E

2018-06-01

Older spousal caregiving relationships involve support that may be affected by the health of either the caregiver or care recipient. We conducted a longitudinal analysis using pooled data from 4,632 community-dwelling spousal care recipients and caregivers aged ⩾50 from the 2002 to 2014 waves of the Health and Retirement Study. We specified logistic and negative binomial regression models using lagged predictor variables to assess the role of partner health status on spousal caregiver and care recipient health care utilization and physical functioning outcomes. Care recipients' odds of hospitalization, odds ratio (OR): 0.83, p<.001, decreased when caregivers had more ADL difficulties. When spouses were in poorer versus better health, care recipients' bed days decreased (4.69 vs. 2.54) while caregivers' bed days increased (0.20 vs. 0.96). Providers should consider the dual needs of caregivers caring for care recipients and their own health care needs, in adopting a family-centered approach to management of older adult long-term care needs.

Mental disorders and employment status in the São Paulo Metropolitan Area, Brazil: gender differences and use of health services.

PubMed

França, Mariane Henriques; Barreto, Sandhi Maria; Pereira, Flavia Garcia; Andrade, Laura Helena Silveira Guerra de; Paiva, Maria Cristina Alochio de; Viana, Maria Carmen

2017-10-09

Mental disorders are associated with employment status as significant predictors and as consequences of unemployment and early retirement. This study describes the estimates and associations of 12-month DSM-IV prevalence rates of mental disorders and use of health services with employment status by gender in the São Paulo Metropolitan Area, Brazil. Data from the São Paulo Megacity Mental Health Survey was analyzed (n = 5,037). This is a population-based study assessing the prevalence and determinants of mental disorders among adults, using the Composite International Diagnostic Interview. The associations were estimated by odds ratios obtained through binomial and multinomial logistic regression. This study demonstrates that having mental disorders, especially mood disorders, is associated with being inactive or unemployed among men and inactive among women, but only having a substance use disorder is associated with being unemployed among women. Among those with mental disorders, seeking health care services is less frequent within unemployed.

Meta-analysis of diagnostic tests accounting for disease prevalence: a new model using trivariate copulas.

PubMed

Hoyer, A; Kuss, O

2015-05-20

In real life and somewhat contrary to biostatistical textbook knowledge, sensitivity and specificity (and not only predictive values) of diagnostic tests can vary with the underlying prevalence of disease. In meta-analysis of diagnostic studies, accounting for this fact naturally leads to a trivariate expansion of the traditional bivariate logistic regression model with random study effects. In this paper, a new model is proposed using trivariate copulas and beta-binomial marginal distributions for sensitivity, specificity, and prevalence as an expansion of the bivariate model. Two different copulas are used, the trivariate Gaussian copula and a trivariate vine copula based on the bivariate Plackett copula. This model has a closed-form likelihood, so standard software (e.g., SAS PROC NLMIXED) can be used. The results of a simulation study have shown that the copula models perform at least as good but frequently better than the standard model. The methods are illustrated by two examples. Copyright © 2015 John Wiley & Sons, Ltd.

Perceived interparental conflict and early adolescents' friendships: the role of attachment security and emotion regulation.

PubMed

Schwarz, Beate; Stutz, Melanie; Ledermann, Thomas

2012-09-01

Although there is strong evidence for the effect of interparental conflict on adolescents' internalizing and externalizing problems, little is known about the effect on the quality of adolescents' relationships. The current study investigates the link between adolescents' friendships and interparental conflict as reported by both parents and adolescents. It considers early adolescents' emotion regulation ability and attachment security as mediators. The analysis is based on a longitudinal study with two waves separated by 12 months. The participants were 180 two-parent families and their adolescent children (50.5 % girls), the average age of the latter being 10.61 years (SD = 0.41) at the outset (Time 1). Binomial logistic regression analysis revealed that perceived interparental conflict increased the risk of instability in friendship relationships across the 1-year period. Structural equation modeling analysis indicated that the association between perceived interparental conflict and friendship quality was mediated by emotion regulation and attachment security. The discussion focuses on mechanisms whereby interparental conflict influences early adolescents' friendship relationships.

Alone on the Inside: The Impact of Social Isolation and Helping Others on AOD Use and Criminal Activity

PubMed Central

Johnson, Byron R.; Pagano, Maria E.; Lee, Matthew T.; Post, Stephen G.

2015-01-01

Because addiction is a socially isolating disease, social support for recovery is an important element of treatment planning. This study examines the relationship between social isolation, giving and receiving social support in Alcoholics Anonymous during treatment, and post-treatment outcomes among juvenile offenders court-referred to addiction treatment. Adolescents (N = 195) aged 14 to 18 years were prospectively assessed at treatment admission, treatment discharge, 6 months, and 12 months after treatment discharge. The influence of social isolation variables on relapse and severe criminal activity in the 12-months post-treatment was examined using negative binomial logistic regressions and event history methods. Juveniles entering treatment with social estrangement were significantly more likely to relapse, be incarcerated, and commit a violent crime in the 12-months post-treatment. Giving help to others in Alcoholics Anonymous during treatment significantly reduced the risk of relapse, incarceration, and violent crime in the 12-months post-treatment whereas receiving help did not. PMID:29628533

The Effect of Sharrows, Painted Bicycle Lanes and Physically Protected Paths on the Severity of Bicycle Injuries Caused by Motor Vehicles.

PubMed

Wall, Stephen P; Lee, David C; Frangos, Spiros G; Sethi, Monica; Heyer, Jessica H; Ayoung-Chee, Patricia; DiMaggio, Charles J

2016-01-01

We conducted individual and ecologic analyses of prospectively collected data from 839 injured bicyclists who collided with motorized vehicles and presented to Bellevue Hospital, an urban Level-1 trauma center in New York City, from December 2008 to August 2014. Variables included demographics, scene information, rider behaviors, bicycle route availability, and whether the collision occurred before the road segment was converted to a bicycle route. We used negative binomial modeling to assess the risk of injury occurrence following bicycle path or lane implementation. We dichotomized U.S. National Trauma Data Bank Injury Severity Scores (ISS) into none/mild (0-8) versus moderate, severe, or critical (>8) and used adjusted multivariable logistic regression to model the association of ISS with collision proximity to sharrows (i.e., bicycle lanes designated for sharing with cars), painted bicycle lanes, or physically protected paths. Negative binomial modeling of monthly counts, while adjusting for pedestrian activity, revealed that physically protected paths were associated with 23% fewer injuries. Painted bicycle lanes reduced injury risk by nearly 90% (IDR 0.09, 95% CI 0.02-0.33). Holding all else equal, compared to no bicycle route, a bicycle injury nearby sharrows was nearly twice as likely to be moderate, severe, or critical (adjusted odds ratio 1.94; 95% confidence interval (CI) 0.91-4.15). Painted bicycle lanes and physically protected paths were 1.52 (95% CI 0.85-2.71) and 1.66 (95% CI 0.85-3.22) times as likely to be associated with more than mild injury respectively.

Drivers of multidimensional eco-innovation: empirical evidence from the Brazilian industry.

PubMed

da Silva Rabêlo, Olivan; de Azevedo Melo, Andrea Sales Soares

2018-03-08

The study analyses the relationships between the main drivers of eco-innovation introduced by innovative industries, focused on cooperation strategy. Eco-innovation is analysed by means of a multidimensional identification strategy, showing the relationships between the independent variables and the variable of interest. The literature discussing environmental innovation is different from the one discussing other types of innovation inasmuch as it seeks to grasp its determinants and to mostly highlight the relevance of environmental regulation. The key feature of this paper is that it ascribes special relevance to cooperation strategy with external partners and to the propensity of innovative industry introducing eco-innovation. A sample of 35,060 Brazilian industries were analysed, between 2003 and 2011, by means of Binomial, Multinomial and Ordinal logistic regressions with microdata collected with the research and innovation department (PINTEC) from the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística). The econometric results estimated by the Logit Multinomial method suggest that the cooperation with external partners practiced by innovative industries facilitates the adoption of eco-innovation in dimension 01 with probability of 64.59%, 57.63% in dimension 02 and 81.02% in dimension 03. The data reveal that the higher the degree of eco-innovation complexity, the harder industries seek to obtain cooperation with external partners. When calculating with the Logit Ordinal and Binomial models, cooperation increases the probability that the industry is eco-innovative in 65.09% and 89.34%, respectively. Environmental regulation and innovation in product and information management were also positively correlated as drivers of eco-innovation.

The analysis of incontinence episodes and other count data in patients with overactive bladder by Poisson and negative binomial regression.

PubMed

Martina, R; Kay, R; van Maanen, R; Ridder, A

2015-01-01

Clinical studies in overactive bladder have traditionally used analysis of covariance or nonparametric methods to analyse the number of incontinence episodes and other count data. It is known that if the underlying distributional assumptions of a particular parametric method do not hold, an alternative parametric method may be more efficient than a nonparametric one, which makes no assumptions regarding the underlying distribution of the data. Therefore, there are advantages in using methods based on the Poisson distribution or extensions of that method, which incorporate specific features that provide a modelling framework for count data. One challenge with count data is overdispersion, but methods are available that can account for this through the introduction of random effect terms in the modelling, and it is this modelling framework that leads to the negative binomial distribution. These models can also provide clinicians with a clearer and more appropriate interpretation of treatment effects in terms of rate ratios. In this paper, the previously used parametric and non-parametric approaches are contrasted with those based on Poisson regression and various extensions in trials evaluating solifenacin and mirabegron in patients with overactive bladder. In these applications, negative binomial models are seen to fit the data well. Copyright © 2014 John Wiley & Sons, Ltd.

Introductory Statistics in the Garden

ERIC Educational Resources Information Center

Wagaman, John C.

2017-01-01

This article describes four semesters of introductory statistics courses that incorporate service learning and gardening into the curriculum with applications of the binomial distribution, least squares regression and hypothesis testing. The activities span multiple semesters and are iterative in nature.

Harnessing Youth and Young Adult Culture: Improving the Reach and Engagement of the truth® Campaign.

PubMed

Hair, Elizabeth; Pitzer, Lindsay; Bennett, Morgane; Halenar, Michael; Rath, Jessica; Cantrell, Jennifer; Dorrler, Nicole; Asche, Eric; Vallone, Donna

2017-07-01

The national youth and young adult tobacco prevention mass media campaign, truth®, relaunched in 2014 with the goal of creating "the generation that ends smoking." The objective of this study was to assess whether the strategy of airing truth ads during popular, culturally relevant televised events was associated with higher ad and brand awareness and increases in social media engagement. Awareness of six truth advertisements that aired during popular television events and self-reported social media engagement were assessed via cross-sectional online surveys of youth and young adults aged 15-21 years. Social engagement was also measured using separate Twitter and YouTube metrics. Logistic regression models predicted self-reported social engagement and any ad awareness, and a negative binomial regression predicted the total social media engagement across digital platforms. The study found that viewing a popular televised event was associated with higher odds of ad awareness and social engagement. The results also indicate that levels of social media engagement for an event period are greater than for a nonevent period. The findings demonstrate that premiering advertisements during a popular, culturally relevant televised event is associated with higher awareness of truth ads and increased social engagement related to the campaign, controlling for variables that might also influence the response to campaign messages.

Measuring moral hazard and adverse selection by propensity scoring in the mixed health care economy of Hong Kong.

PubMed

Wong, Irene O L; Lindner, Michael J; Cowling, Benjamin J; Lau, Eric H Y; Lo, Su-Vui; Leung, Gabriel M

2010-04-01

To evaluate the presence of moral hazard, adjusted for the propensity to have self-purchased insurance policies, employer-based medical benefits, and welfare-associated medical benefits in Hong Kong. Based on 2005 population survey, we used logistic regression and zero-truncated negative binomial/Poisson regressions to assess the presence of moral hazard by comparing inpatient and outpatient utilization between insured and uninsured individuals. We fitted each enabling factor specific to the type of service covered, and adjusted for predisposing socioeconomic and demographic factors. We used a propensity score approach to account for potential adverse selection. Employment-based benefits coverage was associated with increased access and intensity of use for both inpatient and outpatient care, except for public hospital use. Similarly, welfare-based coverage had comparable effect sizes as employment-based schemes, except for the total number of public ambulatory episodes. Self-purchased insurance facilitated access but did not apparently induce greater demand of services among ever users. Nevertheless, there was no evidence of moral hazard in public hospital use. Our findings suggest that employment-based benefits coverage lead to the greatest degree of moral hazard in Hong Kong. Future studies should focus on confirming these observational findings using a randomized design. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

Socioeconomic Differences in and Predictors of Home-Based Palliative Care Health Service Use in Ontario, Canada

PubMed Central

Cai, Jiaoli; Guerriere, Denise N.; Zhao, Hongzhong; Coyte, Peter C.

2017-01-01

The use of health services may vary across people with different socioeconomic statuses, and may be determined by many factors. The purposes of this study were (i) to examine the socioeconomic differences in the propensity and intensity of use for three main home-based health services, that is, home-based palliative care physician visits, nurse visits and personal support worker (PSW) hours; and (ii) to explore the determinants of the use of home-based palliative care services. A prospective cohort study was employed. A total of 181 caregivers were interviewed biweekly over the course of the palliative care trajectory, yielding a total of 994 interviews. The propensity and intensity of health service use were examined using logistic regression and negative binomial regression, respectively. The results demonstrated that both the propensity and intensity of home-based nurse and PSW visits fell with socioeconomic status. The use of home-based palliative care services was not concentrated in high socioeconomic status groups. The common predictors of health service use in the three service categories were patient age, the Palliative Performance Scale (PPS) score and place of death. These findings may assist health service planners in the appropriate allocation of resources and service packages to meet the complex needs of palliative care populations. PMID:28718797

Use of acute care hospital services by immigrant seniors in Ontario: A linkage study.

PubMed

Ng, Edward; Sanmartin, Claudia; Tu, Jack; Manuel, Doug

2014-10-01

Seniors constitute the largest group of hospital users. The increasing share of immigrants in Canada's senior population can affect the demand for hospital care. This study used the linked 2006 Census-Hospital Discharge Abstract Database to examine hospitalization during the 2004-to-2006 period, by immigrant status, of Ontario seniors living in the community. Hospitalization was assessed with logistic regressions; cumulative length of stay, with zero-truncated negative binomial regressions. All-cause hospitalization and hospitalizations specific to circulatory and digestive diseases were examined. Immigrant seniors had significantly low age-/sex-adjusted odds of hospitalization, compared with Canadian-born seniors (OR = 0.81). The odds varied from 0.4 among East Asians to 0.89 among Europeans, and rose with length of time since arrival from 0.54 for recent (1994 to 2003) to 0.86 for long-term (before 1984) immigrants. Adjustment for demographic and socio-economic characteristics did not change the overall patterns. Immigrants' cumulated length of hospital stay tended to be shorter than or similar to that of Canadian-born seniors. Immigrant seniors, especially recent arrivals, had lower odds of hospitalization and similar time in hospital, compared with Canadian-born seniors. These patterns likely reflect differences in health status. Variations by world region and disease reflect the diverse health care needs of immigrant seniors.

Determinants of the lethality of climate-related disasters in the Caribbean Community (CARICOM): a cross-country analysis

PubMed Central

Andrewin, Aisha N.; Rodriguez-Llanes, Jose M.; Guha-Sapir, Debarati

2015-01-01

Floods and storms are climate-related hazards posing high mortality risk to Caribbean Community (CARICOM) nations. However risk factors for their lethality remain untested. We conducted an ecological study investigating risk factors for flood and storm lethality in CARICOM nations for the period 1980–2012. Lethality - deaths versus no deaths per disaster event- was the outcome. We examined biophysical and social vulnerability proxies and a decadal effect as predictors. We developed our regression model via multivariate analysis using a generalized logistic regression model with quasi-binomial distribution; removal of multi-collinear variables and backward elimination. Robustness was checked through subset analysis. We found significant positive associations between lethality, percentage of total land dedicated to agriculture (odds ratio [OR] 1.032; 95% CI: 1.013–1.053) and percentage urban population (OR 1.029, 95% CI 1.003–1.057). Deaths were more likely in the 2000–2012 period versus 1980–1989 (OR 3.708, 95% CI 1.615–8.737). Robustness checks revealed similar coefficients and directions of association. Population health in CARICOM nations is being increasingly impacted by climate-related disasters connected to increasing urbanization and land use patterns. Our findings support the evidence base for setting sustainable development goals (SDG). PMID:26153115

Secondhand smoke exposure in the workplace.

PubMed

Skeer, Margie; Cheng, Debbie M; Rigotti, Nancy A; Siegel, Michael

2005-05-01

Currently, there is little understanding of the relationship between the strength of workplace smoking policies and the likelihood and duration, not just the likelihood, of exposure to secondhand smoke at work. This study assessed self-reported exposure to secondhand smoke at work in hours per week among a cross-sectional sample of 3650 Massachusetts adults who were employed primarily at a single worksite outside the home that was not mainly outdoors. The sample data were from a larger longitudinal study designed to examine the effect of community-based tobacco control interventions on adult and youth smoking behavior. Participants were identified through a random-digit-dialing telephone survey. Multiple logistic regression and zero-inflated negative binomial regression models were used to estimate the independent effect of workplace smoking policies on the likelihood and duration of exposure to secondhand smoke. Compared to employees whose workplace banned smoking completely, those whose workplace provided designated smoking areas had 2.9 times the odds of being exposed to secondhand smoke and 1.74 times the duration of exposure, while those with no restrictions had 10.27 times the odds of being exposed and 6.34 times the duration of exposure. Workplace smoking policies substantially reduce the likelihood of self-reported secondhand smoke exposure among employees in the workplace and also greatly affect the duration of exposure.

Predictors of postoperative bleeding after vitrectomy for vitreous hemorrhage in patients with diabetic retinopathy.

PubMed

Motoda, Saori; Shiraki, Nobuhiko; Ishihara, Takuma; Sakaguchi, Hirokazu; Kabata, Daijiro; Takahara, Mitsuyoshi; Kimura, Takekazu; Kozawa, Junji; Imagawa, Akihisa; Nishida, Kohji; Shintani, Ayumi; Iwahashi, Hiromi; Shimomura, Iichiro

2017-12-19

To clarify the association between perioperative variables and postoperative bleeding in pars plana vitrectomy for vitreous hemorrhage in diabetic retinopathy. The present retrospective study enrolled 72 eyes of 64 patients who were admitted to Osaka University Hospital between April 2010 and March 2014, and underwent vitrectomy for vitreous hemorrhage as a result of diabetic retinopathy. Postoperative bleeding developed in 12 eyes. Using binomial logistic regression analysis, we found that the duration of operation was the only significant variable associated with postoperative bleeding within 12 weeks after vitrectomy. Furthermore, Poisson regression analysis identified fasting blood glucose just before vitrectomy, no treatment with antiplatelet drugs and treatment with antihypertensive drugs, as well as duration of operation, to be significantly associated with the frequency of bleeding within 52 weeks after vitrectomy. Long duration of operation can be used to predict bleeding within both 12 and 52 weeks after vitrectomy. In addition, fasting blood glucose just before vitrectomy, no treatment with antiplatelet drugs and treatment with antihypertensive drugs might be risk factors for postoperative bleeding up to 1 year after vitrectomy. © 2017 The Authors. Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd.

Socioeconomic Differences in and Predictors of Home-Based Palliative Care Health Service Use in Ontario, Canada.

PubMed

Cai, Jiaoli; Guerriere, Denise N; Zhao, Hongzhong; Coyte, Peter C

2017-07-18

The use of health services may vary across people with different socioeconomic statuses, and may be determined by many factors. The purposes of this study were (i) to examine the socioeconomic differences in the propensity and intensity of use for three main home-based health services, that is, home-based palliative care physician visits, nurse visits and personal support worker (PSW) hours; and (ii) to explore the determinants of the use of home-based palliative care services. A prospective cohort study was employed. A total of 181 caregivers were interviewed biweekly over the course of the palliative care trajectory, yielding a total of 994 interviews. The propensity and intensity of health service use were examined using logistic regression and negative binomial regression, respectively. The results demonstrated that both the propensity and intensity of home-based nurse and PSW visits fell with socioeconomic status. The use of home-based palliative care services was not concentrated in high socioeconomic status groups. The common predictors of health service use in the three service categories were patient age, the Palliative Performance Scale (PPS) score and place of death. These findings may assist health service planners in the appropriate allocation of resources and service packages to meet the complex needs of palliative care populations.

Prevalence and associations of anisometropia with spherical ametropia, cylindrical power, age, and sex in refractive surgery candidates.

PubMed

Linke, Stephan J; Richard, Gisbert; Katz, Toam

2011-09-29

To analyze the prevalence and associations of anisometropia with spherical ametropia, astigmatism, age, and sex in a refractive surgery population. Medical records of 27,070 eyes of 13,535 refractive surgery candidates were reviewed. Anisometropia, defined as the absolute difference in mean spherical equivalent powers between right and left eyes, was analyzed for subjective (A(subj)) and cycloplegic refraction (A(cycl)). Correlations between anisometropia (>1 diopter) and spherical ametropia, cylindrical power, age, and sex, were analyzed using χ² and nonparametric Kruskal-Wallis or Mann-Whitney tests and binomial logistic regression analyses. Power vector analysis was applied for further analysis of cylindrical power. Prevalence of A(subj) was 18.5% and of A(cycl) was 19.3%. In hyperopes, logistic regression analysis revealed that only spherical refractive error (odds ratio [OR], 0.72) and age (OR, 0.97) were independently associated with anisometropia. A(subj) decreased with increasing spherical ametropia and advancing age. Cylindrical power and sex did not significantly affect A(subj). In myopes all explanatory variables (spherical power OR, 0.93; cylindrical power OR, 0.75; age OR, 1.02; sex OR, 0.8) were independently associated with anisometropia. Cylindrical power was most strongly associated with anisometropia. Advancing age and increasing spherical/cylindrical power correlated positively with increasing anisometropia in myopic subjects. Female sex was more closely associated with anisometropia. This large-scale retrospective analysis confirmed an independent association between anisometropia and both spherical ametropia and age in refractive surgery candidates. Notably, an inverse relationship between these parameters in hyperopes was observed. Cylindrical power and female sex were independently associated with anisometropia in myopes.

Tachycardia in breast reconstructive microsurgery: Affirmation of the IMA tachycardia syndrome.

PubMed

Sachanandani, N S; Kale, S S; Skolnick, G B; Barbour, J R; Myckatyn, T M

2015-06-01

The internal mammary vessels are frequently chosen as recipient vessels for breast free flap reconstruction. We have noticed that when using the internal mammary recipients that these patients have a propensity for tachycardia that was not previously observed. Our aim was to investigate the factors related to perioperative tachycardia in the microsurgical breast reconstruction population and to address whether use of the internal mammary system is a causative factor in tachycardia. A retrospective chart review was conducted to identify patients who underwent abdominal-based microvascular breast reconstruction at the Washington University School of Medicine between 2002 and 2012 to identify the presence of tachycardia. After application of exclusion criteria, 76 microvascular abdominal-based free flap reconstructions were identified. The internal mammary (IM) TRAM group (n = 24) and the thoracodorsal (TD) TRAM group (n = 52) were compared. A binomial logistic regression was performed with the presence of tachycardia as the dependent variable. There was a higher incidence of tachycardia in the IM TRAM group when compared to the TD TRAM group (p = 0.004). The variables predictive of tachycardia in our logistic regression model were IMA recipient (p = 0.04), need for transfusion (p = 0.03), and presence of fever (p = 0.01). Our study reaffirms that there are several factors that are predictive of tachycardia in the setting of microvascular breast reconstruction. The IMA syndrome should be a recognized cause of tachycardia as using these recipient vessels are shown to be predictive of postoperative tachycardia as shown in our study. Copyright © 2015 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

Perceived discrimination In Primary Healthcare in Europe: evidence from the cross-sectional QUALICOPC study.

PubMed

Hanssens, Lise G M; Detollenaere, Jens D J; Van Pottelberge, Amelie; Baert, Stijn; Willems, Sara J T

2017-03-01

Recent figures show that discrimination in healthcare is still persistent in the European Union. Research has confirmed these results but focused mainly on the outcomes of perceived discrimination. Studies that take into account socioeconomic determinants of discrimination limit themselves to either ethnicity, income or education. This article explores the influence of several socioeconomic indicators (e.g. gender, age, income, education and ethnicity) on perceived discrimination in 30 European countries. Data from the QUALICOPC study were used. These data were collected between October 2011 and December 2013 in the participating countries. In total, 7183 GPs (general practitioners) and 61932 patients participated in the study, which had an average response rate of 74.1%. Data collection was co-ordinated by NIVEL (Dutch Institute for Research of Health Care). Bivariate binomial logistic regressions were used to estimate the impact of each socioeconomic indicator on perceived discrimination. Multivariate logistic regressions were used to estimate the unique effect of each indicator. Results indicate that in Europe, overall 7% of the respondents felt discriminated, ranging between 1.4% and 12.8% at the country level. With regard to socioeconomic determinants in perceived discrimination, income and age are both important indicators, with lower income groups and younger people having a higher chance to feel discriminated. In addition, we find significant influences of education, gender, age and ethnicity in several countries. In most countries, higher educated people, older people, women and the indigenous population appeared to feel less discriminated. In conclusion, perceived discrimination in healthcare is reported in almost all European countries, but there is large variation between European countries. A high prevalence of perceived discrimination within a country also does not imply a correlation between socioeconomic indicators and perceived discrimination. © 2016 John Wiley & Sons Ltd.

Vitamin D status and 3-month Glasgow Outcome Scale scores in patients in neurocritical care: prospective analysis of 497 patients.

PubMed

Guan, Jian; Karsy, Michael; Brock, Andrea A; Eli, Ilyas M; Manton, Gabrielle M; Ledyard, Holly K; Hawryluk, Gregory W J; Park, Min S

2018-06-01

OBJECTIVE Vitamin D deficiency has been associated with a variety of negative outcomes in critically ill patients, but little focused study on the effects of hypovitaminosis D has been performed in the neurocritical care population. In this study, the authors examined the effect of vitamin D deficiency on 3-month outcomes after discharge from a neurocritical care unit (NCCU). METHODS The authors prospectively analyzed 25-hydroxy vitamin D levels in patients admitted to the NCCU of a quaternary care center over a 6-month period. Glasgow Outcome Scale (GOS) scores were used to evaluate their 3-month outcome, and univariate and multivariate logistic regression was used to evaluate the effects of vitamin D deficiency. RESULTS Four hundred ninety-seven patients met the inclusion criteria. In the binomial logistic regression model, patients without vitamin D deficiency (> 20 ng/dl) were significantly more likely to have a 3-month GOS score of 4 or 5 than those who were vitamin D deficient (OR 1.768 [95% CI 1.095-2.852]). Patients with a higher Simplified Acute Physiology Score (SAPS II) (OR 0.925 [95% CI 0.910-0.940]) and those admitted for stroke (OR 0.409 [95% CI 0.209-0.803]) or those with an "other" diagnosis (OR 0.409 [95% CI 0.217-0.772]) were significantly more likely to have a 3-month GOS score of 3 or less. CONCLUSIONS Vitamin D deficiency is associated with worse 3-month postdischarge GOS scores in patients admitted to an NCCU. Additional study is needed to determine the role of vitamin D supplementation in the NCCU population.

Loneliness, Insomnia and Suicidal Behavior among School-Going Adolescents in Western Pacific Island Countries: Role of Violence and Injury.

PubMed

Sharma, Bimala; Lee, Tae Ho; Nam, Eun Woo

2017-07-15

This study aimed to examine whether being bullied, fighting, and injury, regarded in terms of frequency and nature, were significantly associated with psychological distress and suicidal behavior, independent of substance abuse and parental support in adolescents. Secondary analysis of data from the Global School-based Student Health Survey from Kiribati, the Solomon Islands, and Vanuatu was conducted. Binomial logistic regression analysis was used to examine the association of being bullied, fighting and injury with psychological health outcomes (loneliness, insomnia, suicidal ideation and suicide attempt) at a 5% level of significance. A total of 4122 students were included; 45.5% were male, and 52.0% were 14 years of age or younger. Of the total, 9.3% felt lonely and 9.5% had insomnia most of the time over the last 12 months; 27.6% had suicidal ideation, and 30.9% reported at least one suicide attempt in the last 12 months. Multivariable logistic regression analysis showed that being bullied, fighting and injury were significantly associated with psychological health outcomes; adjusted odds ratios (AORs) of loneliness, insomnia, suicidal ideation and suicide attempt increased with increased exposure to bullying, fighting, and injury compared to non-exposed group. Among the types of bullying victimization, the highest AORs of insomnia and suicide attempt were among students who were left out of activities, compared to the non-bullied. Among the causes of injury, adolescents injured due to a physical attack were the most likely to report the highest AORs of loneliness, insomnia and suicidal ideation compared to those not injured. Preventing violence and injury among adolescents might contribute to better mental health and reduction of suicidal behavior.

Association Between Socioeconomic Status and Participation in Colonoscopy Screening Program in First Degree Relatives of Colorectal Cancer Patients.

PubMed

Chouhdari, Arezoo; Yavari, Parvin; Pourhoseingholi, Mohammad Amin; Sohrabi, Mohammad-Reza

2016-04-01

Approximately 15% to 25% of colorectal cancer (CRC) cases have positive family history for disease. Colonoscopy screening test is the best way for prevention and early diagnosis. Studies have found that first degree relatives (FDRs) with low socioeconomic status are less likely to participate in colonoscopy screening program. The aim of this study is to determine the association between socioeconomic status and participation in colonoscopy screening program in FDRs. This descriptive cross-sectional, study has been conducted on 200 FDRs who were consulted for undergoing colonoscopy screening program between 2007 and 2013 in research institute for gastroenterology and liver disease of Shahid Beheshti University of Medical Sciences, Tehran, Iran. They were interviewed via phone by a valid questionnaire about socioeconomic status. For data analysis, chi-square, exact fisher and multiple logistic regression were executed by SPSS 19. The results indicated 58.5% participants underwent colonoscopy screening test at least once to the time of the interview. There was not an association between participation in colonoscopy screening program and socioeconomic status to the time of the interview in binomial analysis. But statistical significance between intention to participate and educational and income level were found. We found, in logistic regression analysis, that high educational level (Diploma and University degree in this survey) was a predictor to participate in colonoscopy screening program in FDRs. According to this survey low socioeconomic status is an important factor to hinder participation of FDRs in colonoscopy screening program. Therefore, planned interventions for elevation knowledge and attitude in FDRs with low educational level are necessary. Also, reducing colonoscopy test costs should be a major priority for policy makers.

Loneliness, Insomnia and Suicidal Behavior among School-Going Adolescents in Western Pacific Island Countries: Role of Violence and Injury

PubMed Central

Sharma, Bimala; Lee, Tae Ho; Nam, Eun Woo

2017-01-01

This study aimed to examine whether being bullied, fighting, and injury, regarded in terms of frequency and nature, were significantly associated with psychological distress and suicidal behavior, independent of substance abuse and parental support in adolescents. Secondary analysis of data from the Global School-based Student Health Survey from Kiribati, the Solomon Islands, and Vanuatu was conducted. Binomial logistic regression analysis was used to examine the association of being bullied, fighting and injury with psychological health outcomes (loneliness, insomnia, suicidal ideation and suicide attempt) at a 5% level of significance. A total of 4122 students were included; 45.5% were male, and 52.0% were 14 years of age or younger. Of the total, 9.3% felt lonely and 9.5% had insomnia most of the time over the last 12 months; 27.6% had suicidal ideation, and 30.9% reported at least one suicide attempt in the last 12 months. Multivariable logistic regression analysis showed that being bullied, fighting and injury were significantly associated with psychological health outcomes; adjusted odds ratios (AORs) of loneliness, insomnia, suicidal ideation and suicide attempt increased with increased exposure to bullying, fighting, and injury compared to non-exposed group. Among the types of bullying victimization, the highest AORs of insomnia and suicide attempt were among students who were left out of activities, compared to the non-bullied. Among the causes of injury, adolescents injured due to a physical attack were the most likely to report the highest AORs of loneliness, insomnia and suicidal ideation compared to those not injured. Preventing violence and injury among adolescents might contribute to better mental health and reduction of suicidal behavior. PMID:28714893

Predictors of the number of under-five malnourished children in Bangladesh: application of the generalized poisson regression model

PubMed Central

2013-01-01

Background Malnutrition is one of the principal causes of child mortality in developing countries including Bangladesh. According to our knowledge, most of the available studies, that addressed the issue of malnutrition among under-five children, considered the categorical (dichotomous/polychotomous) outcome variables and applied logistic regression (binary/multinomial) to find their predictors. In this study malnutrition variable (i.e. outcome) is defined as the number of under-five malnourished children in a family, which is a non-negative count variable. The purposes of the study are (i) to demonstrate the applicability of the generalized Poisson regression (GPR) model as an alternative of other statistical methods and (ii) to find some predictors of this outcome variable. Methods The data is extracted from the Bangladesh Demographic and Health Survey (BDHS) 2007. Briefly, this survey employs a nationally representative sample which is based on a two-stage stratified sample of households. A total of 4,460 under-five children is analysed using various statistical techniques namely Chi-square test and GPR model. Results The GPR model (as compared to the standard Poisson regression and negative Binomial regression) is found to be justified to study the above-mentioned outcome variable because of its under-dispersion (variance < mean) property. Our study also identify several significant predictors of the outcome variable namely mother’s education, father’s education, wealth index, sanitation status, source of drinking water, and total number of children ever born to a woman. Conclusions Consistencies of our findings in light of many other studies suggest that the GPR model is an ideal alternative of other statistical models to analyse the number of under-five malnourished children in a family. Strategies based on significant predictors may improve the nutritional status of children in Bangladesh. PMID:23297699

Topics in Bayesian Hierarchical Modeling and its Monte Carlo Computations

NASA Astrophysics Data System (ADS)

Tak, Hyung Suk

The first chapter addresses a Beta-Binomial-Logit model that is a Beta-Binomial conjugate hierarchical model with covariate information incorporated via a logistic regression. Various researchers in the literature have unknowingly used improper posterior distributions or have given incorrect statements about posterior propriety because checking posterior propriety can be challenging due to the complicated functional form of a Beta-Binomial-Logit model. We derive data-dependent necessary and sufficient conditions for posterior propriety within a class of hyper-prior distributions that encompass those used in previous studies. Frequency coverage properties of several hyper-prior distributions are also investigated to see when and whether Bayesian interval estimates of random effects meet their nominal confidence levels. The second chapter deals with a time delay estimation problem in astrophysics. When the gravitational field of an intervening galaxy between a quasar and the Earth is strong enough to split light into two or more images, the time delay is defined as the difference between their travel times. The time delay can be used to constrain cosmological parameters and can be inferred from the time series of brightness data of each image. To estimate the time delay, we construct a Gaussian hierarchical model based on a state-space representation for irregularly observed time series generated by a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian approach jointly infers model parameters via a Gibbs sampler. We also introduce a profile likelihood of the time delay as an approximation of its marginal posterior distribution. The last chapter specifies a repelling-attracting Metropolis algorithm, a new Markov chain Monte Carlo method to explore multi-modal distributions in a simple and fast manner. This algorithm is essentially a Metropolis-Hastings algorithm with a proposal that consists of a downhill move in density that aims to make local modes repelling, followed by an uphill move in density that aims to make local modes attracting. The downhill move is achieved via a reciprocal Metropolis ratio so that the algorithm prefers downward movement. The uphill move does the opposite using the standard Metropolis ratio which prefers upward movement. This down-up movement in density increases the probability of a proposed move to a different mode.

An examination of sources of sensitivity of consumer surplus estimates in travel cost models.

PubMed

Blaine, Thomas W; Lichtkoppler, Frank R; Bader, Timothy J; Hartman, Travis J; Lucente, Joseph E

2015-03-15

We examine sensitivity of estimates of recreation demand using the Travel Cost Method (TCM) to four factors. Three of the four have been routinely and widely discussed in the TCM literature: a) Poisson verses negative binomial regression; b) application of Englin correction to account for endogenous stratification; c) truncation of the data set to eliminate outliers. A fourth issue we address has not been widely modeled: the potential effect on recreation demand of the interaction between income and travel cost. We provide a straightforward comparison of all four factors, analyzing the impact of each on regression parameters and consumer surplus estimates. Truncation has a modest effect on estimates obtained from the Poisson models but a radical effect on the estimates obtained by way of the negative binomial. Inclusion of an income-travel cost interaction term generally produces a more conservative but not a statistically significantly different estimate of consumer surplus in both Poisson and negative binomial models. It also generates broader confidence intervals. Application of truncation, the Englin correction and the income-travel cost interaction produced the most conservative estimates of consumer surplus and eliminated the statistical difference between the Poisson and the negative binomial. Use of the income-travel cost interaction term reveals that for visitors who face relatively low travel costs, the relationship between income and travel demand is negative, while it is positive for those who face high travel costs. This provides an explanation of the ambiguities on the findings regarding the role of income widely observed in the TCM literature. Our results suggest that policies that reduce access to publicly owned resources inordinately impact local low income recreationists and are contrary to environmental justice. Copyright © 2014 Elsevier Ltd. All rights reserved.

Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data.

PubMed

Holsclaw, Tracy; Hallgren, Kevin A; Steyvers, Mark; Smyth, Padhraic; Atkins, David C

2015-12-01

Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased Type I and Type II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in online supplemental materials. (c) 2016 APA, all rights reserved).

Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data

PubMed Central

Holsclaw, Tracy; Hallgren, Kevin A.; Steyvers, Mark; Smyth, Padhraic; Atkins, David C.

2015-01-01

Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non-normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased type-I and type-II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally-technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in supplementary materials. PMID:26098126

Goodness-of-fit tests and model diagnostics for negative binomial regression of RNA sequencing data.

PubMed

Mi, Gu; Di, Yanming; Schafer, Daniel W

2015-01-01

This work is about assessing model adequacy for negative binomial (NB) regression, particularly (1) assessing the adequacy of the NB assumption, and (2) assessing the appropriateness of models for NB dispersion parameters. Tools for the first are appropriate for NB regression generally; those for the second are primarily intended for RNA sequencing (RNA-Seq) data analysis. The typically small number of biological samples and large number of genes in RNA-Seq analysis motivate us to address the trade-offs between robustness and statistical power using NB regression models. One widely-used power-saving strategy, for example, is to assume some commonalities of NB dispersion parameters across genes via simple models relating them to mean expression rates, and many such models have been proposed. As RNA-Seq analysis is becoming ever more popular, it is appropriate to make more thorough investigations into power and robustness of the resulting methods, and into practical tools for model assessment. In this article, we propose simulation-based statistical tests and diagnostic graphics to address model adequacy. We provide simulated and real data examples to illustrate that our proposed methods are effective for detecting the misspecification of the NB mean-variance relationship as well as judging the adequacy of fit of several NB dispersion models.

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.

Mapping CHU9D Utility Scores from the PedsQLTM 4.0 SF-15.

PubMed

Mpundu-Kaambwa, Christine; Chen, Gang; Russo, Remo; Stevens, Katherine; Petersen, Karin Dam; Ratcliffe, Julie

2017-04-01

The Pediatric Quality of Life Inventory™ 4.0 Short Form 15 Generic Core Scales (hereafter the PedsQL) and the Child Health Utility-9 Dimensions (CHU9D) are two generic instruments designed to measure health-related quality of life in children and adolescents in the general population and paediatric patient groups living with specific health conditions. Although the PedsQL is widely used among paediatric patient populations, presently it is not possible to directly use the scores from the instrument to calculate quality-adjusted life-years (QALYs) for application in economic evaluation because it produces summary scores which are not preference-based. This paper examines different econometric mapping techniques for estimating CHU9D utility scores from the PedsQL for the purpose of calculating QALYs for cost-utility analysis. The PedsQL and the CHU9D were completed by a community sample of 755 Australian adolescents aged 15-17 years. Seven regression models were estimated: ordinary least squares estimator, generalised linear model, robust MM estimator, multivariate factorial polynomial estimator, beta-binomial estimator, finite mixture model and multinomial logistic model. The mean absolute error (MAE) and the mean squared error (MSE) were used to assess predictive ability of the models. The MM estimator with stepwise-selected PedsQL dimension scores as explanatory variables had the best predictive accuracy using MAE and the equivalent beta-binomial model had the best predictive accuracy using MSE. Our mapping algorithm facilitates the estimation of health-state utilities for use within economic evaluations where only PedsQL data is available and is suitable for use in community-based adolescents aged 15-17 years. Applicability of the algorithm in younger populations should be assessed in further research.

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.

Work experience and gender differences in chronic disease risk in older Mexicans.

PubMed

Salinas, Jennifer J; Peek, M Kristen

2008-08-01

The purpose of this study is to examine the relationship between labor force participation and gender differences in the prevalence of arthritis, diabetes, and hypertension. The Mexican Health and Aging Survey (MHAS) data is nationally representative sample of older Mexicans 50 years and older. Binomial logistic regression models were performed to examine differences between older Mexican men and women in the prevalence of arthritis, diabetes, and hypertension. Interaction effects were also estimated between gender and occupation, length of time in the labor force, and pension eligibility. Older Mexican women have a significantly greater risk of having arthritis, diabetes, and hypertension. Findings from this study suggest that within the same occupational classification, women suffer from the damaging effects on health to a greater extent than men. Interaction effects show that women who work in services or in client's home are particularly susceptible to arthritis. Moreover, women who work in sales were at a significantly greater risk of hypertension than men. Older Mexican women are at greater risk of chronic disease and part of their vulnerability is a result of the type of work that they do.

Association between Low-density lipoprotein cholesterol and occipital periventricular hyperintensities in a group of Chinese patients: an observational study.

PubMed

Duan, Dazhi; Shen, Lin; Cui, Chun; Shu, Tongsheng; Zheng, Jian

2017-02-27

While occipital periventricular hyperintensities (OPVHs) are among the most common mild white matter hyperintensities, the clinical factors associated with OPVHs remain unclear. In this study, we investigated the role of clinical factors in development of pure OPVHs. This study included 97 patients with OPVHs and 73 healthy controls. Univariate analysis of clinical factors in OPVH patients and controls was followed by binomial logistic regression analysis to identify clinical factors significantly associated with OPVHs. Univariate analysis indicated that age, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and apolipoprotein-B (Apo-B) levels differed significantly between the OPVH patients and controls (p < 0.05). Age and gender were correlated with OPVH scores (p < 0.05), while LDL-C, triglycerides, Apo-B and TC were anti-correlated with OPVHs scores (p < 0.05). Multivariate analysis indicated that LDL-C is negatively correlated with OPVHs (p < 0.05), and age is positively correlated with OPVHs (p < 0.001). In summary, LDL-C was negatively and age was positively associated with OPVHs among Chinese patients in a hospital.

Sexual Violence in the Backlands: Toward a Macro-Level Understanding of Rural Sex Crimes.

PubMed

Braithwaite, Jeremy

2015-10-01

This research focuses on structural covariates of sex crimes in rural communities (using urban and urbanizing communities as comparison groups), with particular analysis on exploring how the magnitude and direction of such covariates differ with respect to type of sex crime. Using 2000 sex crime data from the National Incident-Based Reporting System (NIBRS) for the population of reporting U.S. cities, negative binomial and logistic regression procedures were used to explore the relationship between resource disadvantage, local investment, and economic inequality and sex crime subtypes. For sex crimes that occurred almost exclusively in the home, urban and urbanizing community rates were largely influenced by resource disadvantage and local investment, while these measures did not reach significance for explaining rural rates. Conversely, local investment was a significant predictor of sex crimes that occurred outside the home in rural communities. This research indicates that a structural analysis of sexual victimization (widely absent from the scientific literature) does yield significant findings and that disaggregation of crime into subtypes allows for a more detailed differentiation between urban and rural communities. © The Author(s) 2014.

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.

Predictors for hospitalization and outpatient visits in patients with inflammatory bowel disease: results from the Swiss Inflammatory Bowel Disease Cohort Study.

