A Study of the Relationship between Social Support and Clergy Family Stress among Korean-American Baptist Pastors and Their Wives

ERIC Educational Resources Information Center

Shin, Min Young

2012-01-01

Problem: The first problem of this study was to determine the relationship between the clergy family stress scores as measured by the Clergy Family Inventory (CFLI) and the specified predictor variables of social support among Korean-American Baptist pastors. The specified predictor variables included tangible support, appraisal support,…

Seasonal precipitation forecasting for the Melbourne region using a Self-Organizing Maps approach

NASA Astrophysics Data System (ADS)

Pidoto, Ross; Wallner, Markus; Haberlandt, Uwe

2017-04-01

The Melbourne region experiences highly variable inter-annual rainfall. For close to a decade during the 2000s, below average rainfall seriously affected the environment, water supplies and agriculture. A seasonal rainfall forecasting model for the Melbourne region based on the novel approach of a Self-Organizing Map has been developed and tested for its prediction performance. Predictor variables at varying lead times were first assessed for inclusion within the model by calculating their importance via Random Forests. Predictor variables tested include the climate indices SOI, DMI and N3.4, in addition to gridded global sea surface temperature data. Five forecasting models were developed: an annual model and four seasonal models, each individually optimized for performance through Pearson's correlation r and the Nash-Sutcliffe Efficiency. The annual model showed a prediction performance of r = 0.54 and NSE = 0.14. The best seasonal model was for spring, with r = 0.61 and NSE = 0.31. Autumn was the worst performing seasonal model. The sea surface temperature data contributed fewer predictor variables compared to climate indices. Most predictor variables were supplied at a minimum lead, however some predictors were found at lead times of up to a year.

Predictors of stroke in patients with impaired glucose tolerance: results from the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research trial.

PubMed

Preiss, David; Giles, Thomas D; Thomas, Laine E; Sun, Jie-Lena; Haffner, Steven M; Holman, Rury R; Standl, Eberhard; Mazzone, Theodore; Rutten, Guy E; Tognoni, Gianni; Chiang, Fu-Tien; McMurray, John J V; Califf, Robert M

2013-09-01

Risk factors for stroke are well-established in general populations but sparsely studied in individuals with impaired glucose tolerance. We identified predictors of stroke among participants with impaired glucose tolerance in the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) trial. Cox proportional-hazard regression models were constructed using baseline variables, including the 2 medications studied, valsartan and nateglinide. Among 9306 participants, 237 experienced a stroke over 6.4 years. Predictors of stroke included classical risk factors such as existing cerebrovascular and coronary heart disease, higher pulse pressure, higher low-density lipoprotein cholesterol, older age, and atrial fibrillation. Other factors, including previous venous thromboembolism, higher waist circumference, lower estimated glomerular filtration rate, lower heart rate, and lower body mass index, provided additional important predictive information, yielding a C-index of 0.72. Glycemic measures were not predictive of stroke. Variables associated with stroke were similar in participants with no prior history of cerebrovascular disease at baseline. The most powerful predictors of stroke in patients with impaired glucose tolerance included a combination of established risk factors and novel variables, such as previous venous thromboembolism and elevated waist circumference, allowing moderately effective identification of high-risk individuals.

Heart rate variability: Pre-deployment predictor of post-deployment PTSD symptoms

PubMed Central

Pyne, Jeffrey M.; Constans, Joseph I.; Wiederhold, Mark D.; Gibson, Douglas P.; Kimbrell, Timothy; Kramer, Teresa L.; Pitcock, Jeffery A.; Han, Xiaotong; Williams, D. Keith; Chartrand, Don; Gevirtz, Richard N.; Spira, James; Wiederhold, Brenda K.; McCraty, Rollin; McCune, Thomas R.

2017-01-01

Heart rate variability is a physiological measure associated with autonomic nervous system activity. This study hypothesized that lower pre-deployment HRV would be associated with higher post-deployment post-traumatic stress disorder (PTSD) symptoms. Three-hundred-forty-three Army National Guard soldiers enrolled in the Warriors Achieving Resilience (WAR) study were analyzed. The primary outcome was PTSD symptom severity using the PTSD Checklist – Military version (PCL) measured at baseline, 3- and 12-month post-deployment. Heart rate variability predictor variables included: high frequency power (HF) and standard deviation of the normal cardiac inter-beat interval (SDNN). Generalized linear mixed models revealed that the pre-deployment PCL*ln(HF) interaction term was significant (p < 0.0001). Pre-deployment SDNN was not a significant predictor of post-deployment PCL. Covariates included age, pre-deployment PCL, race/ethnicity, marital status, tobacco use, childhood abuse, pre-deployment traumatic brain injury, and previous combat zone deployment. Pre-deployment heart rate variability predicts post-deployment PTSD symptoms in the context of higher pre-deployment PCL scores. PMID:27773678

Socioeconomic, emotional, and physical execution variables as predictors of cognitive performance in a Spanish sample of middle-aged and older community-dwelling participants.

PubMed

González, Mari Feli; Facal, David; Juncos-Rabadán, Onésimo; Yanguas, Javier

2017-10-01

Cognitive performance is not easily predicted, since different variables play an important role in the manifestation of age-related declines. The objective of this study is to analyze the predictors of cognitive performance in a Spanish sample over 50 years from a multidimensional perspective, including socioeconomic, affective, and physical variables. Some of them are well-known predictors of cognition and others are emergent variables in the study of cognition. The total sample, drawn from the "Longitudinal Study Aging in Spain (ELES)" project, consisted of 832 individuals without signs of cognitive impairment. Cognitive function was measured with tests evaluating episodic and working memory, visuomotor speed, fluency, and naming. Thirteen independent variables were selected as predictors belonging to socioeconomic, emotional, and physical execution areas. Multiple linear regressions, following the enter method, were calculated for each age group in order to study the influence of these variables in cognitive performance. Education is the variable which best predicts cognitive performance in the 50-59, 60-69, and 70-79 years old groups. In the 80+ group, the best predictor is objective economic status and education does not enter in the model. Age-related decline can be modified by the influence of educational and socioeconomic variables. In this context, it is relevant to take into account how easy is to modify certain variables, compared to others which depend on each person's life course.

Role of maternal health and infant inflammation in nutritional and neurodevelopmental outcomes of two-year-old Bangladeshi children.

PubMed

Donowitz, Jeffrey R; Cook, Heather; Alam, Masud; Tofail, Fahmida; Kabir, Mamun; Colgate, E Ross; Carmolli, Marya P; Kirkpatrick, Beth D; Nelson, Charles A; Ma, Jennie Z; Haque, Rashidul; Petri, William A

2018-05-01

Previous studies have shown maternal, inflammatory, and socioeconomic variables to be associated with growth and neurodevelopment in children from low-income countries. However, these outcomes are multifactorial and work describing which predictors most strongly influence them is lacking. We conducted a longitudinal study of Bangladeshi children from birth to two years to assess oral vaccine efficacy. Variables pertaining to maternal and perinatal health, socioeconomic status, early childhood enteric and systemic inflammation, and anthropometry were collected. Bayley-III neurodevelopmental assessment was conducted at two years. As a secondary analysis, we employed hierarchical cluster and random forests techniques to identify and rank which variables predicted growth and neurodevelopment. Cluster analysis demonstrated three distinct groups of predictors. Mother's weight and length-for-age Z score (LAZ) at enrollment were the strongest predictors of LAZ at two years. Cognitive score on Bayley-III was strongly predicted by weight-for-age (WAZ) at enrollment, income, and LAZ at enrollment. Top predictors of language included Rotavirus vaccination, plasma IL 5, sCD14, TNFα, mother's weight, and male gender. Motor function was best predicted by fecal calprotectin, WAZ at enrollment, fecal neopterin, and plasma CRP index. The strongest predictors for social-emotional score included plasma sCD14, income, WAZ at enrollment, and LAZ at enrollment. Based on the random forests' predictions, the estimated percentage of variation explained was 35.4% for LAZ at two years, 34.3% for ΔLAZ, 42.7% for cognitive score, 28.1% for language, 40.8% for motor, and 37.9% for social-emotional score. Birth anthropometry and maternal weight were strong predictors of growth while enteric and systemic inflammation had stronger associations with neurodevelopment. Birth anthropometry was a powerful predictor for all outcomes. These data suggest that further study of stunting in low-income settings should include variables relating to maternal and prenatal health, while investigations focusing on neurodevelopmental outcomes should additionally target causes of systemic and enteric inflammation.

Predictors of posttraumatic stress symptoms following childbirth

PubMed Central

2014-01-01

Background Posttraumatic stress disorder (PTSD) following childbirth has gained growing attention in the recent years. Although a number of predictors for PTSD following childbirth have been identified (e.g., history of sexual trauma, emergency caesarean section, low social support), only very few studies have tested predictors derived from current theoretical models of the disorder. This study first aimed to replicate the association of PTSD symptoms after childbirth with predictors identified in earlier research. Second, cognitive predictors derived from Ehlers and Clark’s (2000) model of PTSD were examined. Methods N = 224 women who had recently given birth completed an online survey. In addition to computing single correlations between PTSD symptom severities and variables of interest, in a hierarchical multiple regression analyses posttraumatic stress symptoms were predicted by (1) prenatal variables, (2) birth-related variables, (3) postnatal social support, and (4) cognitive variables. Results Wellbeing during pregnancy and age were the only prenatal variables contributing significantly to the explanation of PTSD symptoms in the first step of the regression analysis. In the second step, the birth-related variables peritraumatic emotions and wellbeing during childbed significantly increased the explanation of variance. Despite showing significant bivariate correlations, social support entered in the third step did not predict PTSD symptom severities over and above the variables included in the first two steps. However, with the exception of peritraumatic dissociation all cognitive variables emerged as powerful predictors and increased the amount of variance explained from 43% to a total amount of 68%. Conclusions The findings suggest that the prediction of PTSD following childbirth can be improved by focusing on variables derived from a current theoretical model of the disorder. PMID:25026966

An assessment of environmental literacy and analysis of predictors of responsible environmental behavior held by secondary teachers in Hualien County of Taiwan

NASA Astrophysics Data System (ADS)

Hsu, Shih-Jang

The major purpose of this study was to determine the relative contribution of nine variables in predicting teachers' responsible environmental behavior (REB). The theoretic framework of this study was based on the Hines model, the Hungerford and Volk model, and the environmental literacy framework proposed by Environmental Literacy Assessment Consortium. A nine-page instrument was administered by mailed questionnaire to 300 randomly selected secondary teachers in Hualien County of Taiwan with a 78.7% response rate. Correlation and stepwise multiple regression analyses were conducted. The following conclusions were drawn: (1) For all the respondents, all the nine environmental literacy variables were significant correlates of REB. These correlates included: perceived knowledge of environmental action strategies (KNOW; r =.46), intention to act (IA; r =.46), perceived skill in using environmental action strategies (SKILL; r =.45), perceived knowledge of environmental problems and issues (KISSU; r =.34), environmental sensitivity (r =.28), environmental responsibility (r =.27), perceived knowledge of ecology and environmental science (r =.27), locus of control (r =.27), and environmental attitudes (r =.21). (2) When only the nine environmental literacy variables were considered, the most parsimonious set of predictors of REB for all the teachers included: (a) KNOW, (Rsp2 =.2116); (b) IA, (Rsp2 =.0916); and (c) SKILL, (Rsp2 =.0205). For the urban teachers, the most parsimonious set of predictors included: (a) IA (Rsp2 =.2559); (b) SKILL (Rsp2.0926); and (c) environmental responsibility (Rsp2 =.0219). For the rural teachers, the most parsimonious set of predictors included: (a) KNOW (Rsp2 =.1872); (b) IA (Rsp2 =.0816); and (c) KISSU (Rsp2 =.0318). (3) When the environmental literacy variables as well as demographic and experience variables were considered, the most parsimonious set of predictors for all the teachers included: (a) KNOW, (Rsp2 =.2834); (b) IA, (Rsp2 =.0696); (c) area of residence, (Rsp2 =.0174); and (d) SKILL, (Rsp2 =.0163). For the urban teachers, the most parsimonious set of predictors included: (a) IA (Rsp2 =.3199); (b) SKILL (Rsp2 =.0840); (c) major sources of environmental information (Rsp2 =.0432); and (d) membership in environmental organizations, (Rsp2 =.0240). Implications for environmental education program development and instructional practice were presented. Recommendations for further research were also provided.

Prognostic value of echocardiographic indices of left atrial morphology and function in dogs with myxomatous mitral valve disease

PubMed Central

Romito, Giovanni; Guglielmini, Carlo; Diana, Alessia; Pelle, Nazzareno G.; Contiero, Barbara; Cipone, Mario

2018-01-01

Background The prognostic relevance of left atrial (LA) morphological and functional variables, including those derived from speckle tracking echocardiography (STE), has been little investigated in veterinary medicine. Objectives To assess the prognostic value of several echocardiographic variables, with a focus on LA morphological and functional variables in dogs with myxomatous mitral valve disease (MMVD). Animals One‐hundred and fifteen dogs of different breeds with MMVD. Methods Prospective cohort study. Conventional morphologic and echo‐Doppler variables, LA areas and volumes, and STE‐based LA strain analysis were performed in all dogs. A survival analysis was performed to test for the best echocardiographic predictors of cardiac‐related death. Results Most of the tested variables, including all LA STE‐derived variables were univariate predictors of cardiac death in Cox proportional hazard analysis. Because of strong correlation between many variables, only left atrium to aorta ratio (LA/Ao > 1.7), mitral valve E wave velocity (MV E vel > 1.3 m/s), LA maximal volume (LAVmax > 3.53 mL/kg), peak atrial longitudinal strain (PALS < 30%), and contraction strain index (CSI per 1% increase) were entered in the univariate analysis, and all were predictors of cardiac death. However, only the MV E vel (hazard ratio [HR], 4.45; confidence interval [CI], 1.76‐11.24; P < .001) and LAVmax (HR, 2.32; CI, 1.10‐4.89; P = .024) remained statistically significant in the multivariable analysis. Conclusions and Clinical Importance The assessment of LA dimension and function provides useful prognostic information in dogs with MMVD. Considering all the LA variables, LAVmax appears the strongest predictor of cardiac death, being superior to LA/Ao and STE‐derived variables. PMID:29572938

Predicting the onset of smoking in boys and girls.

PubMed

Charlton, A; Blair, V

1989-01-01

The problem of the high prevalence of smoking among girls and young women is of great concern. In an attempt to identify the factors which influence girls and boys respectively to attempt smoking, the study examines social background, advertising and brand awareness, knowledge, teaching and personal beliefs in conjunction as predictors of smoking. In this study which involved the administration of identical pre- and post-test questionnaires to a sample of boys and girls aged 12 and 13 years, nine variables expressed by never-smokers at pre-test stage were assessed as predictors of immediate future smoking. The two tests were administered 4 months apart to 1125 boys and 1213 girls in northern England. The nine variables included were parental smoking, best friends' smoking, perceived positive values of smoking, perceived negative values of smoking, correct health knowledge, cigarette-brand awareness, having a favourite cigarette advertisement, having a cigarette-brand sponsored sport in four top favourites on television. One group received teaching about smoking between the pre- and post-tests and this was also included as a variable. For boys, no variable investigated had any consistently statistically significant correlation with the uptake of smoking. The most important predictor of smoking for boys, having a best friend who smoked, was significant on application of the chi 2 test (P 0.037), although it was non-significant when included singly in a logistic regression model (0.094); the discrepancy was probably due to the small number of best friends known to smoke. For girls, four variables were found to be significant predictors of smoking when included singly in a logistic regression.(ABSTRACT TRUNCATED AT 250 WORDS)

Preadmission Predictors of On-time Graduation in a Doctor of Pharmacy Program.

PubMed

Allen, Rondall E; Diaz, Carroll; Gant, Kisha; Taylor, Ashley; Onor, Ifeanyi

2016-04-25

Objective. To determine which preadmission variables or combination of variables are able to predict on-time graduation in a doctor of pharmacy program. Methods. Transcripts and student files were reviewed for 460 students who entered the college between 2007 and 2009. Results. The preadmission variables with significant correlations to on-time graduation included having a prior degree, student type, the number of unsatisfactory grades (nonscience and math-science courses, and the combination), prepharmacy cumulative grade point average (GPA), and math-science GPA. Of these variables, the significant predictors of on-time graduation were prior degree, the presence of no unsatisfactory grades in nonscience courses, and prepharmacy cumulative GPA. Conclusion. Having a prior degree, lack of unsatisfactory grades in nonscience courses, and prepharmacy GPA were identified as significant predictors of on-time graduation.

Preadmission Predictors of On-time Graduation in a Doctor of Pharmacy Program

PubMed Central

Diaz, Carroll; Gant, Kisha; Taylor, Ashley; Onor, Ifeanyi

2016-01-01

Objective. To determine which preadmission variables or combination of variables are able to predict on-time graduation in a doctor of pharmacy program. Methods. Transcripts and student files were reviewed for 460 students who entered the college between 2007 and 2009. Results. The preadmission variables with significant correlations to on-time graduation included having a prior degree, student type, the number of unsatisfactory grades (nonscience and math-science courses, and the combination), prepharmacy cumulative grade point average (GPA), and math-science GPA. Of these variables, the significant predictors of on-time graduation were prior degree, the presence of no unsatisfactory grades in nonscience courses, and prepharmacy cumulative GPA. Conclusion. Having a prior degree, lack of unsatisfactory grades in nonscience courses, and prepharmacy GPA were identified as significant predictors of on-time graduation. PMID:27170814

Evaluating the performance of different predictor strategies in regression-based downscaling with a focus on glacierized mountain environments

NASA Astrophysics Data System (ADS)

Hofer, Marlis; Nemec, Johanna

2016-04-01

This study presents first steps towards verifying the hypothesis that uncertainty in global and regional glacier mass simulations can be reduced considerably by reducing the uncertainty in the high-resolution atmospheric input data. To this aim, we systematically explore the potential of different predictor strategies for improving the performance of regression-based downscaling approaches. The investigated local-scale target variables are precipitation, air temperature, wind speed, relative humidity and global radiation, all at a daily time scale. Observations of these target variables are assessed from three sites in geo-environmentally and climatologically very distinct settings, all within highly complex topography and in the close proximity to mountain glaciers: (1) the Vernagtbach station in the Northern European Alps (VERNAGT), (2) the Artesonraju measuring site in the tropical South American Andes (ARTESON), and (3) the Brewster measuring site in the Southern Alps of New Zealand (BREWSTER). As the large-scale predictors, ERA interim reanalysis data are used. In the applied downscaling model training and evaluation procedures, particular emphasis is put on appropriately accounting for the pitfalls of limited and/or patchy observation records that are usually the only (if at all) available data from the glacierized mountain sites. Generalized linear models and beta regression are investigated as alternatives to ordinary least squares regression for the non-Gaussian target variables. By analyzing results for the three different sites, five predictands and for different times of the year, we look for systematic improvements in the downscaling models' skill specifically obtained by (i) using predictor data at the optimum scale rather than the minimum scale of the reanalysis data, (ii) identifying the optimum predictor allocation in the vertical, and (iii) considering multiple (variable, level and/or grid point) predictor options combined with state-of-art empirical feature selection tools. First results show that in particular for air temperature, those downscaling models based on direct predictor selection show comparative skill like those models based on multiple predictors. For all other target variables, however, multiple predictor approaches can considerably outperform those models based on single predictors. Including multiple variable types emerges as the most promising predictor option (in particular for wind speed at all sites), even if the same predictor set is used across the different cases.

Multivariate analyses of tinnitus complaint and change in tinnitus complaint: a masker study.

PubMed

Jakes, S; Stephens, S D

1987-11-01

Multivariate statistical techniques were used to re-analyse the data from the recent DHSS multi-centre masker study. These analyses were undertaken to three ends. First, to clarify and attempt to replicate the previously found factor structure of complaints about tinnitus. Secondly, to attempt to identify common factors in the change or improvement measures pre- and post-masker treatment. Thirdly, to identify predictors of any such outcome factors. Two complaint factors were identified; 'Distress' and 'intrusiveness'. A series of analyses were conducted on change measures using different numbers of subjects and variables. When only semantic differential scales were used, the change factors were very similar to the complaint factors noted above. When variables measuring other aspects of improvement were included, several other factors were identified. These included; 'tinnitus helped', 'masking effects', 'residual inhibition' and 'matched loudness'. Twenty-five conceptually distinct predictors of outcome were identified. These predictor variables were quite different for different outcome factors. For example, high-frequency hearing loss was a predictor of tinnitus being helped by the masker, and a low frequency match and a low masking threshold predicted therapeutic success on residual inhibition. Decrease in matched loudness was predicted by louder tinnitus initially.

Teacher and child predictors of achieving IEP goals of children with autism.

PubMed

Ruble, Lisa; McGrew, John H

2013-12-01

It is encouraging that children with autism show a strong response to early intervention, yet more research is needed for understanding the variability in responsiveness to specialized programs. Treatment predictor variables from 47 teachers and children who were randomized to receive the COMPASS intervention (Ruble et al. in The collaborative model for promoting competence and success for students with ASD. Springer, New York, 2012a) were analyzed. Predictors evaluated against child IEP goal attainment included child, teacher, intervention practice, and implementation practice variables based on an implementation science framework (Dunst and Trivette in J Soc Sci 8:143-148, 2012). Findings revealed one child (engagement), one teacher (exhaustion), two intervention quality (IEP quality for targeted and not targeted elements), and no implementation quality variables accounted for variance in child outcomes when analyzed separately. When the four significant variables were compared against each other in a single regression analysis, IEP quality accounted for one quarter of the variance in child outcomes.

Teacher and Child Predictors of Achieving IEP Goals of Children with Autism

PubMed Central

Ruble, Lisa; McGrew, John H.

2013-01-01

It is encouraging that children with autism show a strong response to early intervention, yet more research is needed for understanding the variability in responsiveness to specialized programs. Treatment predictor variables from 47 teachers and children who were randomized to receive the COMPASS intervention (Ruble et al. in The collaborative model for promoting competence and success for students with ASD. Springer, New York, 2012a) were analyzed. Predictors evaluated against child IEP goal attainment included child, teacher, intervention practice, and implementation practice variables based on an implementation science framework (Dunst and Trivette in J Soc Sci 8:143–148, 2012). Findings revealed one child (engagement), one teacher (exhaustion), two intervention quality (IEP quality for targeted and not targeted elements), and no implementation quality variables accounted for variance in child outcomes when analyzed separately. When the four significant variables were compared against each other in a single regression analysis, IEP quality accounted for one quarter of the variance in child outcomes. PMID:23838728

Advanced statistics: linear regression, part II: multiple linear regression.

PubMed

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Kindergarten predictors of second versus eighth grade reading comprehension impairments.

PubMed

Adlof, Suzanne M; Catts, Hugh W; Lee, Jaehoon

2010-01-01

Multiple studies have shown that kindergarten measures of phonological awareness and alphabet knowledge are good predictors of reading achievement in the primary grades. However, less attention has been given to the early predictors of later reading achievement. This study used a modified best-subsets variable-selection technique to examine kindergarten predictors of early versus later reading comprehension impairments. Participants included 433 children involved in a longitudinal study of language and reading development. The kindergarten test battery assessed various language skills in addition to phonological awareness, alphabet knowledge, naming speed, and nonverbal cognitive ability. Reading comprehension was assessed in second and eighth grades. Results indicated that different combinations of variables were required to optimally predict second versus eighth grade reading impairments. Although some variables effectively predicted reading impairments in both grades, their relative contributions shifted over time. These results are discussed in light of the changing nature of reading comprehension over time. Further research will help to improve the early identification of later reading disabilities.

Verbal and Nonverbal Predictors of Spelling Performance

ERIC Educational Resources Information Center

Sadoski, Mark; Willson, Victor L.; Holcomb, Angelia; Boulware-Gooden, Regina

2005-01-01

Verbal and nonverbal predictors of spelling performance in Grades 1-12 were investigated using the national norming data from a standardized spelling test. Verbal variables included number of letters, phonemes, syllables, digraphs, blends, silent markers, r-controlled vowels, and the proportion of grapheme-phoneme correspondence. The nonverbal…

Prognostic value of echocardiographic indices of left atrial morphology and function in dogs with myxomatous mitral valve disease.

PubMed

Baron Toaldo, Marco; Romito, Giovanni; Guglielmini, Carlo; Diana, Alessia; Pelle, Nazzareno G; Contiero, Barbara; Cipone, Mario

2018-05-01

The prognostic relevance of left atrial (LA) morphological and functional variables, including those derived from speckle tracking echocardiography (STE), has been little investigated in veterinary medicine. To assess the prognostic value of several echocardiographic variables, with a focus on LA morphological and functional variables in dogs with myxomatous mitral valve disease (MMVD). One-hundred and fifteen dogs of different breeds with MMVD. Prospective cohort study. Conventional morphologic and echo-Doppler variables, LA areas and volumes, and STE-based LA strain analysis were performed in all dogs. A survival analysis was performed to test for the best echocardiographic predictors of cardiac-related death. Most of the tested variables, including all LA STE-derived variables were univariate predictors of cardiac death in Cox proportional hazard analysis. Because of strong correlation between many variables, only left atrium to aorta ratio (LA/Ao > 1.7), mitral valve E wave velocity (MV E vel > 1.3 m/s), LA maximal volume (LAVmax > 3.53 mL/kg), peak atrial longitudinal strain (PALS < 30%), and contraction strain index (CSI per 1% increase) were entered in the univariate analysis, and all were predictors of cardiac death. However, only the MV E vel (hazard ratio [HR], 4.45; confidence interval [CI], 1.76-11.24; P < .001) and LAVmax (HR, 2.32; CI, 1.10-4.89; P = .024) remained statistically significant in the multivariable analysis. The assessment of LA dimension and function provides useful prognostic information in dogs with MMVD. Considering all the LA variables, LAVmax appears the strongest predictor of cardiac death, being superior to LA/Ao and STE-derived variables. Copyright © 2018 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

Predictors of physical performance and functional ability in people 50+ with and without fibromyalgia.

PubMed

Jones, C Jessie; Rutledge, Dana N; Aquino, Jordan

2010-07-01

The purposes of this study were to determine whether people with and without fibromyalgia (FM) age 50 yr and above showed differences in physical performance and perceived functional ability and to determine whether age, gender, depression, and physical activity level altered the impact of FM status on these factors. Dependent variables included perceived function and 6 performance measures (multidimensional balance, aerobic endurance, overall functional mobility, lower body strength, and gait velocity-normal or fast). Independent (predictor) variables were FM status, age, gender, depression, and physical activity level. Results indicated significant differences between adults with and without FM on all physical-performance measures and perceived function. Linear-regression models showed that the contribution of significant predictors was in expected directions. All regression models were significant, accounting for 16-65% of variance in the dependent variables.

Factors predicting desired autonomy in medical decisions: Risk-taking and gambling behaviors

PubMed Central

Fortune, Erica E; Shotwell, Jessica J; Buccellato, Kiara; Moran, Erin

2016-01-01

This study investigated factors that influence patients’ desired level of autonomy in medical decisions. Analyses included previously supported demographic variables in addition to risk-taking and gambling behaviors, which exhibit a strong relationship with overall health and decision-making, but have not been investigated in conjunction with medical autonomy. Participants (N = 203) completed measures on Amazon’s Mechanical Turk, including two measures of autonomy. Two hierarchical regressions revealed that the predictors explained a significant amount of variance for both measures, but the contribution of predictor variables was incongruent between models. Possible causes for this incongruence and implications for patient–physician interactions are discussed. PMID:28070406

Can You Hack It? Validating Predictors for IT Boot Camps

NASA Astrophysics Data System (ADS)

Gear, Courtney C.

Given the large number of information technology jobs open and lack of qualified individuals to fill them, coding boot camps have sprung up in response to this skill gap by offering a specialized training program in an accelerated format. This fast growth has created a need to measure these training programs and understand their effectiveness. In the present study, a series of analyses examined whether specific or combinations of predictors were valid for training performance in this coding academy. Self-rated, daily efficacy scores were used as outcome variables of training success and correlation results showed a positive relationship with efficacy scores and the logic test score as a predictor. Exploratory analyses indicated a Dunning-Kruger effect where students with lower education levels experience higher overall mood during the training program. Limitations of the study included small sample size, severe range restriction in predictor scores, lack of variance in predictor scores, and low variability in training program success. These limitations made identifying jumps between training stages difficult to identify. By identifying which predictors matter most for each stage of skill acquisition, further research should consider more objective variables such as instructor scores which can serve as a guideline to better asses what stage learners join at and how to design curriculum and assignments accordingly (Honken, 2013).

Predictors of intelligence at the age of 5: family, pregnancy and birth characteristics, postnatal influences, and postnatal growth.

PubMed

Eriksen, Hanne-Lise Falgreen; Kesmodel, Ulrik Schiøler; Underbjerg, Mette; Kilburn, Tina Røndrup; Bertrand, Jacquelyn; Mortensen, Erik Lykke

2013-01-01

Parental education and maternal intelligence are well-known predictors of child IQ. However, the literature regarding other factors that may contribute to individual differences in IQ is inconclusive. The aim of this study was to examine the contribution of a number of variables whose predictive status remain unclarified, in a sample of basically healthy children with a low rate of pre- and postnatal complications. 1,782 5-year-old children sampled from the Danish National Birth Cohort (2003-2007) were assessed with a short form of the Wechsler Preschool and Primary Scale of Intelligence - Revised. Information on parental characteristics, pregnancy and birth factors, postnatal influences, and postnatal growth was collected during pregnancy and at follow-up. A model including study design variables and child's sex explained 7% of the variance in IQ, while parental education and maternal IQ increased the explained variance to 24%. Other predictors were parity, maternal BMI, birth weight, breastfeeding, and the child's head circumference and height at follow-up. These variables, however, only increased the explained variance to 29%. The results suggest that parental education and maternal IQ are major predictors of IQ and should be included routinely in studies of cognitive development. Obstetrical and postnatal factors also predict IQ, but their contribution may be of comparatively limited magnitude.

Most Likely to Succeed: Exploring Predictor Variables for the Counselor Preparation Comprehensive Examination

ERIC Educational Resources Information Center

Hartwig, Elizabeth Kjellstrand; Van Overschelde, James P.

2016-01-01

The authors investigated predictor variables for the Counselor Preparation Comprehensive Examination (CPCE) to examine whether academic variables, demographic variables, and test version were associated with graduate counseling students' CPCE scores. Multiple regression analyses revealed all 3 variables were statistically significant predictors of…

Intra-Personal and Extra-Personal Predictors of Suicide Attempts of South Korean Adolescents

ERIC Educational Resources Information Center

Lee, Ji-Young; Bae, Sung-Man

2015-01-01

The purpose of this study was to explore significant variables predicting adolescent suicidal attempts. Socio-environmental variables such as gender, school record, school grade, school adaptation, and family intimacy together with intra-individual variables including depression, anxiety, delinquency, stress, and self-esteem were considered as…

Antecedents of narcotic use and addiction. A study of 898 Vietnam veterans.

PubMed

Helzer, J E; Robins, L N; Davis, D H

1976-02-01

Previous studies of predictors of narcotic abuse have been retrospective and based on samples of long-term addicts obtained from legal or medical channels. There are several methodological problems in this approach. The present study is an attempt to test certain alleged predictors of narcotic use in a cohort of 898 Vietnam veterans. The design overcomes several of the methodological weaknesses of previous studies. Eight variables which have been reported as predictors of drug use or addiction in the drug literature were inquired about during a personal interview which included the premilitary life of each subject. The antecedent variables were socioeconomic background, inner city residence, psychiatric illness, broken home, race, employment history, education and antisocial history. Using information obtained from interviews and military records, we then tested the predictive value of each of these antecedents by comparing narcotic used and addiction in Vietman and use after Vietnam in men differing with respect to each antecedent. Results indicate that some of the variables were very poor, and others very good predictors of the various levels of narcotic involvement. The predictive value and overall importance of each of the variables we tested are discussed.

Childhood Depression: Relation to Adaptive, Clinical and Predictor Variables

PubMed Central

Garaigordobil, Maite; Bernarás, Elena; Jaureguizar, Joana; Machimbarrena, Juan M.

2017-01-01

The study had two goals: (1) to explore the relations between self-assessed childhood depression and other adaptive and clinical variables (2) to identify predictor variables of childhood depression. Participants were 420 students aged 7–10 years old (53.3% boys, 46.7% girls). Results revealed: (1) positive correlations between depression and clinical maladjustment, school maladjustment, emotional symptoms, internalizing and externalizing problems, problem behaviors, emotional reactivity, and childhood stress; and (2) negative correlations between depression and personal adaptation, global self-concept, social skills, and resilience (sense of competence and affiliation). Linear regression analysis including the global dimensions revealed 4 predictors of childhood depression that explained 50.6% of the variance: high clinical maladjustment, low global self-concept, high level of stress, and poor social skills. However, upon introducing the sub-dimensions, 9 predictor variables emerged that explained 56.4% of the variance: many internalizing problems, low family self-concept, high anxiety, low responsibility, low personal self-assessment, high social stress, few aggressive behaviors toward peers, many health/psychosomatic problems, and external locus of control. The discussion addresses the importance of implementing prevention programs for childhood depression at early ages. PMID:28572787

Do Cognitive Models Help in Predicting the Severity of Posttraumatic Stress Disorder, Phobia, and Depression After Motor Vehicle Accidents? A Prospective Longitudinal Study

PubMed Central

Ehring, Thomas; Ehlers, Anke; Glucksman, Edward

2008-01-01

The study investigated the power of theoretically derived cognitive variables to predict posttraumatic stress disorder (PTSD), travel phobia, and depression following injury in a motor vehicle accident (MVA). MVA survivors (N = 147) were assessed at the emergency department on the day of their accident and 2 weeks, 1 month, 3 months, and 6 months later. Diagnoses were established with the Structured Clinical Interview for DSM–IV. Predictors included initial symptom severities; variables established as predictors of PTSD in E. J. Ozer, S. R. Best, T. L. Lipsey, and D. S. Weiss's (2003) meta-analysis; and variables derived from cognitive models of PTSD, phobia, and depression. Results of nonparametric multiple regression analyses showed that the cognitive variables predicted subsequent PTSD and depression severities over and above what could be predicted from initial symptom levels. They also showed greater predictive power than the established predictors, although the latter showed similar effect sizes as in the meta-analysis. In addition, the predictors derived from cognitive models of PTSD and depression were disorder-specific. The results support the role of cognitive factors in the maintenance of emotional disorders following trauma. PMID:18377119

Advanced statistics: linear regression, part I: simple linear regression.

PubMed

Marill, Keith A

2004-01-01

Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

Prematriculation Program Grades as Predictors of Black and Other Nontraditional Students' First-Year Academic Performances.

ERIC Educational Resources Information Center

Hesser, Al; Lewis, Lloyd

1992-01-01

A study explored predictors of African-American and other nontraditional medical students' first-year academic performance at the Medical College of Georgia. Variables included undergraduate grades and grades in a summer prematriculation program (SPP) featuring biochemistry, anatomy, and immunology courses. SPP grades were found useful in…

Predicting Responsiveness to Treatment of Children with Autism: A Retrospective Study of the Importance of Physical Dysmorphology

ERIC Educational Resources Information Center

Stoelb, M.; Yarnal, R.; Miles, J.; Takahashi, T. N.; Farmer, J. E.; McCathren, R. B.