PubMed

Sulz, Michael C; Siebert, Uwe; Arvandi, Marjan; Gothe, Raffaella M; Wurm, Johannes; von Känel, Roland; Vavricka, Stephan R; Meyenberger, Christa; Sagmeister, Markus

2013-07-01

Patients with inflammatory bowel disease (IBD) have a high resource consumption, with considerable costs for the healthcare system. In a system with sparse resources, treatment is influenced not only by clinical judgement but also by resource consumption. We aimed to determine the resource consumption of IBD patients and to identify its significant predictors. Data from the prospective Swiss Inflammatory Bowel Disease Cohort Study were analysed for the resource consumption endpoints hospitalization and outpatient consultations at enrolment [1187 patients; 41.1% ulcerative colitis (UC), 58.9% Crohn's disease (CD)] and at 1-year follow-up (794 patients). Predictors of interest were chosen through an expert panel and a review of the relevant literature. Logistic regressions were used for binary endpoints, and negative binomial regressions and zero-inflated Poisson regressions were used for count data. For CD, fistula, use of biologics and disease activity were significant predictors for hospitalization days (all P-values <0.001); age, sex, steroid therapy and biologics were significant predictors for the number of outpatient visits (P=0.0368, 0.023, 0.0002, 0.0003, respectively). For UC, biologics, C-reactive protein, smoke quitters, age and sex were significantly predictive for hospitalization days (P=0.0167, 0.0003, 0.0003, 0.0076 and 0.0175 respectively); disease activity and immunosuppressive therapy predicted the number of outpatient visits (P=0.0009 and 0.0017, respectively). The results of multivariate regressions are shown in detail. Several highly significant clinical predictors for resource consumption in IBD were identified that might be considered in medical decision-making. In terms of resource consumption and its predictors, CD and UC show a different behaviour.

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…

Factors Associated with Sexual Violence against Men Who Have Sex with Men and Transgendered Individuals in Karnataka, India

PubMed Central

Shaw, Souradet Y.; Lorway, Robert R.; Deering, Kathleen N.; Avery, Lisa; Mohan, H. L.; Bhattacharjee, Parinita; Reza-Paul, Sushena; Isac, Shajy; Ramesh, Banadakoppa M.; Washington, Reynold; Moses, Stephen; Blanchard, James F.

2012-01-01

Objectives There is a lack of information on sexual violence (SV) among men who have sex with men and transgendered individuals (MSM-T) in southern India. As SV has been associated with HIV vulnerability, this study examined health related behaviours and practices associated with SV among MSM-T. Design Data were from cross-sectional surveys from four districts in Karnataka, India. Methods Multivariable logistic regression models were constructed to examine factors related to SV. Multivariable negative binomial regression models examined the association between physician visits and SV. Results A total of 543 MSM-T were included in the study. Prevalence of SV was 18% in the past year. HIV prevalence among those reporting SV was 20%, compared to 12% among those not reporting SV (p = .104). In multivariable models, and among sex workers, those reporting SV were more likely to report anal sex with 5+ casual sex partners in the past week (AOR: 4.1; 95%CI: 1.2–14.3, p = .029). Increased physician visits among those reporting SV was reported only for those involved in sex work (ARR: 1.7; 95%CI: 1.1–2.7, p = .012). Conclusions These results demonstrate high levels of SV among MSM-T populations, highlighting the importance of integrating interventions to reduce violence as part of HIV prevention programs and health services. PMID:22448214

Intimate partner violence and women's economic and non-economic activities in Minya, Egypt.

PubMed

Yount, Kathryn M; Zureick-Brown, Sarah; salem, Rania

2014-06-01

Intimate partner violence (IPV) against women is widespread, but its implications for their economic and non-economic activities are understudied. Leveraging new data from 564 ever-married women aged 22–65 in rural Minya, Egypt, we estimated logistic regressions and zero-inflated negative binomial regressions to test spillover, compensation, and patriarchal bargaining theories about the influences of women's exposure to IPV on their engagement in and time spent on market, subsistence, domestic, and care work. Supporting compensation theory, exposures to lifetime, recent, and chronic physical or sexual IPV were associated with higher adjusted odds of performing market work in the prior month, and exposures to recent and chronic IPV were associated with higher adjusted odds of performing subsistence work in this period. Supporting compensation and patriarchal bargaining theories, exposures to recent and chronic IPV were associated with more time spent on domestic work in the prior day. Supporting spillover and patriarchal bargaining theories, exposures to lifetime IPV of all forms were associated with lower adjusted odds of performing mostly nonspousal care work in the prior day, and this association was partially mediated by women's generalized anxiety. Women in rural Minya who are exposed to IPV may escalate their housework to fulfill local norms of feminine domesticity while substituting economic activities for nonspousal care work to enhance their economic independence from violent partners.

Is there an Appalachian disparity in dental caries in Pennsylvania schoolchildren?

PubMed

Polk, Deborah E; Kim, Sunghee; Manz, Michael; Weyant, Robert J

2015-02-01

To determine whether there is an Appalachian disparity in caries prevalence or extent in children living in Pennsylvania. We conducted a cross-sectional clinical assessment of caries in a sample representing 1st, 3rd, 9th, and 11th grade students across Pennsylvania. We used logistic regression and zero-inflated negative binomial regression controlling for age to examine the association of residence in an Appalachian county with caries prevalence and extent in the primary and permanent dentitions. Compared with children living outside Appalachia, more children living in Appalachia had a dft >0 (OR = 1.37, 95% CI = 1.07-1.76) and more had a DMFT >0 (OR = 1.32, 95% CI = 1.06-1.64). In addition, compared with children living outside Appalachia, children living in Appalachia had a greater primary but not permanent caries extent (IRR = 1.10, 95% CI = 1.01-1.19). We found Appalachian disparities in caries prevalence in both the primary and permanent dentitions and an Appalachian disparity in caries extent in the primary dentition. None of the disparities was moderated by age. This suggests that the search for the mechanism or mechanisms for the Appalachian disparities should focus on differential exposures to risk factors occurring prior to and at the start of elementary school. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Perfluoroalkyl substances measured in breast milk and child neuropsychological development in a Norwegian birth cohort study.

PubMed

Forns, J; Iszatt, N; White, R A; Mandal, S; Sabaredzovic, A; Lamoree, M; Thomsen, C; Haug, L S; Stigum, H; Eggesbø, M

2015-10-01

Perfluoroalkyl substances (PFASs) are chemicals with potential neurotoxic effects although the current evidence is still limited. This study investigated the association between perinatal exposure to perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) and neuropsychological development assessed at 6, 12 and 24 months. We measured PFOS and PFOA in breast milk samples collected one month after delivery by mothers of children participating in the HUMIS study (Norway). Cognitive and psychomotor development was measured at 6 and at 24 months using the Ages and Stages Questionnaire (ASQ-II). Behavioral development was assessed using the infant-toddler symptom checklist (ITSC) at 12 and at 24 months. Weighted logistic regression and weighted negative binomial regression models were applied to analyze the associations between PFASs and ASQ-II and ITSC, respectively. The median concentration of PFOS was 110 ng/L, while the median for PFOA was 40 ng/L. We did not detect an increased risk of having an abnormal score in ASQ-II at 6 months or 24 months. Moreover, no consistent increase in behavioral problems assessed at 12 and 24 months by ITSC questionnaire was detected. We observed no association between perinatal PFOS and PFOA exposure and early neuropsychological development. Further longitudinal studies are needed to confirm the effects of these compounds on neuropsychological development in older children. Copyright © 2015. Published by Elsevier Ltd.

Fire characteristics associated with firefighter injury on large federal wildland fires.

PubMed

Britton, Carla; Lynch, Charles F; Torner, James; Peek-Asa, Corinne

2013-02-01

Wildland fires present many injury hazards to firefighters. We estimate injury rates and identify fire-related factors associated with injury. Data from the National Interagency Fire Center from 2003 to 2007 provided the number of injuries in which the firefighter could not return to his or her job assignment, person-days worked, and fire characteristics (year, region, season, cause, fuel type, resistance to control, and structures destroyed). We assessed fire-level risk factors of having at least one reported injury using logistic regression. Negative binomial regression was used to examine incidence rate ratios associated with fire-level risk factors. Of 867 fires, 9.5% required the most complex management and 24.7% required the next-highest level of management. Fires most often occurred in the western United States (82.8%), during the summer (69.6%), caused by lightening (54.9%). Timber was the most frequent fuel source (40.2%). Peak incident management level, person-days of exposure, and the fire's resistance to control were significantly related to the odds of a fire having at least one reported injury. However, the most complex fires had a lower injury incidence rate than less complex fires. Although fire complexity and the number of firefighters were associated with the risk for at least one reported injury, the more experienced and specialized firefighting teams had lower injury incidence. Copyright © 2013 Elsevier Inc. All rights reserved.

Is There an Appalachian Disparity in Dental Caries in Pennsylvania Schoolchildren?

PubMed Central

Polk, Deborah E.; Kim, Sunghee; Manz, Michael; Weyant, Robert J.

2015-01-01

Objectives To determine whether there is an Appalachian disparity in caries prevalence or extent in children living in Pennsylvania. Methods We conducted a cross-sectional clinical assessment of caries in a sample representing 1st, 3rd, 9th, and 11th grade students across Pennsylvania. We used logistic regression and zero-inflated negative binomial regression controlling for age to examine the association of residence in an Appalachian county with caries prevalence and extent in the primary and permanent dentitions. Results Compared with children living outside Appalachia, more children living in Appalachia had a dft > 0 (OR = 1.37, 95% CI = 1.07 – 1.76) and more had a DMFT > 0 (OR = 1.32, 95% CI = 1.06 – 1.64). In addition, compared with children living outside Appalachia, children living in Appalachia had a greater primary but not permanent caries extent (IRR = 1.10, 95% CI = 1.01 – 1.19). Conclusions We found Appalachian disparities in caries prevalence in both the primary and permanent dentitions and an Appalachian disparity in caries extent in the primary dentition. None of the disparities was moderated by age. This suggests that the search for the mechanism or mechanisms for the Appalachian disparities should focus on differential exposures to risk factors occurring prior to and at the start of elementary school. PMID:25470650

The effect of cotrimoxazole prophylactic treatment on malaria, birth outcomes, and postpartum CD4 count in HIV-infected women.

PubMed

Dow, Anna; Kayira, Dumbani; Hudgens, Michael G; Van Rie, Annelies; King, Caroline C; Ellington, Sascha; Chome, Nelecy; Kourtis, Athena; Turner, Abigail Norris; Kacheche, Zebrone; Jamieson, Denise J; Chasela, Charles; van der Horst, Charles

2013-01-01

Limited data exist on cotrimoxazole prophylactic treatment (CPT) in pregnant women, including protection against malaria versus standard intermittent preventive therapy with sulfadoxine-pyrimethamine (IPTp). Using observational data we examined the effect of CPT in HIV-infected pregnant women on malaria during pregnancy, low birth weight and preterm birth using proportional hazards, logistic, and log binomial regression, respectively. We used linear regression to assess effect of CPT on CD4 count. Data from 468 CPT-exposed and 768 CPT-unexposed women were analyzed. CPT was associated with protection against malaria versus IPTp (hazard ratio: 0.35, 95% Confidence Interval (CI): 0.20, 0.60). After adjustment for time period this effect was not statistically significant (adjusted hazard ratio: 0.66, 95% CI: 0.28, 1.52). Among women receiving and not receiving CPT, rates of low birth weight (7.1% versus 7.6%) and preterm birth (23.5% versus 23.6%) were similar. CPT was associated with lower CD4 counts 24 weeks postpartum in women receiving (-77.6 cells/ μ L, 95% CI: -125.2, -30.1) and not receiving antiretrovirals (-33.7 cells/ μ L, 95% CI: -58.6, -8.8). Compared to IPTp, CPT provided comparable protection against malaria in HIV-infected pregnant women and against preterm birth or low birth weight. Possible implications of CPT-associated lower CD4 postpartum warrant further examination.

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…

Comparing Environmental Influences on Coral Bleaching Across and within Species using Clustered Binomial Regression

EPA Science Inventory

Differential susceptibility among reef-building coral species can lead to community shifts and loss of diversity as a result of temperature-induced mass bleaching events. However, the influence of the local environment on species-specific bleaching susceptibilities has not been ...

Discrimination of Picea chihuahuana Martinez populations on the basis of climatic, edaphic, dendrometric, genetic and population traits

PubMed Central

Dominguez-Guerrero, Iliana Karina; del Rocío Mariscal-Lucero, Samantha; Hernández-Díaz, José Ciro; Heinze, Berthold; Prieto-Ruiz, José Ángel

2017-01-01

Background Picea chihuahuana, which is endemic to Mexico, is currently listed as “Endangered” on the Red List. Chihuahua spruce is only found in the Sierra Madre Occidental (SMO), Mexico. About 42,600 individuals are distributed in forty populations. These populations are fragmented and can be classified into three geographically distinct clusters in the SMO. The total area covered by P. chihuahuana populations is less than 300 ha. A recent study suggested assisted migration as an alternative to the ex situ conservation of P. chihuahuana, taking into consideration the genetic structure and diversity of the populations and the predictions regarding the future climate of the habitat. However, detailed background information is required to enable development of plans for protecting and conserving species and for successful assisted migration. Thus, it is important to identify differences between populations in relation to environmental conditions. The genetic diversity of populations, which affect vigor, evolution and adaptability of the species, must also be considered. In this study, we examined 14 populations of P. chihuahuana, with the overall aim of discriminating the populations and form clusters of this species. Methods Each population was represented by one 50 × 50 m plot established in the center of its respective location. Climate, soil, dasometric, density variables and genetic and species diversities were assessed in these plots for further analyses. The putatively neutral and adaptive AFLP markers were used to calculate genetic diversity. Affinity Propagation (AP) clustering technique and k-means clustering algorithm were used to classify the populations in the optimal number of clusters. Later stepwise binomial logistic regression was applied to test for significant differences in variables of the southern and northern P. chihuahuana populations. Spearman’s correlation test was used to analyze the relationships among all variables studied. Results The binomial logistic regression analysis revealed that seven climate variables, the geographical longitude and sand proportion in the soil separated the southern from northern populations. The northern populations grow in more arid and continental conditions and on soils with lower sand proportion. The mean genetic diversity using all AFLP studied of P. chihuahuana was significantly correlated with the mean temperature in the warmest month, where warmer temperatures are associated to larger genetic diversity. Genetic diversity of P. chihuahuana calculated with putatively adaptive AFLP was not statistically significantly correlated with any environmental factor. Discussion Future reforestation programs should take into account that at least two different groups (the northern and southern cluster) of P. chihuahuana exist, as local adaptation takes place because of different environmental conditions. PMID:28626616

Race/Ethnicity and Success in Academic Medicine: Findings From a Longitudinal Multi-Institutional Study.

PubMed

Kaplan, Samantha E; Raj, Anita; Carr, Phyllis L; Terrin, Norma; Breeze, Janis L; Freund, Karen M

2017-10-24

To understand differences in productivity, advancement, retention, satisfaction, and compensation comparing underrepresented medical (URM) faculty with other faculty at multiple institutions. A 17-year follow-up was conducted of the National Faculty Survey, a random sample from 24 U.S. medical schools, oversampled for URM faculty. The authors examined academic productivity, advancement, retention, satisfaction, and compensation, comparing white, URM, and non-URM faculty. Retention, productivity, and advancement data were obtained from public sources for nonrespondents. Covariates included gender, specialty, time distribution, and years in academia. Negative binomial regression was used for count data, logistic regression for binary outcomes, and linear regression for continuous outcomes. In productivity analyses, advancement, and retention, 1,270 participants were included; 604 participants responded to the compensation and satisfaction survey. Response rates were lower for African American (26%) and Hispanic faculty (39%) than white faculty (52%, P < .0001). URM faculty had lower rates of peer-reviewed publications (relative number 0.64; 95% CI: 0.51, 0.79), promotion to professor (OR = 0.53; CI: 0.30, 0.93), and retention in academic medicine (OR = 0.49; CI: 0.32, 0.75). No differences were identified in federal grant acquisition, senior leadership roles, career satisfaction, or compensation between URM and white faculty. URM and white faculty had similar career satisfaction, grant support, leadership, and compensation; URM faculty had fewer publications and were less likely to be promoted and retained in academic careers. Successful retention of URM faculty requires comprehensive institutional commitment to changing the academic climate and deliberative programming to support productivity and advancement.

Longitudinal Analysis of Gender Differences in Academic Productivity among Medical Faculty across 24 Medical Schools in the United States

PubMed Central

Raj, Anita; Carr, Phyllis L.; Kaplan, Samantha E.; Terrin, Norma; Breeze, Janis L.; Freund, Karen M.

2017-01-01

Purpose This study examines gender differences in academic productivity, as indicated by publications and federal grant funding acquisition, among a longitudinal cohort of medical faculty from 24 medical schools across the United States, 1995 to 2012. Method Data for this research was taken from the National Faculty Study involving a survey with medical faculty recruited from medical schools in 1995, and followed up in 2012. Data included surveys and publication and grant funding databases. Outcomes were number of publications, h-index and principal investigator on a federal grant in the prior two years. Gender differences were assessed using negative binomial regression models for publication and h-index outcomes, and logistic regression for the grant funding outcome; analyses adjusted for race/ethnicity, rank, specialty area and years since first academic appointment. Results Data were available for 1,244 of the 1,275 (98%) subjects eligible for the follow up study. Men were significantly more likely than women to be married/partnered, have children, and hold the rank of professor (P < .0001). Adjusted regression models document that women have a lower rate of publication (relative number = .71; 95% CI = .63, .81; P < .0001) and h-index (relative number = .81; 95% CI = .73, .90; P < .0001) relative to men, though there was no gender difference in grant funding. Conclusions Women faculty acquire federal funding at similar rates as male faculty, yet lag behind in terms of publications and their impact. Medical academia must consider how to help address ongoing gender disparities in publication records. PMID:27276002

Estimating the Prevalence of Atrial Fibrillation from A Three-Class Mixture Model for Repeated Diagnoses

PubMed Central

Li, Liang; Mao, Huzhang; Ishwaran, Hemant; Rajeswaran, Jeevanantham; Ehrlinger, John; Blackstone, Eugene H.

2016-01-01

Atrial fibrillation (AF) is an abnormal heart rhythm characterized by rapid and irregular heart beat, with or without perceivable symptoms. In clinical practice, the electrocardiogram (ECG) is often used for diagnosis of AF. Since the AF often arrives as recurrent episodes of varying frequency and duration and only the episodes that occur at the time of ECG can be detected, the AF is often underdiagnosed when a limited number of repeated ECGs are used. In studies evaluating the efficacy of AF ablation surgery, each patient undergo multiple ECGs and the AF status at the time of ECG is recorded. The objective of this paper is to estimate the marginal proportions of patients with or without AF in a population, which are important measures of the efficacy of the treatment. The underdiagnosis problem is addressed by a three-class mixture regression model in which a patient’s probability of having no AF, paroxysmal AF, and permanent AF is modeled by auxiliary baseline covariates in a nested logistic regression. A binomial regression model is specified conditional on a subject being in the paroxysmal AF group. The model parameters are estimated by the EM algorithm. These parameters are themselves nuisance parameters for the purpose of this research, but the estimators of the marginal proportions of interest can be expressed as functions of the data and these nuisance parameters and their variances can be estimated by the sandwich method. We examine the performance of the proposed methodology in simulations and two real data applications. PMID:27983754

An analysis of first-time blood donors return behaviour using regression models.

PubMed

Kheiri, S; Alibeigi, Z

2015-08-01

Blood products have a vital role in saving many patients' lives. The aim of this study was to analyse blood donor return behaviour. Using a cross-sectional follow-up design of 5-year duration, 864 first-time donors who had donated blood were selected using a systematic sampling. The behaviours of donors via three response variables, return to donation, frequency of return to donation and the time interval between donations, were analysed based on logistic regression, negative binomial regression and Cox's shared frailty model for recurrent events respectively. Successful return to donation rated at 49·1% and the deferral rate was 13·3%. There was a significant reverse relationship between the frequency of return to donation and the time interval between donations. Sex, body weight and job had an effect on return to donation; weight and frequency of donation during the first year had a direct effect on the total frequency of donations. Age, weight and job had a significant effect on the time intervals between donations. Aging decreases the chances of return to donation and increases the time interval between donations. Body weight affects the three response variables, i.e. the higher the weight, the more the chances of return to donation and the shorter the time interval between donations. There is a positive correlation between the frequency of donations in the first year and the total number of return to donations. Also, the shorter the time interval between donations is, the higher the frequency of donations. © 2015 British Blood Transfusion Society.

Estimating the prevalence of atrial fibrillation from a three-class mixture model for repeated diagnoses.

PubMed

Li, Liang; Mao, Huzhang; Ishwaran, Hemant; Rajeswaran, Jeevanantham; Ehrlinger, John; Blackstone, Eugene H

2017-03-01

Atrial fibrillation (AF) is an abnormal heart rhythm characterized by rapid and irregular heartbeat, with or without perceivable symptoms. In clinical practice, the electrocardiogram (ECG) is often used for diagnosis of AF. Since the AF often arrives as recurrent episodes of varying frequency and duration and only the episodes that occur at the time of ECG can be detected, the AF is often underdiagnosed when a limited number of repeated ECGs are used. In studies evaluating the efficacy of AF ablation surgery, each patient undergoes multiple ECGs and the AF status at the time of ECG is recorded. The objective of this paper is to estimate the marginal proportions of patients with or without AF in a population, which are important measures of the efficacy of the treatment. The underdiagnosis problem is addressed by a three-class mixture regression model in which a patient's probability of having no AF, paroxysmal AF, and permanent AF is modeled by auxiliary baseline covariates in a nested logistic regression. A binomial regression model is specified conditional on a subject being in the paroxysmal AF group. The model parameters are estimated by the Expectation-Maximization (EM) algorithm. These parameters are themselves nuisance parameters for the purpose of this research, but the estimators of the marginal proportions of interest can be expressed as functions of the data and these nuisance parameters and their variances can be estimated by the sandwich method. We examine the performance of the proposed methodology in simulations and two real data applications. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

School Violence: The Role of Parental and Community Involvement

ERIC Educational Resources Information Center

Lesneskie, Eric; Block, Steven

2017-01-01

This study utilizes the School Survey on Crime and Safety to identify variables that predict lower levels of violence from four domains: school security, school climate, parental involvement, and community involvement. Negative binomial regression was performed and the findings indicate that statistically significant results come from all four…

Predicting Children's Asthma Hospitalizations: Rural and Urban Differences in Texas

ERIC Educational Resources Information Center

Grineski, Sara E.

2009-01-01

Asthma is the number one chronic health condition facing children today; however, little is known about rural-urban inequalities in asthma. This "area effects on health" study examines rural-urban differences in childhood asthma hospitalizations within the state of Texas using negative binomial regression models. Effects associated with…

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.

What Is Threatening the Effectiveness of Insecticide-Treated Bednets? A Case-Control Study of Environmental, Behavioral, and Physical Factors Associated with Prevention Failure.

PubMed

Obala, Andrew A; Mangeni, Judith Nekesa; Platt, Alyssa; Aswa, Daniel; Abel, Lucy; Namae, Jane; Prudhomme O'Meara, Wendy

2015-01-01

Insecticide-treated nets are the cornerstone of global malaria control and have been shown to reduce malaria morbidity by 50-60%. However, some areas are experiencing a resurgence in malaria following successful control. We describe an efficacy decay framework to understand why high malaria burden persists even under high ITN coverage in a community in western Kenya. We enrolled 442 children hospitalized with malaria and paired them with age, time, village and gender-matched controls. We completed comprehensive household and neighborhood assessments including entomological surveillance. The indicators are grouped into five domains in an efficacy decay framework: ITN ownership, compliance, physical integrity, vector susceptibility and facilitating factors. After variable selection, case-control data were analyzed using conditional logistic regression models and mosquito data were analyzed using negative binomial regression. Predictive margins were calculated from logistic regression models. Measures of ITN coverage and physical integrity were not correlated with hospitalized malaria in our study. However, consistent ITN use (Adjusted Odds Ratio (AOR) = 0.23, 95%CI: 0.12-0.43), presence of nearby larval sites (AOR = 1.137, 95%CI: 1.02-1.27), and specific types of crops (AOR (grains) = 0.446, 95%CI: 0.24-0.82) were significantly correlated with malaria amongst children who owned an ITN. The odds of hospitalization for febrile malaria nearly tripled when one other household member had symptomatic malaria infection (AOR-2.76, 95%CI:1.83-4.18). Overall, perfect household adherence could reduce the probability of hospitalization for malaria to less than 30% (95%CI:0.12-0.46) and adjusting environmental factors such as elimination of larval sites and growing grains nearby could reduce the probability of hospitalization for malaria to less than 20% (95%CI:0.04-0.31). Availability of ITNs is not the bottleneck for malaria prevention in this community. Behavior change interventions to improve compliance and environmental management of mosquito breeding habitats may greatly enhance ITN efficacy. A better understanding of the relationship between agriculture and mosquito survival and feeding success is needed.

The effectiveness of a monetary incentive offer on survey response rates and response completeness in a longitudinal study.

PubMed

Yu, Shengchao; Alper, Howard E; Nguyen, Angela-Maithy; Brackbill, Robert M; Turner, Lennon; Walker, Deborah J; Maslow, Carey B; Zweig, Kimberly C

2017-04-26

Achieving adequate response rates is an ongoing challenge for longitudinal studies. The World Trade Center Health Registry is a longitudinal health study that periodically surveys a cohort of ~71,000 people exposed to the 9/11 terrorist attacks in New York City. Since Wave 1, the Registry has conducted three follow-up surveys (Waves 2-4) every 3-4 years and utilized various strategies to increase survey participation. A promised monetary incentive was offered for the first time to survey non-respondents in the recent Wave 4 survey, conducted 13-14 years after 9/11. We evaluated the effectiveness of a monetary incentive in improving the response rate five months after survey launch, and assessed whether or not response completeness was compromised due to incentive use. The study compared the likelihood of returning a survey for those who received an incentive offer to those who did not, using logistic regression models. Among those who returned surveys, we also examined whether those receiving an incentive notification had higher rate of response completeness than those who did not, using negative binomial regression models and logistic regression models. We found that a $10 monetary incentive offer was effective in increasing Wave 4 response rates. Specifically, the $10 incentive offer was useful in encouraging initially reluctant participants to respond to the survey. The likelihood of returning a survey increased by 30% for those who received an incentive offer (AOR = 1.3, 95% CI: 1.1, 1.4), and the incentive increased the number of returned surveys by 18%. Moreover, our results did not reveal any significant differences on response completeness between those who received an incentive offer and those who did not. In the face of the growing challenge of maintaining a high response rate for the World Trade Center Health Registry follow-up surveys, this study showed the value of offering a monetary incentive as an additional refusal conversion strategy. Our findings also suggest that an incentive offer could be particularly useful near the end of data collection period when an immediate boost in response rate is needed.

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…

Estimating relative risks for common outcome using PROC NLP.

PubMed

Yu, Binbing; Wang, Zhuoqiao

2008-05-01

In cross-sectional or cohort studies with binary outcomes, it is biologically interpretable and of interest to estimate the relative risk or prevalence ratio, especially when the response rates are not rare. Several methods have been used to estimate the relative risk, among which the log-binomial models yield the maximum likelihood estimate (MLE) of the parameters. Because of restrictions on the parameter space, the log-binomial models often run into convergence problems. Some remedies, e.g., the Poisson and Cox regressions, have been proposed. However, these methods may give out-of-bound predicted response probabilities. In this paper, a new computation method using the SAS Nonlinear Programming (NLP) procedure is proposed to find the MLEs. The proposed NLP method was compared to the COPY method, a modified method to fit the log-binomial model. Issues in the implementation are discussed. For illustration, both methods were applied to data on the prevalence of microalbuminuria (micro-protein leakage into urine) for kidney disease patients from the Diabetes Control and Complications Trial. The sample SAS macro for calculating relative risk is provided in the appendix.

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.

Postnatal depression among rural women in South India: do socio-demographic, obstetric and pregnancy outcome have a role to play?

PubMed

Shivalli, Siddharudha; Gururaj, Nandihal

2015-01-01

Postnatal depression (PND) is one of the most common psychopathology and is considered as a serious public health issue because of its devastating effects on mother, family, and infant or the child. To elicit socio-demographic, obstetric and pregnancy outcome predictors of Postnatal Depression (PND) among rural postnatal women in Karnataka state, India. Hospital based analytical cross sectional study. A rural tertiary care hospital of Mandya District, Karnataka state, India. PND prevalence based estimated sample of 102 women who came for postnatal follow up from 4th to 10th week of lactation. Study participants were interviewed using validated kannada version of Edinburgh Postnatal Depression Scale (EPDS). Cut-off score of ≥ 13 was used as high risk of PND. The percentage of women at risk of PND was estimated, and differences according to socio-demographic, obstetric and pregnancy outcome were described. Logistic regression was applied to identify the independent predictors of PND risk. Prevalence, Odds ratio (OR) and adjusted (adj) OR of PND. Prevalence of PND was 31.4% (95% CI 22.7-41.4%). PND showed significant (P < 0.05) association with joint family, working women, non-farmer husbands, poverty, female baby and pregnancy complications or known medical illness. In binomial logistic regression poverty (adjOR: 11.95, 95% CI:1.36-105), birth of female baby (adjOR: 3.6, 95% CI:1.26-10.23) and pregnancy complications or known medical illness (adjOR: 17.4, 95% CI:2.5-121.2) remained as independent predictors of PND. Risk of PND among rural postnatal women was high (31.4%). Birth of female baby, poverty and complications in pregnancy or known medical illness could predict the high risk of PND. PND screening should be an integral part of postnatal care. Capacity building of grass root level workers and feasibility trials for screening PND by them are needed.

The Bright Side and Dark Side of Workplace Social Capital: Opposing Effects of Gender on Overweight among Japanese Employees

PubMed Central

Kobayashi, Tomoko; Suzuki, Etsuji; Oksanen, Tuula; Kawachi, Ichiro; Takao, Soshi

2014-01-01

Background A growing number of studies have sought to examine the health associations of workplace social capital; however, evidence of associations with overweight is sparse. We examined the association between individual perceptions of workplace social capital and overweight among Japanese male and female employees. Methodology/Principal Findings We conducted a cross-sectional survey among full-time employees at a company in Osaka prefecture in February 2012. We used an 8-item measure to assess overall and sub-dimensions of workplace social capital, divided into tertiles. Of 1050 employees, 849 responded, and 750 (624 men and 126 women) could be linked to annual health check-up data in the analysis. Binomial logistic regression models were used to calculate odds ratios and 95% confidence intervals for overweight (body mass index: ≥25 kg/m2, calculated from measured weight and height) separately for men and women. The prevalence of overweight was 24.5% among men and 14.3% among women. Among men, low levels of bonding and linking social capital in the workplace were associated with a nearly 2-fold risk of overweight compared to high corresponding dimensions of social capital when adjusted for age, sleep hours, physiological distress, and lifestyle. In contrast, among women we found lower overall and linking social capital to be associated with lower odds for overweight even after covariate adjustment. Subsequently, we used multinomial logistic regression analyses to assess the relationships between a 1 standard deviation (SD) decrease in mean social capital and odds of underweight/overweight relative to normal weight. Among men, a 1-SD decrease in overall, bonding, and linking social capital was significantly associated with higher odds of overweight, but not with underweight. Among women, no significant associations were found for either overweight or underweight. Conclusions/Significance We found opposite gender relationships between perceived low linking workplace social capital and overweight among Japanese employees. PMID:24498248

The bright side and dark side of workplace social capital: opposing effects of gender on overweight among Japanese employees.

PubMed

Kobayashi, Tomoko; Suzuki, Etsuji; Oksanen, Tuula; Kawachi, Ichiro; Takao, Soshi

2014-01-01

A growing number of studies have sought to examine the health associations of workplace social capital; however, evidence of associations with overweight is sparse. We examined the association between individual perceptions of workplace social capital and overweight among Japanese male and female employees. We conducted a cross-sectional survey among full-time employees at a company in Osaka prefecture in February 2012. We used an 8-item measure to assess overall and sub-dimensions of workplace social capital, divided into tertiles. Of 1050 employees, 849 responded, and 750 (624 men and 126 women) could be linked to annual health check-up data in the analysis. Binomial logistic regression models were used to calculate odds ratios and 95% confidence intervals for overweight (body mass index: ≥ 25 kg/m(2), calculated from measured weight and height) separately for men and women. The prevalence of overweight was 24.5% among men and 14.3% among women. Among men, low levels of bonding and linking social capital in the workplace were associated with a nearly 2-fold risk of overweight compared to high corresponding dimensions of social capital when adjusted for age, sleep hours, physiological distress, and lifestyle. In contrast, among women we found lower overall and linking social capital to be associated with lower odds for overweight even after covariate adjustment. Subsequently, we used multinomial logistic regression analyses to assess the relationships between a 1 standard deviation (SD) decrease in mean social capital and odds of underweight/overweight relative to normal weight. Among men, a 1-SD decrease in overall, bonding, and linking social capital was significantly associated with higher odds of overweight, but not with underweight. Among women, no significant associations were found for either overweight or underweight. We found opposite gender relationships between perceived low linking workplace social capital and overweight among Japanese employees.

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.

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).

General Strain Theory as a Basis for the Design of School Interventions

ERIC Educational Resources Information Center

Moon, Byongook; Morash, Merry

2013-01-01

The research described in this article applies general strain theory to identify possible points of intervention for reducing delinquency of students in two middle schools. Data were collected from 296 youths, and separate negative binomial regression analyses were used to identify predictors of violent, property, and status delinquency. Emotional…

The Effectiveness of an Electronic Security Management System in a Privately Owned Apartment Complex

ERIC Educational Resources Information Center

Greenberg, David F.; Roush, Jeffrey B.

2009-01-01

Poisson and negative binomial regression methods are used to analyze the monthly time series data to determine the effects of introducing an integrated security management system including closed-circuit television (CCTV), door alarm monitoring, proximity card access, and emergency call boxes to a large privately-owned complex of apartment…

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.

Socio-environmental predictors of Barmah forest virus transmission in coastal areas, Queensland, Australia.

PubMed

Naish, Suchithra; Hu, Wenbiao; Nicholls, Neville; Mackenzie, John S; Dale, Pat; McMichael, Anthony J; Tong, Shilu

2009-02-01

To assess the socio-environmental predictors of Barmah forest virus (BFV) transmission in coastal areas, Queensland, Australia. Data on BFV notified cases, climate, tidal levels and socioeconomic index for area (SEIFA) in six coastal cities, Queensland, for the period 1992-2001 were obtained from the relevant government agencies. Negative binomial regression models were used to assess the socio-environmental predictors of BFV transmission. The results show that maximum and minimum temperature, rainfall, relative humidity, high and low tide were statistically significantly associated with BFV incidence at lags 0-2 months. The fitted negative binomial regression models indicate a significant independent association of each of maximum temperature (beta = 0.139, P = 0.000), high tide (beta = 0.005, P = 0.000) and SEIFA index (beta = -0.010, P = 0.000) with BFV transmission after adjustment for confounding variables. The transmission of BFV disease in Queensland coastal areas seemed to be determined by a combination of local social and environmental factors. The model developed in this study may have applications in the control and prevention of BFV disease in these areas.

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.

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.

Analyzing hospitalization data: potential limitations of Poisson regression.

PubMed

Weaver, Colin G; Ravani, Pietro; Oliver, Matthew J; Austin, Peter C; Quinn, Robert R

2015-08-01

Poisson regression is commonly used to analyze hospitalization data when outcomes are expressed as counts (e.g. number of days in hospital). However, data often violate the assumptions on which Poisson regression is based. More appropriate extensions of this model, while available, are rarely used. We compared hospitalization data between 206 patients treated with hemodialysis (HD) and 107 treated with peritoneal dialysis (PD) using Poisson regression and compared results from standard Poisson regression with those obtained using three other approaches for modeling count data: negative binomial (NB) regression, zero-inflated Poisson (ZIP) regression and zero-inflated negative binomial (ZINB) regression. We examined the appropriateness of each model and compared the results obtained with each approach. During a mean 1.9 years of follow-up, 183 of 313 patients (58%) were never hospitalized (indicating an excess of 'zeros'). The data also displayed overdispersion (variance greater than mean), violating another assumption of the Poisson model. Using four criteria, we determined that the NB and ZINB models performed best. According to these two models, patients treated with HD experienced similar hospitalization rates as those receiving PD {NB rate ratio (RR): 1.04 [bootstrapped 95% confidence interval (CI): 0.49-2.20]; ZINB summary RR: 1.21 (bootstrapped 95% CI 0.60-2.46)}. Poisson and ZIP models fit the data poorly and had much larger point estimates than the NB and ZINB models [Poisson RR: 1.93 (bootstrapped 95% CI 0.88-4.23); ZIP summary RR: 1.84 (bootstrapped 95% CI 0.88-3.84)]. We found substantially different results when modeling hospitalization data, depending on the approach used. Our results argue strongly for a sound model selection process and improved reporting around statistical methods used for modeling count data. © The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Equity in health services use and intensity of use in Canada

PubMed Central

Asada, Yukiko; Kephart, George

2007-01-01

Background The Canadian health care system has striven to remove financial or other barriers to access to medically necessary health care services since the establishment of the Canada Health Act 20 years ago. Evidence has been conflicting as to what extent the Canadian health care system has met this goal of equitable access. The objective of this study was to examine whether and where socioeconomic inequities in health care utilization occur in Canada. Methods We used a nationally representative cross-sectional survey, the 2000/01 Canadian Community Health Survey, which provides a large sample size (about 110,000) and permits more comprehensive adjustment for need indicators than previous studies. We separately examined general practitioner, specialist, and hospital services using two-part hurdle models: use versus non-use by logistic regression, and the intensity of use among users by zero-truncated negative binomial regression. Results We found that lower income was associated with less contact with general practitioners, but among those who had contact, lower income and education were associated with greater intensity of use of general practitioners. Both lower income and education were associated with less contact with specialists, but there was no statistically significant relationship between these socioeconomic variables and intensity of specialist use among the users. Neither income nor education was statistically significantly associated with use or intensity of use of hospitals. Conclusion Our study unveiled possible socioeconomic inequities in the use of health care services in Canada. PMID:17349059

Family and parenting characteristics associated with marijuana use by Chilean adolescents

PubMed Central

Bares, Cristina B; Delva, Jorge; Grogan-Kaylor, Andrew; Andrade, Fernando

2011-01-01

Objective Family involvement and several characteristics of parenting have been suggested to be protective factors for adolescent substance use. Some parenting behaviors may have stronger relationships with adolescent behavior while others may have associations with undesirable behavior among youth. Although it is generally acknowledged that families play an important role in the lives of Chilean adolescents, scant research exists on how different family and parenting factors may be associated with marijuana use and related problems in this population which has one of the highest rates of drug use in Latin America. Methods Using logistic regression and negative binomial regression, we examined whether a large number of family and parenting variables were associated with the possibility of Chilean adolescents ever using marijuana, and with marijuana-related problems. Analyses controlled for a number of demographic and peer-related variables. Results Controlling for other parenting and family variables, adolescent reports of parental marijuana use showed a significant and positive association with adolescent marijuana use. The multivariate models also revealed that harsh parenting by fathers was the only family variable associated with the number of marijuana-related problems youth experienced. Conclusion Of all the family and parenting variables studied, perceptions of parental use of marijuana and harsh parenting by fathers were predictors for marijuana use, and the experience of marijuana-related problems. Prevention interventions need to continue emphasizing the critical socializing role that parental behavior plays in their children’s development and potential use of marijuana. PMID:21660209

Investigating the role of health care at birth on inequalities in neonatal survival: evidence from Bangladesh

PubMed Central

2013-01-01

Introduction In countries such as Bangladesh many women may only seek skilled care at birth when complications become evident. This often results in higher neonatal mortality for women who give birth in institutions than for those that give birth at home. However, we hypothesise that this apparent excess mortality is concentrated among less advantaged women. The aim of this paper is to examine the association between place of birth and neonatal mortality in Bangladesh, and how this varies by socio-economic status. Methodology The study is based on pooled data from four Bangladesh Demographic and Household Surveys, and uses descriptive analysis and binomial multivariate logistic regression. It uses regression models stratified for place of delivery to examine the impact of socio-economic status and place of residence on neonatal mortality. Results Poor women from rural areas and those with no education who gave birth in institutions had much worse outcomes than those who gave birth at home. There is no difference for more wealthy women. There is a much stronger socio-economic gradient in neonatal mortality for women who gave birth in institutions than those who delivered at home. Conclusion In Bangladesh babies from lower socio-economic groups and particularly those in rural areas have very poor outcomes if born in a facility. This suggests poorer, rural and less educated women are failing to obtain the timely access to quality maternal health care services needed to improve newborn outcomes. PMID:23496964

Association of post-traumatic stress disorder and work performance: A survey from an emergency medical service, Karachi, Pakistan

PubMed Central

Kerai, Salima; Pasha, Omrana; Khan, Uzma; Islam, Muhammad; Asad, Nargis; Razzak, Junaid

2017-01-01

BACKGROUND: The purpose of the study was to explore the association between post-traumatic stress disorder (PTSD) and work performance of emergency medical services personnel in Karachi, Pakistan. METHODS: Emergency medical service personnel were screened for potential PTSD using Impact of Event Scale-Revised (IES-R). Work performance was assessed on the basis of five variables: number of late arrivals to work, number of days absent, number of days sick, adherence to protocol, and patient satisfaction over a period of 3 months. In order to model outcomes like the number of late arrivals to work, days absent and days late, negative binomial regression was applied, whereas logistic regression was applied for adherence to protocol and linear for patient satisfaction scores. RESULTS: Mean scores of PTSD were 24.0±12.2. No association was found between PTSD and work performance measures: number of late arrivals to work (RRadj 0.99; 0.98–1.00), days absent (RRadj 0.98; 0.96–0.99), days sick (RRadj 0.99; 0.98–1.00), adherence to protocol (ORadj 1.01; 0.99–1.04) and patient satisfaction (β 0.001%–0.03%) after adjusting for years of formal schooling, living status, coping mechanism, social support, working hours, years of experience and anxiety or depression. CONCLUSION: No statistically significant association was found between PTSD and work performance amongst EMS personnel in Karachi, Pakistan. PMID:28680519

Consumption of fast food, sugar-sweetened beverages, artificially-sweetened beverages and allostatic load among young adults.

PubMed

van Draanen, Jenna; Prelip, Michael; Upchurch, Dawn M

2018-06-01

This study investigates the associations between recent consumption of fast foods, sugar-sweetened beverages, and artificially-sweetened beverages on level of allostatic load, a measure of cumulative biological risk, in young adults in the US. Data from Wave IV of the National Longitudinal Study of Adolescent to Adult Health were analyzed. Negative binomial regression models were used to estimate the associations between consumption of fast foods, sugar-sweetened, and artificially-sweetened beverages and allostatic load. Poisson and logistic regression models were used to estimate the associations between these diet parameters and combined biomarkers of physiological subsystems that comprise our measure of allostatic load. All analyses were weighted and findings are representative of young adults in the US, ages 24-34 in 2008 (n = 11,562). Consumption of fast foods, sugar-sweetened, and artificially-sweetened beverages were associated with higher allostatic load at a bivariate level. Accounting for demographics and medication use, only artificially-sweetened beverages remained significantly associated with allostatic load. When all three dietary components were simultaneously included in a model, both sugar- and artificially-sweetened beverage consumption were associated with higher allostatic load. Differences in allostatic load emerge early in the life course and young adults consuming sugar- or artificially-sweetened beverages have higher allostatic load, net of demographics and medication use. Public health messages to young adults may need to include cautions about both sugar- and artificially-sweetened beverages.

Association of post-traumatic stress disorder and work performance: A survey from an emergency medical service, Karachi, Pakistan.

PubMed

Kerai, Salima; Pasha, Omrana; Khan, Uzma; Islam, Muhammad; Asad, Nargis; Razzak, Junaid

2017-01-01

The purpose of the study was to explore the association between post-traumatic stress disorder (PTSD) and work performance of emergency medical services personnel in Karachi, Pakistan. Emergency medical service personnel were screened for potential PTSD using Impact of Event Scale-Revised (IES-R). Work performance was assessed on the basis of five variables: number of late arrivals to work, number of days absent, number of days sick, adherence to protocol, and patient satisfaction over a period of 3 months. In order to model outcomes like the number of late arrivals to work, days absent and days late, negative binomial regression was applied, whereas logistic regression was applied for adherence to protocol and linear for patient satisfaction scores. Mean scores of PTSD were 24.0±12.2. No association was found between PTSD and work performance measures: number of late arrivals to work ( RR adj 0.99; 0.98-1.00), days absent ( RR adj 0.98; 0.96-0.99), days sick ( RR adj 0.99; 0.98-1.00), adherence to protocol ( OR adj 1.01; 0.99-1.04) and patient satisfaction ( β 0.001%-0.03%) after adjusting for years of formal schooling, living status, coping mechanism, social support, working hours, years of experience and anxiety or depression. No statistically significant association was found between PTSD and work performance amongst EMS personnel in Karachi, Pakistan.

The Relationship Between Client Characteristics and Wraparound Services in Substance Use Disorder Treatment Centers.

PubMed

Paino, Maria; Aletraris, Lydia; Roman, Paul

2016-01-01

The National Institute on Drug Abuse (NIDA) recommends a comprehensive treatment program for individuals with substance use disorder (SUD) in order to treat needs they often have in addition to their SUD. Specifically, NIDA suggests providing services related to the following issues: medical care, mental health care, HIV/AIDS, child care, educational, vocational, family counseling, housing, transportation, financial, and legal. By providing a comprehensive model that combines core and wraparound services, treatment centers can deliver a higher quality of treatment. In this article, we assessed the relationship between client characteristics and the availability of wraparound services in SUD treatment centers. We combined two nationally representative samples of treatment centers and used a negative binomial regression and a series of logistic regressions to analyze the relationship between client characteristics and wraparound services. On average, centers offered fewer than half of the wraparound services endorsed by NIDA. Our results indicated that client characteristics were significantly related to the provision of wraparound services. Most notably, the proportion of adolescent clients was positively related to educational services, the proportion of female clients was positively related to child care, but the proportion of clients referred from the criminal justice system was negatively associated with the provision of multiple wraparound services. Our findings have important implications for SUD clients and suggest that, although centers are somewhat responsive to their clients' ancillary needs, most centers do not offer the majority of wraparound services.

The Relationship Between Client Characteristics and Wraparound Services in Substance Use Disorder Treatment Centers

PubMed Central

Paino, Maria; Aletraris, Lydia; Roman, Paul

2016-01-01

Objective: The National Institute on Drug Abuse (NIDA) recommends a comprehensive treatment program for individuals with substance use disorder (SUD) in order to treat needs they often have in addition to their SUD. Specifically, NIDA suggests providing services related to the following issues: medical care, mental health care, HIV/AIDS, child care, educational, vocational, family counseling, housing, transportation, financial, and legal. By providing a comprehensive model that combines core and wraparound services, treatment centers can deliver a higher quality of treatment. In this article, we assessed the relationship between client characteristics and the availability of wraparound services in SUD treatment centers. Method: We combined two nationally representative samples of treatment centers and used a negative binomial regression and a series of logistic regressions to analyze the relationship between client characteristics and wraparound services. Results: On average, centers offered fewer than half of the wraparound services endorsed by NIDA. Our results indicated that client characteristics were significantly related to the provision of wraparound services. Most notably, the proportion of adolescent clients was positively related to educational services, the proportion of female clients was positively related to child care, but the proportion of clients referred from the criminal justice system was negatively associated with the provision of multiple wraparound services. Conclusions: Our findings have important implications for SUD clients and suggest that, although centers are somewhat responsive to their clients’ ancillary needs, most centers do not offer the majority of wraparound services. PMID:26751366

The Effect of Cotrimoxazole Prophylactic Treatment on Malaria, Birth Outcomes, and Postpartum CD4 Count in HIV-Infected Women

PubMed Central

Dow, Anna; Hudgens, Michael G.; Van Rie, Annelies; King, Caroline C.; Ellington, Sascha; Chome, Nelecy; Turner, Abigail Norris; Kacheche, Zebrone; Jamieson, Denise J.; Chasela, Charles; van der Horst, Charles

2013-01-01

Background. Limited data exist on cotrimoxazole prophylactic treatment (CPT) in pregnant women, including protection against malaria versus standard intermittent preventive therapy with sulfadoxine-pyrimethamine (IPTp). Methods. Using observational data we examined the effect of CPT in HIV-infected pregnant women on malaria during pregnancy, low birth weight and preterm birth using proportional hazards, logistic, and log binomial regression, respectively. We used linear regression to assess effect of CPT on CD4 count. Results. Data from 468 CPT-exposed and 768 CPT-unexposed women were analyzed. CPT was associated with protection against malaria versus IPTp (hazard ratio: 0.35, 95% Confidence Interval (CI): 0.20, 0.60). After adjustment for time period this effect was not statistically significant (adjusted hazard ratio: 0.66, 95% CI: 0.28, 1.52). Among women receiving and not receiving CPT, rates of low birth weight (7.1% versus 7.6%) and preterm birth (23.5% versus 23.6%) were similar. CPT was associated with lower CD4 counts 24 weeks postpartum in women receiving (−77.6 cells/μL, 95% CI: −125.2, −30.1) and not receiving antiretrovirals (−33.7 cells/μL, 95% CI: −58.6, −8.8). Conclusions. Compared to IPTp, CPT provided comparable protection against malaria in HIV-infected pregnant women and against preterm birth or low birth weight. Possible implications of CPT-associated lower CD4 postpartum warrant further examination. PMID:24363547

The effects of combat exposure, abusive violence, and sense of coherence on PTSD and depression in Portuguese colonial war veterans.

PubMed

Ferrajão, Paulo Correia; Oliveira, Rui Aragão

2016-01-01

We analyzed the effects of 3 war components-combat exposure (CES), observation of abusive violence (OBS), and participation in abusive violence (PARTC)-and sense of coherence (SOC) on the development of both posttraumatic stress disorder (PTSD) and depression among a sample of war veterans. We also analyzed the role of SOC as a mediator of the effects of CES, OBS, and PARTC on both depression and PTSD symptoms. Sample was composed of 120 Portuguese Colonial War veterans. A binomial logistic regression analysis was performed to determine the effects of these variables on depression and PTSD diagnosis. Mediation test was performed by conducting several hierarchical regression analyses. Results showed that OBS and PARTC, and lower levels of SOC were associated with increased odds for exceeding the clinical cutoff scores for diagnosis of depression. All variables were associated with increased odds for exceeding the clinical cutoff scores for diagnosis of PTSD. In mediation analysis, at first step, PARTC was not a significant predictor of both PTSD and depression symptoms, and PARTC did not enter in subsequent analysis. SOC was a full mediator of the effects of OBS and CES on both depression and PTSD symptoms. We propose that treatment of war veterans should aim the reconciliation of traumatic incongruent experiences in veterans' personal schemas to strengthen veterans' sense of coherence, especially for those exposed to acts of abusive violence. (c) 2016 APA, all rights reserved).

Association between Parental Access to Paid Sick Leave and Children's Access to and Use of Healthcare Services

PubMed Central

Asfaw, Abay; Colopy, Maria

2017-01-01

Background We examined the association between parental access to paid sick leave (PPSL) and children's use of preventive care and reduced likelihood of delayed medical care and emergency room (ER) visits. Methods We used the child sample of the National Health Interview Survey data (linked to the adult and family samples) from 2011 through 2015 and logistic and negative binomial regression models. Results Controlling for covariates, the odds of children with PPSL receiving flu vaccination were 12.5% [95%CI: 1.06–1.19] higher and receiving annual medical checkups were 13.2% [95%CI: 1.04–1.23] higher than those of children without PPSL. With PPSL, the odds of children receiving delayed medical care because of time mismatch were 13.3% [95%CI: 0.76–0.98] lower, and being taken to ER were 53.6% [95%CI: 0.27–0.81] lower than those of children without PPSL. PPSL was associated with 11% [95%CI: 0.82–0.97] fewer ER visits per year. Conclusion PPSL may improve children's access and use of healthcare services and reduce the number of ER visits. PMID:28169438

Socio-Economic and Environmental Factors Associated with Overweight and Obesity in Children Aged 6-8 Years Living in Five Italian Cities (the MAPEC_LIFE Cohort).

PubMed

Grassi, Tiziana; De Donno, Antonella; Bagordo, Francesco; Serio, Francesca; Piscitelli, Prisco; Ceretti, Elisabetta; Zani, Claudia; Viola, Gaia C V; Villarini, Milena; Moretti, Massimo; Levorato, Sara; Carducci, Annalaura; Verani, Marco; Donzelli, Gabriele; Bonetta, Sara; Bonetta, Silvia; Carraro, Elisabetta; Bonizzoni, Silvia; Bonetti, Alberto; Gelatti, Umberto

2016-10-11

The prevalence of obesity among Italian children has reached such alarming levels as to require detailed studies of the causes of the phenomenon. A cross-sectional study was carried out in order to assess the weight status of 1164 Italian children aged 6-8 years (the Monitoring Air Pollution Effects on Children for Supporting Public Health Policy (MAPEC_LIFE) cohort) and to identify any associations between selected socio-economic and environmental factors and overweight/obesity. The data were obtained by means of a questionnaire given to parents, and any associations were examined by binomial logistic regression analyses. Overweight was found to be positively associated with male gender, parents of non-Italian origin, and parents who smoke, and negatively associated with the parents' level of education and employment. In addition, the frequency of overweight varied in relation to the geographical area of residence, with a greater prevalence of overweight children in the cities of central-southern Italy. This study highlights the need to implement appropriate obesity prevention programs in Italy, which should include educational measures concerning lifestyle for parents from the earliest stages of their child's life.

Indicators of Dysphagia in Aged Care Facilities.

PubMed

Pu, Dai; Murry, Thomas; Wong, May C M; Yiu, Edwin M L; Chan, Karen M K

2017-09-18

The current cross-sectional study aimed to investigate risk factors for dysphagia in elderly individuals in aged care facilities. A total of 878 individuals from 42 aged care facilities were recruited for this study. The dependent outcome was speech therapist-determined swallowing function. Independent factors were Eating Assessment Tool score, oral motor assessment score, Mini-Mental State Examination, medical history, and various functional status ratings. Binomial logistic regression was used to identify independent variables associated with dysphagia in this cohort. Two statistical models were constructed. Model 1 used variables from case files without the need for hands-on assessment, and Model 2 used variables that could be obtained from hands-on assessment. Variables positively associated with dysphagia identified in Model 1 were male gender, total dependence for activities of daily living, need for feeding assistance, mobility, requiring assistance walking or using a wheelchair, and history of pneumonia. Variables positively associated with dysphagia identified in Model 2 were Mini-Mental State Examination score, edentulousness, and oral motor assessments score. Cognitive function, dentition, and oral motor function are significant indicators associated with the presence of swallowing in the elderly. When assessing the frail elderly, case file information can help clinicians identify frail elderly individuals who may be suffering from dysphagia.

Socio-demographic predictors of person-organization fit.

PubMed

Merecz-Kot, Dorota; Andysz, Aleksandra

2017-02-21

The aim of this study was to explore the relationship between socio-demographic characteristics and the level of complementary and supplementary person-organization fit (P-O fit). The study sample was a group of 600 Polish workers, urban residents aged 19-65. Level of P-O fit was measured using the Subjective Person-Organization Fit Questionnaire by Czarnota-Bojarska. The binomial multivariate logistic regression was applied. The analyzes were performed separately for the men and women. Socio-demographic variables explained small percentage of the outcome variability. Gender differences were found. In the case of men shift work decreased complementary and supplementary fit, while long working hours decreased complementary fit. In the women, age was a stimulant of a complementary fit, involuntary job losses predicted both complementary and supplementary misfit. Additionally, relational responsibilities increased probability of supplementary P-O fit in the men. Going beyond personality and competences as the factors affecting P-O fit will allow development of a more accurate prediction of P-O fit. Int J Occup Med Environ Health 2017;30(1):133-139. This work is available in Open Access model and licensed under a CC BY-NC 3.0 PL license.

The Structure of Co-Occurring Bullying Experiences and Associations with Suicidal Behaviors in Korean Adolescents.

PubMed

Roh, Beop-Rae; Yoon, Yoewon; Kwon, Ahye; Oh, Seunga; Lee, Soyoung Irene; Ha, Kyunghee; Shin, Yun Mi; Song, Jungeun; Park, Eun Jin; Yoo, Heejung; Hong, Hyun Ju

2015-01-01

This study had two main goals: to examine the structure of co-occurring peer bullying experiences among adolescents in South Korea from the perspective of victims and to determine the effects of bullying on suicidal behavior, including suicidal ideation and suicide attempts, among adolescents. This study used data gathered from 4,410 treatment-seeking adolescents at their initial visits to 31 local mental health centers in Gyeonggi Province, South Korea. The structure of peer bullying was examined using latent class analysis (LCA) to classify participants' relevant experiences. Then, a binomial logistic regression adjusted by propensity scores was conducted to identify relationships between experiences of being bullied and suicidal behaviors. The LCA of experiences with bullying revealed two distinct classes of bullying: physical and non-physical. Adolescents who experienced physical bullying were 3.05 times more likely to attempt suicide than those who were not bullied. Victims of (non-physical) cyber bullying were 2.94 times more likely to attempt suicide than were those who were not bullied. Both physical and non-physical bullying were associated with suicide attempts, with similar effect sizes. Schools and mental health professionals should be more attentive than they currently are to non-physical bullying.

Formal Home Care Utilization Patterns by Rural–Urban Community Residence

PubMed Central

Spector, William; Van Nostrand, Joan

2009-01-01

Background We examined formal home care utilization among civilian adults across metro and nonmetro residential categories before and after adjustment for predisposing, enabling, and need variables. Methods Two years of the Medical Expenditure Panel Survey (MEPS) were combined to produce a nationally representative sample of adults who resided in the community for a calendar year. We established 6 rural–urban categories based upon Urban Influence Codes and examined 2 dependent variables: (a) likelihood of using any formal home care and (b) number of provider days received by users. The Area Resource File provided county-level information. Logistic and negative binomial regression analyses were employed, with adjustments for the MEPS complex sampling design and the combined years. Results Under controls for predisposing, enabling, and need variables, differences in likelihood of any formal home care use disappear, but differences in number of provider days received by users emerged, with fewer provider days in remote areas than in metro and several other nonmetro types. Conclusions It is important to fully account for predisposing, enabling, and need factors when assessing rural and urban home care utilization patterns. The limited provider days in remote counties under controls suggest a possible access problem for adults in these areas. PMID:19196690

Factors affecting the prevalence of mange-mite infestations in stray dogs of Yucatán, Mexico.

PubMed

Rodriguez-Vivas, R I; Ortega-Pacheco, A; Rosado-Aguilar, J A; Bolio, G M E

2003-07-10

The aim of the present study was to determine the factors affecting the prevalence of mange-mite infestations in stray dogs of Yucatán, Mexico. The study was carried out in 200 stray dogs of Mérida capital city of Yucatán, Mexico. Four samples (head, thoracic-abdominal area, extremities and ear) were taken from each animal by skin scraping and examined microscopically in 10% KOH solution to detect the presence of mites. Mites were also collected from the external ear canal of dogs using cotton-tipped swabs. The prevalence of different mite species was calculated. A primary screening was performed using 2xK contingency tables of exposure variables. All variables with P< or =0.20 were analyzed by a logistic-binomial regression. The overall prevalence was 34%. Demodex canis (23.0%) was the most frequent mite, followed by Sarcoptes scabei var. canis (7.0%) and Otodectes cynotis (3.5%). The following factors were found: body condition (bad, OR: 5.35, CI 95%: 1.66-17.3; regular, OR: 3.72, CI 95%: 1.39-9.99) and the presence of macroscopic lesions of dermatosis (OR: 42.80, CI 95%: 13.65-134.24).

Relationship of dropout and psychopathology in a high school sample in Mexico.

PubMed

Chalita, Pablo J; Palacios, Lino; Cortes, Jose F; Landeros-Weisenberger, Angeli; Panza, Kaitlyn E; Bloch, Michael H

2012-01-01

School dropout has significant consequences for both individuals and societies. Only 21% of adults in Mexico achieve the equivalent of a high school education. We examined the relationship between school dropout and self-reported psychiatric symptoms in a middle school in a suburb of Mexico City. We used binomial logistic regression to examine the odds ratio (OR) of school dropout associated with students' self-reported psychopathology. Two-hundred thirty-seven students participated in the study. Psychosis [OR = 8.0 (95% confidence interval, CI: 1.7-37.2)], depression [OR = 4.7 (95% CI: 2.2-9.7)], tic disorders [OR = 3.7 (95% CI: 1.4-9.5)], ADHD [OR = 3.2 (95% CI: 1.5-6.4)], and social phobia [OR = 2.6 (95% CI: 1.2-5.8)] were associated with increased risk of school dropout after controlling for age and gender as covariates. Our study suggested that students' self-reported psychopathology is associated with increased school dropout in Mexico. ADHD and depression may be particularly useful childhood psychiatric disorders to target with public health interventions because they explain the greatest amount of the variance in school dropout of child psychiatric disorders.

Using exceedance probabilities to detect anomalies in routinely recorded animal health data, with particular reference to foot-and-mouth disease in Viet Nam.

PubMed

Richards, K K; Hazelton, M L; Stevenson, M A; Lockhart, C Y; Pinto, J; Nguyen, L

2014-10-01

The widespread availability of computer hardware and software for recording and storing disease event information means that, in theory, we have the necessary information to carry out detailed analyses of factors influencing the spatial distribution of disease in animal populations. However, the reliability of such analyses depends on data quality, with anomalous records having the potential to introduce significant bias and lead to inappropriate decision making. In this paper we promote the use of exceedance probabilities as a tool for detecting anomalies when applying hierarchical spatio-temporal models to animal health data. We illustrate this methodology through a case study data on outbreaks of foot-and-mouth disease (FMD) in Viet Nam for the period 2006-2008. A flexible binomial logistic regression was employed to model the number of FMD infected communes within each province of the country. Standard analyses of the residuals from this model failed to identify problems, but exceedance probabilities identified provinces in which the number of reported FMD outbreaks was unexpectedly low. This finding is interesting given that these provinces are on major cattle movement pathways through Viet Nam. Copyright © 2014 Elsevier Ltd. All rights reserved.

Bruxism in craniocervical dystonia: a prospective study.

PubMed

Borie, Laetitia; Langbour, Nicolas; Guehl, Dominique; Burbaud, Pierre; Ella, Bruno

2016-09-01

Bruxism pathophysiology remains unclear, and its occurrence has been poorly investigated in movement disorders. The aim of this study was to compare the frequency of bruxism in patients with craniocervical dystonia vs. normal controls and to determine its associated clinical features. This is a prospective-control study. A total of 114 dystonic subjects (45 facial dystonia, 69 cervical dystonia) and 182 controls were included. Bruxism was diagnosed using a hetero-questionnaire and a clinical examination performed by trained dentists. Occurrence of bruxism was compared between the different study populations. A binomial logistic regression analysis was used to determine which clinical features influenced bruxism occurrence in each population. The frequency of bruxism was significantly higher in the dystonic group than in normal controls but there was no difference between facial and cervical dystonia. It was also higher in women than in men. Bruxism features were similar between normal controls and dystonic patients except for a higher score of temporomandibular jaw pain in the dystonic group. The higher frequency of bruxism in dystonic patients suggests that bruxism is increased in patients with basal ganglia dysfunction but that its nature does not differ from that seen in bruxers from the normal population.

The Role of Emotional Abuse in Intimate Partner Violence and Health Among Women in Yokohama, Japan

PubMed Central

Horrocks, Julie; Kamano, Saori

2009-01-01

Objectives. As part of the World Health Organization's cross-national research effort, we investigated the relationship between various health indicators and the experience of intimate partner violence (IPV), which included emotional, physical, and sexual abuse, among women in Yokohama, Japan. Methods. We used multivariate logistic and negative binomial regression to examine the relationship between health status and IPV in a stratified cluster sample of 1371 women aged 18 to 49 years. Results. In 9 of 11 health indicators examined, the odds of experiencing health-related problems were significantly higher (P < .05) among those that reported emotional abuse plus physical or sexual violence than among those that reported no IPV, after we controlled for sociodemographic factors, childhood sexual abuse, and adulthood sexual violence perpetrated by someone other than an intimate partner. For most health indicators, there were no significant differences between those that reported emotional abuse only and those that reported emotional abuse plus physical or sexual violence. Conclusions. The similarity of outcomes among those that reported emotional abuse only and those that reported emotional abuse plus physical or sexual violence suggests the need for increased training of health care providers about the effects of emotional abuse. PMID:18703455

The increase in symptoms of anxiety and depressed mood among Icelandic adolescents: time trend between 2006 and 2016.

PubMed

Thorisdottir, Ingibjorg E; Asgeirsdottir, Bryndis B; Sigurvinsdottir, Rannveig; Allegrante, John P; Sigfusdottir, Inga D

2017-10-01

Both research and popular media reports suggest that adolescent mental health has been deteriorating across societies with advanced economies. This study sought to describe the trends in self-reported symptoms of depressed mood and anxiety among Icelandic adolescents. Data for this study come from repeated, cross-sectional, population-based school surveys of 43 482 Icelandic adolescents in 9th and 10th grade, with six waves of pooled data from 2006 to 2016. We used analysis of variance, linear regression and binomial logistic regression to examine trends in symptom scores of anxiety and depressed mood over time. Gender differences in trends of high symptoms were also tested for interactions. Linear regression analysis showed a significant linear increase over the course of the study period in mean symptoms of anxiety and depressed mood for girls only; however, symptoms of anxiety among boys decreased. The proportion of adolescents reporting high depressive symptoms increased by 1.6% for boys and 6.8% for girls; the proportion of those reporting high anxiety symptoms increased by 1.3% for boys and 8.6% for girls. Over the study period, the odds for reporting high depressive symptoms and high anxiety symptoms were significantly higher for both genders. Girls were more likely to report high symptoms of anxiety and depressed mood than boys. Self-reported symptoms of anxiety and depressed mood have increased over time among Icelandic adolescents. Our findings suggest that future research needs to look beyond mean changes and examine the trends among those adolescents who report high symptoms of emotional distress. © The Author 2017. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

Peak Weight and Height Velocity to Age 36 Months and Asthma Development: The Norwegian Mother and Child Cohort Study

PubMed Central

Magnus, Maria C.; Stigum, Hein; Håberg, Siri E.; Nafstad, Per; London, Stephanie J.; Nystad, Wenche

2015-01-01

Background The immediate postnatal period is the period of the fastest growth in the entire life span and a critical period for lung development. Therefore, it is interesting to examine the association between growth during this period and childhood respiratory disorders. Methods We examined the association of peak weight and height velocity to age 36 months with maternal report of current asthma at 36 months (n = 50,311), recurrent lower respiratory tract infections (LRTIs) by 36 months (n = 47,905) and current asthma at 7 years (n = 24,827) in the Norwegian Mother and Child Cohort Study. Peak weight and height velocity was calculated using the Reed1 model through multilevel mixed-effects linear regression. Multivariable log-binomial regression was used to calculate adjusted relative risks (adj.RR) and 95% confidence intervals (CI). We also conducted a sibling pair analysis using conditional logistic regression. Results Peak weight velocity was positively associated with current asthma at 36 months [adj.RR 1.22 (95%CI: 1.18, 1.26) per standard deviation (SD) increase], recurrent LRTIs by 36 months [adj.RR 1.14 (1.10, 1.19) per SD increase] and current asthma at 7 years [adj.RR 1.13 (95%CI: 1.07, 1.19) per SD increase]. Peak height velocity was not associated with any of the respiratory disorders. The positive association of peak weight velocity and asthma at 36 months remained in the sibling pair analysis. Conclusions Higher peak weight velocity, achieved during the immediate postnatal period, increased the risk of respiratory disorders. This might be explained by an influence on neonatal lung development, shared genetic/epigenetic mechanisms and/or environmental factors. PMID:25635872

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.

Perceived Prevalence of Teasing and Bullying Predicts High School Dropout Rates

ERIC Educational Resources Information Center

Cornell, Dewey; Gregory, Anne; Huang, Francis; Fan, Xitao

2013-01-01

This prospective study of 276 Virginia public high schools found that the prevalence of teasing and bullying (PTB) as perceived by both 9th-grade students and teachers was predictive of dropout rates for this cohort 4 years later. Negative binomial regression indicated that one standard deviation increases in student- and teacher-reported PTB were…

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.

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.

A statistical model to estimate the impact of a hepatitis A vaccination programme.

PubMed

Oviedo, Manuel; Pilar Muñoz, M; Domínguez, Angela; Borras, Eva; Carmona, Gloria

2008-11-11

A program of routine hepatitis A+B vaccination in preadolescents was introduced in 1998 in Catalonia, a region situated in the northeast of Spain. The objective of this study was to quantify the reduction in the incidence of hepatitis A in order to differentiate the natural reduction of the incidence of hepatitis A from that produced due to the vaccination programme and to predict the evolution of the disease in forthcoming years. A generalized linear model (GLM) using negative binomial regression was used to estimate the incidence rates of hepatitis A in Catalonia by year, age group and vaccination. Introduction of the vaccine reduced cases by 5.5 by year (p-value<0.001), but there was a significant interaction between the year of report and vaccination that smoothed this reduction (p-value<0.001). The reduction was not equal in all age groups, being greater in the 12-18 years age group, which fell from a mean rate of 8.15 per 100,000 person/years in the pre-vaccination period (1992-1998) to 1.4 in the vaccination period (1999-2005). The model predicts the evolution accurately for the group of vaccinated subjects. Negative binomial regression is more appropriate than Poisson regression when observed variance exceeds the observed mean (overdispersed count data), can cause a variable apparently contribute more on the model of what really makes it.

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.

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.

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.

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...

Regular exercise and related factors in patients with Parkinson's disease: Applying zero-inflated negative binomial modeling of exercise count data.

PubMed

Lee, JuHee; Park, Chang Gi; Choi, Moonki

2016-05-01

This study was conducted to identify risk factors that influence regular exercise among patients with Parkinson's disease in Korea. Parkinson's disease is prevalent in the elderly, and may lead to a sedentary lifestyle. Exercise can enhance physical and psychological health. However, patients with Parkinson's disease are less likely to exercise than are other populations due to physical disability. A secondary data analysis and cross-sectional descriptive study were conducted. A convenience sample of 106 patients with Parkinson's disease was recruited at an outpatient neurology clinic of a tertiary hospital in Korea. Demographic characteristics, disease-related characteristics (including disease duration and motor symptoms), self-efficacy for exercise, balance, and exercise level were investigated. Negative binomial regression and zero-inflated negative binomial regression for exercise count data were utilized to determine factors involved in exercise. The mean age of participants was 65.85 ± 8.77 years, and the mean duration of Parkinson's disease was 7.23 ± 6.02 years. Most participants indicated that they engaged in regular exercise (80.19%). Approximately half of participants exercised at least 5 days per week for 30 min, as recommended (51.9%). Motor symptoms were a significant predictor of exercise in the count model, and self-efficacy for exercise was a significant predictor of exercise in the zero model. Severity of motor symptoms was related to frequency of exercise. Self-efficacy contributed to the probability of exercise. Symptom management and improvement of self-efficacy for exercise are important to encourage regular exercise in patients with Parkinson's disease. Copyright © 2015 Elsevier Inc. All rights reserved.

A Negative Binomial Regression Model for Accuracy Tests

ERIC Educational Resources Information Center

Hung, Lai-Fa

2012-01-01

Rasch used a Poisson model to analyze errors and speed in reading tests. An important property of the Poisson distribution is that the mean and variance are equal. However, in social science research, it is very common for the variance to be greater than the mean (i.e., the data are overdispersed). This study embeds the Rasch model within an…

Which Types of Televised Anti-Tobacco Campaigns Prompt More Quitline Calls from Disadvantaged Groups?

ERIC Educational Resources Information Center

Durkin, Sarah J.; Wakefield, Melanie A.; Spittal, Matthew J.

2011-01-01

To examine the efficacy of different types of mass media ads in driving lower socio-economic smokers (SES) to utilize quitlines. This study collected all 33 719 calls to the Victorian quitline in Australia over a 2-year period. Negative binomial regressions examined the relationship between weekly levels of exposure to different types of…

A Time Series Analysis: Weather Factors, Human Migration and Malaria Cases in Endemic Area of Purworejo, Indonesia, 2005–2014

PubMed Central

REJEKI, Dwi Sarwani Sri; NURHAYATI, Nunung; AJI, Budi; MURHANDARWATI, E. Elsa Herdiana; KUSNANTO, Hari

2018-01-01

Background: Climatic and weather factors become important determinants of vector-borne diseases transmission like malaria. This study aimed to prove relationships between weather factors with considering human migration and previous case findings and malaria cases in endemic areas in Purworejo during 2005–2014. Methods: This study employed ecological time series analysis by using monthly data. The independent variables were the maximum temperature, minimum temperature, maximum humidity, minimum humidity, precipitation, human migration, and previous malaria cases, while the dependent variable was positive malaria cases. Three models of count data regression analysis i.e. Poisson model, quasi-Poisson model, and negative binomial model were applied to measure the relationship. The least Akaike Information Criteria (AIC) value was also performed to find the best model. Negative binomial regression analysis was considered as the best model. Results: The model showed that humidity (lag 2), precipitation (lag 3), precipitation (lag 12), migration (lag1) and previous malaria cases (lag 12) had a significant relationship with malaria cases. Conclusion: Weather, migration and previous malaria cases factors need to be considered as prominent indicators for the increase of malaria case projection. PMID:29900134

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

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.

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.

Evaluation of the Johne's disease risk assessment and management plan on dairy farms in Ontario, Canada.

PubMed

Pieper, Laura; Sorge, Ulrike S; DeVries, Trevor J; Godkin, Ann; Lissemore, Kerry; Kelton, David F

2015-10-01

Johne's disease (JD) is a production-limiting gastrointestinal disease in cattle. To minimize the effects of JD, the Ontario dairy industry launched the Ontario Johne's Education and Management Assistance Program in 2010. As part of the program, trained veterinarians conducted a risk assessment and management plan (RAMP), an on-farm questionnaire where high RAMP scores are associated with high risk of JD transmission. Subsequently, veterinarians recommended farm-specific management practices for JD prevention. Milk or serum ELISA results from the milking herd were used to determine the herd ELISA status (HES) and within-herd prevalence. After 3.5 yr of implementation of the program, the aim of this study was to evaluate the associations among RAMP scores, HES, and recommendations. Data from 2,103 herds were available for the analyses. A zero-inflated negative binomial model for the prediction of the number of ELISA-positive animals per farm was built. The model included individual RAMP questions about purchasing animals in the logistic portion, indicating risks for between-herd transmission, and purchasing bulls, birth of calves outside the designated calving area, colostrum and milk feeding management, and adult cow environmental hygiene in the negative binomial portion, indicating risk factors for within-herd transmission. However, farms which fed low-risk milk compared with milk replacer had fewer seropositive animals. The model additionally included the JD herd history in the negative binomial and the logistic portion, indicating that herds with a JD herd history were more likely to have at least 1 positive animal and to have a higher number of positive animals. Generally, a positive association was noted between RAMP scores and the odds of receiving a recommendation for the respective risk area; however, the relationship was not always linear. For general JD risk and calving area risk, seropositive herds had higher odds of receiving recommendations compared with seronegative herds if the section scores were low. This study suggests that the RAMP is a valuable tool to assess the risk for JD transmission within and between herds and to determine farm-specific recommendations for JD prevention. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Some considerations for excess zeroes in substance abuse research.

PubMed

Bandyopadhyay, Dipankar; DeSantis, Stacia M; Korte, Jeffrey E; Brady, Kathleen T

2011-09-01

Count data collected in substance abuse research often come with an excess of "zeroes," which are typically handled using zero-inflated regression models. However, there is a need to consider the design aspects of those studies before using such a statistical model to ascertain the sources of zeroes. We sought to illustrate hurdle models as alternatives to zero-inflated models to validate a two-stage decision-making process in situations of "excess zeroes." We use data from a study of 45 cocaine-dependent subjects where the primary scientific question was to evaluate whether study participation influences drug-seeking behavior. The outcome, "the frequency (count) of cocaine use days per week," is bounded (ranging from 0 to 7). We fit and compare binomial, Poisson, negative binomial, and the hurdle version of these models to study the effect of gender, age, time, and study participation on cocaine use. The hurdle binomial model provides the best fit. Gender and time are not predictive of use. Higher odds of use versus no use are associated with age; however once use is experienced, odds of further use decrease with increase in age. Participation was associated with higher odds of no-cocaine use; once there is use, participation reduced the odds of further use. Age and study participation are significantly predictive of cocaine-use behavior. The two-stage decision process as modeled by a hurdle binomial model (appropriate for bounded count data with excess zeroes) provides interesting insights into the study of covariate effects on count responses of substance use, when all enrolled subjects are believed to be "at-risk" of use.

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.

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.

Do Barriers to Crime Prevention Moderate the Effects of Situational Crime Prevention Policies on Violent Crime in High Schools?

ERIC Educational Resources Information Center

Sevigny, Eric L.; Zhang, Gary

2018-01-01

This study investigates how barriers to school-based crime prevention programming moderate the effects of situational crime prevention (SCP) policies on levels of violent crime in U.S. public high schools. Using data from the 2008 School Survey on Crime and Safety, we estimate a series of negative binomial regression models with interactions to…

Relationship between suicide rate and economic growth and stock market in the People's Republic of China: 2004-2013.

PubMed

Yin, Honglei; Xu, Lin; Shao, Yechang; Li, Liping; Wan, Chengsong

2016-01-01

The objective of this study was to estimate the features of suicide rate and its association with economic development and stock market during the past decade in the People's Republic of China. Official data were gathered and analyzed in the People's Republic of China during the period 2004-2013. Nationwide suicide rate was stratified by four year age-groups, sex, urban/rural areas, and regions (East, Central, and West). Annual economic indexes including gross domestic product (GDP) per capita and rural and urban income per capita were all adjusted for inflation. Variation coefficient of market index (VCMI) was also included as an economic index to measure the fluctuation of the stock market. Negative binomial regression was performed to examine the time trend of region-level suicide rates and effects of sex, age, urban/rural area, region, and economic index on the suicide rates. Suicide rates of each age-group, sex, urban/rural area, and region were generally decreased from 2004 to 2013, while annual GDP per capita and rural and urban income per capita were generally increased by year. VCMI fluctuated largely, which peaked around 2009 and decreased after that time. Negative binomial regression showed that the decreased suicide rate in East and Central rural areas was the main cause of the decrease in suicide rate in the People's Republic of China. Suicide rate in the People's Republic of China for the study period increased with age and was higher in rural than in urban area, higher in males than in females, and the highest in the Central region. When GDP per capita increased by 2,787 RMB, the suicide rate decreased by 0.498 times. VCMI showed no significant relationship with suicide rate in the negative binomial regression. Suicide rate decreased in 2004-2013; varied among different age-groups, sex, urban/rural areas, and regions; and was negatively associated with the economic growth in the People's Republic of China. Stock market showed no relationship with suicide rate, but this finding needs to be verified in a future study.

Relationship between suicide rate and economic growth and stock market in the People’s Republic of China: 2004–2013

PubMed Central

Yin, Honglei; Xu, Lin; Shao, Yechang; Li, Liping; Wan, Chengsong

2016-01-01

Objectives The objective of this study was to estimate the features of suicide rate and its association with economic development and stock market during the past decade in the People’s Republic of China. Methods Official data were gathered and analyzed in the People’s Republic of China during the period 2004–2013. Nationwide suicide rate was stratified by four year age-groups, sex, urban/rural areas, and regions (East, Central, and West). Annual economic indexes including gross domestic product (GDP) per capita and rural and urban income per capita were all adjusted for inflation. Variation coefficient of market index (VCMI) was also included as an economic index to measure the fluctuation of the stock market. Negative binomial regression was performed to examine the time trend of region-level suicide rates and effects of sex, age, urban/rural area, region, and economic index on the suicide rates. Results Suicide rates of each age-group, sex, urban/rural area, and region were generally decreased from 2004 to 2013, while annual GDP per capita and rural and urban income per capita were generally increased by year. VCMI fluctuated largely, which peaked around 2009 and decreased after that time. Negative binomial regression showed that the decreased suicide rate in East and Central rural areas was the main cause of the decrease in suicide rate in the People’s Republic of China. Suicide rate in the People’s Republic of China for the study period increased with age and was higher in rural than in urban area, higher in males than in females, and the highest in the Central region. When GDP per capita increased by 2,787 RMB, the suicide rate decreased by 0.498 times. VCMI showed no significant relationship with suicide rate in the negative binomial regression. Conclusion Suicide rate decreased in 2004–2013; varied among different age-groups, sex, urban/rural areas, and regions; and was negatively associated with the economic growth in the People’s Republic of China. Stock market showed no relationship with suicide rate, but this finding needs to be verified in a future study. PMID:27994468

Exploring Genitoanal Injury and HIV Risk Among Women: Menstrual Phase, Hormonal Birth Control, and Injury Frequency and Prevalence.

PubMed

Brawner, Bridgette M; Sommers, Marilyn S; Moore, Kendra; Aka-James, Rose; Zink, Therese; Brown, Kathleen M; Fargo, Jamison D

2016-02-01

Genital, anal, and oral injuries sustained from sexual intercourse may explain HIV transmission among women. We determined the variability in genitoanal injury frequency and prevalence in women after consensual sexual intercourse, exploring the role of menstrual phase and hormonal birth control. We used a longitudinal observational design with a convenience sample of 393 women aged 21 years and older. Participants had a baseline interview with gynecological examination, followed by consensual sexual intercourse with a male partner and a second gynecological examination. We analyzed injury prevalence with logistic regression and injury frequency with negative binomial regression among women who were (1) menstrual, not using hormonal birth control, (2) menstrual, using hormonal birth control, or (3) menopausal. We also compared injury among menstrual women in the follicular, ovulatory, and luteal phases. Women using hormonal birth control had 38% more external genitalia injuries [adjusted rate ratio (ARR) = 1.38, P = 0.030] and more than twice the anal injuries (ARR = 2.67, P = 0.005) as the nonhormonal birth control menstruating group. Menopausal women had more than 3 times the anal injuries (ARR = 3.36, P = 0.020) than those in the nonhormonal menstrual group. Among menstrual women, those in the follicular phase had a greater prevalence and frequency of external genitalia injuries than those in other phases. Increased rates of postcoital genitoanal injuries are noted among women using hormonal birth control and/or in the follicular phase of menstruation. Biological factors that influence women's risk for HIV warrant further investigation.

Exploring genitoanal injury and HIV risk among women: Menstrual phase, hormonal birth control, and injury frequency and prevalence

PubMed Central

Brawner, Bridgette M.; Sommers, Marilyn S.; Moore, Kendra; Aka-James, Rose; Zink, Therese; Brown, Kathleen M.; Fargo, Jamison D.

2015-01-01

Background Genital, anal and oral injuries sustained from sexual intercourse may explain HIV transmission among women. We determined the variability in genitoanal injury frequency and prevalence in women following consensual sexual intercourse, exploring the role of menstrual phase and hormonal birth control. Methods We used a longitudinal, observational design with a convenience sample of 393 women aged 21 and older. Participants had a baseline interview with gynecologic examination, followed by consensual sexual intercourse with a male partner and a second gynecologic examination. We analyzed injury prevalence with logistic regression and injury frequency with negative binomial regression among women who were: a) menstrual, not using hormonal birth control, b) menstrual, using hormonal birth control, or c) menopausal. We also compared injury among menstrual women in the follicular, ovulatory and luteal phases. Findings Women using hormonal birth control had 38% more external genitalia injuries (adjusted rate ratio [ARR] = 1.38, p = 0.030) and more than twice the anal injuries (ARR = 2.67, p = 0.005) as the non-hormonal birth control menstruating group. Menopausal women had more than three times the anal injuries (ARR = 3.36, p = 0.020) than those in the non-hormonal menstrual group. Among menstrual women, those in the follicular phase had a greater prevalence and frequency of external genitalia injuries than those in other phases. Interpretation Increased rates of post-coital genitoanal injuries are noted among women using hormonal birth control and/or in the follicular phase of menstruation. Biological factors that influence women's risk for HIV warrant further investigation. PMID:26334741

A workforce-based study of occupational exposures and asthma symptoms in cleaning workers.

PubMed

Vizcaya, David; Mirabelli, Maria C; Antó, Josep-Maria; Orriols, Ramon; Burgos, Felip; Arjona, Lourdes; Zock, Jan-Paul

2011-12-01

To study associations between use of cleaning products and asthma symptoms in cleaning workers. Information on respiratory symptoms, history of asthma, workplaces, use of cleaning products and acute inhalation incidents were obtained through a self-administered questionnaire. 917 employees of 37 cleaning companies in Barcelona were studied. 761 (83%) were current cleaners, 86 (9%) former cleaners and 70 (8%) had never worked as cleaners. Multivariable logistic regression analyses were used to evaluate the associations between specific exposures among current cleaners and wheeze without having a cold, chronic cough and current asthma. Associations with an asthma symptom score were also studied using negative binomial regression analyses to report mean ratios. After adjusting for sex, age, nationality and smoking status, the prevalence of current asthma was non-significantly higher among current (OR 1.9; 95% CI 0.5 to 7.8) and former cleaners (OR 1.9; CI 0.6 to 5.5) than in never cleaners. Cleaners working in hospitals during the last year had a significantly increased prevalence of wheeze, current asthma and a 1.8 (95% CI 1.2 to 2.8) times higher mean asthma score. Use of hydrochloric acid was strongly associated with asthma score (mean ratio 1.7; 95% CI 1.1 to 2.6). Use of ammonia, degreasers, multiple purpose products and waxes was also associated with asthma score. Cleaning work in places with high demand for disinfection, high cleaning standards and use of cleaning products containing respiratory irritants is associated with higher risk of asthma symptoms. This suggests irritants have an important role in cleaning-related asthma.

Increased diagnostic activity in general practice during the year preceding colorectal cancer diagnosis.

PubMed

Hansen, Pernille Libach; Hjertholm, Peter; Vedsted, Peter

2015-08-01

Accurate diagnostic activity in general practice before colorectal cancer (CRC) diagnosis is crucial for an early detection of CRC. This study aimed to investigate the rates of daytime consultations, hemoglobin (Hb) measurements and medicine prescriptions for hemorrhoids in general practice in the year preceding CRC diagnosis. Using Danish registries, we conducted a population-based matched cohort study including CRC patients aged 40-80 years (n = 19,209) and matched references (n = 192,090). We calculated odds ratios (ORs) using a conditional logistical regression model and incidence rate ratios (IRRs) using a negative binomial regression model. The CRC patients had significantly more consultations from 9 months before diagnosis and significantly increased rates of Hb measurements from up to 17 months before diagnosis compared with references. Furthermore, up to 18 months before diagnosis, CRC patients had significantly higher rates of prescriptions for hemorrhoids; and 2 months before diagnosis, the IRR was 12.24 (95% confidence interval (CI): 10.29-14.55) for men. The positive predictive value (PPV) of CRC for having a first-time prescription for hemorrhoids was highest among men aged 70-80 years [PPV = 3.2% (95% CI: 2.8-3.7)]. High prescription rates were predominantly seen among rectal cancer patients, whereas colon cancer patients had higher rates of consultations and Hb measurements. This study revealed a significant increase in healthcare seeking and diagnostic activity in general practice in the year prior to CRC diagnosis, which indicates the presence of a "diagnostic time window" and a potential for earlier diagnosis of CRC based on clinical signs and symptoms. © 2015 UICC.