2004-01-01

This retrospective study examined predictors of outcome for children with autism following 6 and 12 months of early intensive behavioral intervention. Potential predictor variables included pretreatment functioning, age at onset of treatment, treatment intensity, family involvement, and physical characteristics (e.g., brain abnormalities,…

Inadequate Response to Therapy as a Predictor of Suicide.

ERIC Educational Resources Information Center

Dahlsgaard, Katherine K.; Beck, Aaron T.; Brown, Gregory K.

1998-01-01

The role of response to cognitive therapy as a predictor of suicide was investigated by comparing 17 outpatients with mood disorders who committed suicide with 17 matched patients who did not commit suicide. Significant differences were found on several variables including higher levels of hopelessness at termination of therapy. (Author/EMK)

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…

Childhood maltreatment history as a risk factor for sexual harassment among U.S. Army soldiers.

PubMed

Rosen, L N; Martin, L

1998-01-01

Four different types of childhood maltreatment were examined as predictors of unwanted sexual experiences and acknowledged sexual harassment among male and female active duty soldiers in the United States Army. Predictor variables included childhood sexual abuse, physical-emotional abuse, physical neglect, and emotional neglect. Three types of unwanted sexual experiences in the workplace were examined as outcome variables: gender harassment, unwanted sexual attention, and coercion. Both sexual and physical-emotional abuse during childhood were found to be predictors of unwanted sexual experiences and of acknowledged sexual harassment in the workplace. Among female soldiers, the most severe type of unwanted experience-coercion-was predicted only by childhood physical-emotional abuse. Among male soldiers childhood sexual abuse was the strongest predictor of coercion. A greater variety of types of childhood maltreatment predicted sexual harassment outcomes for male soldiers. Childhood maltreatment and adult sexual harassment were predictors of psychological well-being for soldiers of both genders.

Dysthymic Disorder and Double Depression: Prediction of 10-Year Course Trajectories and Outcomes

PubMed Central

Klein, Daniel N.; Shankman, Stewart A.; Rose, Suzanne

2008-01-01

We sought to identify baseline predictors of 10-year course trajectories and outcomes in patients with dysthymic disorder and double depression. Eighty-seven outpatients with early-onset (< 21 years) dysthymic disorder, with or without superimposed major depression, were assessed five times at 30-month intervals for 10 years. Baseline evaluations included semi-structured diagnostic interviews for Axis I and II psychopathology and childhood adversity. Direct interview and family history data were collected on first-degree relatives. Follow-up assessments included the Longitudinal Follow-up Evaluation and Hamilton Depression Rating Scale. Using mixed effects growth curve models, univariate predictors of depression severity and functional impairment at 10-year outcome included older age, less education, concurrent anxiety disorder, greater familial loading for chronic depression, a history of a poorer maternal relationship in childhood, and a history of childhood sexual abuse. In addition, longer duration of dysthymic disorder also predicted greater impairment 10 years later. Predictors of a poorer trajectory of depressive symptoms over time included ethnicity and personality disorders; predictors of a poorer trajectory of social functioning included familial loading of chronic depression and quality of the childhood maternal relationship. Thus, demographic, clinical, family history, and early adversity variables all contribute to predicting the long term trajectory and outcome of DD. These variables should be routinely assessed in clinical evaluations and can provide clinicians with valuable prognostic information. PMID:17466334

Dysthymic disorder and double depression: prediction of 10-year course trajectories and outcomes.

PubMed

Klein, Daniel N; Shankman, Stewart A; Rose, Suzanne

2008-04-01

We sought to identify baseline predictors of 10-year course trajectories and outcomes in patients with dysthymic disorder and double depression. Eighty-seven outpatients with early-onset (<21 years) dysthymic disorder, with or without superimposed major depression, were assessed five times at 30-month intervals for 10 years. Baseline evaluations included semi-structured diagnostic interviews for Axis I and II psychopathology and childhood adversity. Direct interview and family history data were collected on first-degree relatives. Follow-up assessments included the Longitudinal Follow-up Evaluation and Hamilton Depression Rating Scale. Using mixed effects growth curve models, univariate predictors of depression severity and functional impairment at 10-year outcome included older age, less education, concurrent anxiety disorder, greater familial loading for chronic depression, a history of a poorer maternal relationship in childhood, and a history of childhood sexual abuse. In addition, longer duration of dysthymic disorder also predicted greater impairment 10 years later. Predictors of a poorer trajectory of depressive symptoms over time included ethnicity and personality disorders; predictors of a poorer trajectory of social functioning included familial loading of chronic depression and quality of the childhood maternal relationship. Thus, demographic, clinical, family history, and early adversity variables all contribute to predicting the long-term trajectory and outcome of DD. These variables should be routinely assessed in clinical evaluations and can provide clinicians with valuable prognostic information.

Suppressor Variables: The Difference between "Is" versus "Acting As"

ERIC Educational Resources Information Center

Ludlow, Larry; Klein, Kelsey

2014-01-01

Correlated predictors in regression models are a fact of life in applied social science research. The extent to which they are correlated will influence the estimates and statistics associated with the other variables they are modeled along with. These effects, for example, may include enhanced regression coefficients for the other variables--a…

Nonsuicidal self-injury in community adolescents: A systematic review of prospective predictors, mediators and moderators.

PubMed

Valencia-Agudo, Fatima; Burcher, Georgina Corbet; Ezpeleta, Lourdes; Kramer, Tami

2018-06-01

Nonsuicidal self-injury (NSSI) usually starts during adolescence and is associated with an array of psychological and psychiatric symptoms and future suicide attempts. The aim of this study is to determine prospective predictors, mediators and moderators of NSSI in adolescent community samples in order to target prevention and treatment strategies. Two team members searched online databases independently. Thirty-nine studies were included in the review. Several variables were seen to prospectively predict NSSI: female gender, family-related variables, peer victimisation, depression, previous NSSI and self-concept. Few studies analysed mediators and moderators. Low self-concept was highlighted as a relevant moderator in the relationship between intra/interpersonal variables and NSSI. Implications of these findings are discussed. The considerable heterogeneity between studies posed a limitation to determine robust predictors of NSSI. Further prospective studies using standardised measures of predictors and outcomes are needed to ascertain the most at risk individuals and develop prevention strategies. Copyright © 2018 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Self-care resources and activity as predictors of quality of life in persons after myocardial infarction.

PubMed

Baas, Linda S

2004-01-01

An ex post facto correlational study was conducted to examine predictors of quality of life in persons 3 to 6 months after a myocardial infarction. Self-care resources, self-care knowledge (needs), activity level, and selected demographic variables were examined as predictor variables. A convenience sample of 86 subjects with a mean age of 61 years, was recruited for participation in this study. The study that explained 35% of the variance in quality of life included self-care resources available, activity level, and self-care needs. Modeling and Role Modeling Paradigm provided a useful explanation of how self-care resources and self-care knowledge can be applied to persons recovering from myocardial infarction.

Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species?

PubMed Central

Esperón-Rodríguez, Manuel; Baumgartner, John B.; Beaumont, Linda J.

2017-01-01

Background Shrubs play a key role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood and non-wood products. However, despite their ecological and societal importance, the influence of different environmental variables on shrub distributions remains unclear. We evaluated the influence of climate and soil characteristics, and whether including soil variables improved the performance of a species distribution model (SDM), Maxent. Methods This study assessed variation in predictions of environmental suitability for 29 Australian shrub species (representing dominant members of six shrubland classes) due to the use of alternative sets of predictor variables. Models were calibrated with (1) climate variables only, (2) climate and soil variables, and (3) soil variables only. Results The predictive power of SDMs differed substantially across species, but generally models calibrated with both climate and soil data performed better than those calibrated only with climate variables. Models calibrated solely with soil variables were the least accurate. We found regional differences in potential shrub species richness across Australia due to the use of different sets of variables. Conclusions Our study provides evidence that predicted patterns of species richness may be sensitive to the choice of predictor set when multiple, plausible alternatives exist, and demonstrates the importance of considering soil properties when modeling availability of habitat for plants. PMID:28652933

Downscaling reanalysis data to high-resolution variables above a glacier surface (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Hofer, Marlis; Mölg, Thomas; Marzeion, Ben; Kaser, Georg

2010-05-01

Recently initiated observation networks in the Cordillera Blanca provide temporally high-resolution, yet short-term atmospheric data. The aim of this study is to extend the existing time series into the past. We present an empirical-statistical downscaling (ESD) model that links 6-hourly NCEP/NCAR reanalysis data to the local target variables, measured at the tropical glacier Artesonraju (Northern Cordillera Blanca). The approach is particular in the context of ESD for two reasons. First, the observational time series for model calibration are short (only about two years). Second, unlike most ESD studies in climate research, we focus on variables at a high temporal resolution (i.e., six-hourly values). Our target variables are two important drivers in the surface energy balance of tropical glaciers; air temperature and specific humidity. The selection of predictor fields from the reanalysis data is based on regression analyses and climatologic considerations. The ESD modelling procedure includes combined empirical orthogonal function and multiple regression analyses. Principal component screening is based on cross-validation using the Akaike Information Criterion as model selection criterion. Double cross-validation is applied for model evaluation. Potential autocorrelation in the time series is considered by defining the block length in the resampling procedure. Apart from the selection of predictor fields, the modelling procedure is automated and does not include subjective choices. We assess the ESD model sensitivity to the predictor choice by using both single- and mixed-field predictors of the variables air temperature (1000 hPa), specific humidity (1000 hPa), and zonal wind speed (500 hPa). The chosen downscaling domain ranges from 80 to 50 degrees west and from 0 to 20 degrees south. Statistical transfer functions are derived individually for different months and times of day (month/hour-models). The forecast skill of the month/hour-models largely depends on month and time of day, ranging from 0 to 0.8, but the mixed-field predictors generally perform better than the single-field predictors. At all time scales, the ESD model shows added value against two simple reference models; (i) the direct use of reanalysis grid point values, and (ii) mean diurnal and seasonal cycles over the calibration period. The ESD model forecast 1960 to 2008 clearly reflects interannual variability related to the El Niño/Southern Oscillation, but is sensitive to the chosen predictor type. So far, we have not assessed the performance of NCEP/NCAR reanalysis data against other reanalysis products. The developed ESD model is computationally cheap and applicable wherever measurements are available for model calibration.

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

N-of-1 study of weight loss maintenance assessing predictors of physical activity, adherence to weight loss plan and weight change.

PubMed

Kwasnicka, Dominika; Dombrowski, Stephan U; White, Martin; Sniehotta, Falko F

2017-06-01

Behaviour change interventions are effective in supporting individuals to achieve clinically significant weight loss, but weight loss maintenance (WLM) is less often attained. This study examined predictive variables associated with WLM. N-of-1 study with daily ecological momentary assessment combined with objective measurement of weight and physical activity, collected with wireless devices (Fitbit™) for six months. Eight previously obese adults who had lost over 5% of their body weight in the past year took part. Data were analysed using time series methods. Predictor variables were based on five theoretical themes: maintenance motives, self-regulation, personal resources, habits, and environmental influences. Dependent variables were: objectively estimated step count and weight, and self-reported WLM plan adherence. For all participants, daily fluctuations in self-reported adherence to their WLM plan were significantly associated with most of the explanatory variables, including maintenance motivation and satisfaction with outcomes, self-regulation, habit, and stable environment. Personal resources were not a consistent predictor of plan adherence. This is the first study to assess theoretical predictions of WLM within individuals. WLM is a dynamic process including the interplay of motivation, self-regulation, habit, resources, and perceptions of environmental context. Individuals maintaining their weight have unique psychological profiles which could be accounted for in interventions.

A Comparison between Multiple Regression Models and CUN-BAE Equation to Predict Body Fat in Adults

PubMed Central

Fuster-Parra, Pilar; Bennasar-Veny, Miquel; Tauler, Pedro; Yañez, Aina; López-González, Angel A.; Aguiló, Antoni

2015-01-01

Background Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Methods Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. Results The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). Conclusions There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF. PMID:25821960

A comparison between multiple regression models and CUN-BAE equation to predict body fat in adults.

PubMed

Fuster-Parra, Pilar; Bennasar-Veny, Miquel; Tauler, Pedro; Yañez, Aina; López-González, Angel A; Aguiló, Antoni

2015-01-01

Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF.

Hope & Achievement Goals as Predictors of Student Behavior & Achievement in a Rural Middle School

ERIC Educational Resources Information Center

Walker, Christopher O.; Winn, Tina D.; Adams, Blakely N.; Shepard, Misty R.; Huddleston, Chelsea D.; Godwin, Kayce L.

2009-01-01

Relations among a set of cognitive-motivational variables were examined with the intent being to assess and clarify the nature of their interconnections within a middle school sample. Student perception of hope, which includes perceptions of agency and pathways, was investigated, along with personal achievement goal orientation, as predictors of…

Predictors of Intelligence at the Age of 5: Family, Pregnancy and Birth Characteristics, Postnatal Influences, and Postnatal Growth

PubMed Central

Eriksen, Hanne-Lise Falgreen; Kesmodel, Ulrik Schiøler; Underbjerg, Mette; Kilburn, Tina Røndrup; Bertrand, Jacquelyn; Mortensen, Erik Lykke

2013-01-01

Parental education and maternal intelligence are well-known predictors of child IQ. However, the literature regarding other factors that may contribute to individual differences in IQ is inconclusive. The aim of this study was to examine the contribution of a number of variables whose predictive status remain unclarified, in a sample of basically healthy children with a low rate of pre- and postnatal complications. 1,782 5-year-old children sampled from the Danish National Birth Cohort (2003–2007) were assessed with a short form of the Wechsler Preschool and Primary Scale of Intelligence – Revised. Information on parental characteristics, pregnancy and birth factors, postnatal influences, and postnatal growth was collected during pregnancy and at follow-up. A model including study design variables and child’s sex explained 7% of the variance in IQ, while parental education and maternal IQ increased the explained variance to 24%. Other predictors were parity, maternal BMI, birth weight, breastfeeding, and the child’s head circumference and height at follow-up. These variables, however, only increased the explained variance to 29%. The results suggest that parental education and maternal IQ are major predictors of IQ and should be included routinely in studies of cognitive development. Obstetrical and postnatal factors also predict IQ, but their contribution may be of comparatively limited magnitude. PMID:24236109

Predictors of maternal responsiveness.

PubMed

Drake, Emily E; Humenick, Sharron S; Amankwaa, Linda; Younger, Janet; Roux, Gayle

2007-01-01

To explore maternal responsiveness in the first 2 to 4 months after delivery and to evaluate potential predictors of maternal responsiveness, including infant feeding, maternal characteristics, and demographic factors such as age, socioeconomic status, and educational level. A cross-sectional survey design was used to assess the variables of maternal responsiveness, feeding patterns, and maternal characteristics in a convenience sample of 177 mothers in the first 2 to 4 months after delivery. The 60-item self-report instrument included scales to measure maternal responsiveness, self-esteem, and satisfaction with life as well as infant feeding questions and sociodemographic items. An online data-collection strategy was used, resulting in participants from 41 U.S. states. Multiple regression analysis showed that satisfaction with life, self-esteem, and number of children, but not breastfeeding, explained a significant portion of the variance in self-reported maternal responsiveness scores. In this analysis, sociodemographic variables such as age, education, income, and work status showed little or no relationship to maternal responsiveness scores. This study provides additional information about patterns of maternal behavior in the transition to motherhood and some of the variables that influence that transition. Satisfaction with life was a new predictor of maternal responsiveness. However, with only 15% of the variance explained by the predictors in this study, a large portion of the variance in maternal responsiveness remains unexplained. Further research in this area is needed.

Prediction of placebo responses: a systematic review of the literature

PubMed Central

Horing, Bjoern; Weimer, Katja; Muth, Eric R.; Enck, Paul

2014-01-01

Objective: Predicting who responds to placebo treatment—and under which circumstances—has been a question of interest and investigation for generations. However, the literature is disparate and inconclusive. This review aims to identify publications that provide high quality data on the topic of placebo response (PR) prediction. Methods: To identify studies concerned with PR prediction, independent searches were performed in an expert database (for all symptom modalities) and in PubMed (for pain only). Articles were selected when (a) they assessed putative predictors prior to placebo treatment and (b) an adequate control group was included when the associations of predictors and PRs were analyzed. Results: Twenty studies were identified, most with pain as dependent variable. Most predictors of PRs were psychological constructs related to actions, expected outcomes and the emotional valence attached to these events (goal-seeking, self-efficacy/-esteem, locus of control, optimism). Other predictors involved behavioral control (desire for control, eating restraint), personality variables (fun seeking, sensation seeking, neuroticism), or biological markers (sex, a single nucleotide polymorphism related to dopamine metabolism). Finally, suggestibility and beliefs in expectation biases, body consciousness, and baseline symptom severity were found to be predictive. Conclusions: While results are heterogeneous, some congruence of predictors can be identified. PRs mainly appear to be moderated by expectations of how the symptom might change after treatment, or expectations of how symptom repetition can be coped with. It is suggested to include the listed constructs in future research. Furthermore, a closer look at variables moderating symptom change in control groups seems warranted. PMID:25324797

Predicting change over time in career planning and career exploration for high school students.

PubMed

Creed, Peter A; Patton, Wendy; Prideaux, Lee-Ann

2007-06-01

This study assessed 166 high school students in Grade 8 and again in Grade 10. Four models were tested: (a) whether the T1 predictor variables (career knowledge, indecision, decision-making self efficacy, self-esteem, demographics) predicted the outcome variable (career planning/exploration) at T1; (b) whether the T1 predictor variables predicted the outcome variable at T2; (c) whether the T1 predictor variables predicted change in the outcome variable from T1-T2; and (d) whether changes in the predictor variables from T1-T2 predicted change in the outcome variable from T1-T2. Strong associations (R(2)=34%) were identified for the T1 analysis (confidence, ability and paid work experience were positively associated with career planning/exploration). T1 variables were less useful predictors of career planning/exploration at T2 (R(2)=9%; having more confidence at T1 was associated with more career planning/exploration at T2) and change in career planning/exploration from T1-T2 (R(2)=11%; less confidence and no work experience were associated with change in career planning/exploration from T1-T2). When testing effect of changes in predictor variables predicting changes in outcome variable (R(2)=22%), three important predictors, indecision, work experience and confidence, were identified. Overall, results indicated important roles for self-efficacy and early work experiences in current and future career planning/exploration of high school students.

Predictors of Outcome in Traumatic Brain Injury: New Insight Using Receiver Operating Curve Indices and Bayesian Network Analysis.

PubMed

Zador, Zsolt; Sperrin, Matthew; King, Andrew T

2016-01-01

Traumatic brain injury remains a global health problem. Understanding the relative importance of outcome predictors helps optimize our treatment strategies by informing assessment protocols, clinical decisions and trial designs. In this study we establish importance ranking for outcome predictors based on receiver operating indices to identify key predictors of outcome and create simple predictive models. We then explore the associations between key outcome predictors using Bayesian networks to gain further insight into predictor importance. We analyzed the corticosteroid randomization after significant head injury (CRASH) trial database of 10008 patients and included patients for whom demographics, injury characteristics, computer tomography (CT) findings and Glasgow Outcome Scale (GCS) were recorded (total of 13 predictors, which would be available to clinicians within a few hours following the injury in 6945 patients). Predictions of clinical outcome (death or severe disability at 6 months) were performed using logistic regression models with 5-fold cross validation. Predictive performance was measured using standardized partial area (pAUC) under the receiver operating curve (ROC) and we used Delong test for comparisons. Variable importance ranking was based on pAUC targeted at specificity (pAUCSP) and sensitivity (pAUCSE) intervals of 90-100%. Probabilistic associations were depicted using Bayesian networks. Complete AUC analysis showed very good predictive power (AUC = 0.8237, 95% CI: 0.8138-0.8336) for the complete model. Specificity focused importance ranking highlighted age, pupillary, motor responses, obliteration of basal cisterns/3rd ventricle and midline shift. Interestingly when targeting model sensitivity, the highest-ranking variables were age, severe extracranial injury, verbal response, hematoma on CT and motor response. Simplified models, which included only these key predictors, had similar performance (pAUCSP = 0.6523, 95% CI: 0.6402-0.6641 and pAUCSE = 0.6332, 95% CI: 0.62-0.6477) compared to the complete models (pAUCSP = 0.6664, 95% CI: 0.6543-0.679, pAUCSE = 0.6436, 95% CI: 0.6289-0.6585, de Long p value 0.1165 and 0.3448 respectively). Bayesian networks showed the predictors that did not feature in the simplified models were associated with those that did. We demonstrate that importance based variable selection allows simplified predictive models to be created while maintaining prediction accuracy. Variable selection targeting specificity confirmed key components of clinical assessment in TBI whereas sensitivity based ranking suggested extracranial injury as one of the important predictors. These results help refine our approach to head injury assessment, decision-making and outcome prediction targeted at model sensitivity and specificity. Bayesian networks proved to be a comprehensive tool for depicting probabilistic associations for key predictors giving insight into why the simplified model has maintained accuracy.

Developing Risk Prediction Models for Postoperative Pancreatic Fistula: a Systematic Review of Methodology and Reporting Quality.

PubMed

Wen, Zhang; Guo, Ya; Xu, Banghao; Xiao, Kaiyin; Peng, Tao; Peng, Minhao

2016-04-01

Postoperative pancreatic fistula is still a major complication after pancreatic surgery, despite improvements of surgical technique and perioperative management. We sought to systematically review and critically access the conduct and reporting of methods used to develop risk prediction models for predicting postoperative pancreatic fistula. We conducted a systematic search of PubMed and EMBASE databases to identify articles published before January 1, 2015, which described the development of models to predict the risk of postoperative pancreatic fistula. We extracted information of developing a prediction model including study design, sample size and number of events, definition of postoperative pancreatic fistula, risk predictor selection, missing data, model-building strategies, and model performance. Seven studies of developing seven risk prediction models were included. In three studies (42 %), the number of events per variable was less than 10. The number of candidate risk predictors ranged from 9 to 32. Five studies (71 %) reported using univariate screening, which was not recommended in building a multivariate model, to reduce the number of risk predictors. Six risk prediction models (86 %) were developed by categorizing all continuous risk predictors. The treatment and handling of missing data were not mentioned in all studies. We found use of inappropriate methods that could endanger the development of model, including univariate pre-screening of variables, categorization of continuous risk predictors, and model validation. The use of inappropriate methods affects the reliability and the accuracy of the probability estimates of predicting postoperative pancreatic fistula.

Predictors of Outcomes in Autism Early Intervention: Why Don’t We Know More?

PubMed Central

Vivanti, Giacomo; Prior, Margot; Williams, Katrina; Dissanayake, Cheryl

2014-01-01

Response to early intervention programs in autism is variable. However, the factors associated with positive versus poor treatment outcomes remain unknown. Hence the issue of which intervention/s should be chosen for an individual child remains a common dilemma. We argue that lack of knowledge on “what works for whom and why” in autism reflects a number of issues in current approaches to outcomes research, and we provide recommendations to address these limitations. These include: a theory-driven selection of putative predictors; the inclusion of proximal measures that are directly relevant to the learning mechanisms demanded by the specific educational strategies; the consideration of family characteristics. Moreover, all data on associations between predictor and outcome variables should be reported in treatment studies. PMID:24999470

Beyond a Climate-Centric View of Plant Distribution: Edaphic Variables Add Value to Distribution Models

PubMed Central

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change. PMID:24658097

Beyond a climate-centric view of plant distribution: edaphic variables add value to distribution models.

PubMed

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55,000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change.

A Simulation Investigation of Principal Component Regression.

ERIC Educational Resources Information Center

Allen, David E.

Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…

Determinants of Success on the ETS Business Major Field Exam for Students in an Undergraduate Multisite Regional University Business Program

ERIC Educational Resources Information Center

Bagamery, Bruce D.; Lasik, John J.; Nixon, Don R.

2005-01-01

Extending previous studies, the authors examined a larger set of variables to identify predictors of student performance on the Educational Testing Service Major Field Exam in Business, which has been shown to be an externally valid measure of student learning outcomes. Significant predictors include gender, whether students took the SAT, and…

Do bioclimate variables improve performance of climate envelope models?

USGS Publications Warehouse

Watling, James I.; Romañach, Stephanie S.; Bucklin, David N.; Speroterra, Carolina; Brandt, Laura A.; Pearlstine, Leonard G.; Mazzotti, Frank J.

2012-01-01

Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models.

A hybrid machine learning model to estimate nitrate contamination of production zone groundwater in the Central Valley, California

NASA Astrophysics Data System (ADS)

Ransom, K.; Nolan, B. T.; Faunt, C. C.; Bell, A.; Gronberg, J.; Traum, J.; Wheeler, D. C.; Rosecrans, C.; Belitz, K.; Eberts, S.; Harter, T.

2016-12-01

A hybrid, non-linear, machine learning statistical model was developed within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface in the Central Valley, California. A database of 213 predictor variables representing well characteristics, historical and current field and county scale nitrogen mass balance, historical and current landuse, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6,000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The machine learning method, gradient boosting machine (GBM) was used to screen predictor variables and rank them in order of importance in relation to the groundwater nitrate measurements. The top five most important predictor variables included oxidation/reduction characteristics, historical field scale nitrogen mass balance, climate, and depth to 60 year old water. Twenty-two variables were selected for the final model and final model errors for log-transformed hold-out data were R squared of 0.45 and root mean square error (RMSE) of 1.124. Modeled mean groundwater age was tested separately for error improvement in the model and when included decreased model RMSE by 0.5% compared to the same model without age and by 0.20% compared to the model with all 213 variables. 1D and 2D partial plots were examined to determine how variables behave individually and interact in the model. Some variables behaved as expected: log nitrate decreased with increasing probability of anoxic conditions and depth to 60 year old water, generally decreased with increasing natural landuse surrounding wells and increasing mean groundwater age, generally increased with increased minimum depth to high water table and with increased base flow index value. Other variables exhibited much more erratic or noisy behavior in the model making them more difficult to interpret but highlighting the usefulness of the non-linear machine learning method. 2D interaction plots show probability of anoxic groundwater conditions largely control estimated nitrate concentrations compared to the other predictors.

A Preliminary Investigation of the Predictors of Tanning Dependence

PubMed Central

Heckman, Carolyn J.; Egleston, Brian L.; Wilson, Diane B.; Ingersoll, Karen S.

2014-01-01

Objectives To investigate possible predictors of tanning dependence including demographic variables, exposure and protective behaviors, and other health-related behaviors. Methods This study consisted of an online survey of 400 students and other volunteers from a university community. Results Twenty-seven percent of the sample was classified as tanning dependent. Tanning dependence was predicted by ethnicity and skin type, indoor and outdoor tanning and burning, and lower skin protective behaviors, as well as smoking and body mass index. Conclusions Young adults are at risk for tanning dependence, which can be predicted by specific demographic and behavioral variables. PMID:18241130

Maternal risk factors predicting child physical characteristics and dysmorphology in fetal alcohol syndrome and partial fetal alcohol syndrome.

PubMed

May, Philip A; Tabachnick, Barbara G; Gossage, J Phillip; Kalberg, Wendy O; Marais, Anna-Susan; Robinson, Luther K; Manning, Melanie; Buckley, David; Hoyme, H Eugene

2011-12-01

Previous research in South Africa revealed very high rates of fetal alcohol syndrome (FAS), of 46-89 per 1000 among young children. Maternal and child data from studies in this community summarize the multiple predictors of FAS and partial fetal alcohol syndrome (PFAS). Sequential regression was employed to examine influences on child physical characteristics and dysmorphology from four categories of maternal traits: physical, demographic, childbearing, and drinking. Then, a structural equation model (SEM) was constructed to predict influences on child physical characteristics. Individual sequential regressions revealed that maternal drinking measures were the most powerful predictors of a child's physical anomalies (R² = .30, p < .001), followed by maternal demographics (R² = .24, p < .001), maternal physical characteristics (R²=.15, p < .001), and childbearing variables (R² = .06, p < .001). The SEM utilized both individual variables and the four composite categories of maternal traits to predict a set of child physical characteristics, including a total dysmorphology score. As predicted, drinking behavior is a relatively strong predictor of child physical characteristics (β = 0.61, p < .001), even when all other maternal risk variables are included; higher levels of drinking predict child physical anomalies. Overall, the SEM model explains 62% of the variance in child physical anomalies. As expected, drinking variables explain the most variance. But this highly controlled estimation of multiple effects also reveals a significant contribution played by maternal demographics and, to a lesser degree, maternal physical and childbearing variables. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning.

PubMed

Reid, Colleen E; Jerrett, Michael; Petersen, Maya L; Pfister, Gabriele G; Morefield, Philip E; Tager, Ira B; Raffuse, Sean M; Balmes, John R

2015-03-17

Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires. We estimated PM2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM2.5 concentrations during a major wildfire event.

Clinical predictors of challenging atrioventricular node ablation procedure for rate control in patients with atrial fibrillation.

PubMed

Polin, Baptiste; Behar, Nathalie; Galand, Vincent; Auffret, Vincent; Behaghel, Albin; Pavin, Dominique; Daubert, Jean-Claude; Mabo, Philippe; Leclercq, Christophe; Martins, Raphael P

2017-10-15

Atrioventricular node (AVN) ablation is usually a simple procedure but may sometimes be challenging. We aimed at identifying pre-procedural clinical predictors of challenging AVN ablation. Patients referred for AVN ablation from 2009 to 2015 were retrospectively included. Baseline clinical data, procedural variables and outcomes of AVN ablation were collected. A "challenging procedure" was defined 1) total radiofrequency delivery to get persistent AVN block≥400s, 2) need for left-sided arterial approach or 3) failure to obtain AVN ablation. 200 patients were included (71±10years). A total of 37 (18.5%) patients had "challenging" procedures (including 9 failures, 4.5%), while 163 (81.5%) had "non-challenging" ablations. In multivariable analysis, male sex (Odds ratio (OR)=4.66, 95% confidence interval (CI): 1.74-12.46), body mass index (BMI, OR=1.08 per 1kg/m 2 , 95%CI 1.01-1.16), operator experience (OR=0.40, 95%CI 0.17-0.94), and moderate-to-severe tricuspid regurgitation (TR, OR=3.65, 95%CI 1.63-8.15) were significant predictors of "challenging" ablations. The proportion as a function of number of predictors was analyzed (from 0 to 4, including male sex, operator inexperience, a BMI>23.5kg/m 2 and moderate-to-severe TR). There was a gradual increase in the risk of "challenging" procedure with the number of predictors by patient (No predictor: 0%; 1 predictor: 6.3%; 2 predictors: 16.5%; 3 predictors: 32.5%; 4 predictors: 77.8%). Operator experience, male sex, higher BMI and the degree of TR were independent predictors of "challenging" AVN ablation procedure. The risk increases with the number of predictors by patient. Copyright © 2017. Published by Elsevier B.V.

Regression mixture models: Does modeling the covariance between independent variables and latent classes improve the results?

PubMed Central

Lamont, Andrea E.; Vermunt, Jeroen K.; Van Horn, M. Lee

2016-01-01

Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we test the effects of violating an implicit assumption often made in these models – i.e., independent variables in the model are not directly related to latent classes. Results indicated that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. Additionally, this study tests whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations, but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a re-analysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted. PMID:26881956

Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

PubMed

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

Students' Evaluation of Teaching, Approaches to Learning, and Academic Achievement

ERIC Educational Resources Information Center

Diseth, Age

2007-01-01

Students' evaluation and perception of the learning environment are considered to be important predictors of students' approaches to learning. These variables may also account for variance in academic outcome, such as in examination grades, but previous research has rarely included a comparison between all of these variables. This article…

A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables

NASA Astrophysics Data System (ADS)

Madonna, Erica; Ginsbourger, David; Martius, Olivia

2018-05-01

In Switzerland, hail regularly causes substantial damage to agriculture, cars and infrastructure, however, little is known about its long-term variability. To study the variability, the monthly number of days with hail in northern Switzerland is modeled in a regression framework using large-scale predictors derived from ERA-Interim reanalysis. The model is developed and verified using radar-based hail observations for the extended summer season (April-September) in the period 2002-2014. The seasonality of hail is explicitly modeled with a categorical predictor (month) and monthly anomalies of several large-scale predictors are used to capture the year-to-year variability. Several regression models are applied and their performance tested with respect to standard scores and cross-validation. The chosen model includes four predictors: the monthly anomaly of the two meter temperature, the monthly anomaly of the logarithm of the convective available potential energy (CAPE), the monthly anomaly of the wind shear and the month. This model well captures the intra-annual variability and slightly underestimates its inter-annual variability. The regression model is applied to the reanalysis data back in time to 1980. The resulting hail day time series shows an increase of the number of hail days per month, which is (in the model) related to an increase in temperature and CAPE. The trend corresponds to approximately 0.5 days per month per decade. The results of the regression model have been compared to two independent data sets. All data sets agree on the sign of the trend, but the trend is weaker in the other data sets.

Stress, anger and Mediterranean diet as predictors of metabolic syndrome.

PubMed

Garcia-Silva, Jaqueline; Navarrete Navarrete, Nuria; Ruano Rodríguez, Ana; Peralta-Ramírez, María Isabel; Mediavilla García, Juan Diego; Caballo, Vicente E

2017-10-30

Metabolic syndrome (MetS) is a cluster of metabolic conditions that include abdominal obesity, reduction in cholesterol concentrations linked to high density lipoproteins (HLDc), elevated triglycerides, increased blood pressure and hyperglycaemia. Given that this is a multicausal disease, the aim of this study is to identify the psychological, emotional and lifestyle variables that can have an influence on the different MetS components. A cross-sectional study with 103 patients with diagnostic criteria for MetS (47 male and 56 female). Anthropometric, clinical and analytical measurements were collected to assess the variables associated with MetS. The main psychological and emotional variables were also assessed. Different multiple linear regression tests were performed to identify which variables were predictive of MetS. The dependent variables were body mass index (BMI), abdominal circumference, HDLc, and quality of life, and the predictive variables were psychological stress, anger and adherence to a Mediterranean diet. The results showed that psychological stress was a predictor of quality of life (β=-0.55, P≤0). Similarly, anger was a predictor of BMI (β=0.23, P=.047) and abdominal circumference (β=0.27, P=.021). As expected, adherence to a Mediterranean diet was a predictor of HDLc (β=0.2, P=.045) and of quality of life (β=-0.18, P=.031). The results confirm a link between adherence to certain dietary habits and lifestyle, however they go one step further and show the importance of psychological and emotional factors like psychological stress and anger in some MetS components. Copyright © 2017 Elsevier España, S.L.U. All rights reserved.

Longitudinal follow-up of fibrosing interstitial pneumonia: relationship between physiologic testing, computed tomography changes, and survival rate.