Maternal Smoking during Pregnancy, Prematurity and Recurrent Wheezing in Early Childhood

PubMed Central

Robison, Rachel G; Kumar, Rajesh; Arguelles, Lester M; Hong, Xiumei; Wang, Guoying; Apollon, Stephanie; Bonzagni, Anthony; Ortiz, Kathryn; Pearson, Colleen; Pongracic, Jacqueline A; Wang, Xiaobin

2013-01-01

Summary Background Prenatal maternal smoking and prematurity independently affect wheezing and asthma in childhood. Objective We sought to evaluate the interactive effects of maternal smoking and prematurity upon the development of early childhood wheezing. Methods We evaluated 1448 children with smoke exposure data from a prospective urban birth cohort in Boston. Maternal antenatal and postnatal exposure was determined from standardized questionnaires. Gestational age was assessed by the first day of the last menstrual period and early prenatal ultrasound (preterm<37 weeks gestation). Wheezing episodes were determined from medical record extraction of well and ill/unscheduled visits. The primary outcome was recurrent wheezing, defined as ≥ 4 episodes of physician documented wheezing. Logistic regression models and zero inflated negative binomial regression (for number of episodes of wheeze) assessed the independent and joint association of prematurity and maternal antenatal smoking on recurrent wheeze, controlling for relevant covariates. Results In the cohort, 90 (6%) children had recurrent wheezing, 147 (10%) were exposed to in utero maternal smoke and 419 (29%) were premature. Prematurity (odds ratio [OR] 2.0; 95% CI, 1.3-3.1) was associated with an increased risk of recurrent wheezing, but in utero maternal smoking was not (OR 1.1, 95% CI 0.5-2.4). Jointly, maternal smoke exposure and prematurity caused an increased risk of recurrent wheezing (OR 3.8, 95% CI 1.8-8.0). There was an interaction between prematurity and maternal smoking upon episodes of wheezing (p=0.049). Conclusions We demonstrated an interaction between maternal smoking during pregnancy and prematurity on childhood wheezing in this urban, multiethnic birth cohort. PMID:22290763

Evaluation of real-world mobility in age-related macular degeneration.

PubMed

Sengupta, Sabyasachi; Nguyen, Angeline M; van Landingham, Suzanne W; Solomon, Sharon D; Do, Diana V; Ferrucci, Luigi; Friedman, David S; Ramulu, Pradeep Y

2015-01-30

Previous research has suggested an association between poor vision and decreased mobility, including restricted levels of physical activity and travel away from home. We sought to determine the impact of age-related macular degeneration (AMD) on these measures of mobility. Fifty-seven AMD patients with bilateral, or severe unilateral, visual impairment were compared to 59 controls with normal vision. All study subjects were between the ages of 60 and 80. Subjects wore accelerometers and cellular network-based tracking devices over 7 days of normal activity. Number of steps taken, time spent in moderate-to-vigorous physical activity (MVPA), number of excursions from home, and time spent away from home were the primary outcome measures. In multivariate negative binomial regression models adjusted for age, gender, race, comorbidities, and education, AMD participants took fewer steps than controls (18% fewer steps per day, p = 0.01) and spent significantly less time in MVPA (35% fewer minutes, p < 0.001). In multivariate logistic regression models adjusting for age, sex, race, cognition, comorbidities, and grip strength, AMD subjects showed an increased likelihood of not leaving their home on a given day (odds ratio = 1.36, p = 0.04), but did not show a significant difference in the magnitude of time spent away from home (9% fewer minutes, p = 0.11). AMD patients with poorer vision engage in significantly less physical activity and take fewer excursions away from the home. Further studies identifying the factors mediating the relationship between vision loss and mobility are needed to better understand how to improve mobility among AMD patients.

Powder Cocaine and Crack Use in the United States: An Examination of Risk for Arrest and Socioeconomic Disparities in Use

PubMed Central

Palamar, Joseph J.; Davies, Shelby; Ompad, Danielle C.; Cleland, Charles M.; Weitzman, Michael

2015-01-01

Background In light of the current sentencing disparity (18:1) between crack and powder cocaine possession in the United States, we examined socioeconomic correlates of use of each, and relations between use and arrest, to determine who may be at highest risk for arrest and imprisonment. Methods We conducted secondary data analyses on the National Survey on Drug Use and Health, 2009–2012. Data were analyzed for adults age ≥18 to determine associations between use and arrest. Socioeconomic correlates of lifetime and annual use of powder cocaine and of crack were delineated using multivariable logistic regression and correlates of frequency of recent use were examined using generalized negative binomial regression. Results Crack users were at higher risk than powder cocaine users for reporting a lifetime arrest or multiple recent arrests. Racial minorities were at low risk for powder cocaine use and Hispanics were at low risk for crack use. Blacks were at increased risk for lifetime and recent crack use, but not when controlling for other socioeconomic variables. However, blacks who did use either powder cocaine or crack tended to use at higher frequencies. Higher education and higher family income were negatively associated with crack use although these factors were sometimes risk factors for powder cocaine use. Conclusions Crack users are at higher risk of arrest and tend to be of lower socioeconomic status compared to powder cocaine users. These findings can inform US Congress as they review the proposed Smarter Sentencing Act of 2014, which would help eliminate cocaine-related sentencing disparities. PMID:25702933

Sonographic Differentiation of Complicated From Uncomplicated Appendicitis: Implications for Antibiotics-First Therapy.

PubMed

Xu, Yingding; Jeffrey, R Brooke; Chang, Stephanie T; DiMaio, Michael A; Olcott, Eric W

2017-02-01

To evaluate sonographic findings as indicators of complicated versus uncomplicated appendicitis in the setting of known appendicitis, a necessary distinction in deciding whether to proceed with antibiotic therapy or with appendectomy. With Institutional Review Board approval and Health Insurance Portability and Accountability Act compliance, appendiceal sonograms of 119 patients with histopathologically proven appendicitis were retrospectively blindly reviewed to determine the presence or absence of the normally echogenic submucosal layer, the presence of mural hyperemia, periappendiceal fluid, appendicoliths, and hyperechoic periappendiceal fat and to determine the maximum outside diameter. Results were compared with the presence of complicated versus uncomplicated appendicitis on histopathologic examination and assessed by both univariate and mulitvariate logistic regression; confidence intervals (CIs) of proportions were assessed by the exact binomial test. Thirty-two (26.9%) of the 119 patients had complicated appendicitis, including 11 with gangrenous appendicitis without perforation and 21 with gangrenous appendicitis and perforation. Loss of the submucosal layer was the only independent significant indicator of complicated appendicitis in multivariate regression (P < .001) and provided sensitivity and specificity values of 100.0% (95% CI, 89.1%-100.0%) and 92.0% (95% CI, 84.1%-96.7%), respectively. Loss of the normally echogenic submucosal layer was the most useful sonographic finding for discriminating complicated from uncomplicated appendicitis, being the only finding independently and significantly associated with complicated appendicitis and, additionally, providing both high sensitivity and high specificity. This information may help a physician decide whether to proceed with antibiotic therapy or with appendectomy when treating a patient with appendicitis. © 2016 by the American Institute of Ultrasound in Medicine.

Emotional Support, Negative Interaction and DSM IV Lifetime Disorders among Older African Americans: Findings from the National Survey of American Life (NSAL)

PubMed Central

Lincoln, Karen D.; Taylor, Robert Joseph; Bullard, Kai McKeever; Chatters, Linda M.; Himle, Joseph A.; Woodward, Amanda Toler; Jackson, James S.

2010-01-01

Objectives Both emotional support and negative interaction with family members have been linked to mental health. However, few studies have examined the associations between emotional support and negative interaction and psychiatric disorders in late life. This study investigated the relationship between emotional support and negative interaction on lifetime prevalence of mood and anxiety disorders among older African Americans. Design The analyses utilized the National Survey of American Life. Methods Logistic regression and negative binomial regression analyses were used to examine the effect of emotional support and negative interaction with family members on the prevalence of lifetime DSM-IV mood and anxiety disorders. Participants Data from 786 African Americans aged 55 years and older were used. Measurement The DSM-IV World Mental Health Composite International Diagnostic Interview (WMH-CIDI) was used to assess mental disorders. Three dependent variables were investigated: the prevalence of lifetime mood disorders, the prevalence of lifetime anxiety disorders, and the total number of lifetime mood and anxiety disorders. Results Multivariate analysis found that emotional support was not associated with any of the three dependent variables. Negative interaction was significantly and positively associated with the odds of having a lifetime mood disorder, a lifetime anxiety disorder and the number of lifetime mood and anxiety disorders. Conclusions This is the first study to investigate the relationship between emotional support, negative interaction with family members and psychiatric disorders among older African Americans. Negative interaction was a risk factor for mood and anxiety disorders among older African Americans, whereas emotional support was not significant. PMID:20157904

Animal Ownership and Touching Enrich the Context of Social Contacts Relevant to the Spread of Human Infectious Diseases.

PubMed

Kifle, Yimer Wasihun; Goeyvaerts, Nele; Van Kerckhove, Kim; Willem, Lander; Kucharski, Adam; Faes, Christel; Leirs, Herwig; Hens, Niel; Beutels, Philippe

2015-01-01

Many human infectious diseases originate from animals or are transmitted through animal vectors. We aimed to identify factors that are predictive of ownership and touching of animals, assess whether animal ownership influences social contact behavior, and estimate the probability of a major zoonotic outbreak should a transmissible influenza-like pathogen be present in animals, all in the setting of a densely populated European country. A diary-based social contact survey (n = 1768) was conducted in Flanders, Belgium, from September 2010 until February 2011. Many participants touched pets (46%), poultry (2%) or livestock (2%) on a randomly assigned day, and a large proportion of participants owned such animals (51%, 15% and 5%, respectively). Logistic regression models indicated that larger households are more likely to own an animal and, unsurprisingly, that animal owners are more likely to touch animals. We observed a significant effect of age on animal ownership and touching. The total number of social contacts during a randomly assigned day was modeled using weighted-negative binomial regression. Apart from age, household size and day type (weekend versus weekday and regular versus holiday period), animal ownership was positively associated with the total number of social contacts during the weekend. Assuming that animal ownership and/or touching are at-risk events, we demonstrate a method to estimate the outbreak potential of zoonoses. We show that in Belgium animal-human interactions involving young children (0-9 years) and adults (25-54 years) have the highest potential to cause a major zoonotic outbreak.

Food security among individuals experiencing homelessness and mental illness in the At Home/Chez Soi Trial.

PubMed

O'Campo, Patricia; Hwang, Stephen W; Gozdzik, Agnes; Schuler, Andrée; Kaufman-Shriqui, Vered; Poremski, Daniel; Lazgare, Luis Ivan Palma; Distasio, Jino; Belbraouet, Slimane; Addorisio, Sindi

2017-08-01

Individuals experiencing homelessness are particularly vulnerable to food insecurity. The At Home/Chez Soi study provides a unique opportunity to first examine baseline levels of food security among homeless individuals with mental illness and second to evaluate the effect of a Housing First (HF) intervention on food security in this population. At Home/Chez Soi was a 2-year randomized controlled trial comparing the effectiveness of HF compared with usual care among homeless adults with mental illness, stratified by level of need for mental health services (high or moderate). Logistic regressions tested baseline associations between food security (US Food Security Survey Module), study site, sociodemographic variables, duration of homelessness, alcohol/substance use, physical health and service utilization. Negative binomial regression determined the impact of the HF intervention on achieving levels of high or marginal food security over an 18-month follow-up period (6 to 24 months). Community settings at five Canadian sites (Moncton, Montreal, Toronto, Winnipeg and Vancouver). Homeless adults with mental illness (n 2148). Approximately 41 % of our sample reported high or marginal food security at baseline, but this figure varied with gender, age, mental health issues and substance use problems. High need participants who received HF were more likely to achieve marginal or high food security than those receiving usual care, but only at the Toronto and Moncton sites. Our large multi-site study demonstrated low levels of food security among homeless experiencing mental illness. HF showed promise for improving food security among participants with high levels of need for mental health services, with notable site differences.

Animal Ownership and Touching Enrich the Context of Social Contacts Relevant to the Spread of Human Infectious Diseases

PubMed Central

Kifle, Yimer Wasihun; Goeyvaerts, Nele; Van Kerckhove, Kim; Willem, Lander; Faes, Christel; Leirs, Herwig; Hens, Niel; Beutels, Philippe

2015-01-01

Many human infectious diseases originate from animals or are transmitted through animal vectors. We aimed to identify factors that are predictive of ownership and touching of animals, assess whether animal ownership influences social contact behavior, and estimate the probability of a major zoonotic outbreak should a transmissible influenza-like pathogen be present in animals, all in the setting of a densely populated European country. A diary-based social contact survey (n = 1768) was conducted in Flanders, Belgium, from September 2010 until February 2011. Many participants touched pets (46%), poultry (2%) or livestock (2%) on a randomly assigned day, and a large proportion of participants owned such animals (51%, 15% and 5%, respectively). Logistic regression models indicated that larger households are more likely to own an animal and, unsurprisingly, that animal owners are more likely to touch animals. We observed a significant effect of age on animal ownership and touching. The total number of social contacts during a randomly assigned day was modeled using weighted-negative binomial regression. Apart from age, household size and day type (weekend versus weekday and regular versus holiday period), animal ownership was positively associated with the total number of social contacts during the weekend. Assuming that animal ownership and/or touching are at-risk events, we demonstrate a method to estimate the outbreak potential of zoonoses. We show that in Belgium animal-human interactions involving young children (0–9 years) and adults (25–54 years) have the highest potential to cause a major zoonotic outbreak. PMID:26193480

Understanding poisson regression.

PubMed

Hayat, Matthew J; Higgins, Melinda

2014-04-01

Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.

Mixture models for estimating the size of a closed population when capture rates vary among individuals

USGS Publications Warehouse

Dorazio, R.M.; Royle, J. Andrew

2003-01-01

We develop a parameterization of the beta-binomial mixture that provides sensible inferences about the size of a closed population when probabilities of capture or detection vary among individuals. Three classes of mixture models (beta-binomial, logistic-normal, and latent-class) are fitted to recaptures of snowshoe hares for estimating abundance and to counts of bird species for estimating species richness. In both sets of data, rates of detection appear to vary more among individuals (animals or species) than among sampling occasions or locations. The estimates of population size and species richness are sensitive to model-specific assumptions about the latent distribution of individual rates of detection. We demonstrate using simulation experiments that conventional diagnostics for assessing model adequacy, such as deviance, cannot be relied on for selecting classes of mixture models that produce valid inferences about population size. Prior knowledge about sources of individual heterogeneity in detection rates, if available, should be used to help select among classes of mixture models that are to be used for inference.

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.

The relationship between non-communicable disease occurrence and poverty-evidence from demographic surveillance in Matlab, Bangladesh.

PubMed

Mirelman, Andrew J; Rose, Sherri; Khan, Jahangir Am; Ahmed, Sayem; Peters, David H; Niessen, Louis W; Trujillo, Antonio J

2016-07-01

In low-income countries, a growing proportion of the disease burden is attributable to non-communicable diseases (NCDs). There is little knowledge, however, of their impact on wealth, human capital, economic growth or household poverty. This article estimates the risk of being poor after an NCD death in the rural, low-income area of Matlab, Bangladesh. In a matched cohort study, we estimated the 2-year relative risk (RR) of being poor in Matlab households with an NCD death in 2010. Three separate measures of household economic status were used as outcomes: an asset-based index, self-rated household economic condition and total household landholding. Several estimation methods were used including contingency tables, log-binomial regression and regression standardization and machine learning. Households with an NCD death had a large and significant risk of being poor. The unadjusted RR of being poor after death was 1.19, 1.14 and 1.10 for the asset quintile, self-rated condition and landholding outcomes. Adjusting for household and individual level independent variables with log-binomial regression gave RRs of 1.19 [standard error (SE) 0.09], 1.16 (SE 0.07) and 1.14 (SE 0.06), which were found to be exactly the same using regression standardization (SE: 0.09, 0.05, 0.03). Machine learning-based standardization produced slightly smaller RRs though still in the same order of magnitude. The findings show that efforts to address the burden of NCD may also combat household poverty and provide a return beyond improved health. Future work should attempt to disentangle the mechanisms through which economic impacts from an NCD death occur. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

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.

Exposure to Environmental Air Manganese and Medication ...

EPA Pesticide Factsheets

Manganese (Mn) is an essential element with natural low levels found in water, food, and air, but due to industrialized processes, both workplace and the environmental exposures to Mn have increased. Recently, environmental studies have reported physical and mental health problems associated with air-Mn exposure, but medical record reviews for exposed residents are rare in the literature. When medical records and clinical testing are unavailable, examination of residents’ prescribed medication use may be used as a surrogate of health effects associated with Mn. We examined medication use among adult Ohio residents in two towns with elevated air-Mn (n=185) and one unexposed control town (n=90). Study participants recorded medication use in a health questionnaire and brought their currently prescribed medication, over-the-counter and supplement lists to their interview. Two physicians (family and psychiatric medicine) reviewed the provided medication list and developed medical categories associated with the medications used. The exposed (E) and control (C) groups were compared on the established 12 medication and 1 supplement categories using chi-square tests. The significant medication categories were further analyzed using hierarchical binomial logistic regression adjusting for education, personal income, and years of residency. The two groups were primarily white (E:94.6%; C:96.7%) but differed on education (E:13.8; C:15.2 years), residence length in their re

Association between parental access to paid sick leave and children's access to and use of healthcare services.

PubMed

Asfaw, Abay; Colopy, Maria

2017-03-01

We examined the association between parental access to paid sick leave (PPSL) and children's use of preventive care and reduced likelihood of delayed medical care and emergency room (ER) visits. We used the child sample of the National Health Interview Survey data (linked to the adult and family samples) from 2011 through 2015 and logistic and negative binomial regression models. Controlling for covariates, the odds of children with PPSL receiving flu vaccination were 12.5% [95%CI: 1.06-1.19] higher and receiving annual medical checkups were 13.2% [95%CI: 1.04-1.23] higher than those of children without PPSL. With PPSL, the odds of children receiving delayed medical care because of time mismatch were 13.3% [95%CI: 0.76-0.98] lower, and being taken to ER were 53.6% [95%CI: 0.27-0.81] lower than those of children without PPSL. PPSL was associated with 11% [95%CI: 0.82-0.97] fewer ER visits per year. PPSL may improve children's access and use of healthcare services and reduce the number of ER visits. Am. J. Ind. Med. 60:276-284, 2017. © 2017 Wiley Periodicals, Inc. © Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Health care utilization and receipt of preventive care for patients seen at federally funded health centers compared to other sites of primary care.

PubMed

Laiteerapong, Neda; Kirby, James; Gao, Yue; Yu, Tzy-Chyi; Sharma, Ravi; Nocon, Robert; Lee, Sang Mee; Chin, Marshall H; Nathan, Aviva G; Ngo-Metzger, Quyen; Huang, Elbert S

2014-10-01

To compare utilization and preventive care receipt among patients of federal Section 330 health centers (HCs) versus patients of other settings. A nationally representative sample of adults from the Medical Expenditure Panel Survey (2004-2008). HC patients were defined as those with ≥50 percent of outpatient visits at HCs in the first panel year. Outcomes included utilization and preventive care receipt from the second panel year. We used negative binomial and logistic regression models with propensity score adjustment for confounding differences between HC and non-HC patients. Compared to non-HC patients, HC patients had fewer office visits (adjusted incidence rate ratio [aIRR], 0.63) and hospitalizations (aIRR, 0.43) (both p < .001). HC patients were more likely to receive breast cancer screening than non-HC patients (adjusted odds ratio [aOR] 2.78, p < .01). In subgroup analyses, uninsured HC patients had fewer outpatient and emergency room visits and were more likely to receive dietary advice and breast cancer screening compared to non-HC patients. Health centers add value to the health care system by providing socially and medically disadvantaged patients with care that results in lower utilization and maintained or improved preventive care. © Health Research and Educational Trust.

Color Difference and Memory Recall in Free-Flying Honeybees: Forget the Hard Problem

PubMed Central

Dyer, Adrian G.; Garcia, Jair E.

2014-01-01

Free-flying honeybees acquire color information differently depending upon whether a target color is learnt in isolation (absolute conditioning), or in relation to a perceptually similar color (differential conditioning). Absolute conditioning allows for rapid learning, but color discrimination is coarse. Differential conditioning requires more learning trials, but enables fine discriminations. Currently it is unknown whether differential conditioning to similar colors in honeybees forms a long-term memory, and the stability of memory in a biologically relevant scenario considering similar or saliently different color stimuli. Individual free-flying honeybees (N = 6) were trained to similar color stimuli separated by 0.06 hexagon units for 60 trials and mean accuracy was 81.7% ± 12.2% s.d. Bees retested on subsequent days showed a reduction in the number of correct choices with increasing time from the initial training, and for four of the bees this reduction was significant from chance expectation considering binomially distributed logistic regression models. In contrast, an independent group of 6 bees trained to saliently different colors (>0.14 hexagon units) did not experience any decay in memory retention with increasing time. This suggests that whilst the bees’ visual system can permit fine discriminations, flowers producing saliently different colors are more easily remembered by foraging bees over several days. PMID:26462830

Smothering in UK free-range flocks. Part 2: investigating correlations between disease, housing and management practices.

PubMed

Rayner, A C; Gill, R; Brass, D; Willings, T H; Bright, A

2016-09-10

Smothering, when birds group together in a way that results in death from suffocation, is a welfare and economic concern for the egg industry. This questionnaire-based study explored correlations between disease, housing, management practices and smothering on free-range farms. A binomial logistic regression approach was used to test whether question responses predicted occurrence of nest box smothers (NBS) and panic and recurring smothers (PSRS) on farms. Breed (P=0.008) and nest box manufacturer (P=0.014) predicted NBS. Breed and nest box design have been previously reported to affect nesting behaviour. The affect of nest box manufacturer found in this study may illustrate the effect of nest box design features or house layouts. Nest box manufacturer (P=0.009), feeding oyster grit or grain on the litter (P<0.001) and range use on a sunny day (P<0.001) also predicted PSRS. Implementing some management practices to encourage desirable behaviours (eg ranging) may contribute to smothering, whereas some management practices such as those aimed at occupying birds may be beneficial, illustrating the delicate balance of factors involved in free-range egg production. It is hoped that these results will stimulate further work exploring the suitability of housing design and management of laying hens in light of smothering. British Veterinary Association.

Density of tobacco retail outlets near schools and smoking behaviour among secondary school students.

PubMed

Scully, Maree; McCarthy, Molly; Zacher, Meghan; Warne, Charles; Wakefield, Melanie; White, Victoria

2013-12-01

To investigate whether the density of tobacco retail outlets near schools in Victoria, Australia, is associated with adolescent smoking behaviour. Cross-sectional survey data of 2,044 secondary school students aged 12-17 years was combined with tobacco outlet audit data. Associations between students' self-reported tobacco use and the density of tobacco outlets near schools was examined using multilevel logistic and negative binomial regression models, with cigarette price at local milk bars and key socio-demographic and school-related variables included as covariates. Increased tobacco retail outlet density was associated with a significant increase in the number of cigarettes smoked in the previous seven days among students who smoked in the past month (IRR=1.13; 95% CI 1.02-1.26), but not the odds of smoking in the past month in the larger sample (OR=1.06; 95% CI 0.90-1.24), after controlling for local mean price of cigarettes and socio-demographic and school-related variables. This study suggests there is a positive association between tobacco retail outlet density and cigarette consumption among adolescent smokers, but not smoking prevalence, in the Australian context. There is value in considering policy measures that restrict the supply of tobacco retail outlets in school neighbourhoods as a means of reducing youth cigarette consumption.

Medical Complexity among Children with Special Health Care Needs: A Two-Dimensional View.

PubMed

Coller, Ryan J; Lerner, Carlos F; Eickhoff, Jens C; Klitzner, Thomas S; Sklansky, Daniel J; Ehlenbach, Mary; Chung, Paul J

2016-08-01

To identify subgroups of U.S. children with special health care needs (CSHCN) and characterize key outcomes. Secondary analysis of 2009-2010 National Survey of CSHCN. Latent class analysis grouped individuals into substantively meaningful classes empirically derived from measures of pediatric medical complexity. Outcomes were compared among latent classes with weighted logistic or negative binomial regression. LCA identified four unique CSHCN subgroups: broad functional impairment (physical, cognitive, and mental health) with extensive health care (Class 1), broad functional impairment alone (Class 2), predominant physical impairment requiring family-delivered care (Class 3), and physical impairment alone (Class 4). CSHCN from Class 1 had the highest ED visit rates (IRR 3.3, p < .001) and hospitalization odds (AOR: 12.0, p < .001) and lowest odds of a medical home (AOR: 0.17, p < .001). CSHCN in Class 3, despite experiencing more shared decision making and medical home attributes, had more ED visits and missed school than CSHCN in Class 2 (p < .001); the latter, however, experienced more cost-related difficulties, care delays, and parents having to stop work (p < .001). Recognizing distinct impacts of cognitive and mental health impairments and health care delivery needs on CSHCN outcomes may better direct future intervention efforts. © Health Research and Educational Trust.

The Structure of Co-Occurring Bullying Experiences and Associations with Suicidal Behaviors in Korean Adolescents

PubMed Central

Roh, Beop-Rae; Yoon, Yoewon; Kwon, Ahye; Oh, Seunga; Lee, Soyoung Irene; Ha, Kyunghee; Shin, Yun Mi; Song, Jungeun; Park, Eun Jin; Yoo, Heejung; Hong, Hyun Ju

2015-01-01

Objective This study had two main goals: to examine the structure of co-occurring peer bullying experiences among adolescents in South Korea from the perspective of victims and to determine the effects of bullying on suicidal behavior, including suicidal ideation and suicide attempts, among adolescents. Method This study used data gathered from 4,410 treatment-seeking adolescents at their initial visits to 31 local mental health centers in Gyeonggi Province, South Korea. The structure of peer bullying was examined using latent class analysis (LCA) to classify participants’ relevant experiences. Then, a binomial logistic regression adjusted by propensity scores was conducted to identify relationships between experiences of being bullied and suicidal behaviors. Results The LCA of experiences with bullying revealed two distinct classes of bullying: physical and non-physical. Adolescents who experienced physical bullying were 3.05 times more likely to attempt suicide than those who were not bullied. Victims of (non-physical) cyber bullying were 2.94 times more likely to attempt suicide than were those who were not bullied. Conclusions Both physical and non-physical bullying were associated with suicide attempts, with similar effect sizes. Schools and mental health professionals should be more attentive than they currently are to non-physical bullying. PMID:26619356

Household food insecurity during childhood and adolescent misconduct.

PubMed

Jackson, Dylan B; Vaughn, Michael G

2017-03-01

A large body of research has found that household food insecurity can interfere with the healthy development of children. The link between household food insecurity during childhood and misbehaviors during adolescence, however, is not commonly explored. The objective of the current study is to assess whether household food insecurity across childhood predicts four different forms of misconduct during early adolescence. Data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999 (ECLS-K), a nationally representative sample of U.S. children, were employed in the present study. Associations between household food insecurity during childhood and adolescent misconduct were examined using Logistic and Negative Binomial Regression. Analyses were performed separately for males and females. The results revealed that household food insecurity and food insecurity persistence were predictive of most forms of misconduct for males, and were consistently predictive of engagement in multiple forms of misconduct and a greater variety of forms of misconduct for males. For females, however, household food insecurity generally failed to predict adolescent misconduct. The behavioral development of males during adolescence appears to be sensitive to the presence and persistence of household food insecurity during childhood. Future research should seek to replicate and extend the present findings to late adolescence and adulthood. Copyright © 2017 Elsevier Inc. All rights reserved.

Nurse practitioner and physician assistant students' knowledge, attitudes, and perspectives of chiropractic.

PubMed

Bowden, Briana S; Ball, Lisa

2016-10-01

The purpose of this study was to assess nurse practitioner (NP) and physician assistant (PA) students' views of chiropractic. As the role of these providers progresses in primary care settings, providers' views and knowledge of chiropractic will impact interprofessional collaboration and patient outcomes. Understanding how NP and PA students perceive chiropractic may be beneficial in building integrative health care systems. This descriptive quantitative pilot study utilized a 56-item survey to examine attitudes, knowledge, and perspectives of NP and PA students in their 2nd year of graduate studies. Frequencies and binomial and multinomial logistic regression models were used to examine responses to survey totals. Ninety-two (97%) students completed the survey. There were conflicting results as to whether participants viewed chiropractic as mainstream or alternative. The majority of participants indicated lack of awareness regarding current scientific evidence for chiropractic and indicated a positive interest in learning more about the profession. Students who reported prior experience with chiropractic had higher attitude-positive responses compared to those without experience. Participants were found to have substantial knowledge deficits in relation to chiropractic treatments and scope of practice. The results of this study emphasize the need for increased integrative initiatives and chiropractic exposure in NP and PA education to enhance future interprofessional collaboration in health care.

The relationship between employment and veteran status, disability and gender from 2004-2011 Behavioral Risk Factor Surveillance System (BRFSS).

PubMed

Smith, Diane L

2014-01-01

In 2011, about 1.8 million or 8 percent of the 22.2 million veterans were women in the US. The unemployment rate for female veterans of the wars in Iraq and Afghanistan rose to 13.5%, above the 8.4% for non-veteran adult women. To examine data from the Behavioral Risk Factor Surveillance System (BRFSS), from 2004-2011 to determine the relationship between employment and veteran status, disability and gender. Chi square analysis was used to determine if significant differences existed between the employment rate of female veterans with disabilities and female veterans without disabilities, female non-veterans with disabilities and male veterans with disabilities. Binomial logistic regression analysis was used to determine how veteran status, disability and gender affected the likelihood of not being employed. Significant differences were found in employment rate between female veterans with disabilities and female veterans without disabilities, but not when compared to female non-veterans with disabilities or male veterans with disabilities. Disability was the strongest factor increasing the likelihood of not being employed, though veteran status and female gender were also predictive. Female veterans with disabilities experience low levels of employment. Policies and programs are needed to address the unique needs of these veterans.

An examination of variables which influence high school students to enroll in an undergraduate engineering or physical science major

NASA Astrophysics Data System (ADS)

Porter, Christopher H.

The purpose of this study was to examine the variables which influence a high school student to enroll in an engineering discipline versus a physical science discipline. Data was collected utilizing the High School Activities, Characteristics, and Influences Survey, which was administered to students who were freshmen in an engineering or physical science major at an institution in the Southeastern United States. A total of 413 students participated in the survey. Collected data were analyzed using descriptive statistics, two-sample Wilcoxon tests, and binomial logistic regression techniques. A total of 29 variables were deemed significant between the general engineering and physical science students. The 29 significant variables were further analyzed to see which have an independent impact on a student to enroll in an undergraduate engineering program, as opposed to an undergraduate physical science program. Four statistically significant variables were found to have an impact on a student's decision to enroll in a engineering undergraduate program versus a physical science program: father's influence, participation in Project Lead the Way, and the subjects of mathematics and physics. Recommendations for theory, policy, and practice were discussed based on the results of the study. This study presented suggestions for developing ways to attract, educate, and move future engineers into the workforce.

Occurrence of human respiratory syncytial virus in summer in Japan.

PubMed

Shobugawa, Y; Takeuchi, T; Hibino, A; Hassan, M R; Yagami, R; Kondo, H; Odagiri, T; Saito, R

2017-01-01

In temperate zones, human respiratory syncytial virus (HRSV) outbreaks typically occur in cold weather, i.e. in late autumn and winter. However, recent outbreaks in Japan have tended to start during summer and autumn. This study examined associations of meteorological conditions with the numbers of HRSV cases reported in summer in Japan. Using data from the HRSV national surveillance system and national meteorological data for summer during the period 2007-2014, we utilized negative binomial logistic regression analysis to identify associations between meteorological conditions and reported cases of HRSV. HRSV cases increased when summer temperatures rose and when relative humidity increased. Consideration of the interaction term temperature × relative humidity enabled us to show synergistic effects of high temperature with HRSV occurrence. In particular, HRSV cases synergistically increased when relative humidity increased while the temperature was ⩾28·2 °C. Seasonal-trend decomposition analysis using the HRSV national surveillance data divided by 11 climate divisions showed that summer HRSV cases occurred in South Japan (Okinawa Island), Kyushu, and Nankai climate divisions, which are located in southwest Japan. Higher temperature and higher relative humidity were necessary conditions for HRSV occurrence in summer in Japan. Paediatricians in temperate zones should be mindful of possible HRSV cases in summer, when suitable conditions are present.

Influences on physicians' adoption of electronic detailing (e-detailing).

PubMed

Alkhateeb, Fadi M; Doucette, William R

2009-01-01

E-detailing means using digital technology: internet, video conferencing and interactive voice response. There are two types of e-detailing: interactive (virtual) and video. Currently, little is known about what factors influence physicians' adoption of e-detailing. The objectives of this study were to test a model of physicians' adoption of e-detailing and to describe physicians using e-detailing. A mail survey was sent to a random sample of 2000 physicians practicing in Iowa. Binomial logistic regression was used to test the model of influences on physician adoption of e-detailing. On the basis of Rogers' model of adoption, the independent variables included relative advantage, compatibility, complexity, peer influence, attitudes, years in practice, presence of restrictive access to traditional detailing, type of specialty, academic affiliation, type of practice setting and control variables. A total of 671 responses were received giving a response rate of 34.7%. A total of 141 physicians (21.0%) reported using of e-detailing. The overall adoption model for using either type of e-detailing was found to be significant. Relative advantage, peer influence, attitudes, type of specialty, presence of restrictive access and years of practice had significant influences on physician adoption of e-detailing. The model of adoption of innovation is useful to explain physicians' adoption of e-detailing.

Suicide Risk across Latent Class Subgroups: A Test of the Generalizability of the Interpersonal Psychological Theory of Suicide.

PubMed

Ma, Jennifer S; Batterham, Philip J; Calear, Alison L; Han, Jin

2018-01-06

It remains unclear whether the Interpersonal Psychological Theory of Suicide (IPTS; Joiner, ) is generalizable to the population or holds more explanatory power for certain subgroups compared to others. The aim of this study was to (1) identify subgroups of individuals who endorsed suicide ideation in the past month based on a range of mental health and demographic variables, (2) compare levels of the IPTS constructs within these subgroups, and (3) test the IPTS predictions for suicide ideation and suicide attempt for each group. Latent class, negative binomial, linear, and logistic regression analyses were conducted on population-based data obtained from 1,321 adults recruited from Facebook. Among participants reporting suicide ideation, four distinct patterns of risk factors emerged based on age and severity of mental health symptoms. Groups with highly elevated mental health symptoms reported the highest levels of thwarted belongingness and perceived burdensomeness. Tests of the IPTS interactions provided partial support for the theory, primarily in young adults with elevated mental health symptoms. Lack of support found for the IPTS predictions across the subgroups and full sample in this study raise some questions around the broad applicability of the theory. © 2018 The American Association of Suicidology.

[Impact of level of physical activity on healthcare utilization among Korean adults].

PubMed

Kim, Jiyun; Park, Seungmi

2012-04-01

This study was done to identify the impact of physical activity on healthcare utilization among Korean adults. Drawing from the 2008 Korean National Health and Nutrition Examination Survey (NHANES IV-2), data from 6,521 adults who completed the Health Interview and Health Behavior Surveys were analyzed. Association between physical activity and healthcare utilization was tested using the χ²-test. Multiple logistic regression analysis was used to calculate the odds ratios of using outpatient and inpatient healthcare for different levels of physical activity after adjusting for predisposing, enabling, and need factors. A generalized linear model applying a negative binomial distribution was used to determine how the level of physical activity was related to use of outpatient and inpatient healthcare. Physically active participants were 16% less likely to use outpatient healthcare (OR, 0.84; 95% CI, 0.74-0.97) and 23% less likely to use inpatient healthcare (OR, 0.77; 95% CI, 0.63-0.93) than physically inactive participants. Levels of outpatient and inpatient healthcare use decreased as levels of physical activity increased, after adjusting for relevant factors. An independent association between being physically active and lower healthcare utilization was ascertained among Korean adults indicating a need to develop nursing intervention programs that encourage regular physical activity.

Color Difference and Memory Recall in Free-Flying Honeybees: Forget the Hard Problem.

PubMed

Dyer, Adrian G; Garcia, Jair E

2014-07-30

Free-flying honeybees acquire color information differently depending upon whether a target color is learnt in isolation (absolute conditioning), or in relation to a perceptually similar color (differential conditioning). Absolute conditioning allows for rapid learning, but color discrimination is coarse. Differential conditioning requires more learning trials, but enables fine discriminations. Currently it is unknown whether differential conditioning to similar colors in honeybees forms a long-term memory, and the stability of memory in a biologically relevant scenario considering similar or saliently different color stimuli. Individual free-flying honeybees (N = 6) were trained to similar color stimuli separated by 0.06 hexagon units for 60 trials and mean accuracy was 81.7% ± 12.2% s.d. Bees retested on subsequent days showed a reduction in the number of correct choices with increasing time from the initial training, and for four of the bees this reduction was significant from chance expectation considering binomially distributed logistic regression models. In contrast, an independent group of 6 bees trained to saliently different colors (>0.14 hexagon units) did not experience any decay in memory retention with increasing time. This suggests that whilst the bees' visual system can permit fine discriminations, flowers producing saliently different colors are more easily remembered by foraging bees over several days.

Relationship of Dropout and Psychopathology in a High School Sample in Mexico

PubMed Central

Chalita, Pablo J.; Palacios, Lino; Cortes, Jose F.; Landeros-Weisenberger, Angeli; Panza, Kaitlyn E.; Bloch, Michael H.

2012-01-01

School dropout has significant consequences for both individuals and societies. Only 21% of adults in Mexico achieve the equivalent of a high school education. We examined the relationship between school dropout and self-reported psychiatric symptoms in a middle school in a suburb of Mexico City. We used binomial logistic regression to examine the odds ratio (OR) of school dropout associated with students’ self-reported psychopathology. Two-hundred thirty-seven students participated in the study. Psychosis [OR = 8.0 (95% confidence interval, CI: 1.7–37.2)], depression [OR = 4.7 (95% CI: 2.2–9.7)], tic disorders [OR = 3.7 (95% CI: 1.4–9.5)], ADHD [OR = 3.2 (95% CI: 1.5–6.4)], and social phobia [OR = 2.6 (95% CI: 1.2–5.8)] were associated with increased risk of school dropout after controlling for age and gender as covariates. Our study suggested that students’ self-reported psychopathology is associated with increased school dropout in Mexico. ADHD and depression may be particularly useful childhood psychiatric disorders to target with public health interventions because they explain the greatest amount of the variance in school dropout of child psychiatric disorders. PMID:22419912

Walking or bicycling to school and weight status among adolescents from Montería, Colombia.

PubMed

Arango, Carlos Mario; Parra, Diana C; Eyler, Amy; Sarmiento, Olga; Mantilla, Sonia C; Gomez, Luis Fernando; Lobelo, Felipe

2011-09-01

Active school transport (AST) is a recommended strategy to promote physical activity (PA) and prevent overweight (OW) in school-aged children. In many developing countries, such as Colombia, this association has not been well characterized. To determine the association between AST and weight status in a representative sample of adolescents from Montería, Colombia. Participants were 546 adolescents (278 boys) aged 11 to 18 years old from 14 randomly selected schools in Montería, Colombia in 2008. The PA module of the Global School Health Survey (GSHS-2007) was used to determine the prevalence of AST. To identify OW, participants were classified according to CDC 2000 criteria (BMI ≥ 85th percentile). Association between AST and OW was determined by binomial logistic regression. Odds ratios adjusted for age, sex, location of school, compliance with PA, and screen time recommendations showed that adolescents who reported AST had a significantly lower likelihood to be OW compared with adolescents who reported nonactive transportation (OR = 0.5, 95% CI 0.3-0.8, P < .05). These results support the importance of AST as a useful PA domain with potential implications for overweight prevention, in rapidly developing settings. Further epidemiologic and intervention studies addressing AST are needed in the region.

Need for recovery from work and sleep-related complaints among nursing professionals.

PubMed

Silva-Costa, Aline; Griep, Rosane Harter; Fischer, Frida Marina; Rotenberg, Lúcia

2012-01-01

The concept of need for recovery from work (NFR) was deduced from the effort recuperation model. In this model work produces costs in terms of effort during the working day. When there is enough time and possibilities to recuperate, a worker will arrive at the next working day with no residual symptoms of previous effort. NFR evaluates work characteristics such as psychosocial demands, professional work hours or schedules. However, sleep may be an important part of the recovery process. The aim of the study was to test the association between sleep-related complaints and NFR. A cross-sectional study was carried out at three hospitals. All females nursing professionals engaged in assistance to patients were invited to participate (N = 1,307). Participants answered a questionnaire that included four sleep-related complaints (insomnia, unsatisfactory sleep, sleepiness during work hours and insufficient sleep), work characteristics and NRF scale. Binomial logistic regression analysis showed that all sleep-related complaints are associated with a high need for recovery from work. Those who reported insufficient sleep showed a greater chance of high need for recovery; OR=2.730 (CI 95% 2.074 - 3.593). These results corroborate the hypothesis that sleep is an important aspect of the recovery process and, therefore, should be thoroughly investigated.

Association between Q192R paraoxonase 1 polymorphism and serumadipocyte-fatty acid binding protein (FABP4) levels in Mexican women.

PubMed

Ochoa-Martínez, Ángeles C; Ruíz-Vera, Tania; Orta-García, Sandra T; Domínguez-Cortinas, Gabriela; Jiménez-Avalos, Jorge A; Pérez-Maldonado, Iván N

2017-06-01

This study aimed to evaluate the genetic effects of PON1 Q192R polymorphism on serum FABP4 levels in Mexican women. PON1 Q192R polymorphism was genotyped using a TaqMan allelic discrimination assay and serum FABP4 concentration was measured using an enzyme-linked immunosorbent assay. The distribution of genotype frequencies in the assessed women (PON1 Q192R polymorphism) was QQ = 20%, QR = 48% and RR = 32%. Significantly higher serum FABP4 levels were found in women with genotype QR/RR (20.6 ± 2.20 ng/mL), when compared with the levels found in the QQ group (12.8 ± 1.70 ng/mL) (p = .004). After, the odds ratio (OR) was calculated by binomial logistic regression analysis and a significantly higher OR was found in the QR/RR group when compared with the QQ group (OR = 3.45; 95% CI = 1.80-16.50; p < .05). The results support an association between 192R-allele of the PON1 polymorphism (Q192R) and increased serum FABP4 levels (suggested as an early biomarker of CVDs risk) in assessed Mexican women.

The Impact of Obesity and Diabetes on the Risk of Disease and Death due to Invasive Group A Streptococcus Infections in Adults.

PubMed

Langley, Gayle; Hao, Yongping; Pondo, Tracy; Miller, Lisa; Petit, Susan; Thomas, Ann; Lindegren, Mary Louise; Farley, Monica M; Dumyati, Ghinwa; Como-Sabetti, Kathryn; Harrison, Lee H; Baumbach, Joan; Watt, James; Van Beneden, Chris

2016-04-01

Invasive group A Streptococcus (iGAS) infections cause significant morbidity and mortality worldwide. We analyzed whether obesity and diabetes were associated with iGAS infections and worse outcomes among an adult US population. We determined the incidence of iGAS infections using 2010-2012 cases in adults aged ≥ 18 years from Active Bacterial Core surveillance (ABCs), a population-based surveillance system, as the numerator. For the denominator, we used ABCs catchment area population estimates from the 2011 to 2012 Behavioral Risk Factor Surveillance System (BRFSS) survey. The relative risk (RR) of iGAS was determined by obesity and diabetes status after adjusting for age group, gender, race, and other underlying conditions through binomial logistic regression. Multivariable logistic regression was used to determine whether obesity or diabetes was associated with increased odds of death due to iGAS compared to normal weight and nondiabetic patients, respectively. Between 2010 and 2012, 2927 iGAS cases were identified. Diabetes was associated with an increased risk of iGAS in all racial groups (adjusted risk ratio [aRR] ranged from 2.71 to 5.08). Grade 3 obesity (body mass index [BMI] ≥ 40) was associated with an increased risk of iGAS for whites (aRR = 3.47; 95% confidence interval [CI], 3.00-4.01). Grades 1-2 (BMI = 30.0-<40.0) and grade 3 obesity were associated with an increased odds of death (odds ratio [OR] = 1.55, [95% CI, 1.05, 2.29] and OR = 1.62 [95% CI, 1.01, 2.61], respectively) when compared to normal weight patients. These results may help target vaccines against GAS that are currently under development. Efforts to develop enhanced treatment regimens for iGAS may improve prognoses for obese patients. Published by Oxford University Press for the Infectious Diseases Society of America 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Association between physiological falls risk and physical performance tests among community-dwelling older adults.