PubMed

Hwang, Jeong-Hwa; Misumi, Shigeki; Curran-Everett, Douglas; Brown, Kevin K; Sahin, Hakan; Lynch, David A

2011-08-01

The aim of this study was to evaluate the prognostic implications of computed tomography (CT) and physiologic variables at baseline and on sequential evaluation in patients with fibrosing interstitial pneumonia. We identified 72 patients with fibrosing interstitial pneumonia (42 with idiopathic disease, 30 with collagen vascular disease). Pulmonary function tests and CT were performed at the time of diagnosis and at a median follow-up of 12 months, respectively. Two chest radiologists scored the extent of specific abnormalities and overall disease on baseline and follow-up CT. Rate of survival was estimated using the Kaplan-Meier method. Three Cox proportional hazards models were constructed to evaluate the relationship between CT and physiologic variables and rate of survival: model 1 included only baseline variables, model 2 included only serial change variables, and model 3 included both baseline and serial change variables. On follow-up CT, the extent of mixed ground-glass and reticular opacities (P<0.001), pure reticular opacity (P=0.04), honeycombing (P=0.02), and overall extent of disease (P<0.001) was increased in the idiopathic group, whereas these variables remained unchanged in the collagen vascular disease group. Patients with idiopathic disease had a shorter rate of survival than those with collagen vascular disease (P=0.03). In model 1, the extent of honeycombing on baseline CT was the only independent predictor of mortality (P=0.02). In model 2, progression in honeycombing was the only predictor of mortality (P=0.005). In model 3, baseline extent of honeycombing and progression of honeycombing were the only independent predictors of mortality (P=0.001 and 0.002, respectively). Neither baseline nor serial change physiologic variables, nor the presence of collagen vascular disease, was predictive of rate of survival. The extent of honeycombing at baseline and its progression on follow-up CT are important determinants of rate of survival in patients with fibrosing interstitial pneumonia.

Predictors of post-traumatic stress symptoms in Oklahoma City: exposure, social support, peri-traumatic responses.

PubMed

Tucker, P; Pfefferbaum, B; Nixon, S J; Dickson, W

2000-11-01

Eighty-five adults seeking mental health assistance six months after the Oklahoma City bombing were assessed to determine which of three groups of variables (exposure, peri-traumatic responses, and social support) predicted development of post-traumatic stress disorder (PTSD) symptoms. Variables most highly associated with subsequent PTSD symptoms included having been injured (among exposure variables), feeling nervous or afraid (peri-traumatic responses), and responding that counseling helped (support variables). Combining primary predictors in the three areas, PTSD symptoms were more likely to occur in those reporting counseling to help and those feeling nervous or afraid at the time of the bombing. Implications of these findings are discussed for behavioral health administrators and clinicians planning service delivery to groups of victims seeking mental health intervention after terrorist attacks and other disasters.

Towards an automatic statistical model for seasonal precipitation prediction and its application to Central and South Asian headwater catchments

NASA Astrophysics Data System (ADS)

Gerlitz, Lars; Gafurov, Abror; Apel, Heiko; Unger-Sayesteh, Katy; Vorogushyn, Sergiy; Merz, Bruno

2016-04-01

Statistical climate forecast applications typically utilize a small set of large scale SST or climate indices, such as ENSO, PDO or AMO as predictor variables. If the predictive skill of these large scale modes is insufficient, specific predictor variables such as customized SST patterns are frequently included. Hence statistically based climate forecast models are either based on a fixed number of climate indices (and thus might not consider important predictor variables) or are highly site specific and barely transferable to other regions. With the aim of developing an operational seasonal forecast model, which is easily transferable to any region in the world, we present a generic data mining approach which automatically selects potential predictors from gridded SST observations and reanalysis derived large scale atmospheric circulation patterns and generates robust statistical relationships with posterior precipitation anomalies for user selected target regions. Potential predictor variables are derived by means of a cellwise correlation analysis of precipitation anomalies with gridded global climate variables under consideration of varying lead times. Significantly correlated grid cells are subsequently aggregated to predictor regions by means of a variability based cluster analysis. Finally for every month and lead time, an individual random forest based forecast model is automatically calibrated and evaluated by means of the preliminary generated predictor variables. The model is exemplarily applied and evaluated for selected headwater catchments in Central and South Asia. Particularly the for winter and spring precipitation (which is associated with westerly disturbances in the entire target domain) the model shows solid results with correlation coefficients up to 0.7, although the variability of precipitation rates is highly underestimated. Likewise for the monsoonal precipitation amounts in the South Asian target areas a certain skill of the model could be detected. The skill of the model for the dry summer season in Central Asia and the transition seasons over South Asia is found to be low. A sensitivity analysis by means on well known climate indices reveals the major large scale controlling mechanisms for the seasonal precipitation climate of each target area. For the Central Asian target areas, both, the El Nino Southern Oscillation and the North Atlantic Oscillation are identified as important controlling factors for precipitation totals during moist spring season. Drought conditions are found to be triggered by a warm ENSO phase in combination with a positive phase of the NAO. For the monsoonal summer precipitation amounts over Southern Asia, the model suggests a distinct negative response to El Nino events.

Spin, Unit Climate, and Aggression: Near Term, Long Term, and Reciprocal Predictors of Violence Among Workers in Military Settings

DTIC Science & Technology

2016-08-01

differences in within-person variability in emotional state, known as “spin”) and group level variables (e.g., unit climate) hypothesized to impact...effort includes both individual level variables (e.g., differences in within-person variability in emotional state, known as “spin”) and group level...and unit level factors across time. At the individual level, we will examine within-person variability in emotion and interpersonal behaviors

Factors influencing perceived effectiveness in dealing with self-harming patients in a sample of emergency department staff.

PubMed

Egan, Rachel; Sarma, Kiran M; O'Neill, Meena

2012-12-01

Past self-harming behavior is one of the most significant predictors of future suicide. Each year in Ireland there are approximately 11,000 presentations of self-harm to emergency departments (EDs) across the country. This study examines predictors of perceived personal effectiveness in dealing with self-harming patients as reported by ED staff. The predictors are derived from past research and are influenced by Bandura's Social Cognitive Theory. One hundred twenty-five ED medical staff (28 doctors and 97 nurses) from five EDs in the West and South of Ireland completed a questionnaire. Predictor variables included in the design, and informed by past research, included knowledge of self-harm and suicidal behavior and confidence in dealing with incidents of self-harm. Standard multiple regression suggested a statistically significant model fit between the two predictors and the criterion variable, accounting for 24% of total variance. Knowledge and Confidence were significant contributors to perceived personal effectiveness in dealing with self-harming patients. Little is known regarding specific factors that influence perceived effectiveness in dealing with self-harming patients in the ED setting. These findings have implications for psycho-education and training content for staff. The findings suggest that increasing knowledge of self-harm and confidence in dealing with self-harming patients can lead to more positive perceived personal effectiveness in responding to clients' needs. Copyright © 2012 Elsevier Inc. All rights reserved.

Predictors of Better Self-Care in Patients with Heart Failure after Six Months of Follow-Up Home Visits

PubMed Central

Trojahn, Melina Maria; Ruschel, Karen Brasil; Nogueira de Souza, Emiliane; Mussi, Cláudia Motta; Naomi Hirakata, Vânia; Nogueira Mello Lopes, Alexandra; Rabelo-Silva, Eneida Rejane

2013-01-01

This study aimed to examine the predictors of better self-care behavior in patients with heart failure (HF) in a home visiting program. This is a longitudinal study nested in a randomized controlled trial (ISRCTN01213862) in which the home-based educational intervention consisted of a six-month followup that included four home visits by a nurse, interspersed with four telephone calls. The self-care score was measured at baseline and at six months using the Brazilian version of the European Heart Failure Self-Care Behaviour Scale. The associations included eight variables: age, sex, schooling, having received the intervention, social support, income, comorbidities, and symptom severity. A simple linear regression model was developed using significant variables (P ≤ 0.20), followed by a multivariate model to determine the predictors of better self-care. One hundred eighty-eight patients completed the study. A better self-care behavior was associated with patients who received intervention (P < 0.001), had more years of schooling (P = 0.016), and had more comorbidities (P = 0.008). Having received the intervention (P < 0.001) and having a greater number of comorbidities (P = 0.038) were predictors of better self-care. In the multivariate regression model, being in the intervention group and having more comorbidities were a predictor of better self-care. PMID:24083023

An Interactive Tool For Semi-automated Statistical Prediction Using Earth Observations and Models

NASA Astrophysics Data System (ADS)

Zaitchik, B. F.; Berhane, F.; Tadesse, T.

2015-12-01

We developed a semi-automated statistical prediction tool applicable to concurrent analysis or seasonal prediction of any time series variable in any geographic location. The tool was developed using Shiny, JavaScript, HTML and CSS. A user can extract a predictand by drawing a polygon over a region of interest on the provided user interface (global map). The user can select the Climatic Research Unit (CRU) precipitation or Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as predictand. They can also upload their own predictand time series. Predictors can be extracted from sea surface temperature, sea level pressure, winds at different pressure levels, air temperature at various pressure levels, and geopotential height at different pressure levels. By default, reanalysis fields are applied as predictors, but the user can also upload their own predictors, including a wide range of compatible satellite-derived datasets. The package generates correlations of the variables selected with the predictand. The user also has the option to generate composites of the variables based on the predictand. Next, the user can extract predictors by drawing polygons over the regions that show strong correlations (composites). Then, the user can select some or all of the statistical prediction models provided. Provided models include Linear Regression models (GLM, SGLM), Tree-based models (bagging, random forest, boosting), Artificial Neural Network, and other non-linear models such as Generalized Additive Model (GAM) and Multivariate Adaptive Regression Splines (MARS). Finally, the user can download the analysis steps they used, such as the region they selected, the time period they specified, the predictand and predictors they chose and preprocessing options they used, and the model results in PDF or HTML format. Key words: Semi-automated prediction, Shiny, R, GLM, ANN, RF, GAM, MARS

Predicting In-State Workforce Retention After Graduate Medical Education Training.

PubMed

Koehler, Tracy J; Goodfellow, Jaclyn; Davis, Alan T; Spybrook, Jessaca; vanSchagen, John E; Schuh, Lori

2017-02-01

There is a paucity of literature when it comes to identifying predictors of in-state retention of graduate medical education (GME) graduates, such as the demographic and educational characteristics of these physicians. The purpose was to use demographic and educational predictors to identify graduates from a single Michigan GME sponsoring institution, who are also likely to practice medicine in Michigan post-GME training. We included all residents and fellows who graduated between 2000 and 2014 from 1 of 18 GME programs at a Michigan-based sponsoring institution. Predictor variables identified by logistic regression with cross-validation were used to create a scoring tool to determine the likelihood of a GME graduate to practice medicine in the same state post-GME training. A 6-variable model, which included 714 observations, was identified. The predictor variables were birth state, program type (primary care versus non-primary care), undergraduate degree location, medical school location, state in which GME training was completed, and marital status. The positive likelihood ratio (+LR) for the scoring tool was 5.31, while the negative likelihood ratio (-LR) was 0.46, with an accuracy of 74%. The +LR indicates that the scoring tool was useful in predicting whether graduates who trained in a Michigan-based GME sponsoring institution were likely to practice medicine in Michigan following training. Other institutions could use these techniques to identify key information that could help pinpoint matriculating residents/fellows likely to practice medicine within the state in which they completed their training.

Distribution and predictors of wing shape and size variability in three sister species of solitary bees

PubMed Central

Prunier, Jérôme G.; Dewulf, Alexandre; Kuhlmann, Michael; Michez, Denis

2017-01-01

Morphological traits can be highly variable over time in a particular geographical area. Different selective pressures shape those traits, which is crucial in evolutionary biology. Among these traits, insect wing morphometry has already been widely used to describe phenotypic variability at the inter-specific level. On the contrary, fewer studies have focused on intra-specific wing morphometric variability. Yet, such investigations are relevant to study potential convergences of variation that could highlight micro-evolutionary processes. The recent sampling and sequencing of three solitary bees of the genus Melitta across their entire species range provides an excellent opportunity to jointly analyse genetic and morphometric variability. In the present study, we first aim to analyse the spatial distribution of the wing shape and centroid size (used as a proxy for body size) variability. Secondly, we aim to test different potential predictors of this variability at both the intra- and inter-population levels, which includes genetic variability, but also geographic locations and distances, elevation, annual mean temperature and precipitation. The comparison of spatial distribution of intra-population morphometric diversity does not reveal any convergent pattern between species, thus undermining the assumption of a potential local and selective adaptation at the population level. Regarding intra-specific wing shape differentiation, our results reveal that some tested predictors, such as geographic and genetic distances, are associated with a significant correlation for some species. However, none of these predictors are systematically identified for the three species as an important factor that could explain the intra-specific morphometric variability. As a conclusion, for the three solitary bee species and at the scale of this study, our results clearly tend to discard the assumption of the existence of a common pattern of intra-specific signal/structure within the intra-specific wing shape and body size variability. PMID:28273178

Does emotional intelligence influence success during medical school admissions and program matriculation?: a systematic review

PubMed Central

2016-01-01

Purpose It aimed at determining whether emotional intelligence is a predictor for success in a medical school program and whether the emotional intelligence construct correlated with other markers for admission into medical school. Methods Three databases (PubMed, CINAHL, and ERIC) were searched up to and including July 2016, using relevant terms. Studies written in English were selected if they included emotional intelligence as a predictor for success in medical school, markers of success such as examination scores and grade point average and association with success defined through traditional medical school admission criteria and failures, and details about the sample. Data extraction included the study authors and year, population description, emotional intelligence I tool, outcome variables, and results. Associations between emotional intelligence scores and reported data were extracted and recorded. Results Six manuscripts were included. Overall, study quality was high. Four of the manuscripts examined emotional intelligence as a predictor for success while in medical school. Three of these four studies supported a weak positive relationship between emotional intelligence scores and success during matriculation. Two of manuscripts examined the relationship of emotional intelligence to medical school admissions. There were no significant relevant correlations between emotional intelligence and medical school admission selection. Conclusion Emotional intelligence was correlated with some, but not all, measures of success during medical school matriculation and none of the measures associated with medical school admissions. Variability in success measures across studies likely explains the variable findings. PMID:27838916

Does emotional intelligence influence success during medical school admissions and program matriculation?: a systematic review.

PubMed

Cook, Christian Jaeger; Cook, Chad E; Hilton, Tiffany N

2016-01-01

It aimed at determining whether emotional intelligence is a predictor for success in a medical school program and whether the emotional intelligence construct correlated with other markers for admission into medical school. Three databases (PubMed, CINAHL, and ERIC) were searched up to and including July 2016, using relevant terms. Studies written in English were selected if they included emotional intelligence as a predictor for success in medical school, markers of success such as examination scores and grade point average and association with success defined through traditional medical school admission criteria and failures, and details about the sample. Data extraction included the study authors and year, population description, emotional intelligence I tool, outcome variables, and results. Associations between emotional intelligence scores and reported data were extracted and recorded. Six manuscripts were included. Overall, study quality was high. Four of the manuscripts examined emotional intelligence as a predictor for success while in medical school. Three of these four studies supported a weak positive relationship between emotional intelligence scores and success during matriculation. Two of manuscripts examined the relationship of emotional intelligence to medical school admissions. There were no significant relevant correlations between emotional intelligence and medical school admission selection. Emotional intelligence was correlated with some, but not all, measures of success during medical school matriculation and none of the measures associated with medical school admissions. Variability in success measures across studies likely explains the variable findings.

Clinical Trials With Large Numbers of Variables: Important Advantages of Canonical Analysis.

PubMed

Cleophas, Ton J

2016-01-01

Canonical analysis assesses the combined effects of a set of predictor variables on a set of outcome variables, but it is little used in clinical trials despite the omnipresence of multiple variables. The aim of this study was to assess the performance of canonical analysis as compared with traditional multivariate methods using multivariate analysis of covariance (MANCOVA). As an example, a simulated data file with 12 gene expression levels and 4 drug efficacy scores was used. The correlation coefficient between the 12 predictor and 4 outcome variables was 0.87 (P = 0.0001) meaning that 76% of the variability in the outcome variables was explained by the 12 covariates. Repeated testing after the removal of 5 unimportant predictor and 1 outcome variable produced virtually the same overall result. The MANCOVA identified identical unimportant variables, but it was unable to provide overall statistics. (1) Canonical analysis is remarkable, because it can handle many more variables than traditional multivariate methods such as MANCOVA can. (2) At the same time, it accounts for the relative importance of the separate variables, their interactions and differences in units. (3) Canonical analysis provides overall statistics of the effects of sets of variables, whereas traditional multivariate methods only provide the statistics of the separate variables. (4) Unlike other methods for combining the effects of multiple variables such as factor analysis/partial least squares, canonical analysis is scientifically entirely rigorous. (5) Limitations include that it is less flexible than factor analysis/partial least squares, because only 2 sets of variables are used and because multiple solutions instead of one is offered. We do hope that this article will stimulate clinical investigators to start using this remarkable method.

Alabama's Education Report Card, 2000: Significant Predictors of Student Achievement at the District and School Level. Research Brief.

ERIC Educational Resources Information Center

Miller-Whitehead, Marie

This paper examines Alabama's State Education Report Card for the year 2000. It identifies predictors for student academic achievement at both the district and school levels for 128 public school systems and 1,272 public schools. Separate analyses were conducted for 61 city and 67 county school systems. The variables included number of students,…

Psychometric and demographic predictors of the perceived risk of terrorist threats and the willingness to pay for terrorism risk management programs.

PubMed

Mumpower, Jeryl L; Shi, Liu; Stoutenborough, James W; Vedlitz, Arnold

2013-10-01

A 2009 national telephone survey of 924 U.S. adults assessed perceptions of terrorism and homeland security issues. Respondents rated severity of effects, level of understanding, number affected, and likelihood of four terrorist threats: poisoned water supply; explosion of a small nuclear device in a major U.S. city; an airplane attack similar to 9/11; and explosion of a bomb in a building, train, subway, or highway. Respondents rated perceived risk and willingness to pay (WTP) for dealing with each threat. Demographic, attitudinal, and party affiliation data were collected. Respondents rated bomb as highest in perceived risk but gave the highest WTP ratings to nuclear device. For both perceived risk and WTP, psychometric variables were far stronger predictors than were demographic ones. OLS regression analyses using both types of variables to predict perceived risk found only two significant demographic predictors for any threat--Democrat (a negative predictor for bomb) and white male (a significant positive predictor for airline attack). In contrast, among psychometric variables, severity, number affected, and likelihood were predictors of all four threats and level of understanding was a predictor for one. For WTP, education was a negative predictor for three threats; no other demographic variables were significant predictors for any threat. Among psychometric variables, perceived risk and number affected were positive predictors of WTP for all four threats; severity and likelihood were predictors for three; level of understanding was a significant predictor for two. © 2013 Society for Risk Analysis.

Generic biomass functions for Norway spruce in Central Europe--a meta-analysis approach toward prediction and uncertainty estimation.

PubMed

Wirth, Christian; Schumacher, Jens; Schulze, Ernst-Detlef

2004-02-01

To facilitate future carbon and nutrient inventories, we used mixed-effect linear models to develop new generic biomass functions for Norway spruce (Picea abies (L.) Karst.) in Central Europe. We present both the functions and their respective variance-covariance matrices and illustrate their application for biomass prediction and uncertainty estimation for Norway spruce trees ranging widely in size, age, competitive status and site. We collected biomass data for 688 trees sampled in 102 stands by 19 authors. The total number of trees in the "base" model data sets containing the predictor variables diameter at breast height (D), height (H), age (A), site index (SI) and site elevation (HSL) varied according to compartment (roots: n = 114, stem: n = 235, dry branches: n = 207, live branches: n = 429 and needles: n = 551). "Core" data sets with about 40% fewer trees could be extracted containing the additional predictor variables crown length and social class. A set of 43 candidate models representing combinations of lnD, lnH, lnA, SI and HSL, including second-order polynomials and interactions, was established. The categorical variable "author" subsuming mainly methodological differences was included as a random effect in a mixed linear model. The Akaike Information Criterion was used for model selection. The best models for stem, root and branch biomass contained only combinations of D, H and A as predictors. More complex models that included site-related variables resulted for needle biomass. Adding crown length as a predictor for needles, branches and roots reduced both the bias and the confidence interval of predictions substantially. Applying the best models to a test data set of 17 stands ranging in age from 16 to 172 years produced realistic allocation patterns at the tree and stand levels. The 95% confidence intervals (% of mean prediction) were highest for crown compartments (approximately +/- 12%) and lowest for stem biomass (approximately +/- 5%), and within each compartment, they were highest for the youngest and oldest stands, respectively.

Self-Regulation and Recall: Growth Curve Modeling of Intervention Outcomes for Older Adults

PubMed Central

West, Robin L.; Hastings, Erin C.

2013-01-01

Memory training has often been supported as a potential means to improve performance for older adults. Less often studied are the characteristics of trainees that benefit most from training. Using a self-regulatory perspective, the current project examined a latent growth curve model to predict training-related gains for middle-aged and older adult trainees from individual differences (e.g., education), information processing skills (strategy use) and self-regulatory factors such as self-efficacy, control, and active engagement in training. For name recall, a model including strategy usage and strategy change as predictors of memory gain, along with self-efficacy and self-efficacy change, showed comparable fit to a more parsimonious model including only self-efficacy variables as predictors. The best fit to the text recall data was a model focusing on self-efficacy change as the main predictor of memory change, and that model showed significantly better fit than a model also including strategy usage variables as predictors. In these models, overall performance was significantly predicted by age and memory self-efficacy, and subsequent training-related gains in performance were best predicted directly by change in self-efficacy (text recall), or indirectly through the impact of active engagement and self-efficacy on gains (name recall). These results underscore the benefits of targeting self-regulatory factors in intervention programs designed to improve memory skills. PMID:21604891

Self-regulation and recall: growth curve modeling of intervention outcomes for older adults.

PubMed

West, Robin L; Hastings, Erin C

2011-12-01

Memory training has often been supported as a potential means to improve performance for older adults. Less often studied are the characteristics of trainees that benefit most from training. Using a self-regulatory perspective, the current project examined a latent growth curve model to predict training-related gains for middle-aged and older adult trainees from individual differences (e.g., education), information processing skills (strategy use) and self-regulatory factors such as self-efficacy, control, and active engagement in training. For name recall, a model including strategy usage and strategy change as predictors of memory gain, along with self-efficacy and self-efficacy change, showed comparable fit to a more parsimonious model including only self-efficacy variables as predictors. The best fit to the text recall data was a model focusing on self-efficacy change as the main predictor of memory change, and that model showed significantly better fit than a model also including strategy usage variables as predictors. In these models, overall performance was significantly predicted by age and memory self-efficacy, and subsequent training-related gains in performance were best predicted directly by change in self-efficacy (text recall), or indirectly through the impact of active engagement and self-efficacy on gains (name recall). These results underscore the benefits of targeting self-regulatory factors in intervention programs designed to improve memory skills.

Prevalence and Religious Predictors of Healing Prayer Use in the USA: Findings from the Baylor Religion Survey.

PubMed

Levin, Jeff

2016-08-01

Using data from the 2010 Baylor Religion Survey (N = 1714), this study investigates the prevalence and religious predictors of healing prayer use among US adults. Indicators include prayed for self (lifetime prevalence = 78.8 %), prayed for others (87.4 %), asked for prayer (54.1 %), laying-on-of-hands (26.1 %), and participated in a prayer group (53.0 %). Each was regressed onto eight religious measures, and then again controlling for sociodemographic variables and health. While all religious measures had net effects on at least one healing prayer indicator, the one consistent predictor was a four-item scale assessing a loving relationship with God. Higher scores were associated with more frequent healing prayer use according to every measure, after controlling for all other religious variables and covariates.

Genetic and Psychosocial Predictors of Aggression: Variable Selection and Model Building With Component-Wise Gradient Boosting.

PubMed

Suchting, Robert; Gowin, Joshua L; Green, Charles E; Walss-Bass, Consuelo; Lane, Scott D

2018-01-01

Rationale : Given datasets with a large or diverse set of predictors of aggression, machine learning (ML) provides efficient tools for identifying the most salient variables and building a parsimonious statistical model. ML techniques permit efficient exploration of data, have not been widely used in aggression research, and may have utility for those seeking prediction of aggressive behavior. Objectives : The present study examined predictors of aggression and constructed an optimized model using ML techniques. Predictors were derived from a dataset that included demographic, psychometric and genetic predictors, specifically FK506 binding protein 5 (FKBP5) polymorphisms, which have been shown to alter response to threatening stimuli, but have not been tested as predictors of aggressive behavior in adults. Methods : The data analysis approach utilized component-wise gradient boosting and model reduction via backward elimination to: (a) select variables from an initial set of 20 to build a model of trait aggression; and then (b) reduce that model to maximize parsimony and generalizability. Results : From a dataset of N = 47 participants, component-wise gradient boosting selected 8 of 20 possible predictors to model Buss-Perry Aggression Questionnaire (BPAQ) total score, with R 2 = 0.66. This model was simplified using backward elimination, retaining six predictors: smoking status, psychopathy (interpersonal manipulation and callous affect), childhood trauma (physical abuse and neglect), and the FKBP5_13 gene (rs1360780). The six-factor model approximated the initial eight-factor model at 99.4% of R 2 . Conclusions : Using an inductive data science approach, the gradient boosting model identified predictors consistent with previous experimental work in aggression; specifically psychopathy and trauma exposure. Additionally, allelic variants in FKBP5 were identified for the first time, but the relatively small sample size limits generality of results and calls for replication. This approach provides utility for the prediction of aggression behavior, particularly in the context of large multivariate datasets.

Predictors of seizure freedom after incomplete resection in children.

PubMed

Perry, M S; Dunoyer, C; Dean, P; Bhatia, S; Bavariya, A; Ragheb, J; Miller, I; Resnick, T; Jayakar, P; Duchowny, M

2010-10-19

Incomplete resection of the epileptogenic zone (EZ) is the most important predictor of poor outcome after resective surgery for intractable epilepsy. We analyzed the contribution of preoperative and perioperative variables including MRI and EEG data as predictors of seizure-free (SF) outcome after incomplete resection. We retrospectively reviewed patients <18 years of age with incomplete resection for epilepsy with 2 years of follow-up. Fourteen preoperative and perioperative variables were compared in SF and non-SF (NSF) patients. We compared lesional patients, categorized by reason for incompleteness, to lesional patients with complete resection. We analyzed for effect of complete EEG resection on SF outcome in patients with incompletely resected MRI lesions and vice versa. Eighty-three patients with incomplete resection were included with 41% becoming SF. Forty-eight lesional patients with complete resection were included. Thirty-eight percent (57/151) of patients with incomplete resection and 34% (47/138) with complete resection were excluded secondary to lack of follow-up or incomplete records. Contiguous MRI lesions were predictive of seizure freedom after incomplete resection. Fifty-seven percent of patients incomplete by MRI alone, 52% incomplete by EEG alone, and 24% incomplete by both became SF compared to 77% of patients with complete resection (p = 0.0005). Complete resection of the MRI- and EEG-defined EZ is the best predictor of seizure freedom, though patients incomplete by EEG or MRI alone have better outcome compared to patients incomplete by both. More than one-third of patients with incomplete resection become SF, with contiguous MRI lesions a predictor of SF outcome.

Personality, organizational stress, and attitudes toward work as prospective predictors of professional burnout in hospital nurses

PubMed Central

Hudek-Knežević, Jasna; Kalebić Maglica, Barbara; Krapić, Nada

2011-01-01

Aim To examine to what extent personality traits (extraversion, agreeableness, conscientiousness, neuroticism, and openness), organizational stress, and attitudes toward work and interactions between personality and either organizational stress or attitudes toward work prospectively predict 3 components of burnout. Methods The study was carried out on 118 hospital nurses. Data were analyzed by a set of hierarchical regression analyses, in which personality traits, measures of organizational stress, and attitudes toward work, as well as interactions between personality and either organizational stress or attitudes toward work were included as predictors, while 3 indices of burnout were measured 4 years later as criteria variables. Results Personality traits proved to be significant but weak prospective predictors of burnout and as a group predicted only reduced professional efficacy (R2 = 0.10), with agreeableness being a single negative predictor. Organizational stress was positive, affective-normative commitment negative predictor, while continuance commitment was not related to any dimension of burnout. We found interactions between neuroticism as well as conscientiousness and organizational stress, measured as role conflict and work overload, on reduced professional efficacy (βNRCWO = -0.30; ßcRCWO = -0.26). We also found interactions between neuroticism and affective normative commitment (β = 0.24) and between openness and continuance commitment on reduced professional efficacy (β = -0.23), as well as interactions between conscientiousness and continuance commitment on exhaustion. Conclusion Although contextual variables were strong prospective predictors and personality traits weak predictors of burnout, the results suggested the importance of the interaction between personality and contextual variables in predicting burnout. PMID:21853549

Predictors of self-reported negative mood following a depressive mood induction procedure across previously depressed, currently anxious, and control individuals.

PubMed

Scherrer, Martin C; Dobson, Keith S; Quigley, Leanne

2014-09-01

This study identified and examined a set of potential predictors of self-reported negative mood following a depressive mood induction procedure (MIP) in a sample of previously depressed, clinically anxious, and control participants. The examined predictor variables were selected on the basis of previous research and theories of depression, and included symptoms of depression and anxiety, negative and positive affect, negative and positive automatic thoughts, dysfunctional beliefs, rumination, self-concept, and occurrence and perceived unpleasantness of recent negative events. The sample consisted of 33 previously depressed, 22 currently anxious, and 26 non-clinical control participants, recruited from community sources. Participant group status was confirmed through structured diagnostic interviews. Participants completed the Velten negative self-statement MIP as well as self-report questionnaires of affective, cognitive, and psychosocial variables selected as potential predictors of mood change. Symptoms of anxiety were associated with increased self-reported negative mood shift following the MIP in previously depressed participants, but not clinically anxious or control participants. Increased occurrence of recent negative events was a marginally significant predictor of negative mood shift for the previously depressed participants only. None of the other examined variables was significant predictors of MIP response for any of the participant groups. These results identify factors that may increase susceptibility to negative mood states in previously depressed individuals, with implications for theory and prevention of relapse to depression. The findings also identify a number of affective, cognitive, and psychosocial variables that do not appear to influence mood change following a depressive MIP in previously depressed, currently anxious, and control individuals. Limitations of the study and directions for future research are discussed. Current anxiety symptomatology was a significant predictor and occurrence of recent negative events was a marginally significant predictor of greater negative mood shift following the depressive mood induction for previously depressed individuals. None of the examined variables predicted change in mood following the depressive mood induction for currently anxious or control individuals. These results suggest that anxiety symptoms and experience with negative events may increase risk for experiencing depressive mood states among individuals with a vulnerability to depression. The generalizability of the present results to individuals with comorbid depression and anxiety is limited. Future research employing appropriate statistical approaches for confirmatory research is needed to test and confirm the present results. © 2014 The British Psychological Society.

Situational and Intrapersonal Predictors of School and Life Satisfaction of Elementary School Students

ERIC Educational Resources Information Center

Drost, Amy Linden

2012-01-01

This study examined predictors of school and life satisfaction of fifth-grade students. Two situational predictor variables (school climate and school stress) and two intrapersonal predictor variables (locus of control and academic self-concept) were examined. It was hypothesized that positive school climate, low levels of school stress, internal…

Exploratory Long-Range Models to Estimate Summer Climate Variability over Southern Africa.

NASA Astrophysics Data System (ADS)

Jury, Mark R.; Mulenga, Henry M.; Mason, Simon J.

1999-07-01

Teleconnection predictors are explored using multivariate regression models in an effort to estimate southern African summer rainfall and climate impacts one season in advance. The preliminary statistical formulations include many variables influenced by the El Niño-Southern Oscillation (ENSO) such as tropical sea surface temperatures (SST) in the Indian and Atlantic Oceans. Atmospheric circulation responses to ENSO include the alternation of tropical zonal winds over Africa and changes in convective activity within oceanic monsoon troughs. Numerous hemispheric-scale datasets are employed to extract predictors and include global indexes (Southern Oscillation index and quasi-biennial oscillation), SST principal component scores for the global oceans, indexes of tropical convection (outgoing longwave radiation), air pressure, and surface and upper winds over the Indian and Atlantic Oceans. Climatic targets include subseasonal, area-averaged rainfall over South Africa and the Zambezi river basin, and South Africa's annual maize yield. Predictors and targets overlap in the years 1971-93, the defined training period. Each target time series is fitted by an optimum group of predictors from the preceding spring, in a linear multivariate formulation. To limit artificial skill, predictors are restricted to three, providing 17 degrees of freedom. Models with colinear predictors are screened out, and persistence of the target time series is considered. The late summer rainfall models achieve a mean r2 fit of 72%, contributed largely through ENSO modulation. Early summer rainfall cross validation correlations are lower (61%). A conceptual understanding of the climate dynamics and ocean-atmosphere coupling processes inherent in the exploratory models is outlined.Seasonal outlooks based on the exploratory models could help mitigate the impacts of southern Africa's fluctuating climate. It is believed that an advance warning of drought risk and seasonal rainfall prospects will improve the economic growth potential of southern Africa and provide additional security for food and water supplies.

Data Mine and Forget It?: A Cautionary Tale

NASA Technical Reports Server (NTRS)

Tada, Yuri; Kraft, Norbert Otto; Orasanu, Judith M.

2011-01-01

With the development of new technologies, data mining has become increasingly popular. However, caution should be exercised in choosing the variables to include in data mining. A series of regression trees was created to demonstrate the change in the selection by the program of significant predictors based on the nature of variables.

Multidimensional Social Control Variables as Predictors of Drunkenness among French Adolescents

ERIC Educational Resources Information Center

Begue, Laurent; Roche, Sebastian

2009-01-01

Background: Previous studies of the determinants of drunkenness among youth investigated the contribution of a limited range of variables measuring social control. For the first time in France, this study including 1295 participants aged 14-19 years aimed at assessing the relative contribution of a broad range of multidimensional variables…

Parent predictors of child weight change in family based behavioral obesity treatment.

PubMed

Boutelle, Kerri N; Cafri, Guy; Crow, Scott J

2012-07-01

Family based behavioral treatment for overweight and obese children includes parenting skills targeting the modification of child eating and activity change. The purpose of this study was to examine parenting skills and parent weight change as predictors of child weight change in a sample of 80 parent/child dyads who were enrolled in a family based behavioral weight loss program for childhood obesity. Eighty overweight and obese children and their parents who enrolled in treatment in two sites were included in the study. Variables included those related to parent modeling (parent BMI), home food environment, parenting (parent and child report), and demographics. Results suggested that parent BMI change was a significant predictor of child weight, in that a reduction of 1 BMI unit in the parent was associated with a 0.255 reduction in child BMI. None of the other variables were significant in the final model. This study is consistent with other research showing that parent weight change is a key contributor to child weight change in behavioral treatment for childhood obesity. Researchers and clinicians should focus on encouraging parents to lose weight to assist their overweight and obese child in weight management.

Predictors and Moderators of Treatment Response in Childhood Anxiety Disorders: Results from the CAMS Trial

PubMed Central

Compton, Scott N.; Peris, Tara S.; Almirall, Daniel; Birmaher, Boris; Sherrill, Joel; Kendall, Phillip C.; March, John S.; Gosch, Elizabeth A.; Ginsburg, Golda S.; Rynn, Moira A.; Piacentini, John C.; McCracken, James T.; Keeton, Courtney P.; Suveg, Cynthia M.; Aschenbrand, Sasha G.; Sakolsky, Dara; Iyengar, Satish; Walkup, John T.; Albano, Anne Marie

2014-01-01

Objective To examine predictors and moderators of treatment outcomes among 488 youth ages 7-17 years (50% female; 74% ≤ 12 years) with DSM-IV diagnoses of separation anxiety disorder, social phobia, or generalized anxiety disorder who were randomly assigned to receive either cognitive behavior therapy (CBT), sertraline (SRT), their combination (COMB), or medication management with pill placebo (PBO) in the Child/Adolescent Anxiety Multimodal Study (CAMS). Method Six classes of predictor and moderator variables (22 variables) were identified from the literature and examined using continuous (Pediatric Anxiety Ratings Scale; PARS) and categorical (Clinical Global Impression Scale-Improvement; CGI-I) outcome measures. Results Three baseline variables predicted better outcomes (independent of treatment condition) on the PARS, including low anxiety severity (as measured by parents and independent evaluators) and caregiver strain. No baseline variables were found to predict week 12 responder status (CGI-I). Participant's principal diagnosis moderated treatment outcomes, but only on the PARS. No baseline variables were found to moderate treatment outcomes on week 12 responder status (CGI-I). Discussion Overall, anxious children responded favorably to CAMS treatments. However, having more severe and impairing anxiety, greater caregiver strain, and a principal diagnosis of social phobia were associated with less favorable outcomes. Clinical implications of these findings are discussed. PMID:24417601

Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan

NASA Astrophysics Data System (ADS)

Hassanzadeh, S.; Hosseinibalam, F.; Omidvari, M.

2008-04-01

Data of seven meteorological variables (relative humidity, wet temperature, dry temperature, maximum temperature, minimum temperature, ground temperature and sun radiation time) and ozone values have been used for statistical analysis. Meteorological variables and ozone values were analyzed using both multiple linear regression and principal component methods. Data for the period 1999-2004 are analyzed jointly using both methods. For all periods, temperature dependent variables were highly correlated, but were all negatively correlated with relative humidity. Multiple regression analysis was used to fit the meteorological variables using the meteorological variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the linear regression model of the meteorological variables. In 1999, 2001 and 2002 one of the meteorological variables was weakly influenced predominantly by the ozone concentrations. However, the model did not predict that the meteorological variables for the year 2000 were not influenced predominantly by the ozone concentrations that point to variation in sun radiation. This could be due to other factors that were not explicitly considered in this study.

Flood regionalization: A hybrid geographic and predictor-variable region-of-influence regression method

USGS Publications Warehouse

Eng, K.; Milly, P.C.D.; Tasker, Gary D.

2007-01-01

To facilitate estimation of streamflow characteristics at an ungauged site, hydrologists often define a region of influence containing gauged sites hydrologically similar to the estimation site. This region can be defined either in geographic space or in the space of the variables that are used to predict streamflow (predictor variables). These approaches are complementary, and a combination of the two may be superior to either. Here we propose a hybrid region-of-influence (HRoI) regression method that combines the two approaches. The new method was applied with streamflow records from 1,091 gauges in the southeastern United States to estimate the 50-year peak flow (Q50). The HRoI approach yielded lower root-mean-square estimation errors and produced fewer extreme errors than either the predictor-variable or geographic region-of-influence approaches. It is concluded, for Q50 in the study region, that similarity with respect to the basin characteristics considered (area, slope, and annual precipitation) is important, but incomplete, and that the consideration of geographic proximity of stations provides a useful surrogate for characteristics that are not included in the analysis. ?? 2007 ASCE.

Predictors and Variability of Urinary Paraben Concentrations in Men and Women, Including before and during Pregnancy

PubMed Central

Smith, Kristen W.; Braun, Joe M.; Williams, Paige L.; Ehrlich, Shelley; Correia, Katharine F.; Calafat, Antonia M.; Ye, Xiaoyun; Ford, Jennifer; Keller, Myra; Meeker, John D.

2012-01-01

Background: Parabens are suspected endocrine disruptors and ubiquitous preservatives used in personal care products, pharmaceuticals, and foods. No studies have assessed the variability of parabens in women, including during pregnancy. Objective: We evaluated predictors and variability of urinary paraben concentrations. Methods: We measured urinary concentrations of methyl (MP), propyl (PP), and butyl paraben (BP) among couples from a fertility center. Mixed-effects regression models were fit to examine demographic predictors of paraben concentrations and to calculate intraclass correlation coefficients (ICCs). Results: Between 2005 and 2010, we collected 2,721 spot urine samples from 245 men and 408 women. The median concentrations were 112 µg/L (MP), 24.2 µg/L (PP), and 0.70 µg/L (BP). Urinary MP and PP concentrations were 4.6 and 7.8 times higher in women than men, respectively, and concentrations of both MP and PP were 3.8 times higher in African Americans than Caucasians. MP and PP concentrations we CI re slightly more variable in women (ICC = 0.42, 0.43) than men (ICC = 0.54, 0.51), and were weakly correlated between partners (r = 0.27–0.32). Among 129 pregnant women, urinary paraben concentrations were 25–45% lower during pregnancy than before pregnancy, and MP and PP concentrations were more variable (ICCs of 0.38 and 0.36 compared with 0.46 and 0.44, respectively). Conclusions: Urinary paraben concentrations were more variable in women compared with men, and during pregnancy compared with before pregnancy. However, results for this study population suggest that a single urine sample may reasonably represent an individual’s exposure over several months, and that a single sample collected during pregnancy may reasonably classify gestational exposure. PMID:22721761

Predictors and variability of urinary paraben concentrations in men and women, including before and during pregnancy.

PubMed

Smith, Kristen W; Braun, Joe M; Williams, Paige L; Ehrlich, Shelley; Correia, Katharine F; Calafat, Antonia M; Ye, Xiaoyun; Ford, Jennifer; Keller, Myra; Meeker, John D; Hauser, Russ

2012-11-01

Parabens are suspected endocrine disruptors and ubiquitous preservatives used in personal care products, pharmaceuticals, and foods. No studies have assessed the variability of parabens in women, including during pregnancy. We evaluated predictors and variability of urinary paraben concentrations. We measured urinary concentrations of methyl (MP), propyl (PP), and butyl paraben (BP) among couples from a fertility center. Mixed-effects regression models were fit to examine demographic predictors of paraben concentrations and to calculate intraclass correlation coefficients (ICCs). Between 2005 and 2010, we collected 2,721 spot urine samples from 245 men and 408 women. The median concentrations were 112 µg/L (MP), 24.2 µg/L (PP), and 0.70 µg/L (BP). Urinary MP and PP concentrations were 4.6 and 7.8 times higher in women than men, respectively, and concentrations of both MP and PP were 3.8 times higher in African Americans than Caucasians. MP and PP concentrations were slightly more variable in women (ICC = 0.42, 0.43) than men (ICC = 0.54, 0.51), and were weakly correlated between partners (r = 0.27-0.32). Among 129 pregnant women, urinary paraben concentrations were 25-45% lower during pregnancy than before pregnancy, and MP and PP concentrations were more variable (ICCs of 0.38 and 0.36 compared with 0.46 and 0.44, respectively). Urinary paraben concentrations were more variable in women compared with men, and during pregnancy compared with before pregnancy. However, results for this study population suggest that a single urine sample may reasonably represent an individual's exposure over several months, and that a single sample collected during pregnancy may reasonably classify gestational exposure.

Predictors, Moderators, and Mediators of Treatment Outcome Following Manualised Cognitive-Behavioural Therapy for Eating Disorders: A Systematic Review.

PubMed

Linardon, Jake; de la Piedad Garcia, Xochitl; Brennan, Leah

2017-01-01

This systematic review synthesised the literature on predictors, moderators, and mediators of outcome following Fairburn's CBT for eating disorders. Sixty-five articles were included. The relationship between individual variables and outcome was synthesised separately across diagnoses and treatment format. Early change was found to be a consistent mediator of better outcomes across all eating disorders. Moderators were mostly tested in binge eating disorder, and most moderators did not affect cognitive-behavioural treatment outcome relative to other treatments. No consistent predictors emerged. Findings suggest that it is unclear how and for whom this treatment works. More research testing mediators and moderators is needed, and variables selected for analyses need to be empirically and theoretically driven. Future recommendations include the need for authors to (i) interpret the clinical and statistical significance of findings; (ii) use a consistent definition of outcome so that studies can be directly compared; and (iii) report null and statistically significant findings. Copyright © 2016 John Wiley & Sons, Ltd and Eating Disorders Association. © 2016 John Wiley & Sons, Ltd and Eating Disorders Association.

A Systematic Review of Predictors of, and Reasons for, Adherence to Online Psychological Interventions.

PubMed

Beatty, Lisa; Binnion, Claire

2016-12-01

A key issue regarding the provision of psychological therapy in a self-guided online format is low rates of adherence. The aim of this systematic review was to assess both quantitative and qualitative data on the predictors of adherence, as well as participant reported reasons for adhering or not adhering to online psychological interventions. Database searches of PsycINFO, Medline, and CINAHL identified 1721 potentially relevant articles published between 1 January 2000 and 25 November 2015. A further 34 potentially relevant articles were retrieved from reference lists. Articles that reported predictors of, or reasons for, adherence to an online psychological intervention were included. A total of 36 studies met the inclusion criteria. Predictors assessed included demographic, psychological, characteristics of presenting problem, and intervention/computer-related predictors. Evidence suggested that female gender, higher treatment expectancy, sufficient time, and personalized intervention content each predicted higher adherence. Age, baseline symptom severity, and control group allocation had mixed findings. The majority of assessed variables however, did not predict adherence. Few clear predictors of adherence emerged overall, and most results were either mixed or too preliminary to draw conclusions. More research of predictors associated with adherence to online interventions is warranted.

Preinjury somatization symptoms contribute to clinical recovery after sport-related concussion.

PubMed

Nelson, Lindsay D; Tarima, Sergey; LaRoche, Ashley A; Hammeke, Thomas A; Barr, William B; Guskiewicz, Kevin; Randolph, Christopher; McCrea, Michael A

2016-05-17

To determine the degree to which preinjury and acute postinjury psychosocial and injury-related variables predict symptom duration following sport-related concussion. A total of 2,055 high school and collegiate athletes completed preseason evaluations. Concussed athletes (n = 127) repeated assessments serially (<24 hours and days 8, 15, and 45) postinjury. Cox proportional hazard modeling was used to predict concussive symptom duration (in days). Predictors considered included demographic and history variables; baseline psychological, neurocognitive, and balance functioning; acute injury characteristics; and postinjury clinical measures. Preinjury somatic symptom score (Brief Symptom Inventory-18 somatization scale) was the strongest premorbid predictor of symptom duration. Acute (24-hour) postconcussive symptom burden (Sport Concussion Assessment Tool-3 symptom severity) was the best injury-related predictor of recovery. These 2 predictors were moderately correlated (r = 0.51). Path analyses indicated that the relationship between preinjury somatization symptoms and symptom recovery was mediated by postinjury concussive symptoms. Preinjury somatization symptoms contribute to reported postconcussive symptom recovery via their influence on acute postconcussive symptoms. The findings highlight the relevance of premorbid psychological factors in postconcussive recovery, even in a healthy athlete sample relatively free of psychopathology or medical comorbidities. Future research should elucidate the neurobiopsychosocial mechanisms that explain the role of this individual difference variable in outcome following concussive injury. © 2016 American Academy of Neurology.

Spin, Unit Climate, and Aggression: Near Term, Long Term, and Reciprocal Predictors of Violence Among Workers in Military Settings

DTIC Science & Technology

2015-08-01

exposure to aggression (e.g., drug / alcohol use, burnout , suicidal ideation). The proposed effort includes both individual level variables (e.g...aggression (e.g., drug / alcohol use, burnout , suicidal ideation). The proposed effort includes both individual level variables (e.g., differences in...anger / rage, bullying, harassment, intimate partner violence) and related physical health and mental health concerns (e.g., drug / alcohol use, burnout

Dengue: recent past and future threats

PubMed Central

Rogers, David J.

2015-01-01

This article explores four key questions about statistical models developed to describe the recent past and future of vector-borne diseases, with special emphasis on dengue: (1) How many variables should be used to make predictions about the future of vector-borne diseases?(2) Is the spatial resolution of a climate dataset an important determinant of model accuracy?(3) Does inclusion of the future distributions of vectors affect predictions of the futures of the diseases they transmit?(4) Which are the key predictor variables involved in determining the distributions of vector-borne diseases in the present and future?Examples are given of dengue models using one, five or 10 meteorological variables and at spatial resolutions of from one-sixth to two degrees. Model accuracy is improved with a greater number of descriptor variables, but is surprisingly unaffected by the spatial resolution of the data. Dengue models with a reduced set of climate variables derived from the HadCM3 global circulation model predictions for the 1980s are improved when risk maps for dengue's two main vectors (Aedes aegypti and Aedes albopictus) are also included as predictor variables; disease and vector models are projected into the future using the global circulation model predictions for the 2020s, 2040s and 2080s. The Garthwaite–Koch corr-max transformation is presented as a novel way of showing the relative contribution of each of the input predictor variables to the map predictions. PMID:25688021

Types of social supports predicting health-related quality of life among adult patients with CHD in the Institut Jantung Negara (National Heart Institute), Malaysia.

PubMed

Tye, Sue K; Kandavello, Geetha; Gan, Kah L

2017-01-01

The objectives of this study were to examine which types of social supports - emotional/informational support, tangible support, affectionate support, and positive interactions - are the predictors of health-related quality of life (HRQoL) in adult patients with CHD and to assess the influence of demographic variables and clinical factors on these variables. In total, 205 adult patients with CHD from the National Heart Institute, Malaysia, were recruited. Patients were first screened by cardiology consultants to ensure they fit the inclusion criteria before filling in questionnaires, which were medical outcome studies - social support survey and AQoL-8D. Results/conclusions All social supports and their subscales were found to have mild-to-moderate significant relationships with physical dimension, psychological dimension, and overall HRQoL; however, only positive interaction, marital status, and types of diagnosis were reported as predictors of HRQoL. Surprisingly, with regard to the physical dimension of quality of life, social supports were not significant predictors, but educational level, marital status, and types of diagnosis were significant predictors. Positive interaction, affectionate support, marital status, and types of diagnosis were again found to be predictors in the aspects of the psychological dimension of quality of life. In conclusion, positive interaction and affectionate support, which include elements of fun, relaxation, love, and care, should be included in the care of adult patients with CHD.

Predictors of Cell Phone Use in Distracted Driving: Extending the Theory of Planned Behavior.

PubMed

Tian, Yan; Robinson, James D

2017-09-01

This study examines the predictors of six distracted driving behaviors, and the survey data partially support Ajzen's (1991) Theory of Planned Behavior (TPB). The data suggest that the attitude variable predicted intention to engage in all six distracted driving behaviors (reading and sending text messages, making and answering cell phone calls, reading/viewing social media, and posting on social media while driving). Extending the model to include past experience and the variable perceived safety of technology yielded an improvement in the prediction of the distraction variables. Specifically, past experience predicted all six distracted driving behaviors, and the variable perceived safety of technology predicted intentions to read/view social media and intention to post on social media while driving. The study provides evidence for the importance of incorporating expanded variables into the original TPB model to predict cell phone use behaviors while driving, and it suggests that it is essential to tailor campaign materials for each specific cell phone use behavior to reduce distracted driving.

Predicting dropout using student- and school-level factors: An ecological perspective.

PubMed

Wood, Laura; Kiperman, Sarah; Esch, Rachel C; Leroux, Audrey J; Truscott, Stephen D

2017-03-01

High school dropout has been associated with negative outcomes, including increased rates of unemployment, incarceration, and mortality. Dropout rates vary significantly depending on individual and environmental factors. The purpose of our study was to use an ecological perspective to concurrently explore student- and school-level predictors associated with dropout for the purpose of better understanding how to prevent it. We used the Education Longitudinal Study of 2002 dataset. Participants included 14,106 sophomores across 684 public and private schools. We identified variables of interest based on previous research on dropout and implemented hierarchical generalized linear modeling. In the final model, significant student-level predictors included academic achievement, retention, sex, family socioeconomic status (SES), and extracurricular involvement. Significant school-level predictors included school SES and school size. Race/ethnicity, special education status, born in the United States, English as first language, school urbanicity, and school region did not significantly predict dropout after controlling for the aforementioned predictors. Implications for prevention and intervention efforts within a multitiered intervention model are discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Predictors of Response to Web-Based Cognitive Behavioral Therapy With High-Intensity Face-to-Face Therapist Guidance for Depression: A Bayesian Analysis

PubMed Central

Mittner, Matthias; Lillevoll, Kjersti; Katla, Susanne Kvam; Kolstrup, Nils; Eisemann, Martin; Friborg, Oddgeir; Waterloo, Knut

2015-01-01

Background Several studies have demonstrated the effect of guided Internet-based cognitive behavioral therapy (ICBT) for depression. However, ICBT is not suitable for all depressed patients and there is a considerable level of nonresponse. Research on predictors and moderators of outcome in ICBT is inconclusive. Objective This paper explored predictors of response to an intervention combining the Web-based program MoodGYM and face-to-face therapist guidance in a sample of primary care patients with mild to moderate depressive symptoms. Methods Participants (N=106) aged between 18 and 65 years were recruited from primary care and randomly allocated to a treatment condition or to a delayed treatment condition. The intervention included the Norwegian version of the MoodGYM program, face-to-face guidance from a psychologist, and reminder emails. In this paper, data from the treatment phase of the 2 groups was merged to increase the sample size (n=82). Outcome was improvement in depressive symptoms during treatment as assessed with the Beck Depression Inventory-II (BDI-II). Predictors included demographic variables, severity variables (eg, number of depressive episodes and pretreatment depression and anxiety severity), cognitive variables (eg, dysfunctional thinking), module completion, and treatment expectancy and motivation. Using Bayesian analysis, predictors of response were explored with a latent-class approach and by analyzing whether predictors affected the slope of response. Results A 2-class model distinguished well between responders (74%, 61/82) and nonresponders (26%, 21/82). Our results indicate that having had more depressive episodes, being married or cohabiting, and scoring higher on a measure of life satisfaction had high odds for positively affecting the probability of response. Higher levels of dysfunctional thinking had high odds for a negative effect on the probability of responding. Prediction of the slope of response yielded largely similar results. Bayes factors indicated substantial evidence that being married or cohabiting predicted a more positive treatment response. The effects of life satisfaction and number of depressive episodes were more uncertain. There was substantial evidence that several variables were unrelated to treatment response, including gender, age, and pretreatment symptoms of depression and anxiety. Conclusions Treatment response to ICBT with face-to-face guidance may be comparable across varying levels of depressive severity and irrespective of the presence and severity of comorbid anxiety. Being married or cohabiting, reporting higher life satisfaction, and having had more depressive episodes may predict a more favorable response, whereas higher levels of dysfunctional thinking may be a predictor of poorer response. More studies exploring predictors and moderators of Internet-based treatments are needed to inform for whom this treatment is most effective. Trial Registration Australian New Zealand Clinical Trials Registry number: ACTRN12610000257066; https://www.anzctr.org.au/trial_view.aspx?id=335255 (Archived by WebCite at http://www.webcitation.org/6GR48iZH4). PMID:26333818

Prediction of Academic Achievement in an NATA-Approved Graduate Athletic Training Education Program

PubMed Central

Keskula, Douglas R.; Sammarone, Paula G.; Perrin, David H.

1995-01-01

The Purpose of this investigation was to determine which information used in the applicant selection process would best predict the final grade point average of students in a National Athletic Trainers Association (NATA) graduate athletic training education program. The criterion variable used was the graduate grade-point average (GPAg) calculated at the completion of the program of study. The predictor variables included: 1) Graduate Record Examination-Quantitative (GRE-Q) scores; and 2) Graduate Record Examination-Verbal (GRE-V) scores, 3) preadmission grade point average (GPAp), 4) total athletic training hours (hours), and 5) curriculum or internship undergraduate athletic training education (program). Data from 55 graduate athletic training students during a 5-year period were evaluated. Stepwise multiple regression analysis indicated that GPAp was a significant predictor of GPAg, accounting for 34% of the variance. GRE-Q, GRE-V, hours, and program did not significantly contribute individually or in combination to the prediction of GPAg. The results of this investigation suggest that, of the variables examined, GPAp is the best predictor of academic success in an NATA-approved graduate athletic training education program. PMID:16558312

Predictors and moderators of response to cognitive behavioral therapy and medication for the treatment of binge eating disorder.

PubMed

Grilo, Carlos M; Masheb, Robin M; Crosby, Ross D

2012-10-01

To examine predictors and moderators of response to cognitive behavioral therapy (CBT) and medication treatments for binge-eating disorder (BED). 108 BED patients in a randomized double-blind placebo-controlled trial testing CBT and fluoxetine treatments were assessed prior, throughout, and posttreatment. Demographic factors, psychiatric and personality disorder comorbidity, eating disorder psychopathology, psychological features, and 2 subtyping methods (negative affect, overvaluation of shape/weight) were tested as predictors and moderators for the primary outcome of remission from binge eating and 4 secondary dimensional outcomes (binge-eating frequency, eating disorder psychopathology, depression, and body mass index). Mixed-effects models analyzed all available data for each outcome variable. In each model, effects for baseline value and treatment were included with tests of both prediction and moderator effects. Several demographic and clinical variables significantly predicted and/or moderated outcomes. One demographic variable signaled a statistical advantage for medication only (younger participants had greater binge-eating reductions), whereas several demographic and clinical variables (lower self-esteem, negative affect, and overvaluation of shape/weight) signaled better improvements if receiving CBT. Overvaluation was the most salient predictor/moderator of outcomes. Overvaluation significantly predicted binge-eating remission (29% of participants with vs. 57% of participants without overvaluation remitted). Overvaluation was especially associated with lower remission rates if receiving medication only (10% vs. 42% for participants without overvaluation). Overvaluation moderated dimensional outcomes: Participants with overvaluation had significantly greater reductions in eating disorder psychopathology and depression levels if receiving CBT. Overvaluation predictor/moderator findings persisted after controlling for negative affect. Our findings have clinical utility for prescription of CBT and medication and implications for refinement of the BED diagnosis. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Predictors of public support for nutrition-focused policy, systems and environmental change strategies in Los Angeles County, 2013

PubMed Central

Robles, Brenda; Kuo, Tony

2017-01-01

Background Since 2010, federal and local agencies have invested broadly in a variety of nutrition-focused policy, systems and environmental change (PSE) initiatives in Los Angeles County (LAC). To date, little is known about whether the public supports such efforts. We address this gap in the literature by examining predictors of support for a variety of PSEs. Methods Voters residing in LAC (n=1007) were randomly selected to participate in a cross-sectional telephone survey commissioned by the LAC Department of Public Health. The survey asked questions about attitudes towards the obesity epidemic, nutrition knowledge and behaviours, public opinions about changing business practices/government policies related to nutrition, and sociodemographics. A factor analysis informed outcome variable selection (ie, type of PSEs). Multivariable regression analyses were performed to examine predictors of public support. Predictors in the regression models included (primary regressor) community economic hardship; (control variables) political affiliation, sex, age, race and income; and (independent variables) perceptions about obesity, perceived health and weight status, frequency reading nutrition labels, ease of finding healthy and unhealthy foods, and food consumption behaviours (ie, fruit and vegetables, non-diet soda, fast-food and sit-down restaurant meals). Results 3 types of PSE outcome variables were identified: promotional/incentivising, limiting/restrictive and business practices. Community economic hardship was not found to be a significant predictor of public support for any of the 3 PSE types. However, Republican party affiliation, being female and perceiving obesity as a serious health problem were. Conclusions These findings have implications for public health practice and community planning in local health jurisdictions. PMID:28087545

Predictors of public support for nutrition-focused policy, systems and environmental change strategies in Los Angeles County, 2013.

PubMed

Robles, Brenda; Kuo, Tony

2017-01-13

Since 2010, federal and local agencies have invested broadly in a variety of nutrition-focused policy, systems and environmental change (PSE) initiatives in Los Angeles County (LAC). To date, little is known about whether the public supports such efforts. We address this gap in the literature by examining predictors of support for a variety of PSEs. Voters residing in LAC (n=1007) were randomly selected to participate in a cross-sectional telephone survey commissioned by the LAC Department of Public Health. The survey asked questions about attitudes towards the obesity epidemic, nutrition knowledge and behaviours, public opinions about changing business practices/government policies related to nutrition, and sociodemographics. A factor analysis informed outcome variable selection (ie, type of PSEs). Multivariable regression analyses were performed to examine predictors of public support. Predictors in the regression models included (primary regressor) community economic hardship; (control variables) political affiliation, sex, age, race and income; and (independent variables) perceptions about obesity, perceived health and weight status, frequency reading nutrition labels, ease of finding healthy and unhealthy foods, and food consumption behaviours (ie, fruit and vegetables, non-diet soda, fast-food and sit-down restaurant meals). 3 types of PSE outcome variables were identified: promotional/incentivising, limiting/restrictive and business practices. Community economic hardship was not found to be a significant predictor of public support for any of the 3 PSE types. However, Republican party affiliation, being female and perceiving obesity as a serious health problem were. These findings have implications for public health practice and community planning in local health jurisdictions. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Prediction of employer-employee relationships from sociodemographic variables and social values in Brunei public and private sector workers.

PubMed

Mundia, Lawrence; Mahalle, Salwa; Matzin, Rohani; Nasir Zakaria, Gamal Abdul; Abdullah, Nor Zaiham Midawati; Abdul Latif, Siti Norhedayah

2017-01-01

The purpose of the study was to identify the sociodemographic variables and social value correlates and predictors of employer-employee relationship problems in a random sample of 860 Brunei public and private sector workers of both genders. A quantitative field survey design was used and data were analyzed by correlation and logistic regression. The rationale and justification for using this approach is explained. The main sociodemographic correlates and predictors of employer-employee relationship problems in this study were educational level and the district in which the employee resided and worked. Other correlates, but not necessarily predictors, of employer-employee relationship problems were seeking help from the Bomo (traditional healer); obtaining help from online social networking; and workers with children in the family. The two best and most significant social value correlates and predictors of employer-employee relationship problems included interpersonal communications; and self-regulation and self-direction. Low scorers on the following variables were also associated with high likelihood for possessing employer-employee relationship problems: satisfaction with work achievements; and peace and security, while low scorers on work stress had lower odds of having employer-employee relationship problems. Other significant social value correlates, but not predictors of employer-employee relationship problems were self-presentation; interpersonal trust; peace and security; and general anxiety. Consistent with findings of relevant previous studies conducted elsewhere, there were the variables that correlated with and predicted employer-employee relationship problems in Brunei public and private sector workers. Having identified these, the next step, efforts and priority should be directed at addressing the presenting issues via counseling and psychotherapy with affected employees. Further research is recommended to understand better the problem and its possible solutions.

Prediction of employer–employee relationships from sociodemographic variables and social values in Brunei public and private sector workers

PubMed Central

Mundia, Lawrence; Mahalle, Salwa; Matzin, Rohani; Nasir Zakaria, Gamal Abdul; Abdullah, Nor Zaiham Midawati; Abdul Latif, Siti Norhedayah

2017-01-01

The purpose of the study was to identify the sociodemographic variables and social value correlates and predictors of employer–employee relationship problems in a random sample of 860 Brunei public and private sector workers of both genders. A quantitative field survey design was used and data were analyzed by correlation and logistic regression. The rationale and justification for using this approach is explained. The main sociodemographic correlates and predictors of employer–employee relationship problems in this study were educational level and the district in which the employee resided and worked. Other correlates, but not necessarily predictors, of employer–employee relationship problems were seeking help from the Bomo (traditional healer); obtaining help from online social networking; and workers with children in the family. The two best and most significant social value correlates and predictors of employer–employee relationship problems included interpersonal communications; and self-regulation and self-direction. Low scorers on the following variables were also associated with high likelihood for possessing employer–employee relationship problems: satisfaction with work achievements; and peace and security, while low scorers on work stress had lower odds of having employer–employee relationship problems. Other significant social value correlates, but not predictors of employer–employee relationship problems were self-presentation; interpersonal trust; peace and security; and general anxiety. Consistent with findings of relevant previous studies conducted elsewhere, there were the variables that correlated with and predicted employer–employee relationship problems in Brunei public and private sector workers. Having identified these, the next step, efforts and priority should be directed at addressing the presenting issues via counseling and psychotherapy with affected employees. Further research is recommended to understand better the problem and its possible solutions. PMID:28769597

Predictors and Moderators of Response to Cognitive Behavioral Therapy and Medication for the Treatment of Binge Eating Disorder

PubMed Central

Grilo, Carlos. M.; Masheb, Robin M.; Crosby, Ross D.

2012-01-01

Objective To examine predictors and moderators of response to cognitive-behavioral therapy (CBT) and medication treatments for binge-eating disorder (BED). Method 108 BED patients in a randomized double-blind placebo-controlled trial testing CBT and fluoxetine treatments were assessed prior, throughout-, and post-treatment. Demographic factors, psychiatric and personality-disorder co-morbidity, eating-disorder psychopathology, psychological features, and two sub-typing methods (negative-affect, overvaluation of shape/weight) were tested as predictors and moderators for the primary outcome of remission from binge-eating and four secondary dimensional outcomes (binge-eating frequency, eating-disorder psychopathology, depression, and body mass index). Mixed-effects-models analyzed all available data for each outcome variable. In each model, effects for baseline value and treatment were included with tests of both prediction and moderator effects. Results Several demographic and clinical variables significantly predicted and/or moderated outcomes. One demographic variable signaled a statistical advantage for medication-only (younger participants had greater binge-eating reductions) whereas several demographic and clinical variables (lower self-esteem, negative-affect, and overvaluation of shape/weight) signaled better improvements if receiving CBT. Overvaluation was the most salient predictor/moderator of outcomes. Overvaluation significantly predicted binge-eating remission (29% of participants with versus 57% of participants without overvaluation remitted). Overvaluation was especially associated with lower remission rates if receiving medication-only (10% versus 42% for participants without overvaluation). Overvaluation moderated dimensional outcomes: participants with overvaluation had significantly greater reductions in eating-disorder psychopathology and depression levels if receiving CBT. Overvaluation predictor/moderator findings persisted after controlling for negative-affect. Conclusions Our findings have clinical utility for prescription of CBT and medication and implications for refinement of the BED diagnosis. PMID:22289130

Influence of economic and demographic factors on quality of life in renal transplant recipients.

PubMed

Chisholm, Marie A; Spivey, Christina A; Nus, Audrey Van

2007-01-01

The purpose of this study was to determine the influence of annual income, Medicare status, and demographic variables on the health-related quality of life (HQoL) of renal transplant recipients. A cross-sectional survey was mailed to 146 Georgia renal transplant recipients who had functional grafts. Data were collected using the SF-12 Health Survey (version 2), a demographics survey, and 2003 tax documents. One-way ANOVAs and Pearson's R correlations were used to examine relationships between annual income, Medicare status, demographic variables and SF-12 scores. Significant variables were included in stepwise multiple regression analyses. Data from 130 participants (89% response rate) were collected. Recipients with no Medicare coverage had significantly higher scores on the Physical Functioning and Role Physical SF-12 scales (p = 0.005) compared to recipients with Medicare. Annual income was positively correlated with General Health (p < 0.05). Age and race were significant predictors of Vitality (p = 0.004) and Physical Component Summary (p < 0.001) scores. Age, race, and Medicare status were significant predictors of Physical Functioning and Role Physical scores (p < 0.001). Age, annual income, race, and years post-transplant were significant predictors of General Health score (p < 0.001). Age was the sole predictor of Bodily Pain score (p = 0.002), and marital status was the sole predictor of Social Functioning score (p = 0.005). Interventions designed to offset financial barriers may be needed to bolster renal transplant recipients' HQoL.

Employee Turnover: An Empirical and Methodological Assessment.

ERIC Educational Resources Information Center

Muchinsky, Paul M.; Tuttle, Mark L.

1979-01-01

Reviews research on the prediction of employee turnover. Groups predictor variables into five general categories: attitudinal (job satisfaction), biodata, work-related, personal, and test-score predictors. Consistent relationships between common predictor variables and turnover were found for four categories. Eight methodological problems/issues…

Predictors of fitness to practise declarations in UK medical undergraduates.

PubMed

Paton, Lewis W; Tiffin, Paul A; Smith, Daniel; Dowell, Jon S; Mwandigha, Lazaro M

2018-04-05

Misconduct during medical school predicts subsequent fitness to practise (FtP) events in doctors, but relatively little is known about which factors are associated with such issues during undergraduate education. This study exploits the newly created UK medical education database (UKMED), with the aim of identifying predictors of conduct or health-related issues that could potentially impair FtP. The findings would have implications for policies related to both the selection and support of medical students. Data were available for 14,379 students obtaining provisional registration with the General Medical Council who started medical school in 2007 and 2008. FtP declarations made by students were available, as were various educational and demographic predictor variables, including self-report 'personality measures' for students who participated in UK Clinical Aptitude Test (UKCAT) pilot studies. Univariable and multivariable logistic regression models were developed to evaluate the predictors of FtP declarations. Significant univariable predictors (p < 0.05) for conduct-related declarations included male gender, white ethnicity and a non-professional parental background. Male gender (OR 3.07) and higher 'self-esteem' (OR 1.45) were independently associated with an increased risk of a conduct issue. Female gender, a non-professional background, and lower self-reported 'confidence' were, among others, associated with increased odds of a health-related declaration. Only 'confidence' was a significant independent predictor of a health declaration (OR 0.69). Female gender, higher UKCAT score, a non-professional background and lower 'confidence' scores were significant predictors of reported depression, and the latter two variables were independent predictors of declared depression. White ethnicity and UK nationality were associated with increased odds of both conduct and health-related declarations, as were certain personality traits. Students from non-professional backgrounds may be at increased risk of depression and therefore could benefit from targeted support. The small effect sizes observed for the 'personality measures' suggest they would offer little potential benefit for selection, over and above those measures already in use.

Social Connectedness, Academic, Non-Academic Behaviors Related to Self-Regulation among University Students in Saudi Arabia

ERIC Educational Resources Information Center

Jdaitawi, Malek

2015-01-01

Studies dedicated to examination of self-regulation posit a bi-directional association between self-regulation and other variables including social connectedness, self-efficacy and self-control. However, to date, studies of this caliber have only evidenced that self-regulation is a predictor of other variables. In the present study, the factors…

Academic and Nonacademic Characteristics as Predictors of Persistence in an Associate Degree Nursing Program. AIR Forum 1981 Paper.

ERIC Educational Resources Information Center

Donsky, Aaron P.; Judge, Albert J., Jr.

Academic and nonacademic variables that may predict persistence in the nursing program at Lakeland Community College, Ohio, were studied. The academic variables included American College Testing program standard scores, National League for Nursing (NLN) rank scores, high school grade point average, and previous college grade point average. The…

Student performance on levels 1 and 2-CE of COMLEX-USA: do elective upper-level undergraduate science courses matter?