PubMed

Singh, Devinder K A; Pillai, Sharmila G K; Tan, Sin Thien; Tai, Chu Chiau; Shahar, Suzana

2015-01-01

Physical performance and balance declines with aging and may lead to increased risk of falls. Physical performance tests may be useful for initial fall-risk screening test among community-dwelling older adults. Physiological profile assessment (PPA), a composite falls risk assessment tool is reported to have 75% accuracy to screen for physiological falls risk. PPA correlates with Timed Up and Go (TUG) test. However, the association between many other commonly used physical performance tests and PPA is not known. The aim of the present study was to examine the association between physiological falls risk measured using PPA and a battery of physical performance tests. One hundred and forty older adults from a senior citizens club in Kuala Lumpur, Malaysia (94 females, 46 males), aged 60 years and above (65.77±4.61), participated in this cross-sectional study. Participants were screened for falls risk using PPA. A battery of physical performance tests that include ten-step test (TST), short physical performance battery (SPPB), functional reach test (FRT), static balance test (SBT), TUG, dominant hand-grip strength (DHGS), and gait speed test (GST) were also performed. Spearman's rank correlation and binomial logistic regression were performed to examine the significantly associated independent variables (physical performance tests) with falls risk (dependent variable). Approximately 13% older adults were at high risk of falls categorized using PPA. Significant differences (P<0.05) were demonstrated for age, TST, SPPB, FRT, SBT, TUG between high and low falls risk group. A significant (P<0.01) weak correlation was found between PPA and TST (r=0.25), TUG (r=0.27), SBT (r=0.23), SPPB (r=-0.33), and FRT (r=-0.23). Binary logistic regression results demonstrated that SBT measuring postural sways objectively using a balance board was the only significant predictor of physiological falls risk (P<0.05, odds ratio of 2.12). The reference values of physical performance tests in our study may be used as a guide for initial falls screening to categorize high and low physiological falls risk among community-dwelling older adults. A more comprehensive assessment of falls risk can be performed thereafter for more specific intervention of underlying impairments.

Postnatal Depression among Rural Women in South India: Do Socio-Demographic, Obstetric and Pregnancy Outcome Have a Role to Play?

PubMed Central

Shivalli, Siddharudha; Gururaj, Nandihal

2015-01-01

Introduction Postnatal depression (PND) is one of the most common psychopathology and is considered as a serious public health issue because of its devastating effects on mother, family, and infant or the child. Objective To elicit socio-demographic, obstetric and pregnancy outcome predictors of Postnatal Depression (PND) among rural postnatal women in Karnataka state, India. Design Hospital based analytical cross sectional study Setting A rural tertiary care hospital of Mandya District, Karnataka state, India. Sample PND prevalence based estimated sample of 102 women who came for postnatal follow up from 4th to 10th week of lactation. Method Study participants were interviewed using validated kannada version of Edinburgh Postnatal Depression Scale (EPDS). Cut-off score of ≥13 was used as high risk of PND. The percentage of women at risk of PND was estimated, and differences according to socio-demographic, obstetric and pregnancy outcome were described. Logistic regression was applied to identify the independent predictors of PND risk. Main Outcome Measures Prevalence, Odds ratio (OR) and adjusted (adj) OR of PND Results Prevalence of PND was 31.4% (95% CI 22.7–41.4%). PND showed significant (P<0.05) association with joint family, working women, non-farmer husbands, poverty, female baby and pregnancy complications or known medical illness. In binomial logistic regression poverty (adjOR: 11.95, 95% CI:1.36–105), birth of female baby (adjOR: 3.6, 95% CI:1.26–10.23) and pregnancy complications or known medical illness (adjOR: 17.4, 95% CI:2.5–121.2) remained as independent predictors of PND. Conclusion Risk of PND among rural postnatal women was high (31.4%). Birth of female baby, poverty and complications in pregnancy or known medical illness could predict the high risk of PND. PND screening should be an integral part of postnatal care. Capacity building of grass root level workers and feasibility trials for screening PND by them are needed. PMID:25848761

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.

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…

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.

Modelling infant mortality rate in Central Java, Indonesia use generalized poisson regression method

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Sudarno

2018-05-01

The infant mortality rate is the number of deaths under one year of age occurring among the live births in a given geographical area during a given year, per 1,000 live births occurring among the population of the given geographical area during the same year. This problem needs to be addressed because it is an important element of a country’s economic development. High infant mortality rate will disrupt the stability of a country as it relates to the sustainability of the population in the country. One of regression model that can be used to analyze the relationship between dependent variable Y in the form of discrete data and independent variable X is Poisson regression model. Recently The regression modeling used for data with dependent variable is discrete, among others, poisson regression, negative binomial regression and generalized poisson regression. In this research, generalized poisson regression modeling gives better AIC value than poisson regression. The most significant variable is the Number of health facilities (X1), while the variable that gives the most influence to infant mortality rate is the average breastfeeding (X9).

Maternal Early Life Factors Associated with Hormone Levels and the Risk of Having a Child with an Autism Spectrum Disorder in the Nurses Health Study II

ERIC Educational Resources Information Center

Lyall, Kristen; Pauls, David L.; Santangelo, Susan; Spiegelman, Donna; Ascherio, Alberto

2011-01-01

It is not known whether reproductive factors early in the mother's life influence risk of autism spectrum disorders (ASD). We assessed maternal age at menarche, menstrual cycle characteristics during adolescence, oral contraceptive use prior to first birth, body shape, and body mass index (BMI) in association with ASD using binomial regression in…

Oral health of schoolchildren in Western Australia.

PubMed

Arrow, P

2016-09-01

The West Australian School Dental Service (SDS) provides free, statewide, primary dental care to schoolchildren aged 5-17 years. This study reports on an evaluation of the oral health of children examined during the 2014 calendar year. Children were sampled, based on their date of birth, and SDS clinicians collected the clinical information. Weighted mean values of caries experience were presented. Negative binomial regression modelling was undertaken to test for factors of significance in the rate of caries occurrence. Data from children aged 5-15 years were used (girls = 4616, boys = 4900). Mean dmft (5-10-year-olds), 1.42 SE 0.03; mean DMFT (6-15-year-olds), 0.51 SE 0.01. Negative binomial regression model of permanent tooth caries found higher rates of caries in children who were from non-fluoridated areas (RR 2.1); Aboriginal (RR 2.4); had gingival inflammation (RR 1.5); lower ICSEA level (RR 1.4); and recalled at more than 24-month interval (RR 1.8). The study highlighted poor dental health associated with living in non-fluoridated areas, Aboriginal identity, poor oral hygiene, lower socioeconomic level and having extended intervals between dental checkups. Timely assessments and preventive measures targeted at groups, including extending community water fluoridation, may assist in further improving the oral health of children in Western Australia. © 2015 Australian Dental Association.

Recent patterns in antibiotic use for children with group A streptococcal infections in Japan.

PubMed

Okubo, Yusuke; Michihata, Nobuaki; Morisaki, Naho; Kinoshita, Noriko; Miyairi, Isao; Urayama, Kevin Y; Yasunaga, Hideo

2017-11-13

Antibiotics are the most frequently prescribed medicines for children, however inappropriate antibiotic prescribing is prevalent. This study investigated recent trends in antibiotic use and factors associated with appropriate antibiotic selection among children with group A streptococcal infections in Japan. Records of outpatients aged <18years with a diagnosis of group A streptococcal infection were obtained using the Japan Medical Data Center database. Prescription patterns for antibiotics were investigated and factors associated with penicillin use were evaluated using a multivariable log-binomial regression model. Overall, 5030 patients with a diagnosis of group A streptococcal infection were identified. The most commonly prescribed antibiotics were third-generation cephalosporins (53.3%), followed by penicillins (40.1%). In the multivariable log-binomial regression analysis, out-of-hours visits were independently associated with penicillin prescriptions [prevalence ratio (PR)=1.10, 95% confidence interval (CI) 1.03-1.18], whereas clinical departments other than paediatrics and internal medicine were related to non-penicillin prescriptions (PR=0.57, 95% CI 0.46-0.71). Third-generation cephalosporins were overprescribed for children with group A streptococcal infections. This investigation provides important information for promoting education for physicians and for constructing health policies for appropriate antibiotic prescription. Copyright © 2017. Published by Elsevier Ltd.

Community covariates of malnutrition based mortality among older adults.

PubMed

Lee, Matthew R; Berthelot, Emily R

2010-05-01

The purpose of this study was to identify community level covariates of malnutrition-based mortality among older adults. A community level framework was delineated which explains rates of malnutrition-related mortality among older adults as a function of community levels of socioeconomic disadvantage, disability, and social isolation among members of this group. County level data on malnutrition mortality of people 65 years of age and older for the period 2000-2003 were drawn from the CDC WONDER system databases. County level measures of older adult socioeconomic disadvantage, disability, and social isolation were derived from the 2000 US Census of Population and Housing. Negative binomial regression models adjusting for the size of the population at risk, racial composition, urbanism, and region were estimated to assess the relationships among these indicators. Results from negative binomial regression analysis yielded the following: a standard deviation increase in socioeconomic/physical disadvantage was associated with a 12% increase in the rate of malnutrition mortality among older adults (p < 0.001), whereas a standard deviation increase in social isolation was associated with a 5% increase in malnutrition mortality among older adults (p < 0.05). Community patterns of malnutrition based mortality among older adults are partly a function of levels of socioeconomic and physical disadvantage and social isolation among older adults. 2010 Elsevier Inc. All rights reserved.

Association between adherence to physical activity guidelines and health-related quality of life among individuals with physician-diagnosed arthritis.

PubMed

Austin, Shamly; Qu, Haiyan; Shewchuk, Richard M

2012-10-01

To examine the association between adherence to physical activity guidelines and health-related quality of life (HRQOL) among individuals with arthritis. A cross-sectional sample with 33,071 US adults, 45 years or older with physician-diagnosed arthritis was obtained from 2007 Behavioral Risk Factor Surveillance System survey. We conducted negative binomial regression analysis to examine HRQOL as a function of adherence to physical activity guidelines controlling for physicians' recommendations for physical activity, age, sex, race, education, marital status, employment, annual income, health insurance, personal physician, emotional support, body mass index, activity limitations, health status, and co-morbidities based on Behavioral Model of Health Services Utilization. Descriptive statistics showed that 60% adults with arthritis did not adhere to physical activity guidelines, mean physically and mentally unhealthy days were 7.7 and 4.4 days, respectively. Results from negative binomial regression indicated that individuals who did not adhere to physical activity guidelines had 1.14 days more physically unhealthy days and 1.12 days more mentally unhealthy days than those who adhered controlling for covariates. Adherence to physical activity is important to improve HRQOL for individuals with arthritis. However, adherence is low among this population. Interventions are required to engage individuals with arthritis in physical activity.

Factors Associated with Hospital Length of Stay among Cancer Patients with Febrile Neutropenia

PubMed Central

Rosa, Regis G.; Goldani, Luciano Z.

2014-01-01

Purpose This study sought to evaluate factors associated with hospital length of stay in cancer patients with febrile neutropenia. Methods A prospective cohort study was performed at a single tertiary referral hospital in southern Brazil from October 2009 to August 2011. All adult cancer patients with febrile neutropenia admitted to the hematology ward were evaluated. Stepwise random-effects negative binomial regression was performed to identify risk factors for prolonged length of hospital stay. Results In total, 307 cases of febrile neutropenia were evaluated. The overall median length of hospital stay was 16 days (interquartile range 18 days). According to multiple negative binomial regression analysis, hematologic neoplasms (P = 0.003), high-dose chemotherapy regimens (P<0.001), duration of neutropenia (P<0.001), and bloodstream infection involving Gram-negative multi-drug-resistant bacteria (P = 0.003) were positively associated with prolonged hospital length of stay in patients with febrile neutropenia. The condition index showed no evidence of multi-collinearity effect among the independent variables. Conclusions Hematologic neoplasms, high-dose chemotherapy regimens, prolonged periods of neutropenia, and bloodstream infection with Gram-negative multi-drug-resistant bacteria are predictors of prolonged length hospital of stay among adult cancer patients with febrile neutropenia. PMID:25285790

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.

Impact of health portal enrollment with email reminders on adherence to clinic appointments: a pilot study.

PubMed

Horvath, Monica; Levy, Janet; L'Engle, Pete; Carlson, Boyd; Ahmad, Asif; Ferranti, Jeffrey

2011-05-26

Internet portal technologies that provide access to portions of electronic health records have the potential to revolutionize patients' involvement in their care. However, relatively few descriptions of the demographic characteristics of portal enrollees or of the effects of portal technology on quality outcomes exist. This study examined data from patients who attended one of seven Duke Medicine clinics and who were offered the option of enrolling in and using the Duke Medicine HealthView portal (HVP). The HVP allows patients to manage details of their appointment scheduling and provides automated email appointment reminders in addition to the telephone and mail reminders that all patients receive. Our objective was to test whether portal enrollment with an email reminder functionality is significantly related to decreases in rates of appointment "no-shows," which are known to impair clinic operational efficiency. Appointment activity during a 1-year period was examined for all patients attending one of seven Duke Medicine clinics. Patients were categorized as portal enrollees or as nonusers either by their status at time of appointment or at the end of the 1-year period. Demographic characteristics and no-show rates among these groups were compared. A binomial logistic regression model was constructed to measure the adjusted impact of HVP enrollment on no-show rates, given confounding factors. To demonstrate the effect of HVP use over time, monthly no-show rates were calculated for patient appointment keeping and contrasted between preportal and postportal deployment periods. Across seven clinics, 58,942 patients, 15.7% (9239/58,942) of whom were portal enrollees, scheduled 198,199 appointments with an overall no-show rate of 9.9% (19,668/198,199). We found that HVP enrollees were significantly more likely to be female, white, and privately insured compared with nonusers. Bivariate no-show rate differences between portal enrollment groups varied widely according to patient- and appointment-level attributes. Large reductions in no-show rates were seen among historically disadvantaged groups: Medicaid holders (OR = 2.04 for nonuser/enrollee, 5.6% difference, P < .001), uninsured patients (OR = 2.60, 12.8% difference, P < .001), and black patients (OR = 2.13, 8.0% difference, P < .001). After fitting a binomial logistic regression model for the outcome of appointment arrival, the adjusted odds of arrival increased 39.0% for portal enrollees relative to nonusers (OR = 1.39, 95% CI 1.22 - 1.57, P < .001). Analysis of monthly no-show rates over 2 years demonstrated that patients who registered for portal access and received three reminders of upcoming appointments (email, phone, and mail) had a 2.0% no-show rate reduction (P < .001), whereas patients who did not enroll and only received traditional phone and mail reminders saw no such reduction (P < .09). Monthly no-show rates across all seven Duke Medicine clinics were significantly reduced among patients who registered for portal use, suggesting that in combination with an email reminder feature, this technology may have an important and beneficial effect on clinic operations.

Impact of Health Portal Enrollment With Email Reminders on Adherence to Clinic Appointments: A Pilot Study

PubMed Central

Levy, Janet; L'Engle, Pete; Carlson, Boyd; Ahmad, Asif; Ferranti, Jeffrey

2011-01-01

Background Internet portal technologies that provide access to portions of electronic health records have the potential to revolutionize patients’ involvement in their care. However, relatively few descriptions of the demographic characteristics of portal enrollees or of the effects of portal technology on quality outcomes exist. This study examined data from patients who attended one of seven Duke Medicine clinics and who were offered the option of enrolling in and using the Duke Medicine HealthView portal (HVP). The HVP allows patients to manage details of their appointment scheduling and provides automated email appointment reminders in addition to the telephone and mail reminders that all patients receive. Objective Our objective was to test whether portal enrollment with an email reminder functionality is significantly related to decreases in rates of appointment “no-shows,” which are known to impair clinic operational efficiency. Methods Appointment activity during a 1-year period was examined for all patients attending one of seven Duke Medicine clinics. Patients were categorized as portal enrollees or as nonusers either by their status at time of appointment or at the end of the 1-year period. Demographic characteristics and no-show rates among these groups were compared. A binomial logistic regression model was constructed to measure the adjusted impact of HVP enrollment on no-show rates, given confounding factors. To demonstrate the effect of HVP use over time, monthly no-show rates were calculated for patient appointment keeping and contrasted between preportal and postportal deployment periods. Results Across seven clinics, 58,942 patients, 15.7% (9239/58,942) of whom were portal enrollees, scheduled 198,199 appointments with an overall no-show rate of 9.9% (19,668/198,199). We found that HVP enrollees were significantly more likely to be female, white, and privately insured compared with nonusers. Bivariate no-show rate differences between portal enrollment groups varied widely according to patient- and appointment-level attributes. Large reductions in no-show rates were seen among historically disadvantaged groups: Medicaid holders (OR = 2.04 for nonuser/enrollee, 5.6% difference, P < .001), uninsured patients (OR = 2.60, 12.8% difference, P < .001), and black patients (OR = 2.13, 8.0% difference, P < .001). After fitting a binomial logistic regression model for the outcome of appointment arrival, the adjusted odds of arrival increased 39.0% for portal enrollees relative to nonusers (OR = 1.39, 95% CI 1.22 - 1.57, P < .001). Analysis of monthly no-show rates over 2 years demonstrated that patients who registered for portal access and received three reminders of upcoming appointments (email, phone, and mail) had a 2.0% no-show rate reduction (P < .001), whereas patients who did not enroll and only received traditional phone and mail reminders saw no such reduction (P < .09). Conclusions Monthly no-show rates across all seven Duke Medicine clinics were significantly reduced among patients who registered for portal use, suggesting that in combination with an email reminder feature, this technology may have an important and beneficial effect on clinic operations. PMID:21616784

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.

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.

Statistical inference involving binomial and negative binomial parameters.

PubMed

García-Pérez, Miguel A; Núñez-Antón, Vicente

2009-05-01

Statistical inference about two binomial parameters implies that they are both estimated by binomial sampling. There are occasions in which one aims at testing the equality of two binomial parameters before and after the occurrence of the first success along a sequence of Bernoulli trials. In these cases, the binomial parameter before the first success is estimated by negative binomial sampling whereas that after the first success is estimated by binomial sampling, and both estimates are related. This paper derives statistical tools to test two hypotheses, namely, that both binomial parameters equal some specified value and that both parameters are equal though unknown. Simulation studies are used to show that in small samples both tests are accurate in keeping the nominal Type-I error rates, and also to determine sample size requirements to detect large, medium, and small effects with adequate power. Additional simulations also show that the tests are sufficiently robust to certain violations of their assumptions.

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…

Family structure and the treatment of childhood asthma.

PubMed

Chen, Alex Y; Escarce, José J

2008-02-01

Family structure is known to influence children's behavioral, educational, and cognitive outcomes, and recent studies suggest that family structure affects children's access to health care as well. However, no study has addressed whether family structure is associated with the care children receive for particular conditions or with their physical health outcomes. To assess the effects of family structure on the treatment and outcomes of children with asthma. Our data sources were the 1996-2003 Medical Expenditure Panel Survey (MEPS) and the 2003 National Survey of Children's Health (NSCH). The study samples consisted of children 2-17 years of age with asthma who lived in single-mother or 2-parent families. We assessed the effect of number of parents and number of other children in the household on office visits for asthma and use of asthma medications using negative binomial regression, and we assessed the effect of family structure on the severity of asthma symptoms using binary and ordinal logistic regression. Our regression models adjusted for sociodemographic characteristics, parental experience in child-rearing and in caring for an asthmatic child and, when appropriate, measures of children's health status. Asthmatic children in single-mother families had fewer office visits for asthma and filled fewer prescriptions for controller medications than children with 2 parents. In addition, children living in families with 3 or more other children had fewer office visits and filled fewer prescriptions for reliever and controller medications than children living with no other children. Children from single-mother families had more health difficulties from asthma than children with 2 parents, and children living with 2 or more other children were more likely to have an asthma attack in the past 12 months than children living with no other children. For children with asthma, living with a single mother and the presence of additional children in the household are associated with less treatment for asthma and worse asthma outcomes.

Outcomes and resource utilization associated with underage drinking at a level I trauma center.

PubMed

Psoter, Kevin J; Roudsari, Bahman S; Mack, Christopher; Vavilala, Monica S; Jarvik, Jeffrey G

2014-08-01

To examine the association of blood alcohol content (BAC) on hospital-based outcomes and imaging utilization for patients <21 years admitted to a level I trauma center. Retrospective analysis of alcohol-involved injuries in patients 13-20 years, admitted to a level I trauma center from 1996 to 2010. An injury was considered alcohol involved if the patient had a BAC > 0. Multivariable logistic regression was used to compare mortality, discharge destination (home and skilled nursing facility), intensive care unit admission, and operating room use between patients with and without positive BAC for patients 13-15, 16-17, and 18-20 years. Multivariable linear regression was used to compare length of hospitalization. Finally, multivariable negative binomial regression evaluated radiology resource utilization (x-ray, computed tomography [CT], and magnetic resonance imaging). A total of 7,663 patients, 13-20 years old, were admitted over the study period. A positive BAC was reported in 19% of these patients. In general, the presence of alcohol was not associated with mortality rate, length of hospitalization, intensive care unit, and operating room use or discharge status for any age group. However, the presence of alcohol was associated with higher utilization of head (incidence rate ratio [IRR] 1.13, 95% confidence interval [CI] 1.02-1.26), cervical spine (IRR 1.10, 95% CI 1.01-1.22), and thoracic (IRR 1.30, 95% CI 1.05-1.63) CTs in young adults 18-20 years. No differences in CT use were observed in patients 13-15 or 16-17 years. Positive BAC was not significantly associated with adverse outcomes or resource utilization in younger trauma patients. However, the use of certain body region CTs was associated with positive BAC in patients 18-20 years. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Care burden and its predictive factors in parents of newly diagnosed children with acute lymphoblastic leukemia in academic hospitals in China.

PubMed

Wang, Jingting; Shen, Nanping; Zhang, Xiaoyan; Shen, Min; Xie, Anwei; Howell, Doris; Yuan, Changrong

2017-12-01

Caring for children with acute lymphoblastic leukemia (ALL) is a distressing experience for parents without medical training. The experience can lead to parents' care burden. This study explored care burden among parents of children with ALL and its related factors. A total of 130 parents were surveyed with the Zarit Burden Inventory (ZBI), Perceived Social Support Scale (PSSS), Zung's Self-rating Anxiety Scale (SAS), Zung's Self-rating Depression Scale (SDS), Medical Outcome Study Short Form 36 (SF-36), and a study specific demographic information questionnaire. Independent-samples T test, one-way ANOVA, Pearson correlation analysis and multivariate linear regression analysis (stepwise method), and binomial logistic regression were used in data analysis. The mean score of parents' care burden overall was 37.74 ± 16.57, 17 (13.08%) had little or no burden, 57 (43.85%) had mild-to-moderate burden, 44 (33.84%) had moderate-to-severe burden, and 12 (9.23%) had severe burden. Regression analyses indicated daily care time, anxiety, general health, average monthly family income, social support, and number of co-caregivers were factors associated with care burden. These variables accounted for 51% of the variance in care burden. Other demographic information of parents and children, depression, and other dimensions of SF-36 were not related to care burden. The severe burden level was associated the increase risk of emotional distress compared with little or no burden group (OR = 37.500, 95% CI = 4.515-311.348, P = 0.001). The results indicated that care burden in parents of children newly diagnosed with ALL is high. Parents with lower levels of care burden tend to have less daily care time, more co-caregivers, higher income, less anxiety, better general health, and social support. Strategies are needed to help reduce parents' care burden.

Patterns of care and survival after a cancer of unknown primary (CUP) diagnosis: A population-based nested cohort study in Australian Government Department of Veterans' Affairs clients.

PubMed

Schaffer, Andrea L; Pearson, Sallie-Anne; Dobbins, Timothy A; Er, Chuang C; Ward, Robyn L; Vajdic, Claire M

2015-08-01

Little is known about patterns of care after a cancer of unknown primary (CUP) diagnosis. We performed a retrospective cohort study to describe and compare the treatment, health service use and survival of patients with CUP and metastatic cancer of known primary among 143,956 Australian Government Department of Veterans' Affairs clients, 2004-2007. We randomly matched clients with CUP (C809; n=252) with clients with a first diagnosis of metastatic solid cancer of known primary (n=980). We ascertained health services from the month of diagnosis up to 2 months post-diagnosis for consultations, hospitalizations and emergency department visits, and up to 1 year for treatment. We compared cancer treatments using conditional logistic regression; consultation rates using negative binomial regression; and survival using stratified Cox regression. 30% of CUP patients and 70% of patients with known primary received cancer treatment and the median survival was 37 days and 310 days respectively. CUP patients received fewer cancer medicines (odds ratio (OR)=0.54, 95% confidence interval (CI) 0.33-0.89) and less cancer-related surgery (OR=0.25, 95% CI 0.15-0.41); males with CUP received more radiation therapy (OR=2.88, 95% CI 1.69-4.91). CUP patients had more primary care consultations (incidence rate ratio (IRR)=1.25, 95% CI 1.11-1.41), emergency department visits (IRR=1.86, 95% CI 1.50-2.31) and hospitalizations (IRR=1.18, 95% CI 1.03-1.35), and a higher risk of death within 30 days (hazard ratio=3.30, 95% CI 1.69-6.44). Patients with CUP receive less treatment but use more health services, which may reflect underlying patient and disease characteristics. Copyright © 2015 Commonwealth of Australia. Published by Elsevier Ltd.. All rights reserved.

Effects of Clostridium difficile infection in patients with alcoholic hepatitis.

PubMed

Sundaram, Vinay; May, Folasade P; Manne, Vignan; Saab, Sammy

2014-10-01

Infection increases mortality in patients with alcoholic hepatitis (AH). Little is known about the association between Clostridium difficile infection (CDI) and AH. We examined the prevalence and effects of CDI in patients with AH, compared with those of other infections. We performed a cross-sectional analysis using data collected from the Nationwide Inpatient Sample, from 2008 through 2011. International Classification of Diseases, 9th revision, Clinical Modification codes were used to identify patients with AH. We used multivariable logistic regression to determine risk factors that affect mortality, negative binomial regression to evaluate the effects of CDI on predicted length of stay (LOS), and Poisson regression to determine the effects of CDI on predicted hospital charges. Chi-square and Wilcoxon rank-sum analyses were used to compare mortality, LOS, and hospital charges associated with CDI with those associated with urinary tract infection (UTI) and spontaneous bacterial peritonitis (SBP). Of 10,939 patients with AH, 177 had CDI (1.62%). Patients with AH and CDI had increased odds of inpatient mortality (adjusted odds ratio, 1.75; P = .04), a longer predicted LOS (10.63 vs 5.75 d; P < .001), and greater predicted hospital charges ($36,924.30 vs $29,136.58; P < .001), compared with those without CDI. Compared with UTI, CDI was associated with similar mortality but greater LOS (9 vs 6 d; P < .001) and hospital charges ($45,607 vs $32,087; P < .001). SBP was associated with higher mortality than CDI (17.3% vs 10.1%; P = .045), but similar LOS and hospital charges. In patients with AH, CDI is associated with greater mortality and health care use. These effects appear similar to those for UTI and SBP. We propose further studies to determine the cost effectiveness of screening for CDI among patients with AH. Copyright © 2014 AGA Institute. Published by Elsevier Inc. All rights reserved.

Robust inference in the negative binomial regression model with an application to falls data.

PubMed

Aeberhard, William H; Cantoni, Eva; Heritier, Stephane

2014-12-01

A popular way to model overdispersed count data, such as the number of falls reported during intervention studies, is by means of the negative binomial (NB) distribution. Classical estimating methods are well-known to be sensitive to model misspecifications, taking the form of patients falling much more than expected in such intervention studies where the NB regression model is used. We extend in this article two approaches for building robust M-estimators of the regression parameters in the class of generalized linear models to the NB distribution. The first approach achieves robustness in the response by applying a bounded function on the Pearson residuals arising in the maximum likelihood estimating equations, while the second approach achieves robustness by bounding the unscaled deviance components. For both approaches, we explore different choices for the bounding functions. Through a unified notation, we show how close these approaches may actually be as long as the bounding functions are chosen and tuned appropriately, and provide the asymptotic distributions of the resulting estimators. Moreover, we introduce a robust weighted maximum likelihood estimator for the overdispersion parameter, specific to the NB distribution. Simulations under various settings show that redescending bounding functions yield estimates with smaller biases under contamination while keeping high efficiency at the assumed model, and this for both approaches. We present an application to a recent randomized controlled trial measuring the effectiveness of an exercise program at reducing the number of falls among people suffering from Parkinsons disease to illustrate the diagnostic use of such robust procedures and their need for reliable inference. © 2014, The International Biometric Society.

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.

Zero-truncated negative binomial - Erlang distribution

NASA Astrophysics Data System (ADS)

Bodhisuwan, Winai; Pudprommarat, Chookait; Bodhisuwan, Rujira; Saothayanun, Luckhana

2017-11-01

The zero-truncated negative binomial-Erlang distribution is introduced. It is developed from negative binomial-Erlang distribution. In this work, the probability mass function is derived and some properties are included. The parameters of the zero-truncated negative binomial-Erlang distribution are estimated by using the maximum likelihood estimation. Finally, the proposed distribution is applied to real data, the number of methamphetamine in the Bangkok, Thailand. Based on the results, it shows that the zero-truncated negative binomial-Erlang distribution provided a better fit than the zero-truncated Poisson, zero-truncated negative binomial, zero-truncated generalized negative-binomial and zero-truncated Poisson-Lindley distributions for this data.

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.

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.

Ethnic Disparities in Oral Health Related Quality of Life among Adults in London, England.

PubMed

Abdelrahim, R; Delgado-Angulo, E K; Gallagher, J E; Bernabé, E

2017-06-01

To explore ethnic disparities in oral health related quality of life (OHQoL) among adults, and the role that socioeconomic factors play in that association. Data from 705 adults from a socially deprived, ethnically diverse metropolitan area of London (England) were analysed for this study. Ethnicity was self-assigned based on the 2001 UK Census categories. OHQoL was measured using the Oral Health Impact Profile (OHIP-14), which provides information on the prevalence, extent and intensity of oral impacts on quality of life in the previous 12 months. Ethnic disparities were assessed in logistic regression models for prevalence of oral impacts and negative binomial regression models for extent and intensity of oral impacts. The prevalence of oral impacts was 12.7% (95% CI: 10.2-15.1) and the mean OHIP-14 extent and severity scores were 0.27 (95% CI: 0.20-0.34) and 4.19 (95% CI: 3.74-4.64), respectively. Black adults showed greater and Asian adults lower prevalence, extent and severity of oral impacts than White adults. However, significant differences were only found for the extent of oral impacts; Black adults reporting more and Asian adults fewer OHIP-14 items affected than their White counterparts. After adjustments for socioeconomic factors, Asian adults had significantly fewer OHIP-14 items affected than White adults (rate ratio: 0.28; 95%CI: 0.08-0.94). This study found disparities in OHQoL between the three main ethnic groups in South East London. Asian adults had better and Black adults had similar OHQoL than White adults after accounting for demographic and social factors. Copyright© 2017 Dennis Barber Ltd.

Reactivation of Herpes Simplex Virus Type 2 After Initiation of Antiretroviral Therapy

PubMed Central

Tobian, Aaron A. R.; Grabowski, Mary K.; Serwadda, David; Newell, Kevin; Ssebbowa, Paschal; Franco, Veronica; Nalugoda, Fred; Wawer, Maria J.; Gray, Ronald H.; Quinn, Thomas C.; Reynolds, Steven J.

2013-01-01

Background. The association between initiation of antiretroviral therapy (ART) for human immunodeficiency virus (HIV) infection and possible herpes simplex virus type 2 (HSV-2) shedding and genital ulcer disease (GUD) has not been evaluated. Methods. GUD and vaginal HSV-2 shedding were evaluated among women coinfected with HIV and HSV-2 (n = 440 for GUD and n = 96 for HSV-2 shedding) who began ART while enrolled in a placebo-controlled trial of HSV-2 suppression with acyclovir in Rakai, Uganda. Monthly vaginal swabs were tested for HSV-2 shedding, using a real-time quantitative polymerase chain reaction assay. Prevalence risk ratios (PRRs) of GUD were estimated using log binomial regression. Random effects logistic regression was used to estimate odds ratios (ORs) of HSV-2 shedding. Results. Compared with pre-ART values, GUD prevalence increased significantly within the first 3 months after ART initiation (adjusted PRR, 1.94; 95% confidence interval [CI], 1.04–3.62) and returned to baseline after 6 months of ART (adjusted PRR, 0.80; 95% CI, .35–1.80). Detection of HSV-2 shedding was highest in the first 3 months after ART initiation (adjusted OR, 2.58; 95% CI, 1.48–4.49). HSV-2 shedding was significantly less common among women receiving acyclovir (adjusted OR, 0.13; 95% CI, .04–.41). Conclusions. The prevalence of HSV-2 shedding and GUD increased significantly after ART initiation, possibly because of immune reconstitution inflammatory syndrome. Acyclovir significantly reduced both GUD and HSV-2 shedding and should be considered to mitigate these effects following ART initiation. PMID:23812240

Food insecurity is associated with high risk glycemic control and higher health care utilization among youth and young adults with type 1 diabetes.

PubMed

Mendoza, Jason A; Haaland, Wren; D'Agostino, Ralph B; Martini, Lauren; Pihoker, Catherine; Frongillo, Edward A; Mayer-Davis, Elizabeth J; Liu, Lenna L; Dabelea, Dana; Lawrence, Jean M; Liese, Angela D

2018-04-01

Household food insecurity (FI), i.e., limited availability of nutritionally adequate foods, is associated with poor glycemic control among adults with type 2 diabetes. We evaluated the association of FI among youth and young adults (YYA) with type 1 diabetes to inform recent clinical recommendations from the American Diabetes Association for providers to screen all patients with diabetes for FI. Using data from the Washington and South Carolina SEARCH for Diabetes in Youth Study sites, we conducted an observational, cross-sectional evaluation of associations between FI and glycemic control, hospitalizations, and emergency department (ED) visits among YYA with type 1 diabetes. FI was assessed using the Household Food Security Survey Module, which queries conditions and behaviors typical of households unable to meet basic food needs. Participants' HbA 1c were measured from blood drawn at the research visit; socio-demographics and medical history were collected by survey. The prevalence of FI was 19.5%. In adjusted logistic regression analysis, YYAs from food-insecure households had 2.37 higher odds (95% CI: 1.10, 5.09) of high risk glycemic control, i.e., HbA 1c >9.0%, vs. peers from food-secure households. In adjusted binomial regression analysis for ED visits, YYAs from food-insecure households had an adjusted prevalence rate that was 2.95 times (95% CI [1.17, 7.45]) as great as those from food secure households. FI was associated with high risk glycemic control and more ED visits. Targeted efforts should be developed and tested to alleviate FI among YYA with type 1 diabetes. Copyright © 2018 Elsevier B.V. All rights reserved.

Drain Failure in Intra-Abdominal Abscesses Associated with Appendicitis.

PubMed

Horn, Christopher B; Coleoglou Centeno, Adrian A; Guerra, Jarot J; Mazuski, John E; Bochicchio, Grant V; Turnbull, Isaiah R

2018-04-01

Previous studies have suggested that percutaneous drainage and interval appendectomy is an effective treatment for appendicitis with associated abscess. Few studies to date have analyzed risk factors for failed drain management. We hypothesized that older patients with more co-morbidities would be at higher risk for failing conservative treatment. The 2010-2014 editions of the National Inpatient Sample (NIS) were queried for patients with diagnoses of peri-appendiceal abscesses. Minors and elective admissions were excluded. We identified patients who underwent percutaneous drainage and defined drain failure as undergoing a surgical operation after drainage but during the same inpatient visit to assess for factors associated with failure of drainage alone as a treatment. After univariable analysis, binomial logistic regression was used to assess for independent risk factors. Frequencies were analyzed by χ 2 and continuous variables by Student's t-test. A total of 2,209 patients with appendiceal abscesses received drains; 561 patients (25.4%) failed conservative management and underwent operative intervention. On univariable analysis, patients who failed conservative management were younger, more likely to be Hispanic, have more inpatient diagnoses, and to have undergone drainage earlier in the hospital course. Multivariable regression demonstrated that the number of diagnoses, female sex, and Hispanic race were predictive of failure of drainage alone. Older age, West and Midwest census regions, and later drain placement were predictive of successful treatment with drainage alone. Failure was associated with more charges and longer hospital stay but not with a higher mortality rate. Approximately a quarter of patients will fail management of appendiceal abscess with percutaneous drain placement alone. Risk factors for failure are patient complexity, female sex, earlier drainage, and Hispanic race. Failure of drainage is associated with higher total charges and longer hospital stay; however, no change in the mortality rate was noted.

Angiogenic and inflammatory biomarkers in mid-pregnancy and small-for-gestational age outcomes in Tanzania

PubMed Central

DARLING, Anne Marie; MCDONALD, Chloe R.; CONROY, Andrea L.; HAYFORD, Kyla T.; RAJWANS, Nimerta; WANG, Molin; ABOUD, Said; URASSA, Willy S.; KAIN, Kevin C.; FAWZI, Wafaie W.

2014-01-01

OBJECTIVE To investigate the relationship between a panel of angiogenic and inflammatory biomarkers measured in mid-pregnancy and small-for-gestational age (SGA) outcomes in sub-Saharan Africa. STUDY DESIGN Concentrations of 18 angiogenic and inflammatory biomarkers were determined in 432 pregnant women in Dar es Salaam, Tanzania who participated in a trial examining the effect of multivitamins on pregnancy outcomes. Infants falling below the 10th percentile of birth weight for gestational age relative to the applied growth standards were considered SGA. Multivariate binomial regression models with the log link function were used to determine the relative risk of SGA associated with increasing quartiles of each biomarker. Stepwise cubic restricted splines were used to test for non-linearity of these associations. Receiver operating curves obtained from multivariate logistic regression models were used to assess the discriminatory capability of selected biomarkers. RESULTS A total of 60 participants (13.9%) gave birth to SGA infants. Compared to those in the first quartile, the risk of SGA was reduced among those in the fourth quartiles of VEGF-A (adjusted risk ratio (RR) 0.38, 95% Confidence Interval (CI), 0.19-0.74), PGF (adjusted RR 0.28, 95% CI, 0.12-0.61), sFlt-1 (adjusted RR 0.48, 95% CI, 0.23-1.01), MCP-1 (adjusted RR 0.48, 95% CI, 0.25-0.92), and Leptin (adjusted RR 0.46, 95% CI, 0.22-0.96) CONCLUSION Our findings provide evidence of altered angiogenic and inflammatory mediators, at mid-pregnancy, in women who went on to deliver small for gestational age infants. PMID:24881826

A prospective study of mental health care for comorbid depressed mood in older adults with painful osteoarthritis

PubMed Central

2011-01-01

Background Comorbid depression is common among adults with painful osteoarthritis (OA). We evaluated the relationship between depressed mood and receipt of mental health (MH) care services. Methods In a cohort with OA, annual interviews assessed comorbidity, arthritis severity, and MH (SF-36 mental health score). Surveys were linked to administrative health databases to identify mental health-related visits to physicians in the two years following the baseline interview (1996-98). Prescriptions for anti-depressants were ascertained for participants aged 65+ years (eligible for drug benefits). The relationship between MH scores and MH-related physician visits was assessed using zero-inflated negative binomial regression, adjusting for confounders. For those aged 65+ years, logistic regression examined the probability of receiving any MH-related care (physician visit or anti-depressant prescription). Results Analyses were based on 2,005 (90.1%) individuals (mean age 70.8 years). Of 576 (28.7%) with probable depression (MH score < 60/100), 42.5% experienced one or more MH-related physician visits during follow-up. The likelihood of a physician visit was associated with sex (adjusted OR women vs. men = 5.87, p = 0.005) and MH score (adjusted OR per 10-point decrease in MH score = 1.63, p = 0.003). Among those aged 65+, 56.7% with probable depression received any MH care. The likelihood of receiving any MH care exhibited a significant interaction between MH score and self-reported health status (p = 0.0009); with good general health, worsening MH was associated with increased likelihood of MH care; as general health declined, this effect was attenuated. Conclusions Among older adults with painful OA, more than one-quarter had depressed mood, but almost half received no mental health care, suggesting a care gap. PMID:21910895

A comparison of resource utilization following chemotherapy for acute myeloid leukemia in children discharged versus children that remain hospitalized during neutropenia

PubMed Central

Getz, Kelly D; Miller, Tamara P; Seif, Alix E; Li, Yimei; Huang, Yuan-Shung; Bagatell, Rochelle; Fisher, Brian T; Aplenc, Richard

2015-01-01

Comparisons of early discharge and outpatient postchemotherapy supportive care in pediatric acute myeloid leukemia (AML) patients are limited. We used data from the Pediatric Health Information System on a cohort of children treated for newly diagnosed AML to compare course-specific mortality and resource utilization in patients who were discharged after chemotherapy to outpatient management during neutropenia relative to patients who remained hospitalized. Patients were categorized at each course as early or standard discharge. Discharges within 3 days after chemotherapy completion were considered “early”. Resource utilization was determined based on daily billing data and reported as days of use per 1000 hospital days. Inpatient mortality, occurrence of intensive care unit (ICU)-level care, and duration of hospitalization were compared using logistic, log-binomial and linear regression methods, respectively. Poisson regression with inpatient days as offset was used to compare resource use by discharge status. The study population included 996 patients contributing 2358 treatment courses. Fewer patients were discharged early following Induction I (7%) than subsequent courses (22–24%). Across courses, patients discharged early experienced high readmission rates (69–84%), yet 9–12 fewer inpatient days (all P < 0.001). Inpatient mortality was low across courses and did not differ significantly by discharge status. The overall risk for ICU-level care was 116% higher for early compared to standard discharge patients (adjusted risk ratio: 2.16, 95% confidence interval: 1.50, 3.11). Rates of antibiotic, vasopressor, and supplemental oxygen use were consistently elevated for early discharge patients. Despite similar inpatient mortality to standard discharge patients, early discharge patients may be at greater risk for life-threatening chemotherapy-related complications, including infections. PMID:26105201

The influence of marital status on the stage at diagnosis, treatment, and survival of adult patients with gastric cancer: a population-based study.

PubMed

Zhang, Jieyun; Gan, Lu; Wu, Zhenhua; Yan, Shican; Liu, Xiyu; Guo, Weijian

2017-04-04

Marital status was reported as a prognostic factor in many cancers. However, its role in gastric cancer (GC) hasn't been thoroughly explored. In this study, we aimed to investigate the effect of marital status on survival, stage, treatment, and survival in subgroups. We used the Surveillance, Epidemiology and End Results (SEER) database and identified 16910 GC patients. These patients were categorized into married (58.44%) and unmarred (41.56%) groups. Pearson chi-square, Wilcoxon-Mann-Whitney, Log-rank, multivariate Cox regression, univariate and multivariate binomial or multinomial logistic regression analysis were used in our analysis. Subgroup analyses of married versus unmarried patients were summarized in a forest plot. Married patients had better 5-year overall survival (OS) (32.09% VS 24.61%, P<0.001) and 5-year cancer-caused special survival (CSS) (37.74% VS 32.79%, P<0.001) than unmarried ones. Then we studied several underlying mechanisms. Firstly, married patients weren't in earlier stage at diagnosis (P=0.159). Secondly, married patients were more likely to receive surgery (P < 0.001) or radiotherapy (P < 0.001) compared with the unmarried. Thirdly, in subgroup analyses, married patients still had survival advantage in subgroups with stage II-IV and no radiotherapy. These results showed that marital status was an independently prognostic factor for both OS and CSS in GC patients. Undertreatment and lack of social support in unmarried patients were potential explanations. With the knowledge of heterogeneous effects of marriage in subgroups, we can target unmarried patients with better social support, especially who are diagnosed at late stage and undergo no treatment.