PubMed

Wong, Stanley K; Ramirez, Juan R; Helf, Scott C

2009-11-01

The effect of a variety of preadmission variables, including the number of elective preadmission upper-level science courses, on academic achievement is not well established. To investigate the relationship between number of preadmission variables and overall student academic achievement in osteopathic medical school. Academic records of osteopathic medical students in the 2008 and 2009 graduating classes of Western University of Health Sciences College of Osteopathic Medicine of the Pacific in Pomona, California, were analyzed. Multivariate linear regression analyses were performed to identify predictors of academic achievement based on Medical College Admission Test (MCAT) subscores, undergraduate grade point average (GPA), GPA in medical school basic science (preclinical GPA) and clinical clerkship (clinical GPA), and scores on the Comprehensive Osteopathic Medical Licensing Examination-USA (COMLEX-USA) Level 1 and Level 2-Cognitive Evaluation (CE). Records of 358 osteopathic medical students were evaluated. Analysis of beta coefficients suggested that undergraduate science GPA was the most important predictor of overall student academic achievement (P<.01). Biological sciences MCAT subscore was a more modest but still statistically significant predictor of preclinical GPA and COMLEX-USA Level 1 score (P<.01). Physical sciences MCAT subscore was also a statistically significant predictor of preclinical GPA, and verbal reasoning MCAT subscore was a statistically significant predictor of COMLEX-USA Level 2-CE score (both P<.01). Women had statistically significantly higher preclinical GPA and COMLEX-USA Level 2-CE scores than men (P<.05). Differences in some outcome variables were also associated with racial-ethnic background and age. Number of preadmission elective upper-level science courses taken by students before matriculation was not significantly correlated with any academic achievement variable. Although undergraduate science GPA and MCAT biological sciences subscore were significant predictors of overall academic achievement for osteopathic medical students, the number of elective upper-level science courses taken preadmission had no predictive value.

Prostate cancer: role of pretreatment multiparametric 3-T MRI in predicting biochemical recurrence after radical prostatectomy.

PubMed

Park, Jung Jae; Kim, Chan Kyo; Park, Sung Yoon; Park, Byung Kwan; Lee, Hyun Moo; Cho, Seong Whi

2014-05-01

The purpose of this study is to retrospectively investigate whether pretreatment multiparametric MRI findings can predict biochemical recurrence in patients who underwent radical prostatectomy (RP) for localized prostate cancer. In this study, 282 patients with biopsy-proven prostate cancer who received RP underwent pretreatment MRI using a phased-array coil at 3 T, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI). MRI variables included apparent tumor presence on combined imaging sequences, extracapsular extension, and tumor size on DWI or DCE-MRI. Clinical variables included baseline prostate-specific antigen (PSA) level, clinical stage, and Gleason score at biopsy. The relationship between clinical and imaging variables and biochemical recurrence was evaluated using Cox regression analysis. After a median follow-up of 26 months, biochemical recurrence developed in 61 patients (22%). Univariate analysis revealed that all the imaging and clinical variables were significantly associated with biochemical recurrence (p < 0.01). On multivariate analysis, however, baseline PSA level (p = 0.002), Gleason score at biopsy (p = 0.024), and apparent tumor presence on combined T2WI, DWI, and DCE-MRI (p = 0.047) were the only significant independent predictors of biochemical recurrence. Of the independent predictors, apparent tumor presence on combined T2WI, DWI, and DCE-MRI showed the highest hazard ratio (2.38) compared with baseline PSA level (hazard ratio, 1.05) and Gleason score at biopsy (hazard ratio, 1.34). The apparent tumor presence on combined T2WI, DWI, and DCE-MRI of pretreatment MRI is an independent predictor of biochemical recurrence after RP. This finding may be used to construct a predictive model for biochemical recurrence after surgery.

Measures of Time-Sharing Skill and Gender as Predictors of Flight Simulator Performance.

DTIC Science & Technology

1979-01-01

well as overall e- quations including gender as a variable. Besides gender in the overall equations, measures of time-sharing skill were the best ...study indicated the best predictors of dual or whole-task performance were other dual-tasks. Furthermore, the particular components involved in a dual...switching between tasks, or the use of efficient response strategies " (Damos and Wickens, 1977, p.2). Attentional flexibility. According to Keele

Measuring body composition in dogs using multifrequency bioelectrical impedance analysis and dual energy X-ray absorptiometry.

PubMed

Rae, L S; Vankan, D M; Rand, J S; Flickinger, E A; Ward, L C

2016-06-01

Thirty-five healthy, neutered, mixed breed dogs were used to determine the ability of multifrequency bioelectrical impedance analysis (MFBIA) to predict accurately fat-free mass (FFM) in dogs using dual energy X-ray absorptiometry (DXA)-measured FFM as reference. A second aim was to compare MFBIA predictions with morphometric predictions. MFBIA-based predictors provided an accurate measure of FFM, within 1.5% when compared to DXA-derived FFM, in normal weight dogs. FFM estimates were most highly correlated with DXA-measured FFM when the prediction equation included resistance quotient, bodyweight, and body condition score. At the population level, the inclusion of impedance as a predictor variable did not add substantially to the predictive power achieved with morphometric variables alone; in individual dogs, impedance predictors were more valuable than morphometric predictors. These results indicate that, following further validation, MFBIA could provide a useful tool in clinical practice to objectively measure FFM in canine patients and help improve compliance with prevention and treatment programs for obesity in dogs. Copyright © 2016. Published by Elsevier Ltd.

Predictors in use of mental health resources: The role of behaviour problems in patients with severe mental illness.

PubMed

Bellido-Zanin, Gloria; Vázquez-Morejón, Antonio J; Martín-Rodríguez, Agustín; Pérez-San-Gregorio, Maria Ángeles

2017-09-01

In recent years, more variables are being included in the use of mental health resource prediction models. Some studies have shown that how well the patient can function is important for this prediction. However, the relevance of a variable as important as behaviour problems has scarcely been explored. This study attempted to evaluate the effect of behaviour problems in patients with severe mental illness on the use of mental health resources. A total of 185 patients at a Community Mental Health Unit were evaluated using the Behaviour Problem Inventory. Later, a bivariate logistic regression was done to identify what behaviour problems could be specific predictors of use of mental health resources. The results showed that the general index of behaviour problems predicts both use of hospitalization resources and outpatient attention. Underactivity/social withdrawal is the best predictor of all the different areas. These results confirm the role of behaviour problems as predictors of the use of mental health resources in individuals with a severe mental illness.

Using worldwide edaphic data to model plant species niches: An assessment at a continental extent

PubMed Central

Galvão, Franklin; Villalobos, Fabricio; De Marco Júnior, Paulo

2017-01-01

Ecological niche modeling (ENM) is a broadly used tool in different fields of plant ecology. Despite the importance of edaphic conditions in determining the niche of terrestrial plant species, edaphic data have rarely been included in ENMs of plant species perhaps because such data are not available for many regions. Recently, edaphic data has been made available at a global scale allowing its potential inclusion and evaluation on ENM performance for plant species. Here, we take advantage of such data and address the following main questions: What is the influence of distinct predictor variables (e.g. climatic vs edaphic) on different ENM algorithms? and what is the relationship between the performance of different predictors and geographic characteristics of species? We used 125 plant species distributed over the Neotropical region to explore the effect on ENMs of using edaphic data available from the SoilGrids database and its combination with climatic data from the CHELSA database. In addition, we related these different predictor variables to geographic characteristics of the target species and different ENM algorithms. The use of different predictors (climatic, edaphic, and both) significantly affected model performance and spatial complexity of the predictions. We showed that the use of global edaphic plus climatic variables generates ENMs with similar or better accuracy compared to those constructed only with climate variables. Moreover, the performance of models considering these different predictors, separately or jointly, was related to geographic properties of species records, such as number and distribution range. The large geographic extent, the variability of environments and the different species’ geographical characteristics considered here allowed us to demonstrate that global edaphic data adds useful information for plant ENMs. This is particularly valuable for studies of species that are distributed in regions where more detailed information on soil properties is poor or does not even exist. PMID:29049298

Psychosocial predictors of four health-promoting behaviors for cancer prevention using the stage of change of Transtheoretical Model.

PubMed

Choi, Jean H; Chung, Kyong-Mee; Park, Keeho

2013-10-01

The present study aimed to examine whether demographic as well as psychosocial variables related to the five stages of change of the Transtheoretical Model can predict non-clinical adults' cancer preventive and health-promoting behaviors. This study specifically focused on cancer, one of the major chronic diseases, which is a serious threat of national health. A total of 1530 adults participated in the study and completed questionnaires. Collected data were analyzed by using multinominal logistic regression. The significant predictors of later stages varied among the types of health-promoting behaviors. Certain cancer preventive health-promoting behaviors such as well-balanced diet and exercise were significantly associated with psychosocial variables including cancer prevention-related self-efficacy, personality traits, psychosocial stress, and social support. On the other hand, smoking cessation and moderate or abstinence from drinking were more likely to be predicted by demographic variables including sex and age. The present study found that in addition to self-efficacy-a relatively well-studied psychological variable-other personality traits and psychological factors including introversion, neuroticism, psychosocial stress, and social support also significantly predicted later stages of change with respect to cancer preventive health-promoting behaviors. The implications of this study are also discussed. Copyright © 2013 John Wiley & Sons, Ltd.

Predictors of Burnout Among Nurses in Taiwan.

PubMed

Lee, Huan-Fang; Yen, Miaofen; Fetzer, Susan; Chien, Tsair Wei

2015-08-01

Nurse burnout is a crucial issue for health care professionals and impacts nurse turnover and nursing shortages. Individual and situational factors are related to nurse burnout with predictors of burnout differing among cultures and health care systems. The predictors of nurse burnout in Asia, particularly Taiwan, are unknown. The purpose of this study was to investigate the predictors of burnout among a national sample of nurses in Taiwan. A secondary data analysis of a nationwide database investigated the predictors of burnout among 1,846 nurses in Taiwan. Hierarchical regression analysis determined the relationship between predictors and burnout. Predictors of Taiwanese nurse burnout were age, physical/psychological symptoms, job satisfaction, work engagement, and work environment. The most significant predictors were physical/psychological symptoms and work engagement. The variables explained 35, 39, and 18 % of the emotional exhaustion, personal accomplishment, and depersonalization variance for 54 % of the total variance of burnout. Individual characteristics and nurse self-awareness, especially work, engagement can impact Taiwanese nurses' burnout. Nurse burnout predictors provide administrators with information to develop strategies including education programs and support services to reduce nurse burnout.

How Variables Uncorrelated with the Dependent Variable Can Actually Make Excellent Predictors: The Important Suppressor Variable Case.

ERIC Educational Resources Information Center

Woolley, Kristin K.

Many researchers are unfamiliar with suppressor variables and how they operate in multiple regression analyses. This paper describes the role suppressor variables play in a multiple regression model and provides practical examples that explain how they can change research results. A variable that when added as another predictor increases the total…

Use of Admission Criteria to Predict Performance of Students in an Entry-Level Master's Program on Fieldwork Placements and in Academic Courses.

PubMed

Kirchner, G L; Stone, R G; Holm, M B

2001-01-01

The relationships among clinical outcomes, academic success, and predictors used to screen applicants for entrance into a Master in Occupational Therapy Program (MOT) were examined. The dependent variables were grade point average in occupational therapy courses (OT-GPA), client therapy outcomes at the clinic, and ratings of MOT students by Level II Fieldwork supervisors. Predictor variables included undergraduate GPA, scores on the Graduate Record Examination (GRE), and an essay. Both undergraduate GPA and scores on the GRE were found to predict OT-GPA. The analytical section of the GRE was also positively correlated with fieldwork supervisors' ratings of students.

What makes patients with fibromyalgia feel better? Correlations between Patient Global Impression of Improvement and changes in clinical symptoms and function: a pooled analysis of 4 randomized placebo-controlled trials of duloxetine.

PubMed

Hudson, James I; Arnold, Lesley M; Bradley, Laurence A; Choy, Ernest H S; Mease, Philip J; Wang, Fujun; Ahl, Jonna; Wohlreich, Madelaine M

2009-11-01

To investigate the relationship between changes in clinical rating scale items and endpoint Patient Global Impression of Improvement (PGI-I). Data were pooled from 4 randomized, double-blind, placebo-controlled studies of duloxetine in patients with fibromyalgia (FM). Variables included in the analyses were those that assessed symptoms in FM domains of pain, fatigue, sleep, cognitive difficulties, emotional well-being, physical function, and impact on daily living. The association of endpoint PGI-I with changes from baseline in individual variables was assessed using Pearson product-moment correlations (r). Stepwise linear regression was used to identify those variables for which changes from baseline were statistically significant independent predictors of the endpoint PGI-I ratings. Changes in pain variables and interference of symptoms with the ability to work were highly correlated (r >or= 0.5 or r

Predictors of outcomes of psychological treatments for disordered gambling: A systematic review.

PubMed

Merkouris, S S; Thomas, S A; Browning, C J; Dowling, N A

2016-08-01

This systematic review aimed to synthesise the evidence relating to pre-treatment predictors of gambling outcomes following psychological treatment for disordered gambling across multiple time-points (i.e., post-treatment, short-term, medium-term, and long-term). A systematic search from 1990 to 2016 identified 50 articles, from which 11 socio-demographic, 16 gambling-related, 21 psychological/psychosocial, 12 treatment, and no therapist-related variables, were identified. Male gender and low depression levels were the most consistent predictors of successful treatment outcomes across multiple time-points. Likely predictors of successful treatment outcomes also included older age, lower gambling symptom severity, lower levels of gambling behaviours and alcohol use, and higher treatment session attendance. Significant associations, at a minimum of one time-point, were identified between successful treatment outcomes and being employed, ethnicity, no gambling debt, personality traits and being in the action stage of change. Mixed results were identified for treatment goal, while education, income, preferred gambling activity, problem gambling duration, anxiety, any psychiatric comorbidity, psychological distress, substance use, prior gambling treatment and medication use were not significantly associated with treatment outcomes at any time-point. Further research involving consistent treatment outcome frameworks, examination of treatment and therapist predictor variables, and evaluation of predictors across long-term follow-ups is warranted to advance this developing field of research. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

A comparison of two above-ground biomass estimation techniques integrating satellite-based remotely sensed data and ground data for tropical and semiarid forests in Puerto Rico

NASA Astrophysics Data System (ADS)

Iiames, J. S.; Riegel, J.; Lunetta, R.

2013-12-01

Two above-ground forest biomass estimation techniques were evaluated for the United States Territory of Puerto Rico using predictor variables acquired from satellite based remotely sensed data and ground data from the U.S. Department of Agriculture Forest Inventory Analysis (FIA) program. The U.S. Environmental Protection Agency (EPA) estimated above-ground forest biomass implementing methodology first posited by the Woods Hole Research Center developed for conterminous United States (National Biomass and Carbon Dataset [NBCD2000]). For EPA's effort, spatial predictor layers for above-ground biomass estimation included derived products from the U.S. Geologic Survey (USGS) National Land Cover Dataset 2001 (NLCD) (landcover and canopy density), the USGS Gap Analysis Program (forest type classification), the USGS National Elevation Dataset, and the NASA Shuttle Radar Topography Mission (tree heights). In contrast, the U.S. Forest Service (USFS) biomass product integrated FIA ground-based data with a suite of geospatial predictor variables including: (1) the Moderate Resolution Imaging Spectrometer (MODIS)-derived image composites and percent tree cover; (2) NLCD land cover proportions; (3) topographic variables; (4) monthly and annual climate parameters; and (5) other ancillary variables. Correlations between both data sets were made at variable watershed scales to test level of agreement. Notice: This work is done in support of EPA's Sustainable Healthy Communities Research Program. The U.S EPA funded and conducted the research described in this paper. Although this work was reviewed by the EPA and has been approved for publication, it may not necessarily reflect official Agency policy. Mention of any trade names or commercial products does not constitute endorsement or recommendation for use.

Hypoglycemia in noncritically ill patients receiving total parenteral nutrition: a multicenter study. (Study group on the problem of hyperglycemia in parenteral nutrition; Nutrition area of the Spanish Society of Endocrinology and Nutrition).

PubMed

Olveira, Gabriel; Tapia, María José; Ocón, Julia; Cabrejas-Gómez, Carmen; Ballesteros-Pomar, María D; Vidal-Casariego, Alfonso; Arraiza-Irigoyen, Carmen; Olivares, Josefina; Conde-García, Maria Carmen; García-Manzanares, Álvaro; Botella-Romero, Francisco; Quílez-Toboso, Rosa P; Matía, Pilar; Rubio, Miguel Ángel; Chicharro, Luisa; Burgos, Rosa; Pujante, Pedro; Ferrer, Mercedes; Zugasti, Ana; Petrina, Estrella; Manjón, Laura; Diéguez, Marta; Carrera, Ma José; Vila-Bundo, Anna; Urgelés, Juan Ramón; Aragón-Valera, Carmen; Sánchez-Vilar, Olga; Bretón, Irene; García-Peris, Pilar; Muñoz-Garach, Araceli; Márquez, Efren; Del Olmo, Dolores; Pereira, José Luis; Tous, María C

2015-01-01

Hypoglycemia is a common problem among hospitalized patients. Treatment of hyperglycemia with insulin is potentially associated with an increased risk for hypoglycemia. The aim of this study was to determine the prevalence and predictors of hypoglycemia (capillary blood glucose <70 mg/dL) in hospitalized patients receiving total parenteral nutrition (TPN). This prospective multicenter study involved 19 Spanish hospitals. Noncritically ill adults who were prescribed TPN were included, thus enabling us to collect data on capillary blood glucose and insulin dosage. The study included 605 patients of whom 6.8% (n = 41) had at least one capillary blood glucose <70 mg/dL and 2.6% (n = 16) had symptomatic hypoglycemia. The total number of hypoglycemic episodes per 100 d of TPN was 0.82. In univariate analysis, hypoglycemia was significantly associated with the presence of diabetes, a lower body mass index (BMI), and treatment with intravenous (IV) insulin. Patients with hypoglycemia also had a significantly longer hospital length of stay, PN duration, higher blood glucose variability, and a higher insulin dose. Multiple logistic regression analysis showed that a lower BMI, high blood glucose variability, and TPN duration were risk factors for hypoglycemia. Use of IV insulin and blood glucose variability were predictors of symptomatic hypoglycemia. The occurrence of hypoglycemia in noncritically ill patients receiving PN is low. A lower BMI and a greater blood glucose variability and TPN duration are factors associated with the risk for hypoglycemia. IV insulin and glucose variability were predictors of symptomatic hypoglycemia. Copyright © 2015 Elsevier Inc. All rights reserved.

Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

USGS Publications Warehouse

Zimmermann, N.E.; Edwards, T.C.; Moisen, Gretchen G.; Frescino, T.S.; Blackard, J.A.

2007-01-01

1. Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits. 2. We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics. 3. More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species. 4. Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species. 5. Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change. ?? 2007 The Authors.

Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

PubMed Central

ZIMMERMANN, N E; EDWARDS, T C; MOISEN, G G; FRESCINO, T S; BLACKARD, J A

2007-01-01

Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits. We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics. More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species. Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species. Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change. PMID:18642470

SCD-HeFT: Use of RR Interval Statistics for Long-term Risk Stratification for Arrhythmic Sudden Cardiac Death

PubMed Central

Au-yeung, Wan-tai M.; Reinhall, Per; Poole, Jeanne E.; Anderson, Jill; Johnson, George; Fletcher, Ross D.; Moore, Hans J.; Mark, Daniel B.; Lee, Kerry L.; Bardy, Gust H.

2015-01-01

Background In the SCD-HeFT a significant fraction of the congestive heart failure (CHF) patients ultimately did not die suddenly from arrhythmic causes. CHF patients will benefit from better tools to identify if ICD therapy is needed. Objective To identify predictor variables from baseline SCD-HeFT patients’ RR intervals that correlate to arrhythmic sudden cardiac death (SCD) and mortality and to design an ICD therapy screening test. Methods Ten predictor variables were extracted from pre-randomization Holter data from 475 patients enrolled in the SCD-HeFT ICD arm using novel and traditional heart rate variability methods. All variables were correlated to SCD using Mann Whitney-Wilcoxon test and receiver operating characteristic analysis. ICD therapy screening tests were designed by minimizing the cost of false classifications. Survival analysis, including log-rank test and Cox models, was also performed. Results α1 and α2 from detrended fluctuation analysis, the ratio of low to high frequency power, the number of PVCs per hour and heart rate turbulence slope are all statistically significant for predicting the occurrences of SCD (p<0.001) and survival (log-rank p<0.01). The most powerful multivariate predictor tool using the Cox Proportional Hazards was α2 with a hazard ratio of 0.0465 (95% CI: 0.00528 – 0.409, p<0.01). Conclusion Predictor variables from RR intervals correlate to the occurrences of SCD and distinguish survival among SCD-HeFT ICD patients. We believe SCD prediction models should incorporate Holter based RR interval analysis to refine ICD patient selection especially in removing patients who are unlikely to benefit from ICD therapy. PMID:26096609

Early Predictors of Lumbar Spine Surgery after Occupational Back Injury: Results from a Prospective Study of Workers in Washington State

PubMed Central

Keeney, Benjamin J.; Fulton-Kehoe, Deborah; Turner, Judith A.; Wickizer, Thomas M.; Chan, Kwun Chuen Gary; Franklin, Gary M.

2014-01-01

Study Design Prospective population-based cohort study Objective To identify early predictors of lumbar spine surgery within 3 years after occupational back injury Summary of Background Data Back injuries are the most prevalent occupational injury in the United States. Little is known about predictors of lumbar spine surgery following occupational back injury. Methods Using Disability Risk Identification Study Cohort (D-RISC) data, we examined the early predictors of lumbar spine surgery within 3 years among Washington State workers with new worker’s compensation temporary total disability claims for back injuries. Baseline measures included worker-reported measures obtained approximately 3 weeks after claim submission. We used medical bill data to determine whether participants underwent surgery, covered by the claim, within 3 years. Baseline predictors (P < 0.10) of surgery in bivariate analyses were included in a multivariate logistic regression model predicting lumbar spine surgery. The model’s area under the receiver operating characteristic curve (AUC) was used to determine the model’s ability to identify correctly workers who underwent surgery. Results In the D-RISC sample of 1,885 workers, 174 (9.2%) had a lumbar spine surgery within 3 years. Baseline variables associated with surgery (P < 0.05) in the multivariate model included higher Roland Disability Questionnaire scores, greater injury severity, and surgeon as first provider seen for the injury. Reduced odds of surgery were observed for those under age 35, women, Hispanics, and those whose first provider was a chiropractor. 42.7% of workers who first saw a surgeon had surgery, in contrast to only 1.5% of those who saw a chiropractor. The multivariate model’s AUC was 0.93 (95% CI 0.92–0.95), indicating excellent ability to discriminate between workers who would versus would not have surgery. Conclusion Baseline variables in multiple domains predicted lumbar spine surgery. There was a very strong association between surgery and first provider seen for the injury, even after adjustment for other important variables. PMID:23238486

Early predictors of lumbar spine surgery after occupational back injury: results from a prospective study of workers in Washington State.

PubMed

Keeney, Benjamin J; Fulton-Kehoe, Deborah; Turner, Judith A; Wickizer, Thomas M; Chan, Kwun Chuen Gary; Franklin, Gary M

2013-05-15

Prospective population-based cohort study. To identify early predictors of lumbar spine surgery within 3 years after occupational back injury. Back injuries are the most prevalent occupational injury in the United States. Few prospective studies have examined early predictors of spine surgery after work-related back injury. Using Disability Risk Identification Study Cohort (D-RISC) data, we examined the early predictors of lumbar spine surgery within 3 years among Washington State workers, with new workers compensation temporary total disability claims for back injuries. Baseline measures included worker-reported measures obtained approximately 3 weeks after claim submission. We used medical bill data to determine whether participants underwent surgery, covered by the claim, within 3 years. Baseline predictors (P < 0.10) of surgery in bivariate analyses were included in a multivariate logistic regression model predicting lumbar spine surgery. The area under the receiver operating characteristic curve of the model was used to determine the model's ability to identify correctly workers who underwent surgery. In the D-RISC sample of 1885 workers, 174 (9.2%) had a lumbar spine surgery within 3 years. Baseline variables associated with surgery (P < 0.05) in the multivariate model included higher Roland-Morris Disability Questionnaire scores, greater injury severity, and surgeon as first provider seen for the injury. Reduced odds of surgery were observed for those younger than 35 years, females, Hispanics, and those whose first provider was a chiropractor. Approximately 42.7% of workers who first saw a surgeon had surgery, in contrast to only 1.5% of those who saw a chiropractor. The area under the receiver operating characteristic curve of the multivariate model was 0.93 (95% confidence interval, 0.92-0.95), indicating excellent ability to discriminate between workers who would versus would not have surgery. Baseline variables in multiple domains predicted lumbar spine surgery. There was a very strong association between surgery and first provider seen for the injury even after adjustment for other important variables.

Parental Decisional Regret after Primary Distal Hypospadias Repair: Family and Surgery Variables, and Repair Outcomes.

PubMed

Ghidini, Filippo; Sekulovic, Sasa; Castagnetti, Marco

2016-03-01

Decisional regret is defined as distress after making a health care choice and can be an issue for parents electing distal hypospadias repair for their sons. We assessed the influence on decisional regret of variables related to the family, surgery and outcomes. Charts for 372 patients undergoing primary distal hypospadias repair between 2005 and 2012 were reviewed, and validated questionnaires, including the Decisional Regret Scale, Pediatric Penile Perception Score and Dysfunctional Voiding and Incontinence Scoring System, were administered to parents. Data were available for 172 of 372 families (response rate 46.2%). Of 323 parents 128 (39.6%) presented with moderately strong decisional regret, with good agreement within couples. Predictors of decisional regret included intermediate parental educational level (OR 3.19, 95% CI 1.52-6.69), patient not being the first born (OR 2.01, 95% CI 1.07-3.78), family history of hypospadias (OR 4.42, 95% CI 1.96-9.97), initial desire to avoid surgery (OR 2.07, 95% CI 1.04-4.12), younger age at followup (OR 0.81, 95% CI 0.72-0.91), presence of lower urinary tract symptoms (OR 4.92, 95% CI 1.53-15.81) and lower Pediatric Penile Perception Score (OR 0.86, 95% CI 0.75-0.99). Decisional regret was unrelated to parental desire to avoid circumcision, surgical variables, development of complications and duration of followup. Decisional regret is a problem in a significant proportion of parents electing distal hypospadias repair for their sons. In our experience family variables seemed to be predictors of decisional regret, while surgical variables did not. Predictors of decisional regret included worse parental perception of penile appearance and the presence of lower urinary tract symptoms. However, the latter could be unrelated to surgery. Irrespective of the duration of followup, decisional regret seems decreased in parents of older patients. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Metacognitive and Motivational Predictors of Surface Approach to Studying and Academic Examination Performance

ERIC Educational Resources Information Center

Spada, Marcantonio M.; Moneta, Giovanni B.

2014-01-01

The objective of this study was to verify the structure of a model of how surface approach to studying is influenced by the trait variables of motivation and metacognition and the state variables of avoidance coping and evaluation anxiety. We extended the model to include: (1) the investigation of the relative contribution of the five…

Predictors of Persistent Axial Neck Pain After Cervical Laminoplasty.

PubMed

Kimura, Atsushi; Shiraishi, Yasuyuki; Inoue, Hirokazu; Endo, Teruaki; Takeshita, Katsushi

2018-01-01

Retrospective analysis of prospective data. The aim of this study was to reveal baseline predictors of persistent postlaminoplasty neck pain. Axial neck pain is one of the most common complications after cervical laminoplasty; however, baseline predictors of persistent postlaminoplasty neck pain are unclear. We analyzed data from 156 patients who completed a 2-year follow-up after double-door laminoplasty for degenerative cervical myelopathy. Patients rated the average intensity of axial neck pain in the last month using an 11-point numerical rating scale preoperatively and at the 2-year follow-up. The dependent variable was the presence of moderate-to-severe neck pain (numerical rating scale ≥4) at the 2-year follow-up. The independent variables included patient characteristics, baseline radiological parameters, surgical variables, baseline axial neck pain intensity, and baseline functions, which were measured by the Japanese Orthopaedic Association score and the Short Form-36 survey (SF-36). Logistic regression analysis was performed to identify independent predictors of moderate-to-severe neck pain after laminoplasty. At the 2-year follow-up, 51 patients (32%) had moderate-to-severe neck pain, and 106 patients (68%) had no or mild pain. Univariate analysis revealed that the ratio of cervical anterolisthesis, ratio of current smoking, baseline neck pain intensity, and baseline SF-36 Mental Component Summary differed significantly between the groups. Multivariate logistic regression analysis showed that independent predictors of moderate-to-severe neck pain at the 2-year follow-up include the presence of anterolisthesis, current smoking, moderate-to-severe baseline neck pain, and lower SF-36 Mental Component Summary. The presence of anterolisthesis and moderate-to-severe baseline neck pain were also associated with significantly poorer physical function after surgery. The presence of anterolisthesis was associated not only with the highest odds ratio of persistent neck pain but also with significantly poorer functional outcomes. Indications for cervical laminoplasty should be carefully determined in patients with cervical anterolisthesis. 4.

Predictors of retention in a drug-free unit/substance abuse treatment in prison.

PubMed

Casares-López, María José; González-Menéndez, Ana; Festinger, David S; Fernández-García, Paula; Fernández-Hermida, José Ramón; Secades, Roberto; Matejkowski, Jason

2013-01-01

The high rate of dropout from treatment programs is a recurring problem in the field of drug dependence. The purpose of this study was to identify the predictors of retention in a prison-based drug-free unit (DFU). The relationships among subscales of the Addiction Severity Index (ASI) as well as motivation and personality profiles and length of stay in a DFU, of 57 prisoners admitted for the first time to the program were analyzed. The mean dropout rates were 52.9% at six months and 67.8% at one year. The mean length of stay was 195.05 days. Predictors of retention at six months included the ASI Family Composite Score, the motivation subscale Taking Steps, and Narcissistic personality trait score. Predictors of retention at one year included lower ASI Psychological Composite Score, higher scores on the motivation subscale Ambivalence, and higher number of charges pending at the time of admission to the program. Identification of these predictor variables may be useful for developing strategies to increase retention in the context of in-prison substance abuse treatment. Copyright © 2013. Published by Elsevier Ltd.

Is parenting style a predictor of suicide attempts in a representative sample of adolescents?

PubMed

Donath, Carolin; Graessel, Elmar; Baier, Dirk; Bleich, Stefan; Hillemacher, Thomas

2014-04-26

Suicidal ideation and suicide attempts are serious but not rare conditions in adolescents. However, there are several research and practical suicide-prevention initiatives that discuss the possibility of preventing serious self-harm. Profound knowledge about risk and protective factors is therefore necessary. The aim of this study is a) to clarify the role of parenting behavior and parenting styles in adolescents' suicide attempts and b) to identify other statistically significant and clinically relevant risk and protective factors for suicide attempts in a representative sample of German adolescents. In the years 2007/2008, a representative written survey of N = 44,610 students in the 9th grade of different school types in Germany was conducted. In this survey, the lifetime prevalence of suicide attempts was investigated as well as potential predictors including parenting behavior. A three-step statistical analysis was carried out: I) As basic model, the association between parenting and suicide attempts was explored via binary logistic regression controlled for age and sex. II) The predictive values of 13 additional potential risk/protective factors were analyzed with single binary logistic regression analyses for each predictor alone. Non-significant predictors were excluded in Step III. III) In a multivariate binary logistic regression analysis, all significant predictor variables from Step II and the parenting styles were included after testing for multicollinearity. Three parental variables showed a relevant association with suicide attempts in adolescents - (all protective): mother's warmth and father's warmth in childhood and mother's control in adolescence (Step I). In the full model (Step III), Authoritative parenting (protective: OR: .79) and Rejecting-Neglecting parenting (risk: OR: 1.63) were identified as significant predictors (p < .001) for suicidal attempts. Seven further variables were interpreted to be statistically significant and clinically relevant: ADHD, female sex, smoking, Binge Drinking, absenteeism/truancy, migration background, and parental separation events. Parenting style does matter. While children of Authoritative parents profit, children of Rejecting-Neglecting parents are put at risk - as we were able to show for suicide attempts in adolescence. Some of the identified risk factors contribute new knowledge and potential areas of intervention for special groups such as migrants or children diagnosed with ADHD.

What are the most crucial soil factors for predicting the distribution of alpine plant species?

NASA Astrophysics Data System (ADS)

Buri, A.; Pinto-Figueroa, E.; Yashiro, E.; Guisan, A.

2017-12-01

Nowadays the use of species distribution models (SDM) is common to predict in space and time the distribution of organisms living in the critical zone. The realized environmental niche concept behind the development of SDM imply that many environmental factors must be accounted for simultaneously to predict species distributions. Climatic and topographic factors are often primary included, whereas soil factors are frequently neglected, mainly due to the paucity of soil information available spatially and temporally. Furthermore, among existing studies, most included soil pH only, or few other soil parameters. In this study we aimed at identifying what are the most crucial soil factors for explaining alpine plant distributions and, among those identified, which ones further improve the predictive power of plant SDMs. To test the relative importance of the soil factors, we performed plant SDMs using as predictors 52 measured soil properties of various types such as organic/inorganic compounds, chemical/physical properties, water related variables, mineral composition or grain size distribution. We added them separately to a standard set of topo-climatic predictors (temperature, slope, solar radiation and topographic position). We used ensemble forecasting techniques combining together several predictive algorithms to model the distribution of 116 plant species over 250 sites in the Swiss Alps. We recorded the variable importance for each model and compared the quality of the models including different soil proprieties (one at a time) as predictors to models having only topo-climatic variables as predictors. Results show that 46% of the soil proprieties tested become the second most important variable, after air temperature, to explain spatial distribution of alpine plants species. Moreover, we also assessed that addition of certain soil factors, such as bulk soil water density, could improve over 80% the quality of some plant species models. We confirm that soil pH remains one of the most important soil factor for predicting plant species distributions, closely followed by water, organic and inorganic carbon related properties. Finally, we were able to extract three main categories of important soil properties for plant species distributions: grain size distribution, acidity and water in the soil.

Register-based predictors of violations of animal welfare legislation in dairy herds.

PubMed

Otten, N D; Nielsen, L R; Thomsen, P T; Houe, H

2014-12-01

The assessment of animal welfare can include resource-based or animal-based measures. Official animal welfare inspections in Denmark primarily control compliance with animal welfare legislation based on resource measures (e.g. housing system) and usually do not regard animal response parameters (e.g. clinical and behavioural observations). Herds selected for welfare inspections are sampled by a risk-based strategy based on existing register data. The aim of the present study was to evaluate register data variables as predictors of dairy herds with violations of the animal welfare legislation (VoAWL) defined as occurrence of at least one of the two most frequently violated measures found at recent inspections in Denmark, namely (a) presence of injured animals not separated from the rest of the group and/or (b) animals in a condition warranting euthanasia still being present in the herd. A total of 25 variables were extracted from the Danish Cattle Database and assessed as predictors using a multivariable logistic analysis of a data set including 73 Danish dairy herds, which all had more than 100 cows and cubicle loose-housing systems. Univariable screening was used to identify variables associated with VoAWL at a P-value<0.2 for the inclusion in a multivariable logistic regression analysis. Backward selection procedures identified the following variables for the final model predictive of VoAWL: increasing standard deviation of milk yield for first lactation cows, high bulk tank somatic cell count (⩾250 000 cells/ml) and suspiciously low number of recorded veterinary treatments (⩽25 treatments/100 cow years). The identified predictors may be explained by underlying management factors leading to impaired animal welfare in the herd, such as poor hygiene, feeding and management of dry or calving cows and sick animals. However, further investigations are required for causal inferences to be established.

Older Age and Leg Pain Are Good Predictors of Pain and Disability Outcomes in 2710 Patients Who Receive Lumbar Fusion.

PubMed

Cook, Chad E; Frempong-Boadu, Anthony K; Radcliff, Kristen; Karikari, Isaac; Isaacs, Robert

2015-10-01

Identifying appropriate candidates for lumbar spine fusion is a challenging and controversial topic. The purpose of this study was to identify baseline characteristics related to poor/favorable outcomes at 1 year for a patient who received lumbar spine fusion. The aims of this study were to describe baseline characteristics of those who received lumbar surgery and to identify baseline characteristics from a spine repository that were related to poor and favorable pain and disability outcomes for patient who received lumbar fusion (with or without decompression), who were followed up for 1 full year and discriminate predictor variables that were either or in contrast to prognostic variables reported in the literature. This study analyzed data from 2710 patients who underwent lumbar spine fusion. All patient data was part of a multicenter, multi-national spine repository. Ten relatively commonly captured data variables were used as predictors for the study. Univariate/multivariate logistic regression analyses were run against outcome variables of pain/disability. Multiple univariate findings were associated with pain/disability outcomes at 1 year including age, previous surgical history, baseline disability, baseline pain, baseline quality of life scores, and leg pain greater than back pain. Notably significant multivariate findings for both pain and disability include older age, previous surgical history, and baseline mental summary scores, disability, and pain. Leg pain greater than back pain and older age may yield promising value when predicting positive outcomes. Other significant findings may yield less value since these findings are similar to those that are considered to be prognostic regardless of intervention type.

Predictors of physical and mental health-related quality of life outcomes among myocardial infarction patients

PubMed Central

2013-01-01

Background Health-related quality of life (HRQoL) is an important outcome for patients diagnosed with coronary heart disease. This report describes predictors of physical and mental HRQoL at six months post-hospitalisation for myocardial infarction. Methods Participants were myocardial infarction patients (n=430) admitted to two tertiary referral centres in Brisbane, Australia who completed a six month coronary heart disease secondary prevention trial (ProActive Heart). Outcome variables were HRQoL (Short Form-36) at six months, including a physical and mental summary score. Baseline predictors included demographics and clinical variables, health behaviours, and psychosocial variables. Stepwise forward multiple linear regression analyses were used to identify significant independent predictors of six month HRQoL. Results Physical HRQoL was lower in participants who: were older (p<0.001); were unemployed (p=0.03); had lower baseline physical and mental HRQoL scores (p<0.001); had lower confidence levels in meeting sufficient physical activity recommendations (p<0.001); had no intention to be physically active in the next six months (p<0.001); and were more sedentary (p=0.001). Mental HRQoL was lower in participants who: were younger (p=0.01); had lower baseline mental HRQoL (p<0.001); were more sedentary (p=0.01) were depressed (p<0.001); and had lower social support (p=0.001). Conclusions This study has clinical implications as identification of indicators of lower physical and mental HRQoL outcomes for myocardial infarction patients allows for targeted counselling or coronary heart disease secondary prevention efforts. Trial registration Australian Clinical Trials Registry, Australian New Zealand Clinical Trials Registry, CTRN12607000595415. PMID:24020831

Changes in Situational and Dispositional Factors as Predictors of Job Satisfaction

ERIC Educational Resources Information Center

Keller, Anita C.; Semmer, Norbert K.

2013-01-01

Arguably, job satisfaction is one of the most important variables with regard to work. When explaining job satisfaction, research usually focuses on predictor variables in terms of levels but neglects growth rates. Therefore it remains unclear how potential predictors evolve over time and how their development affects job satisfaction. Using…

Predictors of Perioperative Stroke/Death after Carotid Artery Stenting: A Review Article

PubMed Central

AbuRahma, Ali F.

2018-01-01

Carotid artery stenting (CAS) has been recommended as an alternative treatment to carotid endarterectomy for patients with significant carotid stenosis. Only a few studies have analyzed clinical/anatomical and technical variables that affect perioperative outcomes of CAS. Following a comprehensive Medline search, it was reported that clinical factors, including age of >80 years, chronic renal failure, diabetes mellitus, symptomatic indications, and procedures performed within 2 weeks of transient ischemic attack symptoms, are associated with high perioperative stroke and death rates. They also highlighted that angiographic variables, e.g., ulcerated and calcified plaques, left carotid intervention, >90% stenosis, >10-mm target lesion length, ostial involvement, type III aortic arch, and >60°-angulated internal carotid and common carotid arteries, are predictors of increased stroke rates. Technical factors associated with increased perioperative risk of stroke include percutaneous transluminal angioplasty (PTA) without embolic protection devices, PTA before stent placement, and the use of multiple stents. This review describes the most widely quoted data in defining various predictors of perioperative stroke and death after CAS. (This is a review article based on the invited lecture of the 45th Annual Meeting of Japanese Society for Vascular Surgery.) PMID:29682104

Introduction to statistical modelling 2: categorical variables and interactions in linear regression.

PubMed

Lunt, Mark

2015-07-01

In the first article in this series we explored the use of linear regression to predict an outcome variable from a number of predictive factors. It assumed that the predictive factors were measured on an interval scale. However, this article shows how categorical variables can also be included in a linear regression model, enabling predictions to be made separately for different groups and allowing for testing the hypothesis that the outcome differs between groups. The use of interaction terms to measure whether the effect of a particular predictor variable differs between groups is also explained. An alternative approach to testing the difference between groups of the effect of a given predictor, which consists of measuring the effect in each group separately and seeing whether the statistical significance differs between the groups, is shown to be misleading. © The Author 2013. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis

DOE PAGES

Simms, Laura E.; Engebretson, Mark J.; Pilipenko, Viacheslav; ...

2016-04-07

The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF Bz, Dst, and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the predictionmore » of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current ( Dst), AE, and wave activity.« less

Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis

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

Simms, Laura E.; Engebretson, Mark J.; Pilipenko, Viacheslav

The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF Bz, Dst, and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the predictionmore » of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current ( Dst), AE, and wave activity.« less

Psychological Predictors of Visual and Auditory P300 Brain-Computer Interface Performance

PubMed Central

Hammer, Eva M.; Halder, Sebastian; Kleih, Sonja C.; Kübler, Andrea

2018-01-01

Brain-Computer Interfaces (BCIs) provide communication channels independent from muscular control. In the current study we used two versions of the P300-BCI: one based on visual the other on auditory stimulation. Up to now, data on the impact of psychological variables on P300-BCI control are scarce. Hence, our goal was to identify new predictors with a comprehensive psychological test-battery. A total of N = 40 healthy BCI novices took part in a visual and an auditory BCI session. Psychological variables were measured with an electronic test-battery including clinical, personality, and performance tests. The personality factor “emotional stability” was negatively correlated (Spearman's rho = −0.416; p < 0.01) and an output variable of the non-verbal learning test (NVLT), which can be interpreted as ability to learn, correlated positively (Spearman's rho = 0.412; p < 0.01) with visual P300-BCI performance. In a linear regression analysis both independent variables explained 24% of the variance. “Emotional stability” was also negatively related to auditory P300-BCI performance (Spearman's rho = −0.377; p < 0.05), but failed significance in the regression analysis. Psychological parameters seem to play a moderate role in visual P300-BCI performance. “Emotional stability” was identified as a new predictor, indicating that BCI users who characterize themselves as calm and rational showed worse BCI performance. The positive relation of the ability to learn and BCI performance corroborates the notion that also for P300 based BCIs learning may constitute an important factor. Further studies are needed to consolidate or reject the presented predictors. PMID:29867319

Using Google Flu Trends data in forecasting influenza-like-illness related ED visits in Omaha, Nebraska.

PubMed

Araz, Ozgur M; Bentley, Dan; Muelleman, Robert L

2014-09-01

Emergency department (ED) visits increase during the influenza seasons. It is essential to identify statistically significant correlates in order to develop an accurate forecasting model for ED visits. Forecasting influenza-like-illness (ILI)-related ED visits can significantly help in developing robust resource management strategies at the EDs. We first performed correlation analyses to understand temporal correlations between several predictors of ILI-related ED visits. We used the data available for Douglas County, the biggest county in Nebraska, for Omaha, the biggest city in the state, and for a major hospital in Omaha. The data set included total and positive influenza test results from the hospital (ie, Antigen rapid (Ag) and Respiratory Syncytial Virus Infection (RSV) tests); an Internet-based influenza surveillance system data, that is, Google Flu Trends, for both Nebraska and Omaha; total ED visits in Douglas County attributable to ILI; and ILI surveillance network data for Douglas County and Nebraska as the predictors and data for the hospital's ILI-related ED visits as the dependent variable. We used Seasonal Autoregressive Integrated Moving Average and Holt Winters methods with3 linear regression models to forecast ILI-related ED visits at the hospital and evaluated model performances by comparing the root means square errors (RMSEs). Because of strong positive correlations with ILI-related ED visits between 2008 and 2012, we validated the use of Google Flu Trends data as a predictor in an ED influenza surveillance tool. Of the 5 forecasting models we have tested, linear regression models performed significantly better when Google Flu Trends data were included as a predictor. Regression models including Google Flu Trends data as a predictor variable have lower RMSE, and the lowest is achieved when all other variables are also included in the model in our forecasting experiments for the first 5 weeks of 2013 (with RMSE = 57.61). Google Flu Trends data statistically improve the performance of predicting ILI-related ED visits in Douglas County, and this result can be generalized to other communities. Timely and accurate estimates of ED volume during the influenza season, as well as during pandemic outbreaks, can help hospitals plan their ED resources accordingly and lower their costs by optimizing supplies and staffing and can improve service quality by decreasing ED wait times and overcrowding. Copyright © 2014 Elsevier Inc. All rights reserved.

[Predictors of mean blood glucose control and its variability in diabetic hospitalized patients].

PubMed

Sáenz-Abad, Daniel; Gimeno-Orna, José Antonio; Sierra-Bergua, Beatriz; Pérez-Calvo, Juan Ignacio

2015-01-01

This study was intended to assess the effectiveness and predictors factors of inpatient blood glucose control in diabetic patients admitted to medical departments. A retrospective, analytical cohort study was conducted on patients discharged from internal medicine with a diagnosis related to diabetes. Variables collected included demographic characteristics, clinical data and laboratory parameters related to blood glucose control (HbA1c, basal plasma glucose, point-of-care capillary glucose). The cumulative probability of receiving scheduled insulin regimens was evaluated using Kaplan-Meier analysis. Multivariate regression models were used to select predictors of mean inpatient glucose (MHG) and glucose variability (standard deviation [GV]). The study sample consisted of 228 patients (mean age 78.4 (SD 10.1) years, 51% women). Of these, 96 patients (42.1%) were treated with sliding-scale regular insulin only. Median time to start of scheduled insulin therapy was 4 (95% CI, 2-6) days. Blood glucose control measures were: MIG 181.4 (SD 41.7) mg/dL, GV 56.3 (SD 22.6). The best model to predict MIG (R(2): .376; P<.0001) included HbA1c (b=4.96; P=.011), baseline plasma glucose (b=.056; P=.084), mean capillary blood glucose in the first 24hours (b=.154; P<.0001), home treatment (versus oral agents) with basal insulin only (b=13.1; P=.016) or more complex (pre-mixed insulin or basal-bolus) regimens (b=19.1; P=.004), corticoid therapy (b=14.9; P=.002), and fasting on admission (b=10.4; P=.098). Predictors of inpatient blood glucose control which should be considered in the design of DM management protocols include home treatment, HbA1c, basal plasma glucose, mean blood glucose in the first 24hours, fasting, and corticoid therapy. Copyright © 2014 SEEN. Published by Elsevier España, S.L.U. All rights reserved.

Multiresponse semiparametric regression for modelling the effect of regional socio-economic variables on the use of information technology

NASA Astrophysics Data System (ADS)

Wibowo, Wahyu; Wene, Chatrien; Budiantara, I. Nyoman; Permatasari, Erma Oktania

2017-03-01

Multiresponse semiparametric regression is simultaneous equation regression model and fusion of parametric and nonparametric model. The regression model comprise several models and each model has two components, parametric and nonparametric. The used model has linear function as parametric and polynomial truncated spline as nonparametric component. The model can handle both linearity and nonlinearity relationship between response and the sets of predictor variables. The aim of this paper is to demonstrate the application of the regression model for modeling of effect of regional socio-economic on use of information technology. More specific, the response variables are percentage of households has access to internet and percentage of households has personal computer. Then, predictor variables are percentage of literacy people, percentage of electrification and percentage of economic growth. Based on identification of the relationship between response and predictor variable, economic growth is treated as nonparametric predictor and the others are parametric predictors. The result shows that the multiresponse semiparametric regression can be applied well as indicate by the high coefficient determination, 90 percent.

Early predictors of occupational back reinjury: results from a prospective study of workers in Washington State.

PubMed

Keeney, Benjamin J; Turner, Judith A; Fulton-Kehoe, Deborah; Wickizer, Thomas M; Chan, Kwun Chuen Gary; Franklin, Gary M

2013-01-15

Prospective population-based cohort study. To identify early predictors of self-reported occupational back reinjury within 1 year after work-related back injury. Back injuries are the costliest and most prevalent disabling occupational injuries in the United States. A substantial proportion of workers with back injuries have reinjuries after returning to work, yet there are few studies of risk factors for occupational back reinjuries. We aimed to identify the incidence and early (in the claim) predictors of self-reported back reinjury by approximately 1 year after the index injury among Washington State workers with new work disability claims for back injuries. The Washington Workers' Compensation Disability Risk Identification Study Cohort provided a large, population-based sample with information on variables in 7 domains: sociodemographic, employment-related, pain and function, clinical status, health care, health behavior, and psychological. We conducted telephone interviews with workers 3 weeks and 1 year after submission of a time-loss claim for the injury. We first identified predictors (P < 0.10) of self-reported reinjury within 1 year in bivariate analyses. Those variables were then included in a multivariate logistic regression model predicting occupational back reinjury. A total of 290 (25.8%) of 1123 (70.0% response rate) workers who completed the 1-year follow-up interview and had returned to work reported having reinjured their back at work. Baseline variables significantly associated with reinjury (P < 0.05) in the multivariate model included male sex, constant whole-body vibration at work, previous similar injury, 4 or more previous claims of any type, possessing health insurance, and high fear-avoidance scores. Baseline obesity was associated with reduced odds of reinjury. No other employment-related or psychological variables were significant. One-fourth of the workers who received work disability compensation for a back injury self-reported reinjury after returning to work. Baseline variables in multiple domains predicted occupational back reinjury. Increased knowledge of early risk factors for reinjury may help to lead to interventions, such as efforts to reduce fear avoidance and graded activity to promote recovery, effective in lowering the risk of reinjury.

Prediction of BP reactivity to talking using hybrid soft computing approaches.

PubMed

Kaur, Gurmanik; Arora, Ajat Shatru; Jain, Vijender Kumar

2014-01-01

High blood pressure (BP) is associated with an increased risk of cardiovascular diseases. Therefore, optimal precision in measurement of BP is appropriate in clinical and research studies. In this work, anthropometric characteristics including age, height, weight, body mass index (BMI), and arm circumference (AC) were used as independent predictor variables for the prediction of BP reactivity to talking. Principal component analysis (PCA) was fused with artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS), and least square-support vector machine (LS-SVM) model to remove the multicollinearity effect among anthropometric predictor variables. The statistical tests in terms of coefficient of determination (R (2)), root mean square error (RMSE), and mean absolute percentage error (MAPE) revealed that PCA based LS-SVM (PCA-LS-SVM) model produced a more efficient prediction of BP reactivity as compared to other models. This assessment presents the importance and advantages posed by PCA fused prediction models for prediction of biological variables.

Assessment of Communications-related Admissions Criteria in a Three-year Pharmacy Program

PubMed Central

Tejada, Frederick R.; Lang, Lynn A.; Purnell, Miriam; Acedera, Lisa; Ngonga, Ferdinand

2015-01-01

Objective. To determine if there is a correlation between TOEFL and other admissions criteria that assess communications skills (ie, PCAT variables: verbal, reading, essay, and composite), interview, and observational scores and to evaluate TOEFL and these admissions criteria as predictors of academic performance. Methods. Statistical analyses included two sample t tests, multiple regression and Pearson’s correlations for parametric variables, and Mann-Whitney U for nonparametric variables, which were conducted on the retrospective data of 162 students, 57 of whom were foreign-born. Results. The multiple regression model of the other admissions criteria on TOEFL was significant. There was no significant correlation between TOEFL scores and academic performance. However, significant correlations were found between the other admissions criteria and academic performance. Conclusion. Since TOEFL is not a significant predictor of either communication skills or academic success of foreign-born PharmD students in the program, it may be eliminated as an admissions criterion. PMID:26430273

Assessment of Communications-related Admissions Criteria in a Three-year Pharmacy Program.

PubMed

Parmar, Jayesh R; Tejada, Frederick R; Lang, Lynn A; Purnell, Miriam; Acedera, Lisa; Ngonga, Ferdinand

2015-08-25

To determine if there is a correlation between TOEFL and other admissions criteria that assess communications skills (ie, PCAT variables: verbal, reading, essay, and composite), interview, and observational scores and to evaluate TOEFL and these admissions criteria as predictors of academic performance. Statistical analyses included two sample t tests, multiple regression and Pearson's correlations for parametric variables, and Mann-Whitney U for nonparametric variables, which were conducted on the retrospective data of 162 students, 57 of whom were foreign-born. The multiple regression model of the other admissions criteria on TOEFL was significant. There was no significant correlation between TOEFL scores and academic performance. However, significant correlations were found between the other admissions criteria and academic performance. Since TOEFL is not a significant predictor of either communication skills or academic success of foreign-born PharmD students in the program, it may be eliminated as an admissions criterion.

The Contribution of Vegetation and Landscape Configuration for Predicting Environmental Change Impacts on Iberian Birds

PubMed Central

Triviño, Maria; Thuiller, Wilfried; Cabeza, Mar; Hickler, Thomas; Araújo, Miguel B.

2011-01-01

Although climate is known to be one of the key factors determining animal species distributions amongst others, projections of global change impacts on their distributions often rely on bioclimatic envelope models. Vegetation structure and landscape configuration are also key determinants of distributions, but they are rarely considered in such assessments. We explore the consequences of using simulated vegetation structure and composition as well as its associated landscape configuration in models projecting global change effects on Iberian bird species distributions. Both present-day and future distributions were modelled for 168 bird species using two ensemble forecasting methods: Random Forests (RF) and Boosted Regression Trees (BRT). For each species, several models were created, differing in the predictor variables used (climate, vegetation, and landscape configuration). Discrimination ability of each model in the present-day was then tested with four commonly used evaluation methods (AUC, TSS, specificity and sensitivity). The different sets of predictor variables yielded similar spatial patterns for well-modelled species, but the future projections diverged for poorly-modelled species. Models using all predictor variables were not significantly better than models fitted with climate variables alone for ca. 50% of the cases. Moreover, models fitted with climate data were always better than models fitted with landscape configuration variables, and vegetation variables were found to correlate with bird species distributions in 26–40% of the cases with BRT, and in 1–18% of the cases with RF. We conclude that improvements from including vegetation and its landscape configuration variables in comparison with climate only variables might not always be as great as expected for future projections of Iberian bird species. PMID:22216263

The unusual suspect: Land use is a key predictor of biodiversity patterns in the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Martins, Inês Santos; Proença, Vânia; Pereira, Henrique Miguel

2014-11-01

Although land use change is a key driver of biodiversity change, related variables such as habitat area and habitat heterogeneity are seldom considered in modeling approaches at larger extents. To address this knowledge gap we tested the contribution of land use related variables to models describing richness patterns of amphibians, reptiles and passerines in the Iberian Peninsula. We analyzed the relationship between species richness and habitat heterogeneity at two spatial resolutions (i.e., 10 km × 10 km and 50 km × 50 km). Using both ordinary least square and simultaneous autoregressive models, we assessed the relative importance of land use variables, climate variables and topographic variables. We also compare the species-area relationship with a multi-habitat model, the countryside species-area relationship, to assess the role of the area of different types of habitats on species diversity across scales. The association between habitat heterogeneity and species richness varied with the taxa and spatial resolution. A positive relationship was detected for all taxa at a grain size of 10 km × 10 km, but only passerines responded at a grain size of 50 km × 50 km. Species richness patterns were well described by abiotic predictors, but habitat predictors also explained a considerable portion of the variation. Moreover, species richness patterns were better described by a multi-habitat species-area model, incorporating land use variables, than by the classic power model, which only includes area as the single explanatory variable. Our results suggest that the role of land use in shaping species richness patterns goes beyond the local scale and persists at larger spatial scales. These findings call for the need of integrating land use variables in models designed to assess species richness response to large scale environmental changes.

Women, Physical Activity, and Quality of Life: Self-concept as a Mediator.

PubMed

Gonzalo Silvestre, Tamara; Ubillos Landa, Silvia

2016-02-22

The objectives of this research are: (a) analyze the incremental validity of physical activity's (PA) influence on perceived quality of life (PQL); (b) determine if PA's predictive power is mediated by self-concept; and (c) study if results vary according to a unidimensional or multidimensional approach to self-concept measurement. The sample comprised 160 women from Burgos, Spain aged 18 to 45 years old. Non-probability sampling was used. Two three-step hierarchical regression analyses were applied to forecast PQL. The hedonic quality-of-life indicators, self-concept, self-esteem, and PA were included as independent variables. The first regression analysis included global self-concept as predictor variable, while the second included its five dimensions. Two mediation analyses were conducted to see if PA's ability to predict PQL was mediated by global and physical self-concept. Results from the first regression shows that self-concept, satisfaction with life, and PA were significant predictors. PA slightly but significantly increased explained variance in PQL (2.1%). In the second regression, substituting global self-concept with its five constituent factors, only the physical dimension and satisfaction with life predicted PQL, while PA ceased to be a significant predictor. Mediation analysis revealed that only physical self-concept mediates the relationship between PA and PQL (z = 1.97, p < .050), and not global self-concept. Physical self-concept was the strongest predictor and approximately 32.45 % of PA's effect on PQL was mediated by it. This study's findings support a multidimensional view of self-concept, and represent a more accurate image of the relationship between PQL, PA, and self-concept.

Predictors of HIV/AIDS Programming in African American Churches: Implications for Prevention, Testing and Care

PubMed Central

Stewart, Jennifer M.; Hanlon, Alexandra; Brawner, Bridgette M.

2017-01-01

Using data from the National Congregational Study, we examined predictors of having a HIV/AIDS program in predominately African American churches across the United States. We conducted regression analyses of Wave II data (N = 1,506) isolating the sample to churches with a predominately African American membership. The dependent variable asked whether or not the congregation currently had any program focused on HIV or AIDS. Independent variables included several variables from the individual, organizational, and social levels. Our study revealed that region, clergy age, congregant disclosure of HIV-positive status, permitting cohabiting couples to be members, sponsorship or participation in programs targeted to physical health issues and having a designated person or committee to address health-focused programs significantly increased the likelihood of African American churches having a HIV/AIDS program. A paucity of nationally representative research focuses on the social, organizational and individual level predictors of having HIV/AIDS programs in African American churches. Determining the characteristics of churches with HIV/AIDS programming at multiple levels is a critical and necessary approach with significant implications for partnering with African American churches in HIV initiatives. PMID:27540035

Seasonality in trauma admissions - Are daylight and weather variables better predictors than general cyclic effects?

PubMed

Røislien, Jo; Søvik, Signe; Eken, Torsten

2018-01-01

Trauma is a leading global cause of death, and predicting the burden of trauma admissions is vital for good planning of trauma care. Seasonality in trauma admissions has been found in several studies. Seasonal fluctuations in daylight hours, temperature and weather affect social and cultural practices but also individual neuroendocrine rhythms that may ultimately modify behaviour and potentially predispose to trauma. The aim of the present study was to explore to what extent the observed seasonality in daily trauma admissions could be explained by changes in daylight and weather variables throughout the year. Retrospective registry study on trauma admissions in the 10-year period 2001-2010 at Oslo University Hospital, Ullevål, Norway, where the amount of daylight varies from less than 6 hours to almost 19 hours per day throughout the year. Daily number of admissions was analysed by fitting non-linear Poisson time series regression models, simultaneously adjusting for several layers of temporal patterns, including a non-linear long-term trend and both seasonal and weekly cyclic effects. Five daylight and weather variables were explored, including hours of daylight and amount of precipitation. Models were compared using Akaike's Information Criterion (AIC). A regression model including daylight and weather variables significantly outperformed a traditional seasonality model in terms of AIC. A cyclic week effect was significant in all models. Daylight and weather variables are better predictors of seasonality in daily trauma admissions than mere information on day-of-year.

Gender Differences in Cognitive and Noncognitive Factors Related to Achievement in Organic Chemistry

NASA Astrophysics Data System (ADS)

Turner, Ronna C.; Lindsay, Harriet A.

2003-05-01

For many college students in the sciences, organic chemistry poses a difficult challenge. Indeed, success in organic chemistry has proven pivotal in the careers of a vast number of students in a variety of science disciplines. A better understanding of the factors that contribute to achievement in this course should contribute to efforts to increase the number of students in the science disciplines. Further, an awareness of gender differences in factors associated with achievement should aid efforts to bolster the participation of women in chemistry and related disciplines. Using a correlation research design, the individual relationships between organic chemistry achievement and each of several cognitive variables and noncognitive variables were assessed. In addition, the relationships between organic chemistry achievement and combinations of these independent variables were explored. Finally, gender- and instructor-related differences in the relationships between organic chemistry achievement and the independent variables were investigated. Cognitive variables included the second-semester general chemistry grade, the ACT English, math, reading, and science-reasoning scores, and scores from a spatial visualization test. Noncognitive variables included anxiety, confidence, effectance motivation, and usefulness. The second-semester general chemistry grade was found to be the best indicator of performance in organic chemistry, while the effectiveness of other predictors varied between instructors. In addition, gender differences were found in the explanations of organic chemistry achievement variance provided by this study. In general, males exhibited stronger correlations between predictor variables and organic chemistry achievement than females.

The Dynamic Risk Assessment and Management System: An Assessment of Immediate Risk of Violence for Individuals with Offending and Challenging Behaviour

ERIC Educational Resources Information Center

Lindsay, William R.; Murphy, Lesley; Smith, Gordon; Murphy, Daniel; Edwards, Zoe; Chittock, Chris; Grieve, Alan; Young, Steven J.

2004-01-01

Purpose: Research on dynamic risk assessment has developed over the last 10 years and a number of variables have emerged as being possible predictors of future sexual and violent offences. These variables include hostile attitude/anger and compliance with routine. In 2002, Thornton ("Sexual Abuse: A Journal of Research & Treatment" 14, 139)…

History of Not Completing Courses as Predictor of Academic Difficulty among First-Year Students.

ERIC Educational Resources Information Center

Jackson, Evelyn W.; Dawson-Saunders, Beth

1987-01-01

A study found that variables significant in predicting minority students with academic difficulty include science grade-point average, Medical College Admission Test (MCAT) reading subtest score, and number of course withdrawals. For majority students, they include MCAT biology subtest score and number of incompletes taken in courses. (MSE)

Perceived Social Support and Assertiveness as a Predictor of Candidates Psychological Counselors' Psychological Well-Being

ERIC Educational Resources Information Center

Ates, Bünyamin

2016-01-01

In this research, to what extent the variables of perceived social support (family, friends and special people) and assertiveness predicted the psychological well-being levels of candidate psychological counselors. The research group of this study included totally randomly selected 308 candidate psychological counselors including 174 females…

Developmental precursors of young school-age children's hostile attribution bias.

PubMed

Choe, Daniel Ewon; Lane, Jonathan D; Grabell, Adam S; Olson, Sheryl L

2013-12-01

This prospective longitudinal study provides evidence of preschool-age precursors of hostile attribution bias in young school-age children, a topic that has received little empirical attention. We examined multiple risk domains, including laboratory and observational assessments of children's social-cognition, general cognitive functioning, effortful control, and peer aggression. Preschoolers (N = 231) with a more advanced theory-of-mind, better emotion understanding, and higher IQ made fewer hostile attributions of intent in the early school years. Further exploration of these significant predictors revealed that only certain components of these capacities (i.e., nonstereotypical emotion understanding, false-belief explanation, and verbal IQ) were robust predictors of a hostile attribution bias in young school-age children and were especially strong predictors among children with more advanced effortful control. These relations were prospective in nature-the effects of preschool variables persisted after accounting for similar variables at school age. We conclude by discussing the implications of our findings for future research and prevention. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Prediction of half-marathon race time in recreational female and male runners.

PubMed

Knechtle, Beat; Barandun, Ursula; Knechtle, Patrizia; Zingg, Matthias A; Rosemann, Thomas; Rüst, Christoph A

2014-01-01

Half-marathon running is of high popularity. Recent studies tried to find predictor variables for half-marathon race time for recreational female and male runners and to present equations to predict race time. The actual equations included running speed during training for both women and men as training variable but midaxillary skinfold for women and body mass index for men as anthropometric variable. An actual study found that percent body fat and running speed during training sessions were the best predictor variables for half-marathon race times in both women and men. The aim of the present study was to improve the existing equations to predict half-marathon race time in a larger sample of male and female half-marathoners by using percent body fat and running speed during training sessions as predictor variables. In a sample of 147 men and 83 women, multiple linear regression analysis including percent body fat and running speed during training units as independent variables and race time as dependent variable were performed and an equation was evolved to predict half-marathon race time. For men, half-marathon race time might be predicted by the equation (r(2) = 0.42, adjusted r(2) = 0.41, SE = 13.3) half-marathon race time (min) = 142.7 + 1.158 × percent body fat (%) - 5.223 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.71, p < 0.0001) to the achieved race time. For women, half-marathon race time might be predicted by the equation (r(2) = 0.68, adjusted r(2) = 0.68, SE = 9.8) race time (min) = 168.7 + 1.077 × percent body fat (%) - 7.556 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.89, p < 0.0001) to the achieved race time. The coefficients of determination of the models were slightly higher than for the existing equations. Future studies might include physiological variables to increase the coefficients of determination of the models.

A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA

USGS Publications Warehouse

Ransom, Katherine M.; Nolan, Bernard T.; Traum, Jonathan A.; Faunt, Claudia; Bell, Andrew M.; Gronberg, Jo Ann M.; Wheeler, David C.; Zamora, Celia; Jurgens, Bryant; Schwarz, Gregory E.; Belitz, Kenneth; Eberts, Sandra; Kourakos, George; Harter, Thomas

2017-01-01

Intense demand for water in the Central Valley of California and related increases in groundwater nitrate concentration threaten the sustainability of the groundwater resource. To assess contamination risk in the region, we developed a hybrid, non-linear, machine learning model within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface. A database of 145 predictor variables representing well characteristics, historical and current field and landscape-scale nitrogen mass balances, historical and current land use, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The boosted regression tree (BRT) method was used to screen and rank variables to predict nitrate concentration at the depths of domestic and public well supplies. The novel approach included as predictor variables outputs from existing physically based models of the Central Valley. The top five most important predictor variables included two oxidation/reduction variables (probability of manganese concentration to exceed 50 ppb and probability of dissolved oxygen concentration to be below 0.5 ppm), field-scale adjusted unsaturated zone nitrogen input for the 1975 time period, average difference between precipitation and evapotranspiration during the years 1971–2000, and 1992 total landscape nitrogen input. Twenty-five variables were selected for the final model for log-transformed nitrate. In general, increasing probability of anoxic conditions and increasing precipitation relative to potential evapotranspiration had a corresponding decrease in nitrate concentration predictions. Conversely, increasing 1975 unsaturated zone nitrogen leaching flux and 1992 total landscape nitrogen input had an increasing relative impact on nitrate predictions. Three-dimensional visualization indicates that nitrate predictions depend on the probability of anoxic conditions and other factors, and that nitrate predictions generally decreased with increasing groundwater age.

Oral Language, Sex and Socio-Economic Status as Predictors of Reading Achievement.

ERIC Educational Resources Information Center

Ebert, Dorothy Jo Williamson

This study was designed to discover the degree of relationship between a number of predictor variables and reading achievement for 65 black second grade students in two Austin, Texas, schools. The seven predictor variables used were: oral language performance as measured by the Gloria and David Beginning English, Series 20, Test 6 (GDBE); an…

Subacute casemix classification for stroke rehabilitation in Australia. How well does AN-SNAP v2 explain variance in outcomes?

PubMed

Kohler, Friedbert; Renton, Roger; Dickson, Hugh G; Estell, John; Connolly, Carol E

2011-02-01

We sought the best predictors for length of stay, discharge destination and functional improvement for inpatients undergoing rehabilitation following a stroke and compared these predictors against AN-SNAP v2. The Oxfordshire classification subgroup, sociodemographic data and functional data were collected for patients admitted between 1997 and 2007, with a diagnosis of recent stroke. The data were factor analysed using Principal Components Analysis for categorical data (CATPCA). Categorical regression analyses was performed to determine the best predictors of length of stay, discharge destination, and functional improvement. A total of 1154 patients were included in the study. Principal components analysis indicated that the data were effectively unidimensional, with length of stay being the most important component. Regression analysis demonstrated that the best predictor was the admission motor FIM score, explaining 38.9% of variance for length of stay, 37.4%.of variance for functional improvement and 16% of variance for discharge destination. The best explanatory variable in our inpatient rehabilitation service is the admission motor FIM. AN- SNAP v2 classification is a less effective explanatory variable. This needs to be taken into account when using AN-SNAP v2 classification for clinical or funding purposes.

Stability of Predictors of Mortality after Spinal Cord Injury

PubMed Central

Krause, James S.; Saunders, Lee L.; Zhai, Yusheng

2011-01-01

Objective To identify the stability of socio-environmental, behavioral, and health predictors of mortality over an eight year time frame. Study Design Cohort study. Setting Data were analyzed at a large medical university in the Southeast United States of America (USA). Methods Adults with residual impairment from a spinal cord injury (SCI) who were at least one year post-injury at assessment were recruited through a large specialty hospital in the Southeast USA. 1209 participants were included in the final analysis. A piecewise exponential model with 2 equal time intervals (eight years total) was used to assess the stability of the hazard and the predictors over time. Results The hazard did significantly change over time, where the hazard in the first time interval was significantly lower than the second. There were no interactions between the socio-environmental, behavior, or health factors and time, although there was a significant interaction between age at injury (a demographic variable) and time. Conclusion These results suggest there is stability in the association between the predictors and mortality, even over an eight year time period. Results reinforce the use of historic variables for prediction of mortality in persons with SCI. PMID:22231541

Adherence predictors in an Internet-based Intervention program for depression.