The influence of marital status on the stage at diagnosis, treatment, and survival of adult patients with gastric cancer: a population-based study

PubMed Central

Yan, Shican; Liu, Xiyu; Guo, Weijian

2017-01-01

Background & Aims Marital status was reported as a prognostic factor in many cancers. However, its role in gastric cancer (GC) hasn't been thoroughly explored. In this study, we aimed to investigate the effect of marital status on survival, stage, treatment, and survival in subgroups. Methods We used the Surveillance, Epidemiology and End Results (SEER) database and identified 16910 GC patients. These patients were categorized into married (58.44%) and unmarred (41.56%) groups. Pearson chi-square, Wilcoxon-Mann-Whitney, Log-rank, multivariate Cox regression, univariate and multivariate binomial or multinomial logistic regression analysis were used in our analysis. Subgroup analyses of married versus unmarried patients were summarized in a forest plot. Results Married patients had better 5-year overall survival (OS) (32.09% VS 24.61%, P<0.001) and 5-year cancer-caused special survival (CSS) (37.74% VS 32.79%, P<0.001) than unmarried ones. Then we studied several underlying mechanisms. Firstly, married patients weren't in earlier stage at diagnosis (P=0.159). Secondly, married patients were more likely to receive surgery (P < 0.001) or radiotherapy (P < 0.001) compared with the unmarried. Thirdly, in subgroup analyses, married patients still had survival advantage in subgroups with stage II-IV and no radiotherapy. Conclusions These results showed that marital status was an independently prognostic factor for both OS and CSS in GC patients. Undertreatment and lack of social support in unmarried patients were potential explanations. With the knowledge of heterogeneous effects of marriage in subgroups, we can target unmarried patients with better social support, especially who are diagnosed at late stage and undergo no treatment. PMID:26894860

Differences in musculoskeletal health due to gender in a rural multiethnic cohort: a Project FRONTIER study.

PubMed

Brismée, J M; Yang, S; Lambert, M E; Chyu, M C; Tsai, P; Zhang, Y; Han, J; Hudson, C; Chung, Eunhee; Shen, C L

2016-04-26

Very few studies have investigated differences in musculoskeletal health due to gender in a large rural population. The aim of this study is to investigate factors affecting musculoskeletal health in terms of hand grip strength, musculoskeletal discomfort, and gait disturbance in a rural-dwelling, multi-ethnic cohort. Data for 1117 participants (40 years and older, 70% female) of an ongoing rural healthcare study, Project FRONTIER, were analyzed. Subjects with a history of neurological disease, stroke and movement disorder were excluded. Dominant hand grip strength was assessed by dynamometry. Gait disturbance including stiff, spastic, narrow-based, wide-based, unstable or shuffling gait was rated. Musculoskeletal discomfort was assessed by self-reported survey. Data were analyzed by linear, logistic regression and negative binomial regressions as appropriate. Demographic and socioeconomic factors were adjusted in the multiple variable analyses. In both genders, advanced age was a risk factor for weaker hand grip strength; arthritis was positively associated with musculoskeletal discomfort, and fair or poor health was significantly associated with increased risk of gait disturbance. Greater waist circumference was associated with greater musculoskeletal discomfort in males only. In females, advanced age is the risk factor for musculoskeletal discomfort as well as gait disturbance. Females with fair or poor health had weaker hand grip strength. Higher C-reactive protein and HbA1c levels were also positively associated with gait disturbance in females, but not in males. This cross-sectional study demonstrates how gender affects hand grip strength, musculoskeletal discomfort, and gait in a rural-dwelling multi-ethnic cohort. Our results suggest that musculoskeletal health may need to be assessed differently between males and females.

Trends and Regional Variation in Hospital Mortality, Length of Stay and Cost in Hospital of Ischemic Stroke Patients in Alberta Accompanying the Provincial Reorganization of Stroke Care.

PubMed

Ohinmaa, Arto; Zheng, Yufei; Jeerakathil, Thomas; Klarenbach, Scott; Häkkinen, Unto; Nguyen, Thanh; Friesen, Dan; Ruseski, Jane; Kaul, Padma; Ariste, Ruolz; Jacobs, Philip

2016-12-01

This study aimed to evaluate the trends and regional variation of stroke hospital care in 30-day in-hospital mortality, hospital length of stay (LOS), and 1-year total hospitalization cost after implementation of the Alberta Provincial Stroke Strategy. New ischemic stroke patients (N = 7632) admitted to Alberta acute care hospitals between 2006 and 2011 were followed for 1 year. We analyzed in-hospital mortality with logistic regression, LOS with negative binomial regression, and the hospital costs with generalized gamma model (log link). The risk-adjusted results were compared over years and between zones using observed/expected results. The risk-adjusted mortality rates decreased from 12.6% in 2006/2007 to 9.9% in 2010/2011. The regional variations in mortality decreased from 8.3% units in 2008/2009 to 5.6 in 2010/2011. The LOS of the first episode dropped significantly in 2010/2011 after a 4-year slight increase. The regional variation in LOS was 15.5 days in 2006/2007 and decreased to 10.9 days in 2010/2011. The 1-year hospitalization cost increased initially, and then kept on declining during the last 3 years. The South and Calgary zones had the lowest costs over the study period. However, this gap was diminishing. After implementation of the Alberta Provincial Stroke Strategy, both mortality and hospital costs demonstrated a decreasing trend during the later years of study. The LOS increased slightly during the first 4 years but had a significant drop at the last year. In general, the regional variations in all 3 indicators had a diminishing trend. Copyright © 2016 National Stroke Association. Published by Elsevier Inc. All rights reserved.

The relationship between patient data and pooled clinical management decisions.

PubMed

Ludbrook, G I; O'Loughlin, E J; Corcoran, T B; Grant, C

2013-01-01

A strong relationship between patient data and preoperative clinical decisions could potentially be used to support clinical decisions in preoperative management. The aim of this exploratory study was to determine the relationship between key patient data and pooled clinical opinions on management. In a previous study, panels of anaesthetists compared the quality of computer-assisted patient health assessments with outpatient consultations and made decisions on the need for preoperative tests, no preoperative outpatient assessment, possible postoperative intensive care unit/high dependency unit requirements and aspiration prophylaxis. In the current study, the relationship between patient data and these decisions was examined using binomial logistic regression analysis. Backward stepwise regression was used to identify independent predictors of each decision (at P >0.15), which were then incorporated into a predictive model. The number of factors related to each decision varied: blood picture (four factors), biochemistry (six factors), coagulation studies (three factors), electrocardiography (eight factors), chest X-ray (seven factors), preoperative outpatient assessment (17 factors), intensive care unit requirement (eight factors) and aspiration prophylaxis (one factor). The factor types also varied, but included surgical complexity, age, gender, number of medications or comorbidities, body mass index, hypertension, central nervous system condition, heart disease, sleep apnoea, smoking, persistent pain and stroke. Models based on these relationships usually demonstrated good sensitivity and specificity, with receiver operating characteristics in the following areas under curve: blood picture (0.75), biochemistry (0.86), coagulation studies (0.71), electrocardiography (0.90), chest X-ray (0.85), outpatient assessment (0.85), postoperative intensive care unit requirement (0.88) and aspiration prophylaxis (0.85). These initial results suggest modelling of patient data may have utility supporting clinicians' preoperative decisions.

Medical Records Flag for Suicide Risk: Predictors and Subsequent Use of Care Among Veterans With Substance Use Disorders.

PubMed

Berg, Joanna M; Malte, Carol A; Reger, Mark A; Hawkins, Eric J

2018-06-08

The U.S. Department of Veterans Affairs (VA) health care system established policies to include patient record flags (PRFs) for high suicide risk in the electronic medical record to alert providers and to increase health care contacts. This study identified predictors of new PRFs and described health care utilization before and after PRF initiation among VA patients with substance use disorders. The sample included patients ages ≥18 who received a substance use disorder diagnosis in 2012 (N=474,946). Demographic, clinical, and utilization predictors of PRFs were identified by multivariable logistic regression. Changes in short-term (three months) and longer-term (12 months) health care utilization before and after PRF initiation were compared by negative binomial regression. A total of 8,913 patients received PRFs. Demographic predictors of PRF initiation included being younger than 35, white, and homeless. Clinical predictors were cocaine, opioid, and sedative use disorders; posttraumatic stress, psychotic, bipolar, and depressive disorders; and diagnosis of a suicide attempt. Patients with PRFs averaged 1.33 (95% confidence interval [CI]=1.29-1.38) times more primary care visits, 2.29 (CI=2.24-2.34) times more mental health visits, 4.10 (CI=3.80-4.42) times more substance use visits, and fewer (incidence rate ratio=.55, CI=.53-.58) emergency department visits in the three months following compared with the three months before PRF initiation. Modest increases in mental health- and substance use--related days hospitalized were observed. Veterans received significantly more health care services after PRF initiation. Further research is warranted on the effects of PRFs on clinical outcomes, such as suicide behaviors.

Modified 30-second Sit to Stand test predicts falls in a cohort of institutionalized older veterans

PubMed Central

Chassé, Kathleen

2017-01-01

Physical function performance tests, including sit to stand tests and Timed Up and Go, assess the functional capacity of older adults. Their ability to predict falls warrants further investigation. The objective was to determine if a modified 30-second Sit to Stand test that allowed upper extremity use and Timed Up and Go test predicted falls in institutionalized Veterans. Fifty-three older adult Veterans (mean age = 91 years, 49 men) residing in a long-term care hospital completed modified 30-second Sit to Stand and Timed Up and Go tests. The number of falls over one year was collected. The ability of modified 30-second Sit to Stand or Timed Up and Go to predict if participants had fallen was examined using logistic regression. The ability of these tests to predict the number of falls was examined using negative binomial regression. Both analyses controlled for age, history of falls, cognition, and comorbidities. The modified 30-second Sit to Stand was significantly (p < 0.05) related to if participants fell (odds ratio = 0.75, 95% confidence interval = 0.58, 0.97) and the number of falls (incidence rate ratio = 0.82, 95% confidence interval = 0.68, 0.98); decreased repetitions were associated with increased number of falls. Timed Up and Go was not significantly (p > 0.05) related to if participants fell (odds ratio = 1.03, 95% confidence interval = 0.96, 1.10) or the number of falls (incidence rate ratio = 1.01, 95% confidence interval = 0.98, 1.05). The modified 30-second Sit to Stand that allowed upper extremity use offers an alternative method to screen for fall risk in older adults in long-term care. PMID:28464024

Elevated depressive symptoms and adolescent injury: examining associations by injury frequency, injury type, and gender

PubMed Central

2014-01-01

Background Key risk factors for adolescent injury have been well documented, and include structural, behavioural, and psychosocial indicators. While psychiatric distress has been associated with suicidal behaviour and related self-harm, very little research has examined the role of depression in shaping adolescent injury. This study examines the association of elevated depressive symptoms with injury, including total number of injuries and injury type. Gender differences are also considered. Methods Data were drawn in 2010–11 from a representative sample of 2,989 high school students (14 to18 years of age) from Nova Scotia, Canada. Self-reported injury outcomes were examined using the 17-item Adolescent Injury Checklist, which captures past six-month injuries. Elevated depressive symptoms were assessed using the Centers for Epidemiological Studies Depression scale. Associations of elevated depressive symptoms with total number of injuries were estimated with negative binomial regression, while associations with specific injury types were estimated with logistic regression. Analyses were conducted in 2012. Results Adolescents with elevated depressive symptoms experienced a 40% increase in the total number of injury events occurring in the past six months. The association of elevated depressive symptoms with injury was consistent across injury type; violence-related (OR 2.21, 95% CI 1.61 to 3.03), transport-related (OR 1.53, 95% CI 1.10 to 2.13), and unintentional injuries (OR 1.65, 95% CI 1.20 to 2.27). Gender differences were also observed. Conclusion Elevated depressive symptoms play a role in shaping adolescent injury. Interventions aimed at reducing adolescent injury should look to minimize psychosocial antecedents, such as poor mental health, that put adolescents at an elevated risk. PMID:24555802

Associations between displayed alcohol references on Facebook and problem drinking among college students

PubMed Central

Moreno, Megan A; Christakis, Dimitri A; Egan, Katie G; Brockman, Libby N; Becker, Tara

2011-01-01

Objective Alcohol screening is uncommon among college students; however, many students display references to alcohol on Facebook. The objective of this study was to examine associations between displayed alcohol use and intoxication/problem drinking (I/PD) references on Facebook and self-reported problem drinking using a clinical scale. Design Content analysis and cross-sectional survey Setting www.Facebook.com Participants Undergraduate students from two state universities between the ages of 18 and 20 with public Facebook profiles Main exposures Profiles were categorized into one of three distinct categories: Non-Displayers, Alcohol Displayers and Intoxication/Problem Drinking (I/PD) Displayers. Outcome measures An online survey measured problem drinking using the AUDIT scale. Analyses examined associations between alcohol display category and 1) AUDIT problem drinking category using logistic regression, 2) AUDIT score using negative binomial regression, and 3) alcohol-related injury using Fisher’s exact test. Results Of 307 profiles identified, 224 participants completed the survey (73% response rate). The average age was 18.8 years, 122 (54%) were female, 152 (68%) were Caucasian, and approximately half were from each university. Profile owners who displayed I/PD were more likely (OR=4.4 [95% CI 2.0-9.4]) to score in the problem drinking category of the AUDIT scale, had 64% (IRR=1.64 [95% CI: 1.27-11.0] higher AUDIT scores overall and were more likely to report an alcohol-related injury in the past year (p=0.002). Conclusions Displayed references to I/PD were positively associated with AUDIT scores suggesting problem drinking as well as alcohol-related injury. Results suggest that clinical criteria for problem drinking can be applied to Facebook alcohol references. PMID:21969360

The effect of albendazole treatment on seizure outcomes in patients with symptomatic neurocysticercosis.

PubMed

Romo, Matthew L; Wyka, Katarzyna; Carpio, Arturo; Leslie, Denise; Andrews, Howard; Bagiella, Emilia; Hauser, W Allen; Kelvin, Elizabeth A

2015-11-01

Randomized controlled trials have found an inconsistent effect of anthelmintic treatment on long-term seizure outcomes in neurocysticercosis. The objective of this study was to further explore the effect of albendazole treatment on long-term seizure outcomes and to determine if there is evidence for a differential effect by seizure type. In this trial, 178 patients with active or transitional neurocysticercosis cysts and new-onset symptoms were randomized to 8 days of treatment with albendazole (n=88) or placebo (n=90), both with prednisone, and followed for 24 months. We used negative binomial regression and logistic regression models to determine the effect of albendazole on the number of seizures and probability of recurrent or new-onset seizures, respectively, over follow-up. Treatment with albendazole was associated with a reduction in the number of seizures during 24 months of follow-up, but this was only significant for generalized seizures during months 1-12 (unadjusted rate ratio [RR] 0.19; 95% CI: 0.04-0.91) and months 1-24 (unadjusted RR 0.06; 95% CI: 0.01-0.57). We did not detect a significant effect of albendazole on reducing the number of focal seizures or on the probability of having a seizure, regardless of seizure type or time period. Albendazole treatment may be associated with some symptomatic improvement; however, this association seems to be specific to generalized seizures. Future research is needed to identify strategies to better reduce long-term seizure burden in patients with neurocysticercosis. © The Author 2015. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Male Sex Associated With Increased Risk of Neonatal Abstinence Syndrome.

PubMed

Charles, M Katherine; Cooper, William O; Jansson, Lauren M; Dudley, Judith; Slaughter, James C; Patrick, Stephen W

2017-06-01

Neonatal abstinence syndrome (NAS) is a postnatal opioid withdrawal syndrome. Factors associated with development of the syndrome are poorly understood; however, infant sex may influence the risk of NAS. Our objective was to determine if infant sex was associated with the development or severity of the syndrome in a large population-based cohort. This retrospective cohort study used vital statistics and prescription, outpatient, and inpatient administrative data for mothers and infants enrolled in the Tennessee Medicaid program between 2009 and 2011. Multivariable logistic regression models were used to evaluate the association between male sex and diagnosis of NAS, accounting for potential demographic and clinical confounders. NAS severity, as evidenced by hospital length of stay, was modeled by using negative binomial regression. Of 102 695 infants, 927 infants were diagnosed with NAS (484 male subjects and 443 female subjects). Adjustments were made for the following: maternal age, race, and education; maternal hepatitis C infection, anxiety, or depression; in utero exposure to selective serotonin reuptake inhibitors and cigarettes; infant birth weight, small for gestational age, and year; and the interaction between opioid type and opioid amount. Male infants were more likely than female infants to be diagnosed with NAS (adjusted odds ratio, 1.18 [95% confidence interval, 1.05-1.33]) and NAS requiring treatment (adjusted odds ratio, 1.24 [95% confidence interval, 1.04-1.47]). However, there was no sex-based difference in severity for those diagnosed with NAS. Treatment of NAS should be tailored to an infant's individual risk for the syndrome. Clinicians should be mindful that male sex is an important risk factor in the diagnosis of NAS. Copyright © 2017 by the American Academy of Pediatrics.

National Trends in Emergency Room Visits of Dialysis Patients for Adverse Drug Reactions.

PubMed

Chan, Lili; Saha, Aparna; Poojary, Priti; Chauhan, Kinsuk; Naik, Nidhi; Coca, Steven; Garimella, Pranav S; Nadkarni, Girish N

2018-06-12

Various medications are cleared by the kidneys, therefore patients with impaired renal function, especially dialysis patients are at risk for adverse drug events (ADEs). There are limited studies on ADEs in maintenance dialysis patients. We utilized a nationally representative database, the Nationwide Emergency Department Sample, from 2008 to 2013, to compare emergency department (ED) visits for dialysis and propensity matched non-dialysis patients. Log binomial regression was used to calculate relative risk of hospital admission and logistic regression to calculate ORs for in-hospital mortality while adjusting for patient and hospital characteristics. While ED visits for ADEs decreased in both groups, they were over 10-fold higher in dialysis patients than non-dialysis patients (65.8-88.5 per 1,000 patients vs. 4.6-5.4 per 1,000 patients respectively, p < 0.001). The top medication category associated with ED visits for ADEs in dialysis patients is agents primarily affecting blood constituents, which has increased. After propensity matching, patient admission was higher in dialysis patients than non-dialysis patients, (88 vs. 76%, p < 0.001). Dialysis was associated with a 3% increase in risk of admission and 3 times the odds of in-hospital mortality (adjusted OR 3, 95% CI 2.7-2.3.3). ED visits for ADEs are substantially higher in dialysis patients than non-dialysis patients. In dialysis patients, ADEs associated with agents primarily affecting blood constituents are on the rise. ED visits for ADEs in dialysis patients have higher inpatient admissions and in-hospital mortality. Further studies are needed to identify and implement measures aimed at reducing ADEs in dialysis patients. © 2018 S. Karger AG, Basel.

Modified 30-second Sit to Stand test predicts falls in a cohort of institutionalized older veterans.

PubMed

Applebaum, Eva V; Breton, Dominic; Feng, Zhuo Wei; Ta, An-Tchi; Walsh, Kayley; Chassé, Kathleen; Robbins, Shawn M

2017-01-01

Physical function performance tests, including sit to stand tests and Timed Up and Go, assess the functional capacity of older adults. Their ability to predict falls warrants further investigation. The objective was to determine if a modified 30-second Sit to Stand test that allowed upper extremity use and Timed Up and Go test predicted falls in institutionalized Veterans. Fifty-three older adult Veterans (mean age = 91 years, 49 men) residing in a long-term care hospital completed modified 30-second Sit to Stand and Timed Up and Go tests. The number of falls over one year was collected. The ability of modified 30-second Sit to Stand or Timed Up and Go to predict if participants had fallen was examined using logistic regression. The ability of these tests to predict the number of falls was examined using negative binomial regression. Both analyses controlled for age, history of falls, cognition, and comorbidities. The modified 30-second Sit to Stand was significantly (p < 0.05) related to if participants fell (odds ratio = 0.75, 95% confidence interval = 0.58, 0.97) and the number of falls (incidence rate ratio = 0.82, 95% confidence interval = 0.68, 0.98); decreased repetitions were associated with increased number of falls. Timed Up and Go was not significantly (p > 0.05) related to if participants fell (odds ratio = 1.03, 95% confidence interval = 0.96, 1.10) or the number of falls (incidence rate ratio = 1.01, 95% confidence interval = 0.98, 1.05). The modified 30-second Sit to Stand that allowed upper extremity use offers an alternative method to screen for fall risk in older adults in long-term care.

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.

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…

[Spatial epidemiological study on malaria epidemics in Hainan province].

PubMed

Wen, Liang; Shi, Run-He; Fang, Li-Qun; Xu, De-Zhong; Li, Cheng-Yi; Wang, Yong; Yuan, Zheng-Quan; Zhang, Hui

2008-06-01

To better understand the characteristics of spatial distribution of malaria epidemics in Hainan province and to explore the relationship between malaria epidemics and environmental factors, as well to develop prediction model on malaria epidemics. Data on Malaria and meteorological factors were collected in all 19 counties in Hainan province from May to Oct., 2000, and the proportion of land use types of these counties in this period were extracted from digital map of land use in Hainan province. Land surface temperatures (LST) were extracted from MODIS images and elevations of these counties were extracted from DEM of Hainan province. The coefficients of correlation of malaria incidences and these environmental factors were then calculated with SPSS 13.0, and negative binomial regression analysis were done using SAS 9.0. The incidence of malaria showed (1) positive correlations to elevation, proportion of forest land area and grassland area; (2) negative correlations to the proportion of cultivated area, urban and rural residents and to industrial enterprise area, LST; (3) no correlations to meteorological factors, proportion of water area, and unemployed land area. The prediction model of malaria which came from negative binomial regression analysis was: I (monthly, unit: 1/1,000,000) = exp (-1.672-0.399xLST). Spatial distribution of malaria epidemics was associated with some environmental factors, and prediction model of malaria epidemic could be developed with indexes which extracted from satellite remote sensing images.

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.

Distribution, occupancy, and habitat correlates of American martens (Martes americana) in Rocky Mountain National Park, Colorado

USGS Publications Warehouse

Baldwin, R.A.; Bender, L.C.

2008-01-01

A clear understanding of habitat associations of martens (Martes americana) is necessary to effectively manage and monitor populations. However, this information was lacking for martens in most of their southern range, particularly during the summer season. We studied the distribution and habitat correlates of martens from 2004 to 2006 in Rocky Mountain National Park (RMNP) across 3 spatial scales: site-specific, home-range, and landscape. We used remote-sensored cameras from early August through late October to inventory occurrence of martens and modeled occurrence as a function of habitat and landscape variables using binary response (BR) and binomial count (BC) logistic regression, and occupancy modeling (OM). We also assessed which was the most appropriate modeling technique for martens in RMNP. Of the 3 modeling techniques, OM appeared to be most appropriate given the explanatory power of derived models and its incorporation of detection probabilities, although the results from BR and BC provided corroborating evidence of important habitat correlates. Location of sites in the western portion of the park, riparian mixed-conifer stands, and mixed-conifer with aspen patches were most frequently positively correlated with occurrence of martens, whereas more xeric and open sites were avoided. Additionally, OM yielded unbiased occupancy values ranging from 91% to 100% and 20% to 30% for the western and eastern portions of RMNP, respectively. ?? 2008 American Society of Mammalogists.

Tooth loss patterns in older adults with special needs: a Minnesota cohort

PubMed Central

Chen, Xi; Clark, Jennifer J

2011-01-01

This study was conducted to detail tooth loss patterns in older adults with special needs. A total of 491 elderly subjects with special needs were retrospectively selected and followed during 10/1999-12/2006. Medical, dental, cognitive, and functional assessments were abstracted from dental records and used to predict risk of tooth loss. Tooth loss events were recorded for subjects during follow-up. Chi-squared tests were used to study the association between tooth loss and the selected risk factors. Logistic, poisson, and negative binomial regressions were developed to study tooth loss patterns. Overall, 27% of the subjects lost at least one tooth during follow-up. Fourteen subjects had tooth loss events per 100 person-years. Tooth loss pattern did not differ significantly among different special-needs subgroups (i.e. community-dwelling vs. long-term care, physically disabled vs. functionally independent). Special-needs subjects with three or more active dental conditions at arrival had more than twice the risk of losing teeth than those without any existing conditions. After adjusting other factors, the number of carious teeth or retained roots at arrival was a significant predictor of tooth loss for older adults with special needs (P=0.001). These findings indicate that appropriately managing active caries and associated conditions is important to prevent tooth loss for older adults with special needs. PMID:21449213

Risk Factors Associated with the Occurrence of the Second-Litter Syndrome in Sows in Southeastern Mexico

PubMed Central

Segura Correa, José C.; Alzina-López, Alejandro; Santos-Ricalde, Ronald H.

2013-01-01

The objective was to estimate the incidence of and to determine the effect of some risk factors on the decrease of litter size at parity 2 of sows in three commercial farms in Yucatan, Mexico. Data on 8,592 farrowing records of 4,296 sows were analyzed using a binomial logistic regression procedure. The model included the fixed effect of farm (1, 2, and 3), year of farrowing (2003–2011), season of farrowing (dry, rainy, and windy), number of pigs born alive at first parity (<9, 9-10, 11-12, and >12 piglets), lactation length (<18, 18–24, and >24 days), and weaning to conception intervals (<4, 4–11, and >11 days). Fifty-five point eight percent of all sows presented a reduced or similar litter size at parity 2. The odds of decrease in the second litter size were 1.56 and 2.01 for farms 2 and 3, respectively. Higher odds were found for sows farrowing during the rainy and dry seasons (1.20 and 1.24, resp.) and for sows with large litters at parity 1 (>12 piglets, odds = 33.2). Sows with weaning to conception intervals <4 days and between 4 and 11 days had higher odds of a decrease in the second litter (1.78 and 2.74 pigs, resp.). PMID:24288517

Buprenorphine Treatment and Patient Use of Health Services after the Affordable Care Act in an Integrated Health Care System

PubMed Central

Campbell, Cynthia I.; Parthasarathy, Sujaya; Young-Wolff, Kelly C.; Satre, Derek D.

2017-01-01

Introduction The Affordable Care Act (ACA) was expected to benefit patients with substance use disorders, including opioid use disorders (OUDs). This study examined buprenorphine use and health services utilization by patients with OUDs pre- and post-ACA in a large health care system. Methods Using electronic health record data, we examined demographic and clinical characteristics (substance use, psychiatric and medical conditions) of two patient cohorts using buprenorphine: those newly enrolled in 2012 (“pre-ACA”, N=204) and in 2014 (“post-ACA”, N=258). Logistic and negative binomial regressions were used to model persistent buprenorphine use, and to examine whether persistent use was related to health services utilization. Results Buprenorphine patients were largely similar pre- and post-ACA, although more post-ACA patients had a marijuana use disorder (p<.01). Post-ACA patients were more likely to have high deductible benefit plans (p<.01). Use of psychiatry services was lower post-ACA (IRR: 0.56, p<.01), and high deductible plans were also related to lower use of psychiatry services (IRR: 0.30, p<.01). Conclusion The relationship between marijuana use disorder and prescription opioid use is complex, and deserves further study, particularly with increasingly widespread marijuana legalization. Access to psychiatry services may be more challenging for buprenorphine patients post-ACA, especially for patients with deductible plans. PMID:28426332

Availability of Care Concordant With Patient-centered Medical Home Principles Among Those With Chronic Conditions: Measuring Care Outcomes.

PubMed

Pourat, Nadereh; Charles, Shana A; Snyder, Sophie

2016-03-01

Care delivery redesign in the form of patient-centered medical home (PCMH) is considered as a potential solution to improve patient outcomes and reduce costs, particularly for patients with chronic conditions. But studies of prevalence or impact at the population level are rare. We aimed to assess whether desired outcomes indicating better care delivery and patient-centeredness were associated with receipt of care according to 3 important PCMH principles. We analyzed data from a representative population survey in California in 2009, focusing on a population with chronic condition who had a usual source of care. We used bivariate, logistic, and negative-binomial regressions. The indicators of PCMH concordant care included continuity of care (personal doctor), care coordination, and care management (individual treatment plan). Outcomes included flu shots, count of outpatient visits, any emergency department visit, timely provider communication, and confidence in self-care. We found that patients whose care was concordant with all 3 PCMH principles were more likely to receive flu shots, more outpatient care, and timely response from providers. Concordance with 2 principles led to some desired outcomes. Concordance with only 1 principle was not associated with desired outcomes. Patients who received care that met 3 key aspects of PCMH: coordination, continuity, and management, had better quality of care and more efficient use of the health care system.

Serious psychological distress as a barrier to dental care in community-dwelling adults in the United States.

PubMed

Xiang, Xiaoling; Lee, Wonik; Kang, Sung-wan

2015-01-01

To examine whether serious psychological distress (SPD), a nonspecific indicator of past year mental health problems, was associated with subsequent dental care utilization, dental expenditures, and unmet dental needs. We analyzed data from panel 13 thru 15 of the Medical Expenditure Panel Survey -Household Component (n=31,056). SPD was defined as a score of 13 or higher on the Kessler Psychological Distress Scale (K6). Logistic regression, zero-inflated negative binomial model, and generalized linear model (GLM) with a gamma distribution were used to test the study hypotheses. Adults with SPD had, in the subsequent year, 35 percent lower odds of adhering to annual dental checkups and a twofold increase in the odds of having unmet dental needs. Although adults with SPD did not have significantly more dental visits than those without SPD, they spent 20 percent more on dental care. SPD was a modest independent risk factor for lack of subsequent preventive dental care, greater unmet dental needs, and greater dental expenditures. In addition to expanding adult dental coverage, it is important to develop and evaluate interventions to increase the utilization of dental care particularly preventive dental services among people with mental illness in order to improve oral health and reduce dental expenditures among this vulnerable population. © 2014 American Association of Public Health Dentistry.

Beyond the Effects of Comprehensive Sexuality Education: The Significant Prospective Effects of Youth Assets on Contraceptive Behaviors.

PubMed

Green, Jennifer; Oman, Roy F; Vesely, Sara K; Cheney, Marshall; Carroll, Leslie

2017-12-01

The purpose of the study was to prospectively determine if youth assets were significantly associated with contraception use after accounting for the effects of youths' exposure to comprehensive sexuality education programming. Prospective associations between youth asset scores, comprehensive sexuality education topics received, type of contraceptive used, and consistent contraceptive use were analyzed using multinomial and binomial logistic regression in a sample of 757 sexually active youth. Higher youth asset scores were associated with condom use (adjusted odds ratio [AOR] = 1.51, 95% CI = 1.01-2.28), hormonal birth control use (AOR = 2.71, 95% CI = 1.69-4.35), dual method use (AOR = 2.35, 95% CI = 1.44-3.82), and consistent contraceptive use (AOR = 1.97, 95% CI = 1.38-2.82). After controlling for youths' experience with comprehensive sexuality education, higher youth asset scores remained a significant predictor of hormonal birth control use (AOR = 2.09, 95% CI = 1.28-3.42), dual method use (AOR = 2.58, 95% CI = 1.61-4.15), and consistent contraceptive use (AOR = 1.95, 95% CI = 1.36-2.80). Youth serving organizations that are interested in preventing teen pregnancy should consider widespread implementation of evidence-based youth development programs that focus on building and strengthening specific youth assets. Copyright © 2017 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Does distrust in providers affect health-care utilization in China?

PubMed Central

Duckett, Jane; Hunt, Kate; Munro, Neil; Sutton, Matt

2016-01-01

How trust affects health-care utilization is not well-understood, especially in low- and middle-income countries. This article focuses on China, a middle-income country where low trust in health-care settings has become a prominent issue, but actual levels of distrust and their implications for utilization are unknown. We conducted a nationally representative survey of the Chinese population (November 2012 to January 2013), which resulted in a sample of 3680 adult men and women. Respondents rated their trust in different types of health-care providers. Using multivariate logistic and negative binomial regression models, we estimated the association between distrust in clinics and respondents’ hospital visits in the last year; whether they had sought hospital treatment first for two common symptoms (headache, cold) in the last 2 months; and whether they said they would go first to a hospital if they had a minor or major illness. We analysed these associations before and after adjusting for performance evaluations of clinics and hospitals, controlling for sex, age, education, income, insurance status, household registration and self-assessed health. We found that distrust in hospitals is low, but distrust in clinics is high and strongly associated with increased hospital utilization, especially for minor symptoms and illnesses. Further research is needed to understand the reasons for distrust in clinics because its effects are not fully accounted for by poor evaluations of their competence. PMID:27117483

Prevalence of Functional Constipation and Relationship with Dietary Habits in 3- to 8-Year-Old Children in Japan.

PubMed

Fujitani, Asami; Sogo, Tsuyoshi; Inui, Ayano; Kawakubo, Kiyoshi

2018-01-01

To determine the prevalence and effect of dietary habits on functional constipation in preschool and early elementary school children in Japan. A total of 3595 children aged 3 to 8 years from 28 nursery schools and 22 elementary schools in Yokohama City, Kanagawa Prefecture, Japan, were evaluated. The subjects were divided into a functional constipation group and a nonfunctional constipation group according to the Rome III criteria. Dietary intake data were collected using a brief-type, self-administered, diet-history questionnaire validated for Japanese preschool-aged children. Of the 3595 subjects evaluated, 718 (20.0%) had functional constipation. The association between functional constipation and gender was not statistically significant ( p = 0.617). A decrease in bowel frequency was observed in 15.9% of those with functional constipation. There was no significant difference in the proportion of participants in the constipation group by age ( p = 0.112). Binomial logistic regression analysis indicated that only fat per 100 kcal positively correlated with functional constipation [odds ratio = 1.216, 95% confidence interval: 1.0476-1.412]. Functional constipation is common among children in preschool and early elementary school in urban areas of Japan. Parents should pay attention to constipation-related symptoms other than defecation frequency. A high-fat diet should be avoided to prevent functional constipation.

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.

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 ...

Quasi-Likelihood Techniques in a Logistic Regression Equation for Identifying Simulium damnosum s.l. Larval Habitats Intra-cluster Covariates in Togo.

PubMed

Jacob, Benjamin G; Novak, Robert J; Toe, Laurent; Sanfo, Moussa S; Afriyie, Abena N; Ibrahim, Mohammed A; Griffith, Daniel A; Unnasch, Thomas R

2012-01-01

The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l. a major black-fly vector of Onchoceriasis, postulate models relating observational ecological-sampled parameter estimators to prolific habitats without accounting for residual intra-cluster error correlation effects. Generally, this correlation comes from two sources: (1) the design of the random effects and their assumed covariance from the multiple levels within the regression model; and, (2) the correlation structure of the residuals. Unfortunately, inconspicuous errors in residual intra-cluster correlation estimates can overstate precision in forecasted S.damnosum s.l. riverine larval habitat explanatory attributes regardless how they are treated (e.g., independent, autoregressive, Toeplitz, etc). In this research, the geographical locations for multiple riverine-based S. damnosum s.l. larval ecosystem habitats sampled from 2 pre-established epidemiological sites in Togo were identified and recorded from July 2009 to June 2010. Initially the data was aggregated into proc genmod. An agglomerative hierarchical residual cluster-based analysis was then performed. The sampled clustered study site data was then analyzed for statistical correlations using Monthly Biting Rates (MBR). Euclidean distance measurements and terrain-related geomorphological statistics were then generated in ArcGIS. A digital overlay was then performed also in ArcGIS using the georeferenced ground coordinates of high and low density clusters stratified by Annual Biting Rates (ABR). This data was overlain onto multitemporal sub-meter pixel resolution satellite data (i.e., QuickBird 0.61m wavbands ). Orthogonal spatial filter eigenvectors were then generated in SAS/GIS. Univariate and non-linear regression-based models (i.e., Logistic, Poisson and Negative Binomial) were also employed to determine probability distributions and to identify statistically significant parameter estimators from the sampled data. Thereafter, Durbin-Watson test statistics were used to test the null hypothesis that the regression residuals were not autocorrelated against the alternative that the residuals followed an autoregressive process in AUTOREG. Bayesian uncertainty matrices were also constructed employing normal priors for each of the sampled estimators in PROC MCMC. The residuals revealed both spatially structured and unstructured error effects in the high and low ABR-stratified clusters. The analyses also revealed that the estimators, levels of turbidity and presence of rocks were statistically significant for the high-ABR-stratified clusters, while the estimators distance between habitats and floating vegetation were important for the low-ABR-stratified cluster. Varying and constant coefficient regression models, ABR- stratified GIS-generated clusters, sub-meter resolution satellite imagery, a robust residual intra-cluster diagnostic test, MBR-based histograms, eigendecomposition spatial filter algorithms and Bayesian matrices can enable accurate autoregressive estimation of latent uncertainity affects and other residual error probabilities (i.e., heteroskedasticity) for testing correlations between georeferenced S. damnosum s.l. riverine larval habitat estimators. The asymptotic distribution of the resulting residual adjusted intra-cluster predictor error autocovariate coefficients can thereafter be established while estimates of the asymptotic variance can lead to the construction of approximate confidence intervals for accurately targeting productive S. damnosum s.l habitats based on spatiotemporal field-sampled count data.

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.

Prediction of different ovarian responses using anti-Müllerian hormone following a long agonist treatment protocol for IVF.

PubMed

Heidar, Z; Bakhtiyari, M; Mirzamoradi, M; Zadehmodarres, S; Sarfjoo, F S; Mansournia, M A

2015-09-01

The purpose of this study was to predict the poor and excessive ovarian response using anti-Müllerian hormone (AMH) levels following a long agonist protocol in IVF candidates. Through a prospective cohort study, the type of relationship and appropriate scale for AMH were determined using the fractional polynomial regression. To determine the effect of AMH on the outcomes of ovarian stimulation and different ovarian responses, the multi-nominal and negative binomial regression models were fitted using backward stepwise method. The ovarian response of study subject who entered a standard long-term treatment cycle with GnRH agonist was evaluated using prediction model, separately and in combined models with (ROC) curves. The use of standard long-term treatments with GnRH agonist led to positive pregnancy test results in 30% of treated patients. With each unit increase in the log of AMH, the odds ratio of having poor response compared to normal response decreases by 64% (OR 0.36, 95% CI 0.19-0.68). Also the results of negative binomial regression model indicated that for one unit increase in the log of AMH blood levels, the odds of releasing an oocyte increased 24% (OR 1.24, 95% CI 1.14-1.35). The optimal cut-off points of AMH for predicting excessive and poor ovarian responses were 3.4 and 1.2 ng/ml, respectively, with area under curves of 0.69 (0.60-0.77) and 0.76 (0.66-0.86), respectively. By considering the age of the patient undergoing infertility treatment as a variable affecting ovulation, use of AMH levels showed to be a good test to discriminate between different ovarian responses.

Cocoa Farmers’ Compliance with Safety Precautions in Spraying Agrochemicals and Use of Personal Protective Equipment (PPE) in Cameroon

PubMed Central

2018-01-01

The inability of farmers to comply with essential precautions in the course of spraying agrochemicals remains a policy dilemma, especially in developing countries. The objectives of this paper were to assess compliance of cocoa farmers with agrochemical safety measures, analyse the factors explaining involvement of cocoa farmers in the practice of reusing agrochemical containers and wearing of personal protective equipment (PPE). Data were collected with structured questionnaires from 667 cocoa farmers from the Centre and South West regions in Cameroon. Data analyses were carried out with Probit regression and Negative Binomial regression models. The results showed that average cocoa farm sizes were 3.55 ha and 2.82 ha in South West and Centre regions, respectively, and 89.80% and 42.64% complied with manufacturers’ instructions in the use of insecticides. Eating or drinking while spraying insecticides and fungicides was reported by 4.20% and 5.10% of all farmers in the two regions, respectively. However, 37.78% and 57.57% of all farmers wore hand gloves and safety boots while spraying insecticides in the South West and Centre regions of Cameroon, respectively. In addition, 7.80% of all the farmers would wash agrochemical containers and use them at home, while 42.43% would wash and use them on their farms. Probit regression results showed that probability of reusing agrochemical containers was significantly influenced (p < 0.05) by region of residence of cocoa farmers, gender, possession of formal education and farming as primary occupation. The Negative Binomial regression results showed that the log of number PPE worn was significantly influenced (p < 0.10) by region, marital status, attainment of formal education, good health, awareness of manufacturers’ instructions, land area and contact index. It was among others concluded that efforts to train farmers on the need to be familiar with manufacturers’ instructions and use PPE would enhance their safety in the course of spraying agrochemicals. PMID:29438333

Cocoa Farmers' Compliance with Safety Precautions in Spraying Agrochemicals and Use of Personal Protective Equipment (PPE) in Cameroon.

PubMed

Oyekale, Abayomi Samuel

2018-02-13

The inability of farmers to comply with essential precautions in the course of spraying agrochemicals remains a policy dilemma, especially in developing countries. The objectives of this paper were to assess compliance of cocoa farmers with agrochemical safety measures, analyse the factors explaining involvement of cocoa farmers in the practice of reusing agrochemical containers and wearing of personal protective equipment (PPE). Data were collected with structured questionnaires from 667 cocoa farmers from the Centre and South West regions in Cameroon. Data analyses were carried out with Probit regression and Negative Binomial regression models. The results showed that average cocoa farm sizes were 3.55 ha and 2.82 ha in South West and Centre regions, respectively, and 89.80% and 42.64% complied with manufacturers' instructions in the use of insecticides. Eating or drinking while spraying insecticides and fungicides was reported by 4.20% and 5.10% of all farmers in the two regions, respectively. However, 37.78% and 57.57% of all farmers wore hand gloves and safety boots while spraying insecticides in the South West and Centre regions of Cameroon, respectively. In addition, 7.80% of all the farmers would wash agrochemical containers and use them at home, while 42.43% would wash and use them on their farms. Probit regression results showed that probability of reusing agrochemical containers was significantly influenced ( p < 0.05) by region of residence of cocoa farmers, gender, possession of formal education and farming as primary occupation. The Negative Binomial regression results showed that the log of number PPE worn was significantly influenced ( p < 0.10) by region, marital status, attainment of formal education, good health, awareness of manufacturers' instructions, land area and contact index. It was among others concluded that efforts to train farmers on the need to be familiar with manufacturers' instructions and use PPE would enhance their safety in the course of spraying agrochemicals.

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.

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,…

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.

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.

Distinguishing between Binomial, Hypergeometric and Negative Binomial Distributions

ERIC Educational Resources Information Center

Wroughton, Jacqueline; Cole, Tarah

2013-01-01

Recognizing the differences between three discrete distributions (Binomial, Hypergeometric and Negative Binomial) can be challenging for students. We present an activity designed to help students differentiate among these distributions. In addition, we present assessment results in the form of pre- and post-tests that were designed to assess the…

Library Book Circulation and the Beta-Binomial Distribution.

ERIC Educational Resources Information Center

Gelman, E.; Sichel, H. S.

1987-01-01

Argues that library book circulation is a binomial rather than a Poisson process, and that individual book popularities are continuous beta distributions. Three examples demonstrate the superiority of beta over negative binomial distribution, and it is suggested that a bivariate-binomial process would be helpful in predicting future book…

Matching the Statistical Model to the Research Question for Dental Caries Indices with Many Zero Counts.

PubMed

Preisser, John S; Long, D Leann; Stamm, John W

2017-01-01

Marginalized zero-inflated count regression models have recently been introduced for the statistical analysis of dental caries indices and other zero-inflated count data as alternatives to traditional zero-inflated and hurdle models. Unlike the standard approaches, the marginalized models directly estimate overall exposure or treatment effects by relating covariates to the marginal mean count. This article discusses model interpretation and model class choice according to the research question being addressed in caries research. Two data sets, one consisting of fictional dmft counts in 2 groups and the other on DMFS among schoolchildren from a randomized clinical trial comparing 3 toothpaste formulations to prevent incident dental caries, are analyzed with negative binomial hurdle, zero-inflated negative binomial, and marginalized zero-inflated negative binomial models. In the first example, estimates of treatment effects vary according to the type of incidence rate ratio (IRR) estimated by the model. Estimates of IRRs in the analysis of the randomized clinical trial were similar despite their distinctive interpretations. The choice of statistical model class should match the study's purpose, while accounting for the broad decline in children's caries experience, such that dmft and DMFS indices more frequently generate zero counts. Marginalized (marginal mean) models for zero-inflated count data should be considered for direct assessment of exposure effects on the marginal mean dental caries count in the presence of high frequencies of zero counts. © 2017 S. Karger AG, Basel.