PubMed

Castro, Adoración; López-Del-Hoyo, Yolanda; Peake, Christian; Mayoral, Fermín; Botella, Cristina; García-Campayo, Javier; Baños, Rosa María; Nogueira-Arjona, Raquel; Roca, Miquel; Gili, Margalida

2018-05-01

Internet-delivered psychotherapy has been demonstrated to be effective in the treatment of depression. Nevertheless, the study of the adherence in this type of the treatment reported divergent results. The main objective of this study is to analyze predictors of adherence in a primary care Internet-based intervention for depression in Spain. A multi-center, three arm, parallel, randomized controlled trial was conducted with 194 depressive patients, who were allocated in self-guided or supported-guided intervention. Sociodemographic and clinical characteristics were gathered using a case report form. The Mini international neuropsychiatric interview diagnoses major depression. Beck Depression Inventory was used to assess depression severity. The visual analogic scale assesses the respondent's self-rated health and Short Form Health Survey was used to measure the health-related quality of life. Age results a predictor variable for both intervention groups (with and without therapist support). Perceived health is a negative predictor of adherence for the self-guided intervention when change in depression severity was included in the model. Change in depression severity results a predictor of adherence in the support-guided intervention. Our findings demonstrate that in our sample, there are differences in sociodemographic and clinical variables between active and dropout participants and we provide adherence predictors in each intervention condition of this Internet-based program for depression (self-guided and support-guided). It is important to point that further research in this area is essential to improve tailored interventions and to know specific patients groups can benefit from these interventions.

Cognitive and attitudinal predictors related to graphing achievement among pre-service elementary teachers

NASA Astrophysics Data System (ADS)

Szyjka, Sebastian P.

The purpose of this study was to determine the extent to which six cognitive and attitudinal variables predicted pre-service elementary teachers' performance on line graphing. Predictors included Illinois teacher education basic skills sub-component scores in reading comprehension and mathematics, logical thinking performance scores, as well as measures of attitudes toward science, mathematics and graphing. This study also determined the strength of the relationship between each prospective predictor variable and the line graphing performance variable, as well as the extent to which measures of attitude towards science, mathematics and graphing mediated relationships between scores on mathematics, reading, logical thinking and line graphing. Ninety-four pre-service elementary education teachers enrolled in two different elementary science methods courses during the spring 2009 semester at Southern Illinois University Carbondale participated in this study. Each subject completed five different instruments designed to assess science, mathematics and graphing attitudes as well as logical thinking and graphing ability. Sixty subjects provided copies of primary basic skills score reports that listed subset scores for both reading comprehension and mathematics. The remaining scores were supplied by a faculty member who had access to a database from which the scores were drawn. Seven subjects, whose scores could not be found, were eliminated from final data analysis. Confirmatory factor analysis (CFA) was conducted in order to establish validity and reliability of the Questionnaire of Attitude Toward Line Graphs in Science (QALGS) instrument. CFA tested the statistical hypothesis that the five main factor structures within the Questionnaire of Attitude Toward Statistical Graphs (QASG) would be maintained in the revised QALGS. Stepwise Regression Analysis with backward elimination was conducted in order to generate a parsimonious and precise predictive model. This procedure allowed the researcher to explore the relationships among the affective and cognitive variables that were included in the regression analysis. The results for CFA indicated that the revised QALGS measure was sound in its psychometric properties when tested against the QASG. Reliability statistics indicated that the overall reliability for the 32 items in the QALGS was .90. The learning preferences construct had the lowest reliability (.67), while enjoyment (.89), confidence (.86) and usefulness (.77) constructs had moderate to high reliabilities. The first four measurement models fit the data well as indicated by the appropriate descriptive and statistical indices. However, the fifth measurement model did not fit the data well statistically, and only fit well with two descriptive indices. The results addressing the research question indicated that mathematical and logical thinking ability were significant predictors of line graph performance among the remaining group of variables. These predictors accounted for 41% of the total variability on the line graph performance variable. Partial correlation coefficients indicated that mathematics ability accounted for 20.5% of the variance on the line graphing performance variable when removing the effect of logical thinking. The logical thinking variable accounted for 4.7% of the variance on the line graphing performance variable when removing the effect of mathematics ability.

CORRELATION PURSUIT: FORWARD STEPWISE VARIABLE SELECTION FOR INDEX MODELS

PubMed Central

Zhong, Wenxuan; Zhang, Tingting; Zhu, Yu; Liu, Jun S.

2012-01-01

In this article, a stepwise procedure, correlation pursuit (COP), is developed for variable selection under the sufficient dimension reduction framework, in which the response variable Y is influenced by the predictors X1, X2, …, Xp through an unknown function of a few linear combinations of them. Unlike linear stepwise regression, COP does not impose a special form of relationship (such as linear) between the response variable and the predictor variables. The COP procedure selects variables that attain the maximum correlation between the transformed response and the linear combination of the variables. Various asymptotic properties of the COP procedure are established, and in particular, its variable selection performance under diverging number of predictors and sample size has been investigated. The excellent empirical performance of the COP procedure in comparison with existing methods are demonstrated by both extensive simulation studies and a real example in functional genomics. PMID:23243388

Sick-leave track record and other potential predictors of a disability pension. A population based study of 8,218 men and women followed for 16 years

PubMed Central

Wallman, Thorne; Wedel, Hans; Palmer, Edward; Rosengren, Annika; Johansson, Saga; Eriksson, Henry; Svärdsudd, Kurt

2009-01-01

Background A number of previous studies have investigated various predictors for being granted a disability pension. The aim of this study was to test the efficacy of sick-leave track record as a predictor of being granted a disability pension in a large dataset based on subjects sampled from the general population and followed for a long time. Methods Data from five ongoing population-based Swedish studies was used, supplemented with data on all compensated sick leave periods, disability pensions granted, and vital status, obtained from official registers. The data set included 8,218 men and women followed for 16 years, generated 109,369 person years of observation and 97,160 sickness spells. Various measures of days of sick leave during follow up were used as independent variables and disability pension grant was used as outcome. Results There was a strong relationship between individual sickness spell duration and annual cumulative days of sick leave on the one hand and being granted a disability pension on the other, among both men and women, after adjustment for the effects of marital status, education, household size, smoking habits, geographical area and calendar time period, a proxy for position in the business cycle. The interval between sickness spells showed a corresponding inverse relationship. Of all the variables studied, the number of days of sick leave per year was the most powerful predictor of a disability pension. For both men and women 245 annual sick leave days were needed to reach a 50% probability of transition to disability. The independent variables, taken together, explained 96% of the variation in disability pension grantings. Conclusion The sick-leave track record was the most important predictor of the probability of being granted a disability pension in this study, even when the influences of other variables affecting the outcome were taken into account. PMID:19368715

Predictability of Bristol Bay, Alaska, sockeye salmon returns one to four years in the future

USGS Publications Warehouse

Adkison, Milo D.; Peterson, R.M.

2000-01-01

Historically, forecast error for returns of sockeye salmon Oncorhynchus nerka to Bristol Bay, Alaska, has been large. Using cross-validation forecast error as our criterion, we selected forecast models for each of the nine principal Bristol Bay drainages. Competing forecast models included stock-recruitment relationships, environmental variables, prior returns of siblings, or combinations of these predictors. For most stocks, we found prior returns of siblings to be the best single predictor of returns; however, forecast accuracy was low even when multiple predictors were considered. For a typical drainage, an 80% confidence interval ranged from one half to double the point forecast. These confidence intervals appeared to be appropriately wide.

Predictors of emotional problems and physical aggression among children of Hong Kong Chinese, Mainland Chinese and Filipino immigrants to Canada.

PubMed

Beiser, Morton; Hamilton, Hayley; Rummens, Joanna Anneke; Oxman-Martinez, Jacqueline; Ogilvie, Linda; Humphrey, Chuck; Armstrong, Robert

2010-10-01

Data from the New Canadian Children and Youth Study (NCCYS), a national study of immigrant children and youth in Canada, are used to examine the mental health salience of putatively universal determinants, as well as of immigration-specific factors. Universal factors (UF) include age, gender, family and neighbourhood characteristics. Migration-specific (MS) factors include ethnic background, acculturative stress, prejudice, and the impact of region of resettlement within Canada. In a sample of children from Hong Kong, the Philippines and Mainland China, the study examined the determinants of emotional problems (EP), and physical aggression (PA). A two-step regression analysis entered UF on step 1, and MS variables on step 2. Universal factors accounted for 12.1% of EP variance. Addition of MS variables increased explained variance to 15.6%. Significant UF predictors: parental depression, family dysfunction, and parent's education. Significant MS variables: country of origin, region of resettlement, resettlement stress, prejudice, and limited linguistic fluency. UF accounted for 6.3% of variance in PA scores. Adding migration-specific variables increased variance explained to 9.1%. UF: age, gender, parent's depression, family dysfunction. MS: country of origin, region of resettlement, resettlement stress, and parent's perception of prejudice. Net of the effect of factors affecting the mental health of most, if not all children, migration-specific variables contribute to understanding immigrant children's mental health.

Predictors of health of pre-registration nursing and midwifery students: Findings from a cross-sectional survey.

PubMed

Deasy, Christine; Coughlan, Barry; Pironom, Julie; Jourdan, Didier; Mannix-McNamara, Patricia

2016-01-01

Student nurses/midwives evidence less than exemplary lifestyle habits and poor emotional health, despite exposure to health education/promotion during their educational preparation. Knowledge of the factors that predict nursing/midwifery students' health could inform strategies to enhance their health and increase their credibility as future health promoters/educators. To establish the predictors of nursing/midwifery student emotional health. Cross-sectional survey. The research took place at a university in Ireland. We involved a total sample (n=473) student nurses/midwives. Participants completed the General Health Questionnaire, Lifestyle Behaviour Questionnaire and Ways of Coping Questionnaire to determine their self-reported emotional health, lifestyle behaviour and coping processes. Multivariate regression was performed to identify the predictors of student emotional health (dependent variable). The independent variables were demographics, coping, lifestyle behaviour and students' perceptions of determinants of their health. Many respondents reported significant emotional distress (48.71%) and unhealthy lifestyle behaviours including smoking (27.94%), physical inactivity (34.29%), alcohol consumption (91.7%) and unhealthy diet (28.05%). Multivariate regressions indicated that the predictors of emotional distress included gender, year of study, smoking, passive coping and beliefs that their student life was stressful or/and that worry stress and boredom adversely impacted their diet. Targeting student's beliefs regarding influences upon their health, promotion of positive lifestyles and adaptive coping is necessary to facilitate health gain of future health professionals. Copyright © 2015 Elsevier Ltd. All rights reserved.

Older age, higher perceived disability and depressive symptoms predict the amount and severity of work-related difficulties in persons with multiple sclerosis.

PubMed

Raggi, Alberto; Giovannetti, Ambra Mara; Schiavolin, Silvia; Brambilla, Laura; Brenna, Greta; Confalonieri, Paolo Agostino; Cortese, Francesca; Frangiamore, Rita; Leonardi, Matilde; Mantegazza, Renato Emilio; Moscatelli, Marco; Ponzio, Michela; Torri Clerici, Valentina; Zaratin, Paola; De Torres, Laura

2018-04-16

This cross-sectional study aims to identify the predictors of work-related difficulties in a sample of employed persons with multiple sclerosis as addressed with the Multiple Sclerosis Questionnaire for Job Difficulties. Hierarchical linear regression analysis was conducted to identify predictors of work difficulties: predictors included demographic variables (age, formal education), disease duration and severity, perceived disability and psychological variables (cognitive dysfunction, depression and anxiety). The targets were the questionnaire's overall score and its six subscales. A total of 177 participants (108 females, aged 21-63) were recruited. Age, perceived disability and depression were direct and significant predictors of the questionnaire total score, and the final model explained 43.7% of its variation. The models built on the questionnaire's subscales show that perceived disability and depression were direct and significant predictors of most of its subscales. Our results show that, among patients with multiple sclerosis, those who were older, with higher perceived disability and higher depression symptoms have more and more severe work-related difficulties. The Multiple Sclerosis Questionnaire for Job Difficulties can be fruitfully exploited to plan tailored actions to limit the likelihood of near-future job loss in persons of working age with multiple sclerosis. Implications for rehabilitation Difficulties with work are common among people with multiple sclerosis and are usually addressed in terms of unemployment or job loss. The Multiple Sclerosis Questionnaire for Job Difficulties is a disease-specific questionnaire developed to address the amount and severity of work-related difficulties. We found that work-related difficulties were associated to older age, higher perceived disability and depressive symptoms. Mental health issues and perceived disability should be consistently included in future research targeting work-related difficulties.

Prevalence and predictors of healthcare utilization among older people (60+): focusing on ADL dependency and risk of depression.

PubMed

Sandberg, Magnus; Kristensson, Jimmie; Midlöv, Patrik; Fagerström, Cecilia; Jakobsson, Ulf

2012-01-01

The aim of this study was to investigate healthcare utilization patterns over a six-year period among older people (60+), classified as dependent/independent in Activities of Daily Living (ADL) and/or at/not at risk of depression and to identify healthcare utilization predictors. A sample (n=1402) comprising ten age cohorts aged between 60 and 96 years was drawn from the Swedish National study on Aging and Care (SNAC). Baseline data were collected between 2001 and 2003. Number and length of hospital stays were collected for six years after baseline year. Group differences and mean changes over time were investigated. Healthcare utilization predictors were explored using multiple linear regression analysis. The results revealed that 21-24% had at least one hospital stay in the six years after baseline, 29-37% among ADL dependent subjects and 24-33% among those at risk of depression. There was a significant increase of hospital stays in all groups over time. ADL-dependent subjects and those at risk of depression had significant more hospital stays, except for those at/not at risk of depression in years 2, 4 and 5. The healthcare utilization predictors 5-6 years after baseline were mainly age, previous healthcare utilization and various symptoms and, in 1-2 and 3-4 years after baseline, age, various diagnostic groups and various physical variables. Thus healthcare utilization patterns seem to be similar for the different groups, but it is difficult to find universal predictors. This suggests that different variables should be considered, including both ADL and psychosocial variables, when trying to identify future healthcare users. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Predictors of Using a Microbicide-like Product Among Adolescent Girls

PubMed Central

Short, Mary B.; Succop, Paul A.; Ugueto, Ana M.; Rosenthal, Susan L.

2007-01-01

Purpose This study examined demographic, sexual history and weekly contextual variables, and perceptions about microbicides as predictors of microbicide-like product use. Methods Adolescent girls (N=208; 14-21 years) participated in a 6-month study in which they completed three face-to-face interviews and 24-weekly phone call interviews. Participants were given microbicide-like products (vaginal lubricants) and encouraged to use them with condoms when they had intercourse. Results Seventy-five percent of girls had a sexual opportunity to use the product. Using multi-variable logistic regression, the following variables independently predicted ever using the product: length of sexual experience, number of lifetime vaginal partners, and the Comparison to Condoms subscale on the Perceptions of Microbicides Scale. Using mixed model repeat measure linear regression, the following variables independently predicted frequency of use: week of the study, age, condom frequency prior to the study, and 3 subscales on the Perceptions of Microbicide Scale including the Comparison to Condoms subscale, the Negative Effects subscale, and the Pleasure subscale. Conclusion Most girls used the product, including those who were not protecting themselves with condoms. Girls’ initial perceptions regarding the product predicted initial use and frequency of use. Further research should evaluate the best methods for supporting the use of these products by young or sexually less experienced girls. PMID:17875461

Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills.

PubMed

Brandstätter, Christian; Laner, David; Prantl, Roman; Fellner, Johann

2014-12-01

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed. This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables. The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills. Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies. Copyright © 2014 Elsevier Ltd. All rights reserved.

Social Phobia as a Predictor of Social Competence Perceived by Teenagers

ERIC Educational Resources Information Center

Ates, Bünyamin

2016-01-01

In this research, it was analyzed to what extent the variables of social avoidance, concern for being criticized and sense of individual worthlessness as sub-dimensions of social phobia predicted the perceived social competence levels of teenagers. The study group of this study included totally 648 students including 301 (46.5%) female and 347…

Ethylglucuronide in hair is a top predictor of impaired driving recidivism, alcohol dependence, and a key marker of the highest BAC interlock tests.

PubMed

Marques, Paul R; Tippetts, A Scott; Yegles, Michel

2014-01-01

This study focuses on the predictive and comparative significance of ethyl glucuronide measured in head hair (hEtG) for estimating risks associated with alcohol-impaired driving offenders. Earlier work compared different alcohol biomarkers for estimating rates of failed blood alcohol concentration (BAC) tests logged during 8 months of interlock participation. These analyses evaluate the comparative performance of several alcohol markers including hEtG and other markers, past driver records, and psychometric assessment predictors for the detection of 4 criteria: new driving under the influence (DUI) recidivism, alcohol dependence, and interlock record variables including fail rates and maximal interlock BACs logged. Drivers charged with alcohol impairment (DUI) in Alberta, Canada (n = 534; 64% first offenders, 36% multiple offenders) installed ignition interlock devices and consented to participate in research to evaluate blood-, hair-, and urine-derived alcohol biomarkers; sit for interviews; take psychometric assessments; and permit analyses of driving records and interlock log files. Subject variables included demographics, alcohol dependence at program entry, preprogram prior DUI convictions, postenrollment new DUI convictions, self-reported drinking assessments, morning and overall rates of failed interlock BAC tests, and maximal interlock BAC readings. Recidivism, dependence, high BAC, and combined fail rates were set as criteria; other variables were set as predictors. Area under the receiver operating characteristics (ROC) curve (A') estimates of sensitivity and specificity were calculated. Additional analyses were conducted on baseline hEtG levels. Driver performance and drinking indicators were evaluated against the standard hEtG cutoff for excessive drinking at (30 pg/mg) and a higher criterion of 50 pg/mg. HEtG splits were evaluated with the Mann-Whitney rank statistic. HEtG emerged as a top overall predictor for discriminating new recidivism events that occur after interlock installation, for entry alcohol dependence, and for the highest interlock BACs recorded. Together, hEtG and phosphatidylethanol (PEth) were the top predictors of all criterion measures. By contrast, the hair-derived alcohol biomarkers hEtG and hFAEE (fatty acid ethyl esters) were poorer than other alcohol biomarkers as detectors of interlock BAC test fail rates. This study showed that hEtG, an objective alternative to often unreliable self-reported past representation of drinking levels, yields crucial insight into driver alcohol-related risks early in an interlock program and is a top predictor of new recidivist events. Together with PEth, these markers would be excellent anchors in a panel for detecting alcohol consumption.

HIV-related stigma in pregnancy and early postpartum of mothers living with HIV in Ontario, Canada.

PubMed

Ion, Allyson; Wagner, Anne C; Greene, Saara; Loutfy, Mona R

2017-02-01

HIV-related stigma is associated with many psychological challenges; however, minimal research has explored how perceived HIV-related stigma intersects with psychosocial issues that mothers living with HIV may experience including depression, perceived stress and social isolation. The present study aims to describe the correlates and predictors of HIV-related stigma in a cohort of women living with HIV (WLWH) from across Ontario, Canada during pregnancy and early postpartum. From March 2011 to December 2012, WLWH ≥ 18 years (n = 77) completed a study instrument measuring independent variables including sociodemographic characteristics, perceived stress, depression symptoms, social isolation, social support and perceived racism in the third trimester and 3, 6 and 12 months postpartum. Multivariable linear regression was employed to explore the relationship between HIV-related stigma and multiple independent variables. HIV-related stigma generally increased from pregnancy to postpartum; however, there were no significant differences in HIV-related stigma across all study time points. In multivariable regression, depression symptoms and perceived racism were significant predictors of overall HIV-related stigma from pregnancy to postpartum. The present analysis contributes to our understanding of HIV-related stigma throughout the pregnancy-motherhood trajectory for WLWH including the interactional relationship between HIV-related stigma and other psychosocial variables, most notably, depression and racism.

The Impact of Individual Differences on E-Learning System Behavioral Intention

NASA Astrophysics Data System (ADS)

Liao, Peiwen; Yu, Chien; Yi, Chincheh

This study investigated the impact of contingent variables on the relationship between four predictors and employees' behavioral intention with e-learning. Seven hundred and twenty-two employees in online training and education were asked to answer questionnaires about their learning styles, perceptions of the quality of the proposed predictors and behavioral intention with e-learning systems. The results of analysis showed that three contingent variables, gender, job title and industry, significantly influenced the perceptions of predictors and employees' behavioral intention with the e-learning system. This study also found a statistically significant moderating effect of two contingent variables, gender, job title and industry, on the relationship between predictors and e-learning system behavioral intention. The results suggest that a serious consideration of contingent variables is crucial for improving e-learning system behavioral intention. The implications of these results for the management of e-learning systems are discussed.

Marital status as a predictor of survival in patients with human papilloma virus-positive oropharyngeal cancer.

PubMed

Rubin, Samuel J; Kirke, Diana N; Ezzat, Waleed H; Truong, Minh T; Salama, Andrew R; Jalisi, Scharukh

Determine whether marital status is a significant predictor of survival in human papillomavirus-positive oropharyngeal cancer. A single center retrospective study included patients diagnosed with human papilloma virus-positive oropharyngeal cancer at Boston Medical Center between January 1, 2010 and December 30, 2015, and initiated treatment with curative intent at Boston Medical Center. Demographic data and tumor-related variables were recorded. Univariate analysis was performed using a two-sample t-test, chi-squared test, Fisher's exact test, and Kaplan Meier curves with a log rank test. Multivariate survival analysis was performed using a Cox regression model. A total of 65 patients were included in the study with 24 patients described as married and 41 patients described as single. There was no significant difference in most demographic variables or tumor related variables between the two study groups, except single patients were significantly more likely to have government insurance (p=0.0431). Furthermore, there was no significant difference in 3-year overall survival between married patients and single patients (married=91.67% vs single=87.80%; p=0.6532) or 3-year progression free survival (married=79.17% vs single=85.37%; p=0.8136). After adjusting for confounders including age, sex, race, insurance type, smoking status, treatment, and AJCC combined pathologic stage, marital status was not a significant predictor of survival [HR=0.903; 95% CI (0.126,6.489); p=0.9192]. Although previous literature has demonstrated that married patients with head and neck cancer have a survival benefit compared to single patients with head and neck cancer, we were unable to demonstrate the same survival benefit in a cohort of patients with human papilloma virus-positive oropharyngeal cancer. Copyright © 2017 Elsevier Inc. All rights reserved.

Prediction model for peninsular Indian summer monsoon rainfall using data mining and statistical approaches

NASA Astrophysics Data System (ADS)

Vathsala, H.; Koolagudi, Shashidhar G.

2017-01-01

In this paper we discuss a data mining application for predicting peninsular Indian summer monsoon rainfall, and propose an algorithm that combine data mining and statistical techniques. We select likely predictors based on association rules that have the highest confidence levels. We then cluster the selected predictors to reduce their dimensions and use cluster membership values for classification. We derive the predictors from local conditions in southern India, including mean sea level pressure, wind speed, and maximum and minimum temperatures. The global condition variables include southern oscillation and Indian Ocean dipole conditions. The algorithm predicts rainfall in five categories: Flood, Excess, Normal, Deficit and Drought. We use closed itemset mining, cluster membership calculations and a multilayer perceptron function in the algorithm to predict monsoon rainfall in peninsular India. Using Indian Institute of Tropical Meteorology data, we found the prediction accuracy of our proposed approach to be exceptionally good.

Age of acquisition predicts naming and lexical-decision performance above and beyond 22 other predictor variables: an analysis of 2,342 words.

PubMed

Cortese, Michael J; Khanna, Maya M

2007-08-01

Age of acquisition (AoA) ratings were obtained and were used in hierarchical regression analyses to predict naming and lexical-decision performance for 2,342 words (from Balota, Cortese, Sergent-Marshall, Spieler, & Yap, 2004). In the analyses, AoA was included in addition to the set of predictors used by Balota et al. (2004). AoA significantly predicted latency performance on both tasks above and beyond the standard predictor set. However, AoA was more strongly related to lexical-decision performance than to naming performance. Finally, the previously reported effect of imageability on naming latencies by Balota et al. was not significant with AoA included as a factor. These results are consistent with the idea either that AoA has a semantic/lexical locus or that AoA effects emerge primarily in situations in which the input-output mapping is arbitrary.

Risk versus direct protective factors and youth violence: Seattle social development project.

PubMed

Herrenkohl, Todd I; Lee, Jungeun; Hawkins, J David

2012-08-01

Numerous studies have examined predictors of youth violence associated with the individual child, the family, school, and the surrounding neighborhood or community. However, few studies have examined predictors using a systematic approach to differentiate and compare risk and direct protective factors. This study examines risk and protective factors associated with youth violence in an ongoing longitudinal panel study of 808 students from 18 Seattle public elementary schools followed since 1985 when they were in 5th grade. Predictors span the individual, family, school, peer, and neighborhood domains. Data were collected annually, beginning in 1985, to age 16 years, and then again at age 18 years. This paper provides findings of analyses in which continuous predictor variables, measured at ages 10-12 years, were trichotomized to reflect a risk end of the variable, a direct protective end, and a middle category of scores. Youth violence was measured at ages 13-14 years and 15-18 years. Bivariate analyses of risk and direct protective factors identified the following predictors of violence at ages 13-14 years and 15-18 years. Risk for violence was increased by earlier antisocial behavior (e.g., prior violence, truancy, nonviolent delinquency), attention problems, family conflict, low school commitment, and living in a neighborhood where young people were in trouble. Direct protective factors at ages 10-12 years include a low level of attention problems, low risk-taking, refusal skills, school attachment, and low access and exposure to marijuana at ages 10-12 years. Multivariate regressions showed neighborhood risk factors to be among the most salient and consistent predictors of violence after accounting for all other variables in the tested models. Relatively few direct protective factors were identified in these statistical tests, suggesting the need for further review and possible refinement of the measures and methods that were applied. Implications provide important information for programs and policy. Copyright © 2012 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

A systematic review of studies identifying predictors of poor return to work outcomes following workplace injury.

PubMed

Street, Tamara D; Lacey, Sarah J

2015-06-05

Injuries occurring in the workplace can have serious implications for the health of the individual, the productivity of the employer and the overall economic community. The objective of this paper is to increase the current state of understanding of individual demographic and psychosocial characteristics associated with extended absenteeism from the workforce due to a workplace injury. Studies included in this systematic literature review tracked participants' return to work status over a minimum of three months, identified either demographic, psychosocial or general injury predictors of poor return to work outcomes and included a heterogeneous sample of workplace injuries. Identified predictors of poor return to work outcomes included older age, female gender, divorced marital status, two or more dependent family members, lower education levels, employment variables associated with reduced labour market desirability, severity or sensitive injury locations, negative attitudes and outcome perceptions of the participant. There is a need for clear and consistent definition and measurement of return to work outcomes and a holistic theoretical model integrating injury, psychosocial and demographic predictors of return to work. Through greater understanding of the nature of factors affecting return to work, improved outcomes could be achieved.

On the Misconception of Multicollinearity in Detection of Moderating Effects: Multicollinearity Is Not Always Detrimental.

PubMed

Shieh, Gwowen

2010-05-28

Due to its extensive applicability and computational ease, moderated multiple regression (MMR) has been widely employed to analyze interaction effects between 2 continuous predictor variables. Accordingly, considerable attention has been drawn toward the supposed multicollinearity problem between predictor variables and their cross-product term. This article attempts to clarify the misconception of multicollinearity in MMR studies. The counterintuitive yet beneficial effects of multicollinearity on the ability to detect moderator relationships are explored. Comprehensive treatments and numerical investigations are presented for the simplest interaction model and more complex three-predictor setting. The results provide critical insight that both helps avoid misleading interpretations and yields better understanding for the impact of intercorrelation among predictor variables in MMR analyses.

Macrosomia Predictors in Infants Born to Cuban Mothers with Gestational Diabetes.

PubMed

Cruz, Jeddú; Grandía, Raiden; Padilla, Liset; Rodríguez, Suilbert; Hernández García, Pilar; Lang Prieto, Jacinto; Márquez-Guillén, Antonio

2015-07-01

INTRODUCTION Fetal macrosomia is the most important complication in infants of women with diabetes, whether preconceptional or gestational. Its occurrence is related to certain maternal and fetal conditions and negatively affects maternal and perinatal outcomes. The definitive diagnosis is made at birth if a newborn weighs >4000 g. OBJECTIVE Identify which maternal and fetal conditions could be macrosomia predictors in infants born to Cuban mothers with gestational diabetes. METHODS A case-control study comprising 236 women with gestational diabetes who bore live infants (118 with macrosomia and 118 without) was conducted in the América Arias University Maternity Hospital, Havana, Cuba, during 2002-2012. The dependent variable was macrosomia (birth weight >4000 g). Independent maternal variables included body mass index at pregnancy onset, overweight or obesity at pregnancy onset, gestational age at diabetes diagnosis, pregnancy weight gain, glycemic control, triglycerides and cholesterol. Fetal variables examined included third-semester fetal abdominal circumference, estimated fetal weight at ≥28 weeks (absolute and percentilized by Campbell and Wilkin, and Usher and McLean curves). Chi square was used to compare continuous variables (proportions) and the student t test (X ± SD) for categorical variables, with significance threshold set at p <0.05. ORs and their 95% CIs were calculated. RESULTS Significant differences between cases and controls were found in most variables studied, with the exception of late gestational diabetes diagnosis, total fasting cholesterol and hypercholesterolemia. The highest OR for macrosomia were for maternal hypertriglyceridemia (OR 4.80, CI 2.34-9.84), third-trimester fetal abdominal circumference >75th percentile (OR 7.54, CI 4.04-14.06), and estimated fetal weight >90th percentile by Campbell and Wilkin curves (OR 4.75, CI 1.42-15.84) and by Usher and McLean curves (OR 8.81, CI 4.25-18.26). CONCLUSIONS Most variables assessed were predictors of macrosomia in infants of mothers with gestational diabetes. They should therefore be taken into account for future studies and for patient management. Wide confidence intervals indicate uncertainty about the magnitude of predictive power. KEYWORDS Fetal macrosomia, fetal diseases, gestational diabetes, risk factors, risk prediction, Cuba.

Is parenting style a predictor of suicide attempts in a representative sample of adolescents?

PubMed Central

2014-01-01

Background Suicidal ideation and suicide attempts are serious but not rare conditions in adolescents. However, there are several research and practical suicide-prevention initiatives that discuss the possibility of preventing serious self-harm. Profound knowledge about risk and protective factors is therefore necessary. The aim of this study is a) to clarify the role of parenting behavior and parenting styles in adolescents’ suicide attempts and b) to identify other statistically significant and clinically relevant risk and protective factors for suicide attempts in a representative sample of German adolescents. Methods In the years 2007/2008, a representative written survey of N = 44,610 students in the 9th grade of different school types in Germany was conducted. In this survey, the lifetime prevalence of suicide attempts was investigated as well as potential predictors including parenting behavior. A three-step statistical analysis was carried out: I) As basic model, the association between parenting and suicide attempts was explored via binary logistic regression controlled for age and sex. II) The predictive values of 13 additional potential risk/protective factors were analyzed with single binary logistic regression analyses for each predictor alone. Non-significant predictors were excluded in Step III. III) In a multivariate binary logistic regression analysis, all significant predictor variables from Step II and the parenting styles were included after testing for multicollinearity. Results Three parental variables showed a relevant association with suicide attempts in adolescents – (all protective): mother’s warmth and father’s warmth in childhood and mother’s control in adolescence (Step I). In the full model (Step III), Authoritative parenting (protective: OR: .79) and Rejecting-Neglecting parenting (risk: OR: 1.63) were identified as significant predictors (p < .001) for suicidal attempts. Seven further variables were interpreted to be statistically significant and clinically relevant: ADHD, female sex, smoking, Binge Drinking, absenteeism/truancy, migration background, and parental separation events. Conclusions Parenting style does matter. While children of Authoritative parents profit, children of Rejecting-Neglecting parents are put at risk – as we were able to show for suicide attempts in adolescence. Some of the identified risk factors contribute new knowledge and potential areas of intervention for special groups such as migrants or children diagnosed with ADHD. PMID:24766881

Predicting Change over Time in Career Planning and Career Exploration for High School Students

ERIC Educational Resources Information Center

Creed, Peter A.; Patton, Wendy; Prideaux, Lee-Ann

2007-01-01

This study assessed 166 high school students in Grade 8 and again in Grade 10. Four models were tested: (a) whether the T1 predictor variables (career knowledge, indecision, decision-making selfefficacy, self-esteem, demographics) predicted the outcome variable (career planning/exploration) at T1; (b) whether the T1 predictor variables predicted…

Spatial Scaling of Environmental Variables Improves Species-Habitat Models of Fishes in a Small, Sand-Bed Lowland River

PubMed Central

Radinger, Johannes; Wolter, Christian; Kail, Jochem

2015-01-01

Habitat suitability and the distinct mobility of species depict fundamental keys for explaining and understanding the distribution of river fishes. In recent years, comprehensive data on river hydromorphology has been mapped at spatial scales down to 100 m, potentially serving high resolution species-habitat models, e.g., for fish. However, the relative importance of specific hydromorphological and in-stream habitat variables and their spatial scales of influence is poorly understood. Applying boosted regression trees, we developed species-habitat models for 13 fish species in a sand-bed lowland river based on river morphological and in-stream habitat data. First, we calculated mean values for the predictor variables in five distance classes (from the sampling site up to 4000 m up- and downstream) to identify the spatial scale that best predicts the presence of fish species. Second, we compared the suitability of measured variables and assessment scores related to natural reference conditions. Third, we identified variables which best explained the presence of fish species. The mean model quality (AUC = 0.78, area under the receiver operating characteristic curve) significantly increased when information on the habitat conditions up- and downstream of a sampling site (maximum AUC at 2500 m distance class, +0.049) and topological variables (e.g., stream order) were included (AUC = +0.014). Both measured and assessed variables were similarly well suited to predict species’ presence. Stream order variables and measured cross section features (e.g., width, depth, velocity) were best-suited predictors. In addition, measured channel-bed characteristics (e.g., substrate types) and assessed longitudinal channel features (e.g., naturalness of river planform) were also good predictors. These findings demonstrate (i) the applicability of high resolution river morphological and instream-habitat data (measured and assessed variables) to predict fish presence, (ii) the importance of considering habitat at spatial scales larger than the sampling site, and (iii) that the importance of (river morphological) habitat characteristics differs depending on the spatial scale. PMID:26569119

Parental education predicts change in intelligence quotient after childhood epilepsy surgery.

PubMed

Meekes, Joost; van Schooneveld, Monique M J; Braams, Olga B; Jennekens-Schinkel, Aag; van Rijen, Peter C; Hendriks, Marc P H; Braun, Kees P J; van Nieuwenhuizen, Onno

2015-04-01

To know whether change in the intelligence quotient (IQ) of children who undergo epilepsy surgery is associated with the educational level of their parents. Retrospective analysis of data obtained from a cohort of children who underwent epilepsy surgery between January 1996 and September 2010. We performed simple and multiple regression analyses to identify predictors associated with IQ change after surgery. In addition to parental education, six variables previously demonstrated to be associated with IQ change after surgery were included as predictors: age at surgery, duration of epilepsy, etiology, presurgical IQ, reduction of antiepileptic drugs, and seizure freedom. We used delta IQ (IQ 2 years after surgery minus IQ shortly before surgery) as the primary outcome variable, but also performed analyses with pre- and postsurgical IQ as outcome variables to support our findings. To validate the results we performed simple regression analysis with parental education as the predictor in specific subgroups. The sample for regression analysis included 118 children (60 male; median age at surgery 9.73 years). Parental education was significantly associated with delta IQ in simple regression analysis (p = 0.004), and also contributed significantly to postsurgical IQ in multiple regression analysis (p = 0.008). Additional analyses demonstrated that parental education made a unique contribution to prediction of delta IQ, that is, it could not be replaced by the illness-related variables. Subgroup analyses confirmed the association of parental education with IQ change after surgery for most groups. Children whose parents had higher education demonstrate on average a greater increase in IQ after surgery and a higher postsurgical--but not presurgical--IQ than children whose parents completed at most lower secondary education. Parental education--and perhaps other environmental variables--should be considered in the prognosis of cognitive function after childhood epilepsy surgery. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

Psychosocial predictors of affect in adult patients undergoing orthodontic treatment.