Matching the Statistical Model to the Research Question for Dental Caries Indices with Many Zero Counts

PubMed Central

Preisser, John S.; Long, D. Leann; Stamm, John W.

2017-01-01

Marginalized zero-inflated count regression models have recently been introduced for the statistical analysis of dental caries indices and other zero-inflated count data as alternatives to traditional zero-inflated and hurdle models. Unlike the standard approaches, the marginalized models directly estimate overall exposure or treatment effects by relating covariates to the marginal mean count. This article discusses model interpretation and model class choice according to the research question being addressed in caries research. Two datasets, one consisting of fictional dmft counts in two groups and the other on DMFS among schoolchildren from a randomized clinical trial (RCT) comparing three toothpaste formulations to prevent incident dental caries, are analysed with negative binomial hurdle (NBH), zero-inflated negative binomial (ZINB), and marginalized zero-inflated negative binomial (MZINB) models. In the first example, estimates of treatment effects vary according to the type of incidence rate ratio (IRR) estimated by the model. Estimates of IRRs in the analysis of the RCT were similar despite their distinctive interpretations. Choice of statistical model class should match the study’s purpose, while accounting for the broad decline in children’s caries experience, such that dmft and DMFS indices more frequently generate zero counts. Marginalized (marginal mean) models for zero-inflated count data should be considered for direct assessment of exposure effects on the marginal mean dental caries count in the presence of high frequencies of zero counts. PMID:28291962

Development of binomial sequential sampling plans for forecasting Listronotus maculicollis (Coleoptera: Curculionidae) larvae based on the relationship to adult counts and turfgrass damage.

PubMed

McGraw, Benjamin A; Koppenhöfer, Albrecht M

2009-06-01

Binomial sequential sampling plans were developed to forecast weevil Listronotus maculicollis Kirby (Coleoptera: Curculionidae), larval damage to golf course turfgrass and aid in the development of integrated pest management programs for the weevil. Populations of emerging overwintered adults were sampled over a 2-yr period to determine the relationship between adult counts, larval density, and turfgrass damage. Larval density and composition of preferred host plants (Poa annua L.) significantly affected the expression of turfgrass damage. Multiple regression indicates that damage may occur in moderately mixed P. annua stands with as few as 10 larvae per 0.09 m2. However, > 150 larvae were required before damage became apparent in pure Agrostis stolonifera L. plots. Adult counts during peaks in emergence as well as cumulative counts across the emergence period were significantly correlated to future densities of larvae. Eight binomial sequential sampling plans based on two tally thresholds for classifying infestation (T = 1 and two adults) and four adult density thresholds (0.5, 0.85, 1.15, and 1.35 per 3.34 m2) were developed to forecast the likelihood of turfgrass damage by using adult counts during peak emergence. Resampling for validation of sample plans software was used to validate sampling plans with field-collected data sets. All sampling plans were found to deliver accurate classifications (correct decisions were made between 84.4 and 96.8%) in a practical timeframe (average sampling cost < 22.7 min).

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.

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.

Accounting for non-independent detection when estimating abundance of organisms with a Bayesian approach

USGS Publications Warehouse

Martin, Julien; Royle, J. Andrew; MacKenzie, Darryl I.; Edwards, Holly H.; Kery, Marc; Gardner, Beth

2011-01-01

Summary 1. Binomial mixture models use repeated count data to estimate abundance. They are becoming increasingly popular because they provide a simple and cost-effective way to account for imperfect detection. However, these models assume that individuals are detected independently of each other. This assumption may often be violated in the field. For instance, manatees (Trichechus manatus latirostris) may surface in turbid water (i.e. become available for detection during aerial surveys) in a correlated manner (i.e. in groups). However, correlated behaviour, affecting the non-independence of individual detections, may also be relevant in other systems (e.g. correlated patterns of singing in birds and amphibians). 2. We extend binomial mixture models to account for correlated behaviour and therefore to account for non-independent detection of individuals. We simulated correlated behaviour using beta-binomial random variables. Our approach can be used to simultaneously estimate abundance, detection probability and a correlation parameter. 3. Fitting binomial mixture models to data that followed a beta-binomial distribution resulted in an overestimation of abundance even for moderate levels of correlation. In contrast, the beta-binomial mixture model performed considerably better in our simulation scenarios. We also present a goodness-of-fit procedure to evaluate the fit of beta-binomial mixture models. 4. We illustrate our approach by fitting both binomial and beta-binomial mixture models to aerial survey data of manatees in Florida. We found that the binomial mixture model did not fit the data, whereas there was no evidence of lack of fit for the beta-binomial mixture model. This example helps illustrate the importance of using simulations and assessing goodness-of-fit when analysing ecological data with N-mixture models. Indeed, both the simulations and the goodness-of-fit procedure highlighted the limitations of the standard binomial mixture model for aerial manatee surveys. 5. Overestimation of abundance by binomial mixture models owing to non-independent detections is problematic for ecological studies, but also for conservation. For example, in the case of endangered species, it could lead to inappropriate management decisions, such as downlisting. These issues will be increasingly relevant as more ecologists apply flexible N-mixture models to ecological data.

Association between physiological falls risk and physical performance tests among community-dwelling older adults

PubMed Central

Singh, Devinder KA; Pillai, Sharmila GK; Tan, Sin Thien; Tai, Chu Chiau; Shahar, Suzana

2015-01-01

Background Physical performance and balance declines with aging and may lead to increased risk of falls. Physical performance tests may be useful for initial fall-risk screening test among community-dwelling older adults. Physiological profile assessment (PPA), a composite falls risk assessment tool is reported to have 75% accuracy to screen for physiological falls risk. PPA correlates with Timed Up and Go (TUG) test. However, the association between many other commonly used physical performance tests and PPA is not known. The aim of the present study was to examine the association between physiological falls risk measured using PPA and a battery of physical performance tests. Methods One hundred and forty older adults from a senior citizens club in Kuala Lumpur, Malaysia (94 females, 46 males), aged 60 years and above (65.77±4.61), participated in this cross-sectional study. Participants were screened for falls risk using PPA. A battery of physical performance tests that include ten-step test (TST), short physical performance battery (SPPB), functional reach test (FRT), static balance test (SBT), TUG, dominant hand-grip strength (DHGS), and gait speed test (GST) were also performed. Spearman’s rank correlation and binomial logistic regression were performed to examine the significantly associated independent variables (physical performance tests) with falls risk (dependent variable). Results Approximately 13% older adults were at high risk of falls categorized using PPA. Significant differences (P<0.05) were demonstrated for age, TST, SPPB, FRT, SBT, TUG between high and low falls risk group. A significant (P<0.01) weak correlation was found between PPA and TST (r=0.25), TUG (r=0.27), SBT (r=0.23), SPPB (r=−0.33), and FRT (r=−0.23). Binary logistic regression results demonstrated that SBT measuring postural sways objectively using a balance board was the only significant predictor of physiological falls risk (P<0.05, odds ratio of 2.12). Conclusion The reference values of physical performance tests in our study may be used as a guide for initial falls screening to categorize high and low physiological falls risk among community-dwelling older adults. A more comprehensive assessment of falls risk can be performed thereafter for more specific intervention of underlying impairments. PMID:26316727

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.

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

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…

Not in My Back Yard: A Comparative Analysis of Crime Around Publicly Funded Drug Treatment Centers, Liquor Stores, Convenience Stores, and Corner Stores in One Mid-Atlantic City.

PubMed

Furr-Holden, C Debra M; Milam, Adam J; Nesoff, Elizabeth D; Johnson, Renee M; Fakunle, David O; Jennings, Jacky M; Thorpe, Roland J

2016-01-01

This research examined whether publicly funded drug treatment centers (DTCs) were associated with violent crime in excess of the violence happening around other commercial businesses. Violent crime data and locations of community entities were geocoded and mapped. DTCs and other retail outlets were matched based on a Neighborhood Disadvantage score at the census tract level. Street network buffers ranging from 100 to 1,400 feet were placed around each location. Negative binomial regression models were used to estimate the relationship between the count of violent crimes and the distance from each business type. Compared with the mean count of violent crime around drug treatment centers, the mean count of violent crime (M = 2.87) was significantly higher around liquor stores (M = 3.98; t test; p < .01) and corner stores (M = 3.78; t test; p < .01), and there was no statistically significant difference between the count around convenience stores (M = 2.65; t test; p = .32). In the adjusted negative binomial regression models, there was a negative and significant relationship between the count of violent crime and the distance from drug treatment centers (β = -.069, p < .01), liquor stores (β = -.081, p < .01), corner stores (β = -.116, p < .01), and convenience stores (β = -.154, p < .01). Violent crime associated with drug treatment centers is similar to that associated with liquor stores and is less frequent than that associated with convenience stores and corner stores.

[Epidemiology of scrub typhus and influencing factors in Yunnan province, 2006-2013].

PubMed

Sun, Y; Shi, C; Li, X L; Fang, L Q; Cao, W C

2018-01-10

Objective: To understand the epidemiological characteristics of scrub typhu s and influencing factors in Yunnan province, and provide further information for the prevention and control of scrub typhus. Methods: Based on the incidence data of scrub typhus reported in Yunnan from 2006 to 2013, the epidemiological characteristics of scrub typhus were analyzed and related environmental factors were identified with panel negative binomial regression model. Results: A total of 8 980 scrub typhus cases were reported during 2006-2013 in Yunnan. The average annual incidence was 2.46/100 000, with an uptrend observed. Natural focus expansion was found, affecting 71.3% of the counties in 2013. The epidemic mainly occurred in summer and autumn with the incidence peak during July-October. The annual incidence was higher in females than in males. More cases occurred in children and farmers, the proportions of cases in farmers and pre-school aged children showed an obvious increase. Panel negative binomial regression model indicated that the transmission risk of scrub typhus was positive associated with monthly temperature and monthly relative humidity. Furthermore, an "U" pattern between the risk and the increased coverage of cropland and grassland as well as an "inverted-U" pattern between the risk and increased coverage of shrub were observed. Conclusion: It is necessary to strengthen the scrub typhus surveillance in warm and moist areas as well as the areas with high coverage of cropland and grassland in Yunnan, and the health education in children and farmers who are at high risk.

Use of clinical practice guidelines by dentists: findings from the Japanese dental practice-based research network.

PubMed

Kakudate, Naoki; Yokoyama, Yoko; Sumida, Futoshi; Matsumoto, Yuki; Gordan, Valeria V; Gilbert, Gregg H

2017-02-01

The objectives of this study were to: (1) examine differences in the use of dental clinical practice guidelines among Japanese dentists, and (2) identify characteristics associated with the number of guidelines used by participating dentists. We conducted a cross-sectional study consisting of a questionnaire survey in Japan between July 2014 and May 2015. The study queried dentists working in outpatient dental practices who are affiliated with the Dental Practice-Based Research Network Japan (n = 148). They were asked whether they have used each of 15 Japanese dental clinical guidelines. Associations between the number of guidelines used by participants and specific characteristics were analysed via negative binomial regression analysis. The mean number of guidelines used by participating dentists was 2.5 ± 2.9 [standard deviation (SD)]. Rate of use of guidelines showed substantial variation, from 5% to 34% among dentists. The proportion of dentists that used guidelines was the highest among oral medicine specialists, who had the highest proportion for 10 of 15 guidelines. Negative binomial regression analysis identified three factors significantly associated with the number of guidelines used: 'years since graduation from dental school', 'specialty practice' and 'practice busyness'. These results suggest that the use of clinical practice guidelines by Japanese dentists may still be inadequate. Training in the use of the guidelines could be given to dental students as undergraduate education and to young clinicians as continuing education. © 2016 John Wiley & Sons, Ltd.

Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach

PubMed Central

Chen, Yong; Hong, Chuan; Ning, Yang; Su, Xiao

2018-01-01

When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-binomial model for the meta-analysis of studies with binary outcomes. This model is based on the composite likelihood approach, and has several attractive features compared to the existing models such as bivariate generalized linear mixed model (Chu and Cole, 2006) and Sarmanov beta-binomial model (Chen et al., 2012). The advantages of the proposed marginal model include modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, since the marginal beta-binomial model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-binomial model with the bivariate generalized linear mixed model and the Sarmanov beta-binomial model by simulation studies. Interestingly, the results show that the marginal beta-binomial model performs better than the Sarmanov beta-binomial model, whether or not the true model is Sarmanov beta-binomial, and the marginal beta-binomial model is more robust than the bivariate generalized linear mixed model under model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case-control studies are conducted for illustration. PMID:26303591

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.

Factors affecting the probability of first year medical student dropout in the UK: a logistic analysis for the intake cohorts of 1980-92.

PubMed

Arulampalam, Wiji; Naylor, Robin; Smith, Jeremy

2004-05-01

In the context of the 1997 Report of the Medical Workforce Standing Advisory Committee, it is important that we develop an understanding of the factors influencing medical school retention rates. To analyse the determinants of the probability that an individual medical student will drop out of medical school during their first year of study. Binomial and multinomial logistic regression analysis of individual-level administrative data on 51 810 students in 21 medical schools in the UK for the intake cohorts of 1980-92 was performed. The overall average first year dropout rate over the period 1980-92 was calculated to be 3.8%. We found that the probability that a student would drop out of medical school during their first year of study was influenced significantly by both the subjects studied at A-level and by the scores achieved. For example, achieving 1 grade higher in biology, chemistry or physics reduced the dropout probability by 0.38% points, equivalent to a fall of 10%. We also found that males were about 8% more likely to drop out than females. The medical school attended also had a significant effect on the estimated dropout probability. Indicators of both the social class and the previous school background of the student were largely insignificant. Policies aimed at increasing the size of the medical student intake in the UK and of widening access to students from non-traditional backgrounds should be informed by evidence that student dropout probabilities are sensitive to measures of A-level attainment, such as subject studied and scores achieved. If traditional entry requirements or standards are relaxed, then this is likely to have detrimental effects on medical schools' retention rates unless accompanied by appropriate measures such as focussed student support.

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.

Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach.

PubMed

Chen, Yong; Hong, Chuan; Ning, Yang; Su, Xiao

2016-01-15

When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-binomial model for the meta-analysis of studies with binary outcomes. This model is based on the composite likelihood approach and has several attractive features compared with the existing models such as bivariate generalized linear mixed model (Chu and Cole, 2006) and Sarmanov beta-binomial model (Chen et al., 2012). The advantages of the proposed marginal model include modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, because the marginal beta-binomial model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-binomial model with the bivariate generalized linear mixed model and the Sarmanov beta-binomial model by simulation studies. Interestingly, the results show that the marginal beta-binomial model performs better than the Sarmanov beta-binomial model, whether or not the true model is Sarmanov beta-binomial, and the marginal beta-binomial model is more robust than the bivariate generalized linear mixed model under model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case-control studies are conducted for illustration. Copyright © 2015 John Wiley & Sons, Ltd.

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.

Tobit analysis of vehicle accident rates on interstate highways.

PubMed

Anastasopoulos, Panagiotis Ch; Tarko, Andrew P; Mannering, Fred L

2008-03-01

There has been an abundance of research that has used Poisson models and its variants (negative binomial and zero-inflated models) to improve our understanding of the factors that affect accident frequencies on roadway segments. This study explores the application of an alternate method, tobit regression, by viewing vehicle accident rates directly (instead of frequencies) as a continuous variable that is left-censored at zero. Using data from vehicle accidents on Indiana interstates, the estimation results show that many factors relating to pavement condition, roadway geometrics and traffic characteristics significantly affect vehicle accident rates.

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.

Reliability of environmental sampling culture results using the negative binomial intraclass correlation coefficient.

PubMed

Aly, Sharif S; Zhao, Jianyang; Li, Ben; Jiang, Jiming

2014-01-01

The Intraclass Correlation Coefficient (ICC) is commonly used to estimate the similarity between quantitative measures obtained from different sources. Overdispersed data is traditionally transformed so that linear mixed model (LMM) based ICC can be estimated. A common transformation used is the natural logarithm. The reliability of environmental sampling of fecal slurry on freestall pens has been estimated for Mycobacterium avium subsp. paratuberculosis using the natural logarithm transformed culture results. Recently, the negative binomial ICC was defined based on a generalized linear mixed model for negative binomial distributed data. The current study reports on the negative binomial ICC estimate which includes fixed effects using culture results of environmental samples. Simulations using a wide variety of inputs and negative binomial distribution parameters (r; p) showed better performance of the new negative binomial ICC compared to the ICC based on LMM even when negative binomial data was logarithm, and square root transformed. A second comparison that targeted a wider range of ICC values showed that the mean of estimated ICC closely approximated the true ICC.

Trends in Hospital Admission for Diabetic Ketoacidosis in Adults With Type 1 and Type 2 Diabetes in England, 1998-2013: A Retrospective Cohort Study.

PubMed

Zhong, Victor W; Juhaeri, Juhaeri; Mayer-Davis, Elizabeth J

2018-01-31

This study determined trends in hospital admission for diabetic ketoacidosis (DKA) in adults with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) from 1998 to 2013 in England. The study population included 23,246 adults with T1DM and 241,441 adults with T2DM from the Clinical Practice Research Datalink and Hospital Episode Statistics. All hospital admissions for DKA as the primary diagnosis from 1998 to 2013 were identified. Trends in hospital admission for DKA in incidence, length of hospital stay, 30-day all-cause readmission rate, and 30-day and 1-year all-cause mortality rates were determined using joinpoint regression, negative binomial regression, and logistic regression models. For T1DM, the incidence of hospital admission for DKA increased between 1998 and 2007 and remained static until 2013. The incidence in 2013 was higher than that in 1998 (incidence rate ratio 1.53 [95% CI 1.09-2.16]). For T2DM, the incidence increased 4.24% (2.82-5.69) annually between 1998 and 2013. The length of hospital stay decreased over time for both diabetes types ( P ≤ 0.0004). Adults with T1DM were more likely to be discharged within 2 days compared with adults with T2DM (odds ratio [OR] 1.28 [1.07-1.53]). The 30-day readmission rate was higher in T1DM than in T2DM (OR 1.61 [1.04-2.50]) but remained unchanged for both diabetes types over time. Trends in 30-day and 1-year all-cause mortality rates were also stable, with no difference by diabetes type. In the previous 2 decades in England, hospitalization for DKA increased in adults with T1DM and in those with T2DM, and associated health care performance did not improve except decreased length of hospital stay. © 2018 by the American Diabetes Association.

Drought impact functions as intermediate step towards drought damage assessment

NASA Astrophysics Data System (ADS)

Bachmair, Sophie; Svensson, Cecilia; Prosdocimi, Ilaria; Hannaford, Jamie; Helm Smith, Kelly; Svoboda, Mark; Stahl, Kerstin

2016-04-01

While damage or vulnerability functions for floods and seismic hazards have gained considerable attention, there is comparably little knowledge on drought damage or loss. On the one hand this is due to the complexity of the drought hazard affecting different domains of the hydrological cycle and different sectors of human activity. Hence, a single hazard indicator is likely not able to fully capture this multifaceted hazard. On the other hand, drought impacts are often non-structural and hard to quantify or monetize. Examples are impaired navigability of streams, restrictions on domestic water use, reduced hydropower production, reduced tree growth, and irreversible deterioration/loss of wetlands. Apart from reduced crop yield, data about drought damage or loss with adequate spatial and temporal resolution is scarce, making the development of drought damage functions difficult. As an intermediate step towards drought damage functions we exploit text-based reports on drought impacts from the European Drought Impact report Inventory and the US Drought Impact Reporter to derive surrogate information for drought damage or loss. First, text-based information on drought impacts is converted into timeseries of absence versus presence of impacts, or number of impact occurrences. Second, meaningful hydro-meteorological indicators characterizing drought intensity are identified. Third, different statistical models are tested as link functions relating drought hazard indicators with drought impacts: 1) logistic regression for drought impacts coded as binary response variable; and 2) mixture/hurdle models (zero-inflated/zero-altered negative binomial regression) and an ensemble regression tree approach for modeling the number of drought impact occurrences. Testing the predictability of (number of) drought impact occurrences based on cross-validation revealed a good agreement between observed and modeled (number of) impacts for regions at the scale of federal states or provinces with good data availability. Impact functions representing localized drought impacts are more challenging to construct given that less data is available, yet may provide information that more directly addresses stakeholders' needs. Overall, our study contributes insights into how drought intensity translates into ecological and socioeconomic impacts, and how such information may be used for enhancing drought monitoring and early warning.

Self-harm amongst people of Chinese origin versus White people living in England: a cohort study.

PubMed

Chang, Shu-Sen; Steeg, Sarah; Kapur, Navneet; Webb, Roger T; Yip, Paul S F; Cooper, Jayne

2015-04-14

There has been little previous research on self-harm among people of Chinese origin living in the UK, although this population has grown substantially in recent years and China is now the largest source of international students at UK universities. We conducted a prospective cohort study using self-harm presentation data (1997-2011) collected from three hospitals in the City of Manchester, which has the largest Chinese population across all UK Local Authorities. Rate ratios between the Chinese and White groups were calculated using Poisson regression models. Chi-square tests (or Fisher's exact tests), logistic regression, and log-binomial regression were used to examine differences in characteristics and clinical management between groups. Ethnicity was known in the study cohort for 23,297 (87%) amongst 26,894 individuals aged 15 years and above. A total number of 97/23,297 (0.4%) people of Chinese ethnic origin presented with self-harm over the study period and 20,419 (88%) were White people. Incidence of self-harm in the Chinese group (aged 16-64 years) was less than one fifth of that found in White people (0.6 versus 3.2 per 1000 person-years; rate ratio 0.18, 95% confidence interval 0.13-0.24), and was particularly low amongst men of Chinese origin. Individuals of Chinese origin who presented with self-harm were younger, more likely to be female and students, and more likely to self-injure and describe relationship problems as a precipitant than White people. They were less likely to have clinical risk factors such as drug/alcohol misuse and receiving psychiatric treatment, and were rated to have lower risk of self-harm repetition by treating clinicians. Future research needs to investigate whether the low incidence of self-harm presenting to hospitals amongst people of Chinese origin truly reflects a lower frequency of self-harm, or alternatively is due to markedly different post-episode help-seeking behaviours or student overrepresentation in this ethnic group. Relevant healthcare professionals need to be aware of the risk characteristics of people of Chinese origin who self-harm.

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.

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.

Can SLE classification rules be effectively applied to diagnose unclear SLE cases?

PubMed Central

Mesa, Annia; Fernandez, Mitch; Wu, Wensong; Narasimhan, Giri; Greidinger, Eric L.; Mills, DeEtta K.

2016-01-01

Summary Objective Develop a novel classification criteria to distinguish between unclear SLE and MCTD cases. Methods A total of 205 variables from 111 SLE and 55 MCTD patients were evaluated to uncover unique molecular and clinical markers for each disease. Binomial logistic regressions (BLR) were performed on currently used SLE and MCTD classification criteria sets to obtain six reduced models with power to discriminate between unclear SLE and MCTD patients which were confirmed by Receiving Operating Characteristic (ROC) curve. Decision trees were employed to delineate novel classification rules to discriminate between unclear SLE and MCTD patients. Results SLE and MCTD patients exhibited contrasting molecular markers and clinical manifestations. Furthermore, reduced models highlighted SLE patients exhibit prevalence of skin rashes and renal disease while MCTD cases show dominance of myositis and muscle weakness. Additionally decision trees analyses revealed a novel classification rule tailored to differentiate unclear SLE and MCTD patients (Lu-vs-M) with an overall accuracy of 88%. Conclusions Validation of our novel proposed classification rule (Lu-vs-M) includes novel contrasting characteristics (calcinosis, CPK elevated and anti-IgM reactivity for U1-70K, U1A and U1C) between SLE and MCTD patients and showed a 33% improvement in distinguishing these disorders when compare to currently used classification criteria sets. Pending additional validation, our novel classification rule is a promising method to distinguish between patients with unclear SLE and MCTD diagnosis. PMID:27353506

The FTO gene polymorphism (rs9939609) is associated with metabolic syndrome in morbidly obese subjects from southern Italy.

PubMed

Liguori, Rosario; Labruna, Giuseppe; Alfieri, Andreina; Martone, Domenico; Farinaro, Eduardo; Contaldo, Franco; Sacchetti, Lucia; Pasanisi, Fabrizio; Buono, Pasqualina

2014-08-01

Gene variants in MC4R, SIRT1 and FTO are associated with severe obesity and metabolic impairment in Caucasians. We investigated whether common variants in these genes are associated with metabolic syndrome (MetS) in a large group of morbidly obese young adults from southern Italy. One thousand morbidly obese subjects (62% women, mean body mass index 46.5 kg/m(2), mean age 32.6 years) whose families had lived in southern Italy for at least 2 generations were recruited. Single-nucleotide polymorphisms (SNPs) rs12970134, rs477181, rs502933 (MC4R locus), rs3818292, rs7069102, rs730821, rs2273773, rs12413112 (SIRT1 locus) and rs1421085, rs9939609, 9930506, 1121980 (FTO locus) were genotyped by Taqman assay; blood parameters were assayed by routine methods; the Fat Mass, Fat Free Mass, Respiratory Quotient, Basal Metabolic Rate (BMR) and waist circumference were also determined. Binomial logistic regression showed that the TA heterozygous genotype of SNP rs9939609 in the FTO gene was associated with the presence of MetS in our population [OR (95% CI): 2.53 (1.16-5.55)]. Furthermore, the FTO rs9939609 genotype accounted for 21.3% of the MetS phenotype together with total cholesterol, BMR and age. Our results extend the knowledge on genotype susceptibility for MetS in relation to a specific geographical area of residence. Copyright © 2014 Elsevier Ltd. All rights reserved.

Associations between psychological distress, workplace accidents, workplace failures and workplace successes.

PubMed

Hilton, Michael F; Whiteford, Harvey A

2010-12-01

This study investigates associations between psychological distress and workplace accidents, workplace failures and workplace successes. The Health and Work Performance Questionnaire (HPQ) was distributed to employees of 58 large employers. A total of 60,556 full-time employees were eligible for analysis. The HPQ probed whether the respondent had, in the past 30-days, a workplace accident, success or failure ("yes" or "no"). Psychological distress was quantified using the Kessler 6 (K6) scale and categorised into low, moderate and high psychological distress. Three binomial logistic regressions were performed with the dependent variables being workplace accident, success or failure. Covariates in the models were K6 category, gender, age, marital status, education level, job category, physical health and employment sector. Accounting for all other variables, moderate and high psychological distress significantly (P < 0.0001) increased the odds ratio (OR) for a workplace accident to 1.4 for both levels of distress. Moderate and high psychological distress significantly (P < 0.0001) increased the OR (OR = 2.3 and 2.6, respectively) for a workplace failure and significantly (P < 0.0001) decreased the OR for a workplace success (OR = 0.8 and 0.7, respectively). Moderate and high psychological distress increase the OR's for workplace accidents work failures and decrease the OR of workplace successes at similar levels. As the prevalence of moderate psychological distress is approximately double that of high psychological distress moderate distress consequentially has a greater workplace impact.

Physical therapists familiarity and beliefs about health services utilization and health seeking behaviour.

PubMed

Clewley, Derek; Rhon, Dan; Flynn, Tim; Koppenhaver, Shane; Cook, Chad

2018-02-21

Physical therapists' familiarity, perceptions, and beliefs about health services utilization and health seeking behaviour have not been previously assessed. The purposes of this study were to identify physical therapists' characteristics related to familiarity of health services utilization and health seeking behaviour, and to assess what health seeking behaviour factors providers felt were related to health services utilization. We administered a survey based on the Andersen behavioural model of health services utilization to physical therapists using social media campaigns and email between March and June of 2017. In addition to descriptive statistics, we performed binomial logistic regression analysis. We asked respondents to rate familiarity with health services utilization and health seeking behaviour and collected additional characteristic variables. Physical therapists are more familiar with health services utilization than health seeking behaviour. Those who are familiar with either construct tend to be those who assess for health services utilization, use health services utilization for a prognosis, and believe that health seeking behaviour is measurable. Physical therapists rated need and enabling factors as having more influence on health services utilization than predisposing and health belief factors. Physical therapists are generally familiar with health services utilization and health seeking behaviour; however, there appears to be a disconnect between what is familiar, what is perceived to be important, and what can be assessed for both health services utilization and health seeking behaviour. Copyright © 2018 Associação Brasileira de Pesquisa e Pós-Graduação em Fisioterapia. All rights reserved.

Anthropometrics of Italian Senior Male Rugby Union Players: From Elite to Second Division.

PubMed

Fontana, Federico Y; Colosio, Alessandro; De Roia, Gabriela F; Da Lozzo, Giorgio; Pogliaghi, Silvia

2015-09-01

Anthropometric evaluation of athletes is necessary to optimize talent identification and player development. To provide a specific anthropometric reference database of senior male rugby players competing at different levels in the southern European region. Cross-sectional. In 362 professional players (25 ± 4 y; 138 Italian national team, 97 first-division, and 127 second-division national championships) the authors measured mass, stature, and percentage body fat (plicometry). Mean, SD, and coefficient of variation were calculated for forwards and backs and for positional subgroups. Binomial logistic regression and receiver-operating-characteristic curve were performed to assess which variables best predicted level assignment (international vs national level). For all competitive levels forwards were significantly heavier and taller and had a larger percentage body fat and fat-free mass than backs. The lower the competitive level, the higher the within-role variability observed; furthermore, players in a specific positional subgroup were lighter, shorter, and fatter and had less fat-free mass. Fatfree mass is the variable that best predicts the likelihood of being classified as an international or national player (cutoff value 79.54 kg). The data confirm the specificity in the physical requirements of rugby in individual playing positions at all competitive levels and document significant differences among elite and 1st- and 2nd-division players in the same positional role. These differences may reflect the variable technical abilities, selection, training practices, and requirements of the game among these categories.

The failure rate of adhesively retained composite core build-ups in comparison with metal-added glass ionomer core build-ups.

PubMed

Stober, Thomas; Rammelsberg, P

2005-01-01

The purpose of this study was to evaluate the clinical performance of two adhesively retained composite core materials and compare them with a metal-added glass ionomer. The main objective evaluated was total or partial loss of build-ups during the treatment prior to crown cementation. In 187 patients, 315 vital and non-vital teeth were built up after randomisation with either Rebilda D (RD), Rebilda SC (RSC) or Ketac Silver Aplicap (KSA). The composites were applied in the total-etch-technique with the corresponding dentin bonding agent. The metal-added glass ionomer was used with a conditioner. One group of patients was treated by experienced dentists, the other by dental students, in order to evaluate the effects of different levels of experience. Data were analysed using Mann-Whitney-U-Test and binomial logistic regression. The early failure rate (partial or total loss) of core build-ups before crown cementation was significantly higher for KSA (28.8%), as compared to RSC (15.3%, p=0.037) and RD (15%, p=0.025). Most failures were observed during the removal of the temporary crowns. The rate of replacements was between 3.0 (RD/dentists) and 20.4% (KSA/students). Furthermore, we found that build-ups made by students had a significantly higher risk of loss than those made by dentists (p=0.028). Adhesively retained self-curing composites show a better clinical short-term performance and can be recommended as core build-up materials.

There's no such thing as bad weather, just the wrong clothing: climate, weather and active school transportation in Toronto, Canada.

PubMed

Mitra, Raktim; Faulkner, Guy

2012-07-10

Climatic conditions may enable or deter active school transportation in many North American cities, but the topic remains largely overlooked in the existing literature. This study explores the effect of seasonal climate (i.e., fall versus winter) and weekly weather conditions (i.e., temperature, precipitation) on active travelling to school across different built and policy environments. Home-to-school trips by 11-12-year-old children in the City of Toronto were examined using data from the 2006 Transportation Tomorrow Survey. Binomial logistic regressions were estimated to explore the correlates of the choice of active (i.e., walking) versus non-active (i.e., private automobile, transit and school bus) mode for school trips. Climate and weather-related variables were not associated with choice of school travel mode. Children living within the sidewalk snow-plough zone (i.e., in the inner-suburban neighbourhoods) were less likely to walk to school than children living outside of the zone (i.e., in the inner-city neighbourhoods). Given that seasonality and short-term weather conditions appear not to limit active school transportation in general, built environment interventions designed to facilitate active travel could have benefits that spill over across the entire year rather than being limited to a particular season. Educational campaigns with strategies for making the trip fun and ensuring that the appropriate clothing choices are made are also warranted in complementing built environment modifications.

Associations among Pericolonic Fat, Visceral Fat, and Colorectal Polyps on CT Colonography

PubMed Central

Liu, Jiamin; Pattanaik, Sanket; Yao, Jianhua; Dwyer, Andrew J.; Pickhardt, Perry J.; Choi, J. Richard; Summers, Ronald M.

2014-01-01

OBJECTIVE To determine the association between pericolonic fat and colorectal polyps using CT colonography (CTC). METHODS 1169 patients who underwent CTC and same day optical colonoscopy were assessed. Pericolonic fat was measured on CTC in a band surrounding the colon. Visceral adipose tissue volume was measured at the L2-L3 levels. Student t-tests, odds ratio, logistic regression, binomial statistics and weighted-kappa were performed to ascertain associations with the incidence of colorectal polyps. RESULTS Pericolonic fat volume fractions (PFVF) were 61.5±11.0% versus 58.1±11.5%, 61.6 ±11.1% versus 58.7±11.5%, and 62.4±10.6% versus 58.8±11.5% for patients with and without any polyps, adenomatous polyps, and hyperplastic polyps, respectively (p<0.0001). Similar trends were observed when examining visceral fat volume fractions (VFVF). When patients were ordered by quintiles of PFVF or VFVF, there were 2.49, 2.19 and 2.39-fold increases in odds ratio for the presence of any polyp, adenomatous polyps, or hyperplastic polyps from the first to the fifth quintile for PFVF, and 1.92, 2.00 and 1.71-fold increases in odds ratio for VFVF. Polyps tended to occur more commonly in parts of the colon that had more PFVF than the spatially-adjusted average for patients in the highest quintile of VFVF. CONCLUSION Pericolonic fat accumulations, like visceral fat, are correlated with an increased risk of adenomatous and hyperplastic polyps. PMID:25558027

Comparison of the Perceived Quality of Life between Medical and Veterinary Students in Tehran.

PubMed

Labbafinejad, Yasser; Danesh, Hossein; Imanizade, Zahra

2016-01-01

Medical and veterinary professional programs are demanding and may have an impact on a student's quality of life (QOL). The aim of this study was to compare the perceived QOL of these two groups. In this study, we used the SF-36 questionnaire in which higher scores mean a better perceived QOL. Only the students in the internship phase of their program were selected so that we could compare the two groups in a similar way. In total, 308 valid questionnaires were gathered. Apart from age and body mass index (BMI), the two groups were demographically similar. The scores of five domains (physical activity limitation due to health problems, usual role limitation due to emotional problems, vitality, general mental health, and general health perception) and also the total score were statistically higher in medical students. Only the score of one domain (social activity limitation due to physical or emotional problems) was statistically higher in veterinary students. BMI, physical activity limitation due to health problems, and vitality lost their significance after binomial logistic regression. We found that, in general, veterinary students have lower scores for the perceived QOL with social function being the only exception. It can be assumed that in medical students, interaction with human patients may have a negative impact in the score of this domain. Even though medical students have shown lower perceived QOL than the general population in previous studies, veterinary students appear to have slightly lower perceived QOL than medical students.

Waist circumference cut-off in relation to body mass index and percentage of body fat in adult women from Merida, Mexico.

PubMed

Datta Banik, Sudip; Dickinson, Federico

2015-01-01

Waist circumference (WC) as an index of central obesity is related to body mass index (BMI) and percent body fat (PBF). Waist circumference data were analyzed to identify a WC cut-off for adult women with respect to BMI-based obesity (≥ 30 kg/m²) and PBF. The sample was 138 women aged 22 to 41 years with Maya ancestry (based on surnames) in Merida, Yucatan, measured during 2011 - 2013. Anthropometric parameters included height, body weight (BW), and BMI. The PBF was estimated by bioelectrical impedance. Estimated cut-offs per centimeter WC (80 - 99 cm) were predicted by BMI for obesity (≥ 30 kg m⁻²; binomial: Yes = 1, No = 0) and PBF (continuous variable) using binary logistic regression analyses. Mean age was 32 years, mean BMI was 29 kg m(-2) and mean WC was 89 cm. The sample exhibited high PBF (44 %), and high rates of overweight (44%) and obesity (40%). The threshold WC (≥ 93 cm) had high sensitivity (80%), specificity (82%), Youden Index value (0.62), and correct classification rate (82%). The area under the receiver operating characteristic curve was 88 %. The WC ≥ 93 cm cut-off had corresponding values for mean BMI (34 kg m⁻²) and PBF (47%). The optimal WC cut-off at 93 cm significantly identified central obesity for BMI ≥ 30 kg m⁻² and PBF for this sample.

The association of prenatal media marketing exposure recall with breastfeeding intentions, initiation, and duration.

PubMed

Zhang, Yuanting; Carlton, Ewa; Fein, Sara B

2013-11-01

Infant formula marketing, either directly to consumers or through health care providers, may influence women's breastfeeding intentions, initiation, and duration. However, little is known about the impact of different types of media marketing on infant feeding intentions and behavior. This study investigated whether different types of recalled prenatal media marketing exposure to formula and breastfeeding information are related to breastfeeding intentions and behavior. Data were from the Infant Feeding Practices Study II, a longitudinal study from pregnancy through the infants' first year. Sample sizes ranged from 1384 to 2530. Negative binomial, logistic regression, and survival models were used to examine associations between recalled prenatal exposure to formula or breastfeeding information and breastfeeding intentions and behavior. Exposure to infant formula information from print media was associated with shorter intended duration of exclusive breastfeeding, and formula information from websites was related to lower odds of both intended and actual initiation. Exposure to breastfeeding information from websites was related to higher odds of both intended and actual initiation and longer intended duration of any breastfeeding. Breastfeeding information from print media was associated with longer duration of any breastfeeding, but information from broadcast media was associated with shorter duration of any breastfeeding. Mothers who recall exposure to formula information from print or websites are more likely to intend to use formula or to intend to use formula earlier and are less likely to initiate breastfeeding than mothers who do not recall seeing such information.

Malnutrition-related early childhood exposures and enamel defects in the permanent dentition: A longitudinal study from the Bolivian Amazon.

PubMed

Masterson, Erin E; Fitzpatrick, Annette L; Enquobahrie, Daniel A; Mancl, Lloyd A; Conde, Esther; Hujoel, Philippe P

2017-10-01

We investigated the relationship between early childhood malnutrition-related measures and subsequent enamel defects in the permanent dentition. This cohort study included 349 Amerindian adolescents (10-17 years, 52% male) from the Bolivian Amazon. Exposures included: stunted growth (height-for-age z-scores), underweight (weight-for-age z-scores), anemia (hemoglobin), acute inflammation (C-reactive protein) and parasitic infection (hookworm). We measured the occurrence (no/yes) and extent (<1/3, 1/3-2/3, >2/3) of enamel defects. We estimated associations between childhood exposures and enamel defect measures using log-binomial and multinomial logistic regression. The prevalence of an enamel defect characterized by an orange peel texture on a large central depression on the labial surface of the central maxillary incisors was 92.3%. During childhood (1-4 years), participants had a high prevalence of stunted growth (75.2%), anemia (56.9%), acute inflammation (39.1%), and hookworm infection (49.6%). We observed associations between childhood height-for-age (OR = 0.65; P = 0.028 for >2/3 extent vs. no EH) and gastrointestinal hookworm infection (OR = 3.43; P = 0.035 for >2/3 extent vs. no defects or <1/3 extent) with enamel defects. The study describes a possibly novel form of enamel hypoplasia and provides evidence for associations of malnutrition-related measures in early childhood, including stunted growth and parasitic helminth infection, with the observed enamel defects. © 2017 Wiley Periodicals, Inc.

Self-rated mental health and race/ethnicity in the United States: support for the epidemiological paradox

PubMed Central

2016-01-01

This paper evaluates racial/ethnic differences in self-rated mental health for adults in the United States, while controlling for demographic and socioeconomic characteristics as well as length of stay in the country. Using data from the 2010 National Health Interview Survey Cancer Control Supplement (NHIS-CCS), binomial logistic regression models are fit to estimate the association between race/ethnicity and poor/fair self-reported mental health among US Adults. The size of the analytical sample was 22,844 persons. Overall prevalence of poor/fair self-rated mental health was 7.72%, with lower prevalence among Hispanics (6.93%). Non-Hispanic blacks had the highest prevalence (10.38%). After controls for socioeconomic characteristics are incorporated in the models, Hispanics were found to have a lower probability of reporting poor/fair self-rated mental health in comparison to non-Hispanic whites (OR = 0.70; 95% CI [0.55–0.90]). No difference was found for other minority groups when compared to the reference group in the final model. Contrary to global self-rated health, Hispanics were found to have a lower probability of reporting poor/fair self-rated mental health in comparison to non-Hispanic whites. No difference was found for non-Hispanic blacks when they were compared to non-Hispanic whites. Self-rated mental health is therefore one case of a self-rating of health in which evidence supporting the epidemiological paradox is found among adults in the United States. PMID:27688982

Behaviour-Related Scalar Habitat Use by Cape Buffalo (Syncerus caffer caffer)

PubMed Central

Bennitt, Emily; Bonyongo, Mpaphi Casper; Harris, Stephen

2015-01-01

Studies of habitat use by animals must consider behavioural resource requirements at different scales, which could influence the functional value of different sites. Using Cape buffalo (Syncerus caffer caffer) in the Okavango Delta, Botswana, we tested the hypotheses that behaviour affected use between and within habitats, hereafter referred to as macro- and microhabitats, respectively. We fitted GPS-enabled collars to fifteen buffalo and used the distances and turning angles between consecutive fixes to cluster the resulting data into resting, grazing, walking and relocating behaviours. Distance to water and six vegetation characteristic variables were recorded in sites used for each behaviour, except for relocating, which occurred too infrequently. We used multilevel binomial and multinomial logistic regressions to identify variables that characterised seasonally-preferred macrohabitats and microhabitats used for different behaviours. Our results showed that macrohabitat use was linked to behaviour, although this was least apparent during the rainy season, when resources were most abundant. Behaviour-related microhabitat use was less significant, but variation in forage characteristics could predict some behaviour within all macrohabitats. The variables predicting behaviour were not consistent, but resting and grazing sites were more readily identifiable than walking sites. These results highlight the significance of resting, as well as foraging, site availability in buffalo spatial processes. Our results emphasise the importance of considering several behaviours and scales in studies of habitat use to understand the links between environmental resources and animal behavioural and spatial ecology. PMID:26673623

Risk of selected structural abnormalities in infants after increased nuchal translucency measurement.

PubMed

Baer, Rebecca J; Norton, Mary E; Shaw, Gary M; Flessel, Monica C; Goldman, Sara; Currier, Robert J; Jelliffe-Pawlowski, Laura L

2014-12-01

We sought to examine the association between increased first-trimester fetal nuchal translucency (NT) measurement and major noncardiac structural birth defects in euploid infants. Included were 75,899 singleton infants without aneuploidy or critical congenital heart defects born in California in 2009 through 2010 with NT measured between 11-14 weeks of gestation. Logistic binomial regression was employed to estimate relative risks (RRs) and 95% confidence intervals (CIs) for occurrence of birth defects in infants with an increased NT measurement (by percentile at crown-rump length [CRL] and by ≥3.5 mm compared to those with measurements <90th percentile for CRL). When considered by CRL adjusted percentile and by measurement ≥3.5 mm, infants with a NT ≥95th percentile were at risk of having ≥1 major structural birth defects (any defect, RR, 1.6; 95% CI, 1.3-1.9; multiple defects, RR, 2.1; 95% CI, 1.3-3.4). Infants with a NT measurement ≥95th percentile were at particularly high risk for pulmonary, gastrointestinal, genitourinary, and musculoskeletal anomalies (RR, 1.6-2.7; 95% CI, 1.1-5.4). Our findings demonstrate that risks of major pulmonary, gastrointestinal, genitourinary, and musculoskeletal structural birth defects exist for NT measurements ≥95th percentile. The ≥3-fold risks were observed for congenital hydrocephalus; agenesis, hypoplasia, and dysplasia of the lung; atresia and stenosis of the small intestine; osteodystrophies; and diaphragm anomalies. Copyright © 2014 Elsevier Inc. All rights reserved.

Rural Idaho family physicians' scope of practice.

PubMed

Baker, Ed; Schmitz, David; Epperly, Ted; Nukui, Ayaka; Miller, Carissa Moffat

2010-01-01

Scope of practice is an important factor in both training and recruiting rural family physicians. To assess rural Idaho family physicians' scope of practice and to examine variations in scope of practice across variables such as gender, age and employment status. A survey instrument was developed based on a literature review and was validated by physician educators, practicing family physicians and executives at the state hospital association. This survey was mailed to rural family physicians practicing in Idaho counties with populations of less than 50,000. Descriptive, bivariate and multivariate analyses were employed to describe and compare scope of practice patterns. Responses were obtained from 92 of 248 physicians (37.1% response rate). Idaho rural family physicians reported providing obstetrical services in the areas of prenatal care (57.6%), vaginal delivery (52.2%) and C-sections (37.0%); other operating room services (43.5%); esophagogastroduodenoscopy (EGD) or colonoscopy services (22.5%); emergency room coverage (48.9%); inpatient admissions (88.9%); mental health services (90.1%); nursing home services (88.0%); and supervision to midlevel care providers (72.5%). Bivariate analyses showed differences in scope of practice patterns across gender, age group and employment status. Binomial logistic regression models indicated that younger physicians were roughly 3 times more likely to provide prenatal care and perform vaginal deliveries than older physicians in rural areas. Idaho practicing rural family physicians report a broad scope of practice. Younger, employed and female rural family medicine physicians are important subgroups for further study.

Risk pathways among traumatic stress, posttraumatic stress disorder symptoms, and alcohol and drug problems: a test of four hypotheses.

PubMed

Haller, Moira; Chassin, Laurie

2014-09-01

The present study utilized longitudinal data from a community sample (n = 377; 166 trauma-exposed; 54% males; 73% non-Hispanic Caucasian; 22% Hispanic; 5% other ethnicity) to test whether pretrauma substance use problems increase risk for trauma exposure (high-risk hypothesis) or posttraumatic stress disorder (PTSD) symptoms (susceptibility hypothesis), whether PTSD symptoms increase risk for later alcohol/drug problems (self-medication hypothesis), and whether the association between PTSD symptoms and alcohol/drug problems is attributable to shared risk factors (shared vulnerability hypothesis). Logistic and negative binomial regressions were performed in a path analysis framework. Results provided the strongest support for the self-medication hypothesis, such that PTSD symptoms predicted higher levels of later alcohol and drug problems, over and above the influences of pretrauma family risk factors, pretrauma substance use problems, trauma exposure, and demographic variables. Results partially supported the high-risk hypothesis, such that adolescent substance use problems increased risk for assaultive violence exposure but did not influence overall risk for trauma exposure. There was no support for the susceptibility hypothesis. Finally, there was little support for the shared vulnerability hypothesis. Neither trauma exposure nor preexisting family adversity accounted for the link between PTSD symptoms and later substance use problems. Rather, PTSD symptoms mediated the effect of pretrauma family adversity on later alcohol and drug problems, thereby supporting the self-medication hypothesis. These findings make important contributions to better understanding the directions of influence among traumatic stress, PTSD symptoms, and substance use problems.

Relation of peer effects and school climate to substance use among Asian American adolescents.

PubMed

Ryabov, Igor

2015-07-01

Using a nationally representative, longitudinal sample of Asian American late adolescents/young adults (ages 18-26), this article investigates the link between peer effects, school climate, on the one hand, and substance use, which includes tobacco, alcohol, and other illicit mood altering substance. The sample (N = 1585) is drawn from the National Longitudinal Study of Adolescent Health (Waves I and III). The study is set to empirically test premises of generational, social capital and stage-environment fit theories. The exploratory variables include individual-level (immigrant generation status, ethnic origin, co-ethnic and co-generational peers - peers from the same immigrant generation) as well as school-level measures (average school socio-economic status and school climate). Multilevel modeling (logistic and negative binomial regression) was used to estimate substance use. Results indicate that preference for co-generational friends is inversely associated with frequency of cannabis and other illicit drug use and preference for co-ethnic peers is inversely associated with other illicit drug use. We also find that school climate is a strong and negative predictor of frequency of cannabis and other illicit drug use as well as of heavy episodic drinking. In terms of policy, these findings suggest that Asian American students should benefit from co-ethnic and co-generational peer networks in schools and, above all, from improving school climate. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Socio-economic predictors of stillbirths in Nepal (2001-2011)

PubMed Central

Ghimire, Pramesh Raj; Agho, Kingsley Emwinyore; Renzaho, Andre; Christou, Aliki; Nisha, Monjura Khatun; Dibley, Michael; Raynes-Greenow, Camille

2017-01-01

Introduction Stillbirth has a long-lasting impact on parents and families. This study examined socio-economic predictors associated with stillbirth in Nepal for the year 2001, 2006 and 2011. Methods The Nepalese Demographic and Health Survey (NDHS) data for the period (2001–2011) were pooled to estimate socio-economic predictors associated with stillbirths in Nepal using binomial logistic regression while taking clustering and sampling weights into account. Results A total of 18,386 pregnancies of at least 28 weeks gestation were identified. Of these pregnancies, 335 stillbirths were reported. Stillbirth increased significantly among women that lived in the hills ecological zones (aRR 1.38, 95% CI 1.02, 1.87) or in the mountains ecological zones (aRR 1.71, 95% CI 1.10, 2.66). Women with no schooling (aRR 1.72, 95% CI 1.10, 2.69), women with primary education (aRR 1.81, 95% CI 1.11, 2.97); open defecation (aRR 1.48, 95% CI 1.00, 2.18), and those whose major occupation was agriculture (aRR 1.80, 95% CI 1.16, 2.78) are more likely to report higher stillbirth. Conclusions Low levels of education, ecological zones and open defecation were found to be strong predictors of stillbirth. Access to antenatal care services and skilled birth attendants for women in the mountainous and hilly ecological zones of Nepal is needed to further reduce stillbirth and improved services should also focus on women with low levels of education. PMID:28704548

Malnutrition is independently associated with skin tears in hospital inpatient setting-Findings of a 6-year point prevalence audit.

PubMed

Munro, Emma L; Hickling, Donna F; Williams, Damian M; Bell, Jack J

2018-05-24

Skin tears cause pain, increased length of stay, increased costs, and reduced quality of life. Minimal research reports the association between skin tears, and malnutrition using robust measures of nutritional status. This study aimed to articulate the association between malnutrition and skin tears in hospital inpatients using a yearly point prevalence of inpatients included in the Queensland Patient Safety Bedside Audit, malnutrition audits and skin tear audits conducted at a metropolitan tertiary hospital between 2010 and 2015. Patients were excluded if admitted to mental health wards or were <18 years. A total of 2197 inpatients were included, with a median age of 71 years. The overall prevalence of skin tears was 8.1%. Malnutrition prevalence was 33.5%. Univariate analysis demonstrated associations between age (P ˂ .001), body mass index (BMI) (P < .001) and malnutrition (P ˂ .001) but not gender (P = .319). Binomial logistic regression analysis modelling demonstrated that malnutrition diagnosed using the Subjective Global Assessment was independently associated with skin tear incidence (odds ratio, OR: 1.63; 95% confidence interval, CI: 1.13-2.36) and multiple skin tears (OR 2.48 [95% CI 1.37-4.50]). BMI was not independently associated with skin tears or multiple skin tears. This study demonstrated independent associations between malnutrition and skin tear prevalence and multiple skin tears. It also demonstrated the limitations of BMI as a nutritional assessment measure. © 2018 Medicalhelplines.com Inc and John Wiley & Sons Ltd.

Factors associated with ethnic differences in health insurance coverage and type among Asian Americans.

PubMed

Kao, Dennis

2010-04-01

This study examines the discrepancies in health insurance coverage and type across Asian American ethnic groups and the potential factors that may explain why these differences exist. Asian Americans are often considered as a homogeneous population and consequently, remain largely "invisible" in the current research literature. Recent data have highlighted discrepancies in the health insurance coverage between different Asian American ethnic groups-particularly the high uninsurance rates among Korean and Vietnamese Americans. For this study, the 2003 and 2005 California Health Interview Surveys were pooled to obtain a sample of 6,610 Asian American adults aged 18-64, including those of Chinese, Filipino, Japanese, South Asian, and Vietnamese ethnicity. Binomial and multinomial logistic regression models were used to examine the likelihood of current health coverage and insurance type (employer-based vs. private vs. public), respectively. The results showed that ethnic differences in uninsurance and insurance type were partially explained by socioeconomic and immigration-related characteristics-particularly for Vietnamese Americans and to a lesser extent, for Chinese and Korean Americans. There were also key differences in the extent to which specific ethnic groups purchased private insurance or relied on public programs (e.g., Medicaid) to offset the lack of employer-based coverage. This study reaffirms the tremendous heterogeneity in the Asian American population and the need for more targeted policy approaches. With the lack of adequate national data, more localized studies may help to improve our understanding of the health issues affecting specific Asian ethnic groups.

Social factors related to the clinical severity of influenza cases in Spain during the A (H1N1) 2009 virus pandemic

PubMed Central

2013-01-01

Background During the 2009 influenza pandemic, a change in the type of patients most often affected by influenza was observed. The objective of this study was to assess the role of individual and social determinants in hospitalizations due to influenza A (H1N1) 2009 infection. Methods We studied hospitalized patients (cases) and outpatients (controls) with confirmed influenza A (H1N1) 2009 infection. A standardized questionnaire was used to collect data. Variables that might be related to the hospitalization of influenza cases were compared by estimation of the odds ratio (OR) and 95% confidence intervals (CI) and the variables entered into binomial logistic regression models. Results Hospitalization due to pandemic A (H1N1) 2009 influenza virus infections was associated with non-Caucasian ethnicity (OR: 2.18, 95% CI 1.17 − 4.08), overcrowding (OR: 2.84, 95% CI 1.20 − 6.72), comorbidity and the lack of previous preventive information (OR: 2.69, 95% CI: 1.50 − 4.83). Secondary or higher education was associated with a lower risk of hospitalization (OR 0.56, 95% CI: 0.36 − 0.87) Conclusions In addition to individual factors such as comorbidity, other factors such as educational level, ethnicity or overcrowding were associated with hospitalization due to A (H1N1) 2009 influenza virus infections. PMID:23391376

Perceived Service Need After Hurricane Sandy in a Representative Sample of Survivors: The Roles of Community-Level Damage and Individual-Level Stressors.

PubMed

Sampson, Laura; Lowe, Sarah R; Gruebner, Oliver; Cohen, Gregory H; Galea, Sandro

2016-06-01

We aimed to explore how individually experienced disaster-related stressors and collectively experienced community-level damage influenced perceived need for mental health services in the aftermath of Hurricane Sandy. In a cross-sectional study we analyzed 418 adults who lived in the most affected areas of New York City at the time of the storm. Participants indicated whether they perceived a need for mental health services since the storm and reported on their exposure to disaster-related stressors (eg, displacement, property damage). We located participants in communities (n=293 census tracts) and gathered community-level demographic data through the US Census and data on the number of damaged buildings in each community from the Federal Emergency Management Agency Modeling Task Force. A total of 7.9% of participants reported mental health service need since the hurricane. Through multilevel binomial logistic regression analysis, we found a cross-level interaction (P=0.04) between individual-level exposure to disaster-related stressors and community-level building damage. Individual-level stressors were significantly predictive of individual service needs in communities with building damage (adjusted odds ratio: 2.56; 95% confidence interval: 1.58-4.16) and not in communities without damage. Individuals who experienced individual stressors and who lived in more damaged communities were more likely to report need for services than were other persons after Hurricane Sandy. (Disaster Med Public Health Preparedness. 2016;10:428-435).

Low-level lead exposure and autistic behaviors in school-age children.

PubMed

Kim, Kyoung-Nam; Kwon, Ho-Jang; Hong, Yun-Chul

2016-03-01

The association between lead exposure and autism spectrum disorder is inconclusive. We hypothesized an association between higher blood lead concentrations and more autistic behaviors, including impaired social interactions and communication, stereotypical behaviors, and restricted interests, among school-age children. Data from 2473 Korean children aged 7-8years who had no prior history of developmental disorders were analyzed. Two follow-up surveys were conducted biennially until the children reached 11-12years of age. Blood lead concentrations were measured at every survey, and autistic behaviors were evaluated at 11-12years of age using the Autism Spectrum Screening Questionnaire (ASSQ) and Social Responsiveness Scale (SRS). The associations of blood lead concentration with ASSQ and SRS scores were analyzed using negative binomial, logistic, and linear regression models. Blood lead concentrations at 7-8years of age (geometric mean: 1.64μg/dL), but not at 9-10 and 11-12years of age, were associated with more autistic behaviors at 11-12years of age, according to the ASSQ (β=0.151; 95% confidence interval [CI]: 0.061, 0.242) and SRS (β=2.489; 95% CI: 1.378, 3.600). SRS subscale analysis also revealed associations between blood lead concentrations and social awareness, cognition, communication, motivation, and mannerisms. Even low blood lead concentrations at 7-8years of age are associated with more autistic behaviors at 11-12years of age, underscoring the need for continued efforts to reduce lead exposure. Copyright © 2016. Published by Elsevier B.V.

Influence of height on attained level of education in males at 19 years of age.

PubMed

Szklarska, Alicja; Kozieł, Sławomir; Bielicki, Tadeusz; Malina, Robert M

2007-07-01

In this study it is hypothesized that taller individuals are more likely to move up the scale of educational attainment compared with shorter individuals from the same social background. Three national cohorts of 19-year-old males were considered: 29,464 born in 1967 and surveyed in 1986, 31,062 born in 1976 and surveyed in 1995, and 30,851 born in 1982 and surveyed in 2001. Four social variables were used to describe the social background of each conscript in the three surveys: degree of urbanization, family size, and parental and maternal educational status. The educational status of each conscript was classified into two groups: (1) those who were secondary school students or graduates, or who had entered college, and (2) those who had completed their education at the primary school level or who had gone to a basic trade school. Multiple binomial logistic regressions were used to estimate the relative risk of achieving higher educational status by 19-year-old males relative to height and the four social factors. Consistently across the three cohorts the odd ratios (ORs) indicate that height exerts an independent and significant effect on the attained level of education at the age of 19 years in males (1986: OR=1.24, p<0.001; 1995: OR=1.24, p <0.001; 2001: OR=1.20, p<0.001). Two possible, not mutually exclusive, selective mechanisms are postulated and discussed: 'passive' and 'active' action.

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.

A comparison of observation-level random effect and Beta-Binomial models for modelling overdispersion in Binomial data in ecology & evolution.

PubMed

Harrison, Xavier A

2015-01-01

Overdispersion is a common feature of models of biological data, but researchers often fail to model the excess variation driving the overdispersion, resulting in biased parameter estimates and standard errors. Quantifying and modeling overdispersion when it is present is therefore critical for robust biological inference. One means to account for overdispersion is to add an observation-level random effect (OLRE) to a model, where each data point receives a unique level of a random effect that can absorb the extra-parametric variation in the data. Although some studies have investigated the utility of OLRE to model overdispersion in Poisson count data, studies doing so for Binomial proportion data are scarce. Here I use a simulation approach to investigate the ability of both OLRE models and Beta-Binomial models to recover unbiased parameter estimates in mixed effects models of Binomial data under various degrees of overdispersion. In addition, as ecologists often fit random intercept terms to models when the random effect sample size is low (<5 levels), I investigate the performance of both model types under a range of random effect sample sizes when overdispersion is present. Simulation results revealed that the efficacy of OLRE depends on the process that generated the overdispersion; OLRE failed to cope with overdispersion generated from a Beta-Binomial mixture model, leading to biased slope and intercept estimates, but performed well for overdispersion generated by adding random noise to the linear predictor. Comparison of parameter estimates from an OLRE model with those from its corresponding Beta-Binomial model readily identified when OLRE were performing poorly due to disagreement between effect sizes, and this strategy should be employed whenever OLRE are used for Binomial data to assess their reliability. Beta-Binomial models performed well across all contexts, but showed a tendency to underestimate effect sizes when modelling non-Beta-Binomial data. Finally, both OLRE and Beta-Binomial models performed poorly when models contained <5 levels of the random intercept term, especially for estimating variance components, and this effect appeared independent of total sample size. These results suggest that OLRE are a useful tool for modelling overdispersion in Binomial data, but that they do not perform well in all circumstances and researchers should take care to verify the robustness of parameter estimates of OLRE models.

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.

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.

Review and Recommendations for Zero-inflated Count Regression Modeling of Dental Caries Indices in Epidemiological Studies

PubMed Central

Stamm, John W.; Long, D. Leann; Kincade, Megan E.

2012-01-01

Over the past five to ten years, zero-inflated count regression models have been increasingly applied to the analysis of dental caries indices (e.g., DMFT, dfms, etc). The main reason for that is linked to the broad decline in children’s caries experience, such that dmf and DMF indices more frequently generate low or even zero counts. This article specifically reviews the application of zero-inflated Poisson and zero-inflated negative binomial regression models to dental caries, with emphasis on the description of the models and the interpretation of fitted model results given the study goals. The review finds that interpretations provided in the published caries research are often imprecise or inadvertently misleading, particularly with respect to failing to discriminate between inference for the class of susceptible persons defined by such models and inference for the sampled population in terms of overall exposure effects. Recommendations are provided to enhance the use as well as the interpretation and reporting of results of count regression models when applied to epidemiological studies of dental caries. PMID:22710271

M-Bonomial Coefficients and Their Identities

ERIC Educational Resources Information Center

Asiru, Muniru A.

2010-01-01

In this note, we introduce M-bonomial coefficients or (M-bonacci binomial coefficients). These are similar to the binomial and the Fibonomial (or Fibonacci-binomial) coefficients and can be displayed in a triangle similar to Pascal's triangle from which some identities become obvious.

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.

The association between major depression prevalence and sex becomes weaker with age.

PubMed

Patten, Scott B; Williams, Jeanne V A; Lavorato, Dina H; Wang, Jian Li; Bulloch, Andrew G M; Sajobi, Tolulope

2016-02-01

Women have a higher prevalence of major depressive episodes (MDE) than men, and the annual prevalence of MDE declines with age. Age by sex interactions may occur (a weakening of the sex effect with age), but are easily overlooked since individual studies lack statistical power to detect interactions. The objective of this study was to evaluate age by sex interactions in MDE prevalence. In Canada, a series of 10 national surveys conducted between 1996 and 2013 assessed MDE prevalence in respondents over the age of 14. Treating age as a continuous variable, binomial and linear regression was used to model age by sex interactions in each survey. To increase power, the survey-specific interaction coefficients were then pooled using meta-analytic methods. The estimated interaction terms were homogeneous. In the binomial regression model I (2) was 31.2 % and was not statistically significant (Q statistic = 13.1, df = 9, p = 0.159). The pooled estimate (-0.004) was significant (z = 3.13, p = 0.002), indicating that the effect of sex became weaker with increasing age. This resulted in near disappearance of the sex difference in the 75+ age group. This finding was also supported by an examination of age- and sex-specific estimates pooled across the surveys. The association of MDE prevalence with sex becomes weaker with age. The interaction may reflect biological effect modification. Investigators should test for, and consider inclusion of age by sex interactions in epidemiological analyses of MDE prevalence.

Association between month of birth and melanoma risk: fact or fiction?

PubMed

Fiessler, Cornelia; Pfahlberg, Annette B; Keller, Andrea K; Radespiel-Tröger, Martin; Uter, Wolfgang; Gefeller, Olaf

2017-04-01

Evidence on the effect of ultraviolet radiation (UVR) exposure in infancy on melanoma risk in later life is scarce. Three recent studies suggest that people born in spring carry a higher melanoma risk. Our study aimed at verifying whether such a seasonal pattern of melanoma risk actually exists. Data from the population-based Cancer Registry Bavaria (CRB) on the birth months of 28 374 incident melanoma cases between 2002 and 2012 were analysed and compared with data from the Bavarian State Office for Statistics and Data Processing on the birth month distribution in the Bavarian population. Crude and adjusted analyses using negative binomial regression models were performed in the total study group and supplemented by several subgroup analyses. In the crude analysis, the birth months March-May were over-represented among melanoma cases. Negative binomial regression models adjusted only for sex and birth year revealed a seasonal association between melanoma risk and birth month with 13-21% higher relative incidence rates for March, April and May compared with the reference December. However, after additionally adjusting for the birth month distribution of the Bavarian population, these risk estimates decreased markedly and no association with the birth month was observed any more. Similar results emerged in all subgroup analyses. Our large registry-based study provides no evidence that people born in spring carry a higher risk for developing melanoma in later life and thus lends no support to the hypothesis of higher UVR susceptibility during the first months of life. © The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association

Dispersion models and sampling of cacao mirid bug Sahlbergella singularis (Hemiptera: Miridae) on Theobroma Cacao in southern Cameroon.

PubMed

Bisseleua, D H B; Vidal, Stefan

2011-02-01

The spatio-temporal distribution of Sahlbergella singularis Haglung, a major pest of cacao trees (Theobroma cacao) (Malvaceae), was studied for 2 yr in traditional cacao forest gardens in the humid forest area of southern Cameroon. The first objective was to analyze the dispersion of this insect on cacao trees. The second objective was to develop sampling plans based on fixed levels of precision for estimating S. singularis populations. The following models were used to analyze the data: Taylor's power law, Iwao's patchiness regression, the Nachman model, and the negative binomial distribution. Our results document that Taylor's power law was a better fit for the data than the Iwao and Nachman models. Taylor's b and Iwao's β were both significantly >1, indicating that S. singularis aggregated on specific trees. This result was further supported by the calculated common k of 1.75444. Iwao's α was significantly <0, indicating that the basic distribution component of S. singularis was the individual insect. Comparison of negative binomial (NBD) and Nachman models indicated that the NBD model was appropriate for studying S. singularis distribution. Optimal sample sizes for fixed precision levels of 0.10, 0.15, and 0.25 were estimated with Taylor's regression coefficients. Required sample sizes increased dramatically with increasing levels of precision. This is the first study on S. singularis dispersion in cacao plantations. Sampling plans, presented here, should be a tool for research on population dynamics and pest management decisions of mirid bugs on cacao. © 2011 Entomological Society of America

Individual-level exposure to disaster, neighborhood environmental characteristics, and their independent and combined associations with depressive symptoms in women.

PubMed

Gaston, Symielle A; Volaufova, Julia; Peters, Edward S; Ferguson, Tekeda F; Robinson, William T; Nugent, Nicole; Trapido, Edward J; Rung, Ariane L

2017-09-01

The severity of the stress response to experiencing disaster depends on individual exposure and background stress prior to the event. To date, there is limited research on the interaction between neighborhood environmental stress and experiencing an oil spill, and their effects on depression. The objective of the current study was to assess if the association between exposure to the Deepwater Horizon Oil Spill (DHOS) and depressive symptoms varied by neighborhood characteristics. US Census data (2010) and longitudinal data collected in two waves (2012-2014 and 2014-2016) from female residents [N = 889 (Wave I), 737 (Wave II)] of an area highly affected by the DHOS were analyzed. Multilevel and individual-level negative binomial regressions were performed to estimate associations with depressive symptoms in both waves. An interaction term was included to estimate effect modification of the association between DHOS exposure and depressive symptoms by neighborhood characteristics. Generalized estimating equations were applied to the negative binomial regression testing longitudinal associations. Census tract-level neighborhood characteristics were not associated with depressive symptoms. Exposure to the DHOS and neighborhood physical disorder were associated with depressive symptoms cross-sectionally. There was no evidence of effect modification; however, physical/environmental exposure to the DHOS was associated with increased depressive symptoms only among women living in areas with physical disorder. Exposure to the DHOS remained associated with depressive symptoms over time. Findings support the enduring consequences of disaster exposure on depressive symptoms in women and identify potential targets for post-disaster intervention based on residential characteristics.

Not in My Back Yard: A Comparative Analysis of Crime Around Publicly Funded Drug Treatment Centers, Liquor Stores, Convenience Stores, and Corner Stores in One Mid-Atlantic City

PubMed Central

Furr-Holden, C. Debra M.; Milam, Adam J.; Nesoff, Elizabeth D.; Johnson, Renee M.; Fakunle, David O.; Jennings, Jacky M.; Thorpe, Roland J.

2016-01-01

Objective: This research examined whether publicly funded drug treatment centers (DTCs) were associated with violent crime in excess of the violence happening around other commercial businesses. Method: Violent crime data and locations of community entities were geocoded and mapped. DTCs and other retail outlets were matched based on a Neighborhood Disadvantage score at the census tract level. Street network buffers ranging from 100 to 1,400 feet were placed around each location. Negative binomial regression models were used to estimate the relationship between the count of violent crimes and the distance from each business type. Results: Compared with the mean count of violent crime around drug treatment centers, the mean count of violent crime (M = 2.87) was significantly higher around liquor stores (M = 3.98; t test; p < .01) and corner stores (M = 3.78; t test; p < .01), and there was no statistically significant difference between the count around convenience stores (M = 2.65; t test; p = .32). In the adjusted negative binomial regression models, there was a negative and significant relationship between the count of violent crime and the distance from drug treatment centers (β = -.069, p < .01), liquor stores (β = -.081, p < .01), corner stores (β = -.116, p < .01), and convenience stores (β = -.154, p < .01). Conclusions: Violent crime associated with drug treatment centers is similar to that associated with liquor stores and is less frequent than that associated with convenience stores and corner stores. PMID:26751351

A Methodology for Quantifying Certain Design Requirements During the Design Phase

NASA Technical Reports Server (NTRS)

Adams, Timothy; Rhodes, Russel

2005-01-01

A methodology for developing and balancing quantitative design requirements for safety, reliability, and maintainability has been proposed. Conceived as the basis of a more rational approach to the design of spacecraft, the methodology would also be applicable to the design of automobiles, washing machines, television receivers, or almost any other commercial product. Heretofore, it has been common practice to start by determining the requirements for reliability of elements of a spacecraft or other system to ensure a given design life for the system. Next, safety requirements are determined by assessing the total reliability of the system and adding redundant components and subsystems necessary to attain safety goals. As thus described, common practice leaves the maintainability burden to fall to chance; therefore, there is no control of recurring costs or of the responsiveness of the system. The means that have been used in assessing maintainability have been oriented toward determining the logistical sparing of components so that the components are available when needed. The process established for developing and balancing quantitative requirements for safety (S), reliability (R), and maintainability (M) derives and integrates NASA s top-level safety requirements and the controls needed to obtain program key objectives for safety and recurring cost (see figure). Being quantitative, the process conveniently uses common mathematical models. Even though the process is shown as being worked from the top down, it can also be worked from the bottom up. This process uses three math models: (1) the binomial distribution (greaterthan- or-equal-to case), (2) reliability for a series system, and (3) the Poisson distribution (less-than-or-equal-to case). The zero-fail case for the binomial distribution approximates the commonly known exponential distribution or "constant failure rate" distribution. Either model can be used. The binomial distribution was selected for modeling flexibility because it conveniently addresses both the zero-fail and failure cases. The failure case is typically used for unmanned spacecraft as with missiles.

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.

Performance and structure of single-mode bosonic codes

NASA Astrophysics Data System (ADS)

Albert, Victor V.; Noh, Kyungjoo; Duivenvoorden, Kasper; Young, Dylan J.; Brierley, R. T.; Reinhold, Philip; Vuillot, Christophe; Li, Linshu; Shen, Chao; Girvin, S. M.; Terhal, Barbara M.; Jiang, Liang

2018-03-01

The early Gottesman, Kitaev, and Preskill (GKP) proposal for encoding a qubit in an oscillator has recently been followed by cat- and binomial-code proposals. Numerically optimized codes have also been proposed, and we introduce codes of this type here. These codes have yet to be compared using the same error model; we provide such a comparison by determining the entanglement fidelity of all codes with respect to the bosonic pure-loss channel (i.e., photon loss) after the optimal recovery operation. We then compare achievable communication rates of the combined encoding-error-recovery channel by calculating the channel's hashing bound for each code. Cat and binomial codes perform similarly, with binomial codes outperforming cat codes at small loss rates. Despite not being designed to protect against the pure-loss channel, GKP codes significantly outperform all other codes for most values of the loss rate. We show that the performance of GKP and some binomial codes increases monotonically with increasing average photon number of the codes. In order to corroborate our numerical evidence of the cat-binomial-GKP order of performance occurring at small loss rates, we analytically evaluate the quantum error-correction conditions of those codes. For GKP codes, we find an essential singularity in the entanglement fidelity in the limit of vanishing loss rate. In addition to comparing the codes, we draw parallels between binomial codes and discrete-variable systems. First, we characterize one- and two-mode binomial as well as multiqubit permutation-invariant codes in terms of spin-coherent states. Such a characterization allows us to introduce check operators and error-correction procedures for binomial codes. Second, we introduce a generalization of spin-coherent states, extending our characterization to qudit binomial codes and yielding a multiqudit code.

Household Implementation of Smoke-Free Rules in Homes and Cars: A Focus on Adolescent Smoking Behavior and Secondhand Smoke Exposure.

PubMed

Parks, Michael J; Kingsbury, John H; Boyle, Raymond G; Evered, Sharrilyn

2018-01-01

This study addresses the dearth of population-based research on how comprehensive household smoke-free rules (ie, in the home and car) relate to tobacco use and secondhand smoke (SHS) exposure among adolescents. Analysis of 2014 Minnesota Youth Tobacco Survey. Representative sample of Minnesota youth. A total of 1287 youth who lived with a smoker. Measures included household smoke-free rules (no rules, partial rules-home or car, but not both-and comprehensive rules), lifetime and 30-day cigarette use, 30-day cigarette and other product use, and SHS exposure in past 7 days in home and car. Weighted multivariate logistic, zero-inflated Poisson, and zero-inflated negative binomial regressions were used. Compared to comprehensive rules, partial and no smoke-free rules were significantly and positively related to lifetime cigarette use (respectively, adjusted odds ratio [AOR] = 1.80, 95% confidence interval [CI] = 1.24-2.61; AOR = 2.87, 95% CI = 1.93-4.25), and a similar significant pattern was found for 30-day cigarette use (respectively, AOR = 2.20, 95% CI = 1.21-4.02; AOR = 2.45, 95% CI = 1.34-4.50). No smoke-free rules significantly predicted using cigarettes and other tobacco products compared to comprehensive rules. In both descriptive and regression analyses, we found SHS exposure rates in both the home and car were significantly lower among youth whose household implemented comprehensive smoke-free rules. Comprehensive smoke-free rules protect youth from the harms of caregiver tobacco use. Relative to both partial and no smoke-free rules, comprehensive smoke-free rules have a marked impact on tobacco use and SHS exposure among youth who live with a smoker. Health promotion efforts should promote comprehensive smoke-free rules among all households and particularly households with children and adolescents.

Comparative Effectiveness of Ranolazine Versus Traditional Therapies in Chronic Stable Angina Pectoris and Concomitant Diabetes Mellitus and Impact on Health Care Resource Utilization and Cardiac Interventions.

PubMed

Page, Robert L; Ghushchyan, Vahram; Read, Richard A; Hartsfield, Cynthia L; Koch, Bruce R; Nair, Kavita V

2015-11-01

Comparative studies evaluating traditional versus newer antianginal (AA) medications in chronic stable angina pectoris (CSA) on cardiovascular (CV) outcomes and utilization are limited, particularly in patients with diabetes mellitus (DM). Claims data (2008 to 2012) were analyzed using a commercial database. Patients with CSA receiving a β blocker (BB), calcium channel blocker (CCB), long-acting nitrate (LAN), or ranolazine were identified and followed for 12 months after a change in AA therapy. Patients on traditional AA medications were required to have concurrent sublingual nitroglycerin. Therapy change was defined as adding or switching to another traditional AA medication or ranolazine to identify patients whose angina was inadequately controlled with previous therapy. Four groups were identified (BB, CCB, LAN, or ranolazine users) and matched on relevant characteristics. A DM subset was identified. Logistic regression compared revascularization at 30, 60, 90, 180, and 360 days. Negative binomial regression compared all-cause, CV-, and DM-related (in the DM cohort) health care utilization. A total of 8,008 patients were identified with 2,002 patients in each matched group. Majority were men (mean age 66 years). A subset of 3,724 patients with DM (BB, n = 933; CCB, n = 940; LAN, n = 937; and ranolazine, n = 914) resulted from this cohort. Compared to ranolazine in the overall cohort, traditional AA medication exhibited greater odds for revascularization and higher rates in all-cause outpatient, emergency room visits, inpatient length of stay, and CV-related emergency room visits. In the DM cohort, ranolazine demonstrated similar benefits over traditional AA medication. In conclusion, ranolazine use in patients with inadequately controlled chronic angina is associated with less revascularization and all-cause and CV-related health care utilization compared to traditional AA medication. Copyright © 2015 Elsevier Inc. All rights reserved.

Cyberbullying victimization and mental health in adolescents and the moderating role of family dinners.

PubMed

Elgar, Frank J; Napoletano, Anthony; Saul, Grace; Dirks, Melanie A; Craig, Wendy; Poteat, V Paul; Holt, Melissa; Koenig, Brian W

2014-11-01

This study presents evidence that cyberbullying victimization relates to internalizing, externalizing, and substance use problems in adolescents and that the frequency of family dinners attenuate these associations. To examine the unique association between cyberbullying victimization and adolescent mental health (after controlling differences in involvement in traditional, face-to-face bullying) and to explore the potential moderating role of family contact in this association. This cross-sectional, observational study used survey data on 18,834 students (aged 12-18 years) from 49 schools in a Midwestern US state. Logistic regression analysis tested associations between cyberbullying victimization and the likelihood of mental health and substance use problems. Negative binomial regression analysis tested direct and synergistic contributions of cyberbullying victimization and family dinners on the rates of mental health and substance use problems. Frequency of cyberbullying victimization during the previous 12 months; victimization by traditional (face-to-face) bullying; and perpetration of traditional bullying. Five internalizing mental health problems (anxiety, depression, self-harm, suicide ideation, and suicide attempt), 2 externalizing problems (fighting and vandalism), and 4 substance use problems (frequent alcohol use, frequent binge drinking, prescription drug misuse, and over-the-counter drug misuse). About one-fifth (18.6%) of the sample experienced cyberbullying during the previous 12 months. The frequency of cyberbullying positively related to all 11 internalizing, externalizing, and substance use problems (odds ratios from 2.6 [95% CI, 1.7-3.8] to 4.5 [95% CI, 3.0-6.6]). However, victimization related more closely to rates of problems in adolescents that had fewer family dinners. Cyberbullying relates to mental health and substance use problems in adolescents, even after their involvement in face-to-face bullying is taken into account. Although correlational, these results suggest that family dinners (ie, family contact and communication) are beneficial to adolescent mental health and may help protect adolescents from the harmful consequences of cyberbullying.

Decline in Child Marriage and Changes in Its Effect on Reproductive Outcomes in Bangladesh

PubMed Central

2012-01-01

This paper explores the decline in child marriage and changes in its effect on reproductive outcomes of Bangladeshi women, using the 2007 Bangladesh Demographic and Health Survey data. Chi-square tests, negative binomial Poisson regression and binary logistic regression were performed in analyzing the data. Overall, 82% of women aged 20-49 years were married-off before 18 years of age, and 63% of the marriages took place before 16 years of age. The incidence of child marriage was significantly less among the young women aged 20-24 years compared to their older counterparts. Among others, women's education appeared as the most significant single determinant of child marriage as well as decline in child marriage. Findings revealed that, after being adjusted for sociodemographic factors, child marriage compared to adult marriage appeared to be significantly associated with lower age at first birth (OR=0.81, 95% CI=76-0.86), higher fertility (IRR=1.45, 95% WCI=1.35-1.55), increased risk of child mortality (IRR=1.64, 95% WCI=1.44-1.87), decreased risk of contraceptive-use before any childbirths (OR=0.56, 95% CI=0.50-0.63), higher risk of giving three or more childbirth (OR=3.94, 95% CI=3.38-4.58), elevated risk of unplanned pregnancies (OR=1.21, 95% CI=1.02-1.45), increased risk of pregnancy termination (OR=1.16, 95% CI=1.00-1.34), and higher risk of the use of any current contraceptive method (OR=1.20, 95% CI=1.06-1.35). Increased enforcement of existing policies is crucial for the prevention of child marriage. Special programmes should be undertaken to keep girls in school for longer period to raise the age of females at first marriage in Bangladesh and thereby reduce the adverse reproductive outcomes. PMID:23082634

Frequent emergency department presentations among people who inject drugs: A record linkage study.

PubMed

Nambiar, Dhanya; Stoové, Mark; Dietze, Paul

2017-06-01

People who inject drugs (PWID) have been described as frequent users of health services such as emergency departments (EDs), however few studies have described demographic factors, patterns of substance use and previous health service use associated with frequent use of EDs in this population. Using a combination of self-reported data from a cohort of PWID and administrative ED data obtained through record linkage, we identified longitudinal factors associated with the use of ED services. Bivariate and multivariate analyses were conducted using negative binomial regression to identify exposures associated with both cumulative ED presentations, and logistic regression to identify exposures of frequent ED presentations (defined as three or more annual presentations). Among 612 PWID, over half (58%) presented to EDs at least once and over a third (36%) presented frequently between January 2008 and June 2013. Frequent and cumulative ED presentations were associated with reporting the main drug of choice as cannabis (AOR:1.42, 95%CI:1.07-1.89 and AIRR:2.96, 95%CI:1.44-6.07 respectively) or methamphetamine (AOR:1.62, 95%CI:1.17-2.2 and AIRR:2.42, 95%CI:1.08-5.46 respectively) compared to heroin, and past month use of mental health (AOR:1.42, 95%CI:1.08-1.85 and AIRR:3.32, 95%CI:1.69-6.53 respectively) and outpatient services (AOR:1.47, 95%CI: 1.00-2.16 and AIRR:0.95, 95%CI 1.52-10.28 respectively). PWID who are frequent users of EDs are likely to have complex health and substance use-related needs. EDs should actively refer people who present with cannabis and methamphetamine dependence to harm reduction services. Harm reduction services should ensure people referred from EDs are screened for co-occurring mental health conditions and receive adequate support. Copyright © 2017 Elsevier B.V. All rights reserved.

Increased risks of preterm birth and a low-birth-weight baby in Thai human immunodeficiency virus-positive pregnant women with periodontitis.

PubMed

Pattrapornnan, Pakkaporn; DeRouen, Timothy A; Songpaisan, Yupin

2012-11-01

Many studies have investigated the risks of adverse neonatal outcomes associated with the presence of periodontitis in non-human immunodeficiency virus (HIV)-infected pregnant women. To the best of our knowledge, there has been no study to investigate the risk of neonatal outcomes associated with periodontitis in HIV-infected pregnant women. The aim of this study is to measure the risk of having adverse neonatal outcomes: preterm delivery (<37 weeks of gestation), low birth weight (<2500 g at birth), and preterm and low-birth-weight baby (<37 weeks of gestation and <2500 g at birth) associated with the presence of periodontitis in HIV-infected women. A total of 292 HIV-infected pregnant women were interviewed for demographic information and medical history and were examined for their periodontal status during weeks 16 to 34 of gestation. Follow-up sessions were done after the delivery to record the baby's data. Periodontitis defined by various criteria were evaluated as exposures. Binomial regression (generalized linear model) was used to examine the risk ratios (RRs). Logistic regression, t tests, and χ2 test were used to examine the associations of periodontitis with adverse neonatal outcomes. Forty women had preterm delivery, 39 women delivered a low-birth-weight baby, and 22 women gave birth to a baby that was preterm and low birth weight. We found significant elevated risks of having preterm delivery as RR = 3.08, 95% confidence interval (CI) = 1.29 to 7.38, low birth weight RR = 2.55, 95% CI = 1.04 to 2.65, and preterm and low birth weight as RR = 4.08, 95% CI = 1.55 to 10.76 in women who had at ≥1 5-mm periodontal pocket. This study found a positive risk of adverse neonatal outcomes in HIV-infected pregnant women who had moderate periodontitis.

Factors Influencing the Incidence of Severe Complications in Head and Neck Free Flap Reconstructions

PubMed Central

Broome, Martin; Juilland, Naline; Litzistorf, Yann; Monnier, Yan; Sandu, Kishore; Pasche, Philippe; Plinkert, Peter K.; Federspil, Philippe A.

2016-01-01

Background: Complications after head and neck free-flap reconstructions are detrimental and prolong hospital stay. In an effort to identify related variables in a tertiary regional head and neck unit, the microvascular reconstruction activity over the last 5 years was captured in a database along with patient-, provider-, and volume-outcome–related parameters. Methods: Retrospective cohort study (level of evidence 3), a modified Clavien-Dindo classification, was used to assess severe complications. Results: A database of 217 patients was created with consecutively reconstructed patients from 2009 to 2014. In the univariate analysis of severe complications, we found significant associations (P < 0.05) between type of flap used, American Society of Anesthesiologists classification, T-stage, microscope use, surgeon, flap frequency, and surgeon volume. Within a binomial logistic regression model, less frequently versus frequently performed flap (odds ratio [OR] = 3.2; confidence interval [CI] = 2.9–3.5; P = 0.000), high-volume versus low-volume surgeon (OR = 0.52; CI = −0.22 to 0.82; P = 0.007), and ASA classification (OR = 2.9; CI = 2.4–3.4; P = 0.033) were retained as independent predictors of severe complications. In a Cox-regression model, surgeon (P = 0.011), site of reconstruction (P = 0.000), T-stage (P = 0.001), and presence of severe complications (P = 0.015) correlated with a prolonged hospitalization. Conclusions: In this study, we identified a correlation of patient-related factors with severe complications (ASA score) and prolonged hospital stay (T-stage, site). More importantly, we identified several provider- (surgeon) and volume-related (frequency with which a flap was performed and high-volume surgeon) factors as predictors of severe complications. Our data indicate that provider- and volume-related parameters play an important role in the outcome of microvascular free-flap procedures in the head and neck region. PMID:27826458

Interpersonal Trust across Six Asia-Pacific Countries: Testing and Extending the ‘High Trust Society’ and ‘Low Trust Society’ Theory

PubMed Central

Ward, Paul R.; Mamerow, Loreen; Meyer, Samantha B.

2014-01-01

Background Trust is regarded as a necessary component for the smooth running of society, although societal and political modernising processes have been linked to an increase in mistrust, potentially signalling social and economic problems. Fukuyama developed the notion of ‘high trust’ and ‘low trust’ societies, as a way of understanding trust within different societies. The purpose of this paper is to empirically test and extend Fukuyama’s theory utilising data on interpersonal trust in Taiwan, Hong Kong, South Korea, Japan, Australia and Thailand. This paper focuses on trust in family, neighbours, strangers, foreigners and people with a different religion. Methods Cross-sectional surveys were undertaken in 2009–10, with an overall sample of 6331. Analyses of differences in overall levels of trust between countries were undertaken using Chi square analyses. Multivariate binomial logistic regression analysis was undertaken to identify socio-demographic predictors of trust in each country. Results Our data indicate a tripartite trust model: ‘high trust’ in Australia and Hong Kong; ‘medium trust’ in Japan and Taiwan; and ‘low trust’ in South Korea and Thailand. Trust in family and neighbours were very high across all countries, although trust in people with a different religion, trust in strangers and trust in foreigners varied considerably between countries. The regression models found a consistent group of subpopulations with low trust across the countries: people on low incomes, younger people and people with poor self-rated health. The results were conflicting for gender: females had lower trust in Thailand and Hong Kong, although in Australia, males had lower trust in strangers, whereas females had lower trust in foreigners. Conclusion This paper identifies high, medium and low trust societies, in addition to high and low trusting population subgroups. Our analyses extend the seminal work of Fukuyama, providing both corroboration and refutation for his theory. PMID:24760052