PubMed

Peñacoba, Cecilia; González, M José; Santos, Noelia; Romero, Martín

2014-02-01

In this paper we propose to study the role of psychosocial variables in affect in adult patients undergoing orthodontic treatment, considering that affect is a key variable in treatment adherence. Seventy-four patients (average age 33,24 ± 10,56) with metal multibracket-fixed orthodontic treatment were included. Patients were assessed twice. The first stage, at the beginning of treatment, included assessment of dental impact (Psychosocial Impact of Dental Aesthetics Questionnaire), trait anxiety (State-Trait Anxiety Inventory), self-esteem (Rosenberg's self-esteem scale), and self-efficacy (General Self-efficacy Scale). In the second stage, 6 months later, positive and negative affect towards treatment was assessed using the Positive and Negative Affect Scale. Dental social impact differentiates between patients with high and low negative affect, while self-efficacy differentiates between patients with high and low positive affect. Trait anxiety and self-esteem differentiate between both types of affect (positive and negative). Trait anxiety and self-esteem (when trait anxiety weight is controlled) are significant predictor variables of affective balance. These results have important practical implications, because it seems essential to adopt a bio-psychosocial model incorporating assessment methods focusing on day-to-day changes in mood and well-being.

Assessment of the uncertainty and predictive power of large-scale predictors for nonlinear precipitation downscaling in the European Arctic (Invited)

NASA Astrophysics Data System (ADS)

Sauter, T.

2013-12-01

Despite the extensive research on downscaling methods there is still little consensus about the choice of useful atmospheric predictor variables. Besides the general decision of a proper statistical downscaling model, the selection of an informative predictor set is crucial for the accuracy and stability of the resulting downscaled time series. These requirements must be fullfilled by both the atmospheric variables and the predictor domains in terms of geographical location and spatial extend, to which in general not much attention is paid. However, only a limited number of studies is interested in the predictive capability of the predictor domain size or shape, and the question to what extent variability of neighboring grid points influence local-scale events. In this study we emphasized the spatial relationships between observed daily precipitation and selected number of atmospheric variables for the European Arctic. Several nonlinear regression models are used to link the large-scale predictors obtained from reanalysed Weather Research and Forecast model runs to the local-scale observed precipitation. Inferences on the sources of uncertainty are then drawn from variance based sensitivity measures, which also permit to capture interaction effects between individual predictors. The information is further used to develop more parsimonious downscaling models with only small decreases in accuracy. Individual predictors (without interactions) account for almost 2/3 of the total output variance, while the remaining fraction is solely due to interactions. Neglecting predictor interactions in the screening process will lead to some loss of information. Hence, linear screening methods are insufficient as they neither account for interactions nor for non-additivity as given by many nonlinear prediction algorithms.

Environmental sustainability assessments of pharmaceuticals: an emerging need for simplification in life cycle assessments.

PubMed

De Soete, Wouter; Debaveye, Sam; De Meester, Steven; Van der Vorst, Geert; Aelterman, Wim; Heirman, Bert; Cappuyns, Philippe; Dewulf, Jo

2014-10-21

The pharmaceutical and fine chemical industries are eager to strive toward innovative products and technologies. This study first derives hotspots in resource consumption of 2839 Basic Operations in 40 Active Pharmaceutical Ingredient synthesis steps through Exergetic Life Cycle Assessment (ELCA). Second, since companies are increasingly obliged to quantify the environmental sustainability of their products, two alternative ways of simplifying (E)LCA are discussed. The usage of averaged product group values (R(2) = 3.40 × 10(-30)) is compared with multiple linear regression models (R(2) = 8.66 × 10(-01)) in order to estimate resource consumption of synthesis steps. An optimal set of predictor variables is postulated to balance model complexity and embedded information with usability and capability of merging models with existing Enterprise Resource Planning (ERP) data systems. The amount of organic solvents used, molar efficiency, and duration of a synthesis step were shown to be the most significant predictor variables. Including additional predictor variables did not contribute to the predictive power and eventually weakens the model interpretation. Ideally, an organization should be able to derive its environmental impact from readily available ERP data, linking supply chains back to the cradle of resource extraction, excluding the need for an approximation with product group averages.

Climate drives inter-annual variability in probability of high severity fire occurrence in the western United States

NASA Astrophysics Data System (ADS)

Keyser, Alisa; Westerling, Anthony LeRoy

2017-05-01

A long history of fire suppression in the western United States has significantly changed forest structure and ecological function, leading to increasingly uncharacteristic fires in terms of size and severity. Prior analyses of fire severity in California forests showed that time since last fire and fire weather conditions predicted fire severity very well, while a larger regional analysis showed that topography and climate were important predictors of high severity fire. There has not yet been a large-scale study that incorporates topography, vegetation and fire-year climate to determine regional scale high severity fire occurrence. We developed models to predict the probability of high severity fire occurrence for the western US. We predict high severity fire occurrence with some accuracy, and identify the relative importance of predictor classes in determining the probability of high severity fire. The inclusion of both vegetation and fire-year climate predictors was critical for model skill in identifying fires with high fractional fire severity. The inclusion of fire-year climate variables allows this model to forecast inter-annual variability in areas at future risk of high severity fire, beyond what slower-changing fuel conditions alone can accomplish. This allows for more targeted land management, including resource allocation for fuels reduction treatments to decrease the risk of high severity fire.

Predictors of psychological resilience amongst medical students following major earthquakes.

PubMed

Carter, Frances; Bell, Caroline; Ali, Anthony; McKenzie, Janice; Boden, Joseph M; Wilkinson, Timothy; Bell, Caroline

2016-05-06

To identify predictors of self-reported psychological resilience amongst medical students following major earthquakes in Canterbury in 2010 and 2011. Two hundred and fifty-three medical students from the Christchurch campus, University of Otago, were invited to participate in an electronic survey seven months following the most severe earthquake. Students completed the Connor-Davidson Resilience Scale, the Depression, Anxiety and Stress Scale, the Post-traumatic Disorder Checklist, the Work and Adjustment Scale, and the Eysenck Personality Questionnaire. Likert scales and other questions were also used to assess a range of variables including demographic and historical variables (eg, self-rated resilience prior to the earthquakes), plus the impacts of the earthquakes. The response rate was 78%. Univariate analyses identified multiple variables that were significantly associated with higher resilience. Multiple linear regression analyses produced a fitted model that was able to explain 35% of the variance in resilience scores. The best predictors of higher resilience were: retrospectively-rated personality prior to the earthquakes (higher extroversion and lower neuroticism); higher self-rated resilience prior to the earthquakes; not being exposed to the most severe earthquake; and less psychological distress following the earthquakes. Psychological resilience amongst medical students following major earthquakes was able to be predicted to a moderate extent.

Method of invitation and geographical proximity as predictors of NHS Health Check uptake.

PubMed

Gidlow, Christopher; Ellis, Naomi; Randall, Jason; Cowap, Lisa; Smith, Graham; Iqbal, Zafar; Kumar, Jagdish

2015-06-01

Uptake of NHS Health Checks remains below the national target. Better understanding of predictors of uptake can inform targeting and delivery. We explored invitation method and geographical proximity as predictors of uptake in deprived urban communities. This observational cohort study used data from all 4855 individuals invited for an NHS Health Check (September 2010-February 2014) at five general practices in Stoke-on-Trent, UK. Attendance/non-attendance was the binary outcome variable. Predictor variables included the method of invitation, general practice, demographics, deprivation and distance to Health Check location. Mean attendance (61.6%) was above the city and national average, but varied by practice (47.5-83.3%; P < 0.001). Telephone/verbal invitations were associated with higher uptake than postal invitations (OR = 2.87, 95% CI = 2.26-3.64), yet significant practice-level variation remained. Distance to Health Check was not associated with attendance. Increasing age (OR = 1.04, 95% CI = 1.03-1.04), female gender (OR = 1.48, 95% CI = 1.30-1.68) and living in the least deprived areas (OR = 1.59, 95% CI = 1.23-2.05) were all independent positive predictors of attendance. Using verbal or telephone invitations should be considered to improve Health Check uptake. Other differences in recruitment and delivery that might explain remaining practice-level variation in uptake warrant further exploration. Geographical proximity may not be an important predictor of uptake in urban populations. © The Author 2014. Published by Oxford University Press on behalf of Faculty of Public Health.

Predictors of human rotation.

PubMed

Stochl, Jan; Croudace, Tim

2013-01-01

Why some humans prefer to rotate clockwise rather than anticlockwise is not well understood. This study aims to identify the predictors of the preferred rotation direction in humans. The variables hypothesised to influence rotation preference include handedness, footedness, sex, brain hemisphere lateralisation, and the Coriolis effect (which results from geospatial location on the Earth). An online questionnaire allowed us to analyse data from 1526 respondents in 97 countries. Factor analysis showed that the direction of rotation should be studied separately for local and global movements. Handedness, footedness, and the item hypothesised to measure brain hemisphere lateralisation are predictors of rotation direction for both global and local movements. Sex is a predictor of the direction of global rotation movements but not local ones, and both sexes tend to rotate clockwise. Geospatial location does not predict the preferred direction of rotation. Our study confirms previous findings concerning the influence of handedness, footedness, and sex on human rotation; our study also provides new insight into the underlying structure of human rotation movements and excludes the Coriolis effect as a predictor of rotation.

Argentina soybean yield model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate soybean yields for the country of Argentina. A meteorological data set was obtained for the country by averaging data for stations within the soybean growing area. Predictor variables for the model were derived from monthly total precipitation and monthly average temperature. A trend variable was included for the years 1969 to 1978 since an increasing trend in yields due to technology was observed between these years.

Learning style and concept acquisition of community college students in introductory biology

NASA Astrophysics Data System (ADS)

Bobick, Sandra Burin

This study investigated the influence of learning style on concept acquisition within a sample of community college students in a general biology course. There are two subproblems within the larger problem: (1) the influence of demographic variables (age, gender, number of college credits, prior exposure to scientific information) on learning style, and (2) the correlations between prior scientific knowledge, learning style and student understanding of the concept of the gene. The sample included all students enrolled in an introductory general biology course during two consecutive semesters at an urban community college. Initial data was gathered during the first week of the semester, at which time students filled in a short questionnaire (age, gender, number of college credits, prior exposure to science information either through reading/visual sources or a prior biology course). Subjects were then given the Inventory of Learning Processes-Revised (ILP-R) which measures general preferences in five learning styles; Deep Learning; Elaborative Learning, Agentic Learning, Methodical Learning and Literal Memorization. Subjects were then given the Gene Conceptual Knowledge pretest: a 15 question objective section and an essay section. Subjects were exposed to specific concepts during lecture and laboratory exercises. At the last lab, students were given the Genetics Conceptual Knowledge Posttest. Pretest/posttest gains were correlated with demographic variables and learning styles were analyzed for significant correlations. Learning styles, as the independent variable in a simultaneous multiple regression, were significant predictors of results on the gene assessment tests, including pretest, posttest and gain. Of the learning styles, Deep Learning accounted for the greatest positive predictive value of pretest essay and pretest objective results. Literal Memorization was a significant negative predictor for posttest essay, essay gain and objective gain. Simultaneous multiple regression indicated that demographic variables were significant positive predictors for Methodical, Deep and Elaborative Learning Styles. Stepwise multiple regression resulted in number of credits, Read Science and gender (female) as significant predictors of learning styles. The findings of this study emphasize the importance of learning styles in conceptual understanding of the gene and the correlation of nonformal exposure to science information with learning style and conceptual understanding.

Kinematic predictors of star excursion balance test performance in individuals with chronic ankle instability.

PubMed

Hoch, Matthew C; Gaven, Stacey L; Weinhandl, Joshua T

2016-06-01

The Star Excursion Balance Test has identified dynamic postural control deficits in individuals with chronic ankle instability. While kinematic predictors of Star Excursion Balance Test performance have been evaluated in healthy individuals, this has not been thoroughly examined in individuals with chronic ankle instability. Fifteen individuals with chronic ankle instability completed the anterior reach direction of the Star Excursion Balance Test and weight-bearing dorsiflexion assessments. Maximum reach distances on the Star Excursion Balance Test were measured in cm and normalized to leg length. Three-dimensional trunk, hip, knee, and ankle motion of the stance limb were recorded during each anterior reach trial using a motion capture system. Sagittal, frontal, and transverse plane displacement observed from trial initiation to the point of maximum reach was calculated for each joint or segment and averaged for analysis. Pearson product-moment correlations were performed to examine the relationships between kinematic variables, maximal reach, and weight-bearing dorsiflexion. A backward multiple linear regression model was developed with maximal reach as the criterion variable and kinematic variables as predictors. Frontal plane displacement of the trunk, hip, and ankle and sagittal plane knee displacement were entered into the analysis. The final model (p=0.004) included all three frontal plane variables and explained 81% of the variance in maximal reach. Maximal reach distance and several kinematic variables were significantly related to weight-bearing dorsiflexion. Individuals with chronic ankle instability who demonstrated greater lateral trunk displacement toward the stance limb, hip adduction, and ankle eversion achieved greater maximal reach. Copyright © 2016. Published by Elsevier Ltd.

Predictors of the development of myocarditis or acute renal failure in patients with leptospirosis: An observational study

PubMed Central

2012-01-01

Background Leptospirosis has a varied clinical presentation with complications like myocarditis and acute renal failure. There are many predictors of severity and mortality including clinical and laboratory parameters. Early detection and treatment can reduce complications. Therefore recognizing the early predictors of the complications of leptospirosis is important in patient management. This study was aimed at determining the clinical and laboratory predictors of myocarditis or acute renal failure. Methods This was a prospective descriptive study carried out in the Teaching Hospital, Kandy, from 1st July 2007 to 31st July 2008. Patients with clinical features compatible with leptospirosis case definition were confirmed using the Microscopic Agglutination Test (MAT). Clinical features and laboratory measures done on admission were recorded. Patients were observed for the development of acute renal failure or myocarditis. Chi-square statistics, Fisher's exact test and Mann-Whitney U test were used to compare patients with and without complications. A logistic regression model was used to select final predictor variables. Results Sixty two confirmed leptospirosis patients were included in the study. Seven patients (11.3%) developed acute renal failure and five (8.1%) developed myocarditis while three (4.8%) had both acute renal failure and myocarditis. Conjunctival suffusion - 40 (64.5%), muscle tenderness - 28 (45.1%), oliguria - 20 (32.2%), jaundice - 12 (19.3%), hepatomegaly - 10 (16.1%), arrhythmias (irregular radial pulse) - 8 (12.9%), chest pain - 6 (9.7%), bleeding - 5 (8.1%), and shortness of breath (SOB) 4 (6.4%) were the common clinical features present among the patients. Out of these, only oliguria {odds ratio (OR) = 4.14 and 95% confidence interval (CI) 1.003-17.261}, jaundice (OR = 5.13 and 95% CI 1.149-28.003), and arrhythmias (OR = 5.774 and 95% CI 1.001-34.692), were predictors of myocarditis or acute renal failure and none of the laboratory measures could predict the two complications. Conclusions This study shows that out of clinical and laboratory variables, only oliguria, jaundice and arrhythmia are strong predictors of development of acute renal failure or myocarditis in patients with leptospirosis presented to Teaching Hospital of Kandy, Sri Lanka. PMID:22243770

Cognitive components of a mathematical processing network in 9-year-old children.

PubMed

Szűcs, Dénes; Devine, Amy; Soltesz, Fruzsina; Nobes, Alison; Gabriel, Florence

2014-07-01

We determined how various cognitive abilities, including several measures of a proposed domain-specific number sense, relate to mathematical competence in nearly 100 9-year-old children with normal reading skill. Results are consistent with an extended number processing network and suggest that important processing nodes of this network are phonological processing, verbal knowledge, visuo-spatial short-term and working memory, spatial ability and general executive functioning. The model was highly specific to predicting arithmetic performance. There were no strong relations between mathematical achievement and verbal short-term and working memory, sustained attention, response inhibition, finger knowledge and symbolic number comparison performance. Non-verbal intelligence measures were also non-significant predictors when added to our model. Number sense variables were non-significant predictors in the model and they were also non-significant predictors when entered into regression analysis with only a single visuo-spatial WM measure. Number sense variables were predicted by sustained attention. Results support a network theory of mathematical competence in primary school children and falsify the importance of a proposed modular 'number sense'. We suggest an 'executive memory function centric' model of mathematical processing. Mapping a complex processing network requires that studies consider the complex predictor space of mathematics rather than just focusing on a single or a few explanatory factors.

Promoting retention of nurses: A meta-analytic examination of causes of nurse turnover.

PubMed

Nei, Darin; Snyder, Lori Anderson; Litwiller, Brett J

2015-01-01

Because the health care field is expected to be the fastest growing job field until 2020, an urgent need to focus on nurse retention exists. The aim of this study was to examine the relationships between predictors of turnover (i.e., personal characteristics, role states, job characteristics, group/leader relations, organizational/environmental perceptions, attitudinal reactions) and turnover cognitions and intentions, as well as actual turnover among nurses, in an effort to determine the strongest predictors of voluntary turnover. Meta-analysis was used to determine best estimates of the effect of predictors on turnover based on 106 primary studies of employed nurses. Meta-analyzed correlations were subjected to path analysis to establish the structural relationships among the study variables. Supportive and communicative leadership, network centrality, and organizational commitment are the strongest predictors of voluntary turnover based on meta-analytic correlations. Additional variables that relate to nurse turnover intentions include job strain, role tension, work-family conflict, job control, job complexity, rewards/recognition, and team cohesion. The findings suggest that some factors, such as salary, are relatively less important in prediction of turnover. Administrators concerned about nurse turnover may more effectively direct resources toward altering certain job characteristics and work conditions in the effort to reduce voluntary turnover among nurses.

Relationship between general health of older health service users and their self-esteem in Isfahan in 2014

PubMed Central

Molavi, Razieh; Alavi, Mousa; Keshvari, Mahrokh

2015-01-01

Background: Self-esteem is known to be one of the most important markers of successful aging. Older people's self-esteem is influenced by several factors that particularly may be health related. Therefore, this study aimed to explore some important general health-related predictors of the older people's self-esteem. Materials and Methods: In this study, 200 people, aged 65 years and older, who referred to health care centers were selected through stratified random sampling method. Data were collected by using Rosenberg's self-esteem scale and the 28-item Goldberg's general health questionnaire. Data were analyzed by Pearson's coefficient tests and multiple regression analysis. Results: Findings showed that the entered predictor variables accounted for 49% of the total variance (R2) of self-esteem in the model (P < 0.001, F4,195 = 46.717). Three out of the four predictor variables including somatic signs, anxiety/insomnia, and depression, significantly predicted the self-esteem. The results emphasized on the determinant role of both physical (somatic signs) and mental (anxiety/insomnia and depression) aspects of health in older patients’ self-esteem. Conclusions: The significant general health-related predictors found in the present study emphasize on some of the significant points that should be considered in planning for improving older patients’ self-esteem. PMID:26793259

Cognitive components of a mathematical processing network in 9-year-old children

PubMed Central

Szűcs, Dénes; Devine, Amy; Soltesz, Fruzsina; Nobes, Alison; Gabriel, Florence

2014-01-01

We determined how various cognitive abilities, including several measures of a proposed domain-specific number sense, relate to mathematical competence in nearly 100 9-year-old children with normal reading skill. Results are consistent with an extended number processing network and suggest that important processing nodes of this network are phonological processing, verbal knowledge, visuo-spatial short-term and working memory, spatial ability and general executive functioning. The model was highly specific to predicting arithmetic performance. There were no strong relations between mathematical achievement and verbal short-term and working memory, sustained attention, response inhibition, finger knowledge and symbolic number comparison performance. Non-verbal intelligence measures were also non-significant predictors when added to our model. Number sense variables were non-significant predictors in the model and they were also non-significant predictors when entered into regression analysis with only a single visuo-spatial WM measure. Number sense variables were predicted by sustained attention. Results support a network theory of mathematical competence in primary school children and falsify the importance of a proposed modular ‘number sense’. We suggest an ‘executive memory function centric’ model of mathematical processing. Mapping a complex processing network requires that studies consider the complex predictor space of mathematics rather than just focusing on a single or a few explanatory factors. PMID:25089322

Psychological predictors of mobile phone use while crossing the street among college students: An application of the theory of planned behavior.

PubMed

Jiang, Kang; Ling, Feiyang; Feng, Zhongxiang; Wang, Kun; Guo, Lei

2017-02-17

As the prevalence of mobile phone use has increased globally, experts have verified the effects of mobile phone distraction on traffic safety. However, the psychological factors underlying pedestrians' decisions to use their mobile phones while crossing the street have received little attention. The present study employed the theory of planned behavior (TPB) to investigate the psychological factors that influence pedestrians' intentions to use a mobile phone while crossing the street. The additional predictors of descriptive norms, moral norms, risk perception, mobile phone involvement, and perceived ability to compensate are included. Approximately 40% of participants reported having used a mobile phone while crossing during the previous week and 5.4% had been involved in crossing accidents due to mobile phone distractions. Hierarchical multiple regression analyses revealed overall support for the predictive utility of the TPB. The standard TPB variables accounted for 13.3% of variance in intentions after demographic variables were controlled, and the extended predictors contributed an additional 7.6% beyond the standard constructs. The current study revealed that attitude, perceived behavior control, descriptive norms, mobile phone involvement, and perceived ability to compensate all emerged as significant predictors of intentions. The findings could support the design of more effective safety campaigns and interventions to reduce pedestrians' distracted crossing behaviors.

Low Prevalence of Vitamin D Insufficiency among Nepalese Infants Despite High Prevalence of Vitamin D Insufficiency among Their Mothers

PubMed Central

Haugen, Johanne; Ulak, Manjeswori; Chandyo, Ram K.; Henjum, Sigrun; Thorne-Lyman, Andrew L.; Ueland, Per Magne; Midtun, Øivind; Shrestha, Prakash S.; Strand, Tor A.

2016-01-01

Background: Describing vitamin D status and its predictors in various populations is important in order to target public health measures. Objectives: To describe the status and predictors of vitamin D status in healthy Nepalese mothers and infants. Methods: 500 randomly selected Nepalese mother and infant pairs were included in a cross-sectional study. Plasma 25(OH)D concentrations were measured by LC-MS/MS and multiple linear regression analyses were used to identify predictors of vitamin D status. Results: Among the infants, the prevalence of vitamin D insufficiency (25(OH)D <50 nmol/L) and deficiency (<30 nmol/L) were 3.6% and 0.6%, respectively, in contrast to 59.8% and 14.0% among their mothers. Infant 25(OH)D concentrations were negatively associated with infant age and positively associated with maternal vitamin D status and body mass index (BMI), explaining 22% of the variability in 25(OH)D concentration. Global solar radiation, maternal age and BMI predicted maternal 25(OH)D concentration, explaining 9.7% of its variability. Conclusion: Age and maternal vitamin D status are the main predictors of vitamin D status in infants in Bhaktapur, Nepal, who have adequate vitamin D status despite poor vitamin D status in their mothers. PMID:28009810

Relationship between general health of older health service users and their self-esteem in Isfahan in 2014.

PubMed

Molavi, Razieh; Alavi, Mousa; Keshvari, Mahrokh

2015-01-01

Self-esteem is known to be one of the most important markers of successful aging. Older people's self-esteem is influenced by several factors that particularly may be health related. Therefore, this study aimed to explore some important general health-related predictors of the older people's self-esteem. In this study, 200 people, aged 65 years and older, who referred to health care centers were selected through stratified random sampling method. Data were collected by using Rosenberg's self-esteem scale and the 28-item Goldberg's general health questionnaire. Data were analyzed by Pearson's coefficient tests and multiple regression analysis. Findings showed that the entered predictor variables accounted for 49% of the total variance (R(2)) of self-esteem in the model (P < 0.001, F4,195 = 46.717). Three out of the four predictor variables including somatic signs, anxiety/insomnia, and depression, significantly predicted the self-esteem. The results emphasized on the determinant role of both physical (somatic signs) and mental (anxiety/insomnia and depression) aspects of health in older patients' self-esteem. The significant general health-related predictors found in the present study emphasize on some of the significant points that should be considered in planning for improving older patients' self-esteem.

Evaluation of Land Use Regression Models for Nitrogen Dioxide and Benzene in Four US Cities

PubMed Central

Mukerjee, Shaibal; Smith, Luther; Neas, Lucas; Norris, Gary

2012-01-01

Spatial analysis studies have included the application of land use regression models (LURs) for health and air quality assessments. Recent LUR studies have collected nitrogen dioxide (NO2) and volatile organic compounds (VOCs) using passive samplers at urban air monitoring networks in El Paso and Dallas, TX, Detroit, MI, and Cleveland, OH to assess spatial variability and source influences. LURs were successfully developed to estimate pollutant concentrations throughout the study areas. Comparisons of development and predictive capabilities of LURs from these four cities are presented to address this issue of uniform application of LURs across study areas. Traffic and other urban variables were important predictors in the LURs although city-specific influences (such as border crossings) were also important. In addition, transferability of variables or LURs from one city to another may be problematic due to intercity differences and data availability or comparability. Thus, developing common predictors in future LURs may be difficult. PMID:23226985

Gait Rather Than Cognition Predicts Decline in Specific Cognitive Domains in Early Parkinson's Disease.

PubMed

Morris, Rosie; Lord, Sue; Lawson, Rachael A; Coleman, Shirley; Galna, Brook; Duncan, Gordon W; Khoo, Tien K; Yarnall, Alison J; Burn, David J; Rochester, Lynn

2017-11-09

Dementia is significant in Parkinson's disease (PD) with personal and socioeconomic impact. Early identification of risk is of upmost importance to optimize management. Gait precedes and predicts cognitive decline and dementia in older adults. We aimed to evaluate gait characteristics as predictors of cognitive decline in newly diagnosed PD. One hundred and nineteen participants recruited at diagnosis were assessed at baseline, 18 and 36 months. Baseline gait was characterized by variables that mapped to five domains: pace, rhythm, variability, asymmetry, and postural control. Cognitive assessment included attention, fluctuating attention, executive function, visual memory, and visuospatial function. Mixed-effects models tested independent gait predictors of cognitive decline. Gait characteristics of pace, variability, and postural control predicted decline in fluctuating attention and visual memory, whereas baseline neuropsychological assessment performance did not predict decline. This provides novel evidence for gait as a clinical biomarker for PD cognitive decline in early disease. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America.

Enhancement of hepatitis virus immunoassay outcome predictions in imbalanced routine pathology data by data balancing and feature selection before the application of support vector machines.

PubMed

Richardson, Alice M; Lidbury, Brett A

2017-08-14

Data mining techniques such as support vector machines (SVMs) have been successfully used to predict outcomes for complex problems, including for human health. Much health data is imbalanced, with many more controls than positive cases. The impact of three balancing methods and one feature selection method is explored, to assess the ability of SVMs to classify imbalanced diagnostic pathology data associated with the laboratory diagnosis of hepatitis B (HBV) and hepatitis C (HCV) infections. Random forests (RFs) for predictor variable selection, and data reshaping to overcome a large imbalance of negative to positive test results in relation to HBV and HCV immunoassay results, are examined. The methodology is illustrated using data from ACT Pathology (Canberra, Australia), consisting of laboratory test records from 18,625 individuals who underwent hepatitis virus testing over the decade from 1997 to 2007. Overall, the prediction of HCV test results by immunoassay was more accurate than for HBV immunoassay results associated with identical routine pathology predictor variable data. HBV and HCV negative results were vastly in excess of positive results, so three approaches to handling the negative/positive data imbalance were compared. Generating datasets by the Synthetic Minority Oversampling Technique (SMOTE) resulted in significantly more accurate prediction than single downsizing or multiple downsizing (MDS) of the dataset. For downsized data sets, applying a RF for predictor variable selection had a small effect on the performance, which varied depending on the virus. For SMOTE, a RF had a negative effect on performance. An analysis of variance of the performance across settings supports these findings. Finally, age and assay results for alanine aminotransferase (ALT), sodium for HBV and urea for HCV were found to have a significant impact upon laboratory diagnosis of HBV or HCV infection using an optimised SVM model. Laboratories looking to include machine learning via SVM as part of their decision support need to be aware that the balancing method, predictor variable selection and the virus type interact to affect the laboratory diagnosis of hepatitis virus infection with routine pathology laboratory variables in different ways depending on which combination is being studied. This awareness should lead to careful use of existing machine learning methods, thus improving the quality of laboratory diagnosis.

Early Predictors of Occupational Back Re-Injury: Results from a Prospective Study of Workers in Washington State

PubMed Central

Keeney, Benjamin J.; Turner, Judith A.; Fulton-Kehoe, Deborah; Wickizer, Thomas M.; Chan, Kwun Chuen Gary; Franklin, Gary M.

2014-01-01

Study Design Prospective population-based cohort study Objective To identify early predictors of self-reported occupational back re-injury within 1 year after work-related back injury Summary of Background Data Back injuries are the costliest and most prevalent disabling occupational injuries in the United States. A substantial proportion of workers with back injuries have re-injuries after returning to work, yet there are few studies of risk factors for occupational back re-injuries. Methods We aimed to identify the incidence and early (in the claim) predictors of self-reported back re-injury by approximately 1 year after the index injury among Washington State workers with new work disability claims for back injuries. The Washington Workers’ Compensation Disability Risk Identification Study Cohort (D-RISC) provided a large, population-based sample with information on variables in seven domains: sociodemographic, employment-related, pain and function, clinical status, health care, health behavior, and psychological. We conducted telephone interviews with workers 3 weeks and 1 year after submission of a time-loss claim for the injury. We first identified predictors (p-values < 0.10) of self-reported re-injury within 1 year in bivariate analyses. Those variables were then included in a multivariate logistic regression model predicting occupational back re-injury. Results 290 (25.8%) of 1,123 (70.0% response rate) workers who completed the one-year follow-up interview and had returned to work reported having re-injured their back at work. Baseline variables significantly associated with re-injury (p-value < 0.05) in the multivariate model included male gender, constant whole body vibration at work, a history of previous similar injury, 4 or more previous claims of any type, possessing health insurance, and high fear-avoidance scores. Baseline obesity was associated with reduced odds of re-injury. No other employment-related or psychological variables were significant. Conclusion One-fourth of workers who received work disability compensation for a back injury self-reported re-injury after returning to work. Baseline variables in multiple domains predicted occupational back re-injury. Increased knowledge of early risk factors for re-injury may help lead to interventions, such as efforts to reduce fear-avoidance and graded activity to promote recovery, effective in lowering the risk of re-injury. PMID:22772568

Explicit and Implicit Stigma of Mental Illness as Predictors of the Recovery Attitudes of Assertive Community Treatment Practitioners.

PubMed

Stull, Laura G; McConnell, Haley; McGrew, John; Salyers, Michelle P

2017-01-01

While explicit negative stereotypes of mental illness are well established as barriers to recovery, implicit attitudes also may negatively impact outcomes. The current study is unique in its focus on both explicit and implicit stigma as predictors of recovery attitudes of mental health practitioners. Assertive Community Treatment practitioners (n = 154) from 55 teams completed online measures of stigma, recovery attitudes, and an Implicit Association Test (IAT). Three of four explicit stigma variables (perceptions of blameworthiness, helplessness, and dangerousness) and all three implicit stigma variables were associated with lower recovery attitudes. In a multivariate, hierarchical model, however, implicit stigma did not explain additional variance in recovery attitudes. In the overall model, perceptions of dangerousness and implicitly associating mental illness with "bad" were significant individual predictors of lower recovery attitudes. The current study demonstrates a need for interventions to lower explicit stigma, particularly perceptions of dangerousness, to increase mental health providers' expectations for recovery. The extent to which implicit and explicit stigma differentially predict outcomes, including recovery attitudes, needs further research.

Sociodemographic and Psychiatric Diagnostic Predictors of 3-Year Incidence of DSM-IV Substance Use Disorders among Men and Women in the National Epidemiologic Survey on Alcohol and Related Conditions

PubMed Central

Goldstein, Risë B.; Smith, Sharon M.; Dawson, Deborah A.; Grant, Bridget F.

2016-01-01

Incidence rates of alcohol and drug use disorders (AUDs and DUDs) are consistently higher in men than women, but information on whether sociodemographic and psychiatric diagnostic predictors of AUD and DUD incidence differ by sex is limited. Using data from Waves 1 and 2 of the National Epidemiologic Survey on Alcohol and Related Conditions, sex-specific 3-year incidence rates of AUDs and DUDs among United States adults were compared by sociodemographic variables and baseline psychiatric disorders. Sex-specific logistic regression models estimated odds ratios for prediction of incident AUDs and DUDs, adjusting for potentially confounding baseline sociodemographic and diagnostic variables. Few statistically significant sex differences in predictive relationships were identified and those observed were generally modest. Prospective research is needed to identify predictors of incident DSM-5 AUDs and DUDs and their underlying mechanisms, including whether there is sex specificity by developmental phase, in the role of additional comorbidity in etiology and course, and in outcomes of prevention and treatment. PMID:26727008

Predicting depressive symptoms among the mothers of children with leukaemia: a caregiver stress model perspective.

PubMed

Demirtepe-Saygılı, Dilek; Bozo, Ozlem

2011-05-01

The aim of this study was to find out the predictors of depressive symptoms of mothers of children with leukaemia. The potential predictors were chosen in the light of the caregiver stress model [Pearlin, Mullan, Semple, and Skaff, 1990. Caregiving and the stress process: An overview of concepts and their measures. The Gerontologist, 30(5), 583-594.], which examines the caregiver stress as composed of many factors such as the background variables, primary and secondary stressors, mediators and psychological health outcomes. This study included the caregiving tasks, basic needs of the caregivers and role strain as the stressors; coping strategies and perceived social support as mediators and depressive symptoms as the outcome variable. The participants of this study were 90 mothers of children with leukaemia. The results revealed that the satisfaction level of the basic needs and role strain were the predictors of the depressive symptoms. While emotion-focussed coping and perceived social support mediated the stressors and the depressive symptoms relationship, problem-focussed coping did not. The possible explanations of the results were explored and the implications were discussed.

Predictors of Iranian women's intention to first papanicolaou test practice: An application of protection motivation theory.

PubMed

Dehdari, T; Hassani, L; Shojaeizadeh, D; Hajizadeh, E; Nedjat, S; Abedini, M

2016-01-01

Given the importance of papanicolaou (Pap) test in the early detection and timely treatment of cervical cancer, present study was designed to determine predictors of a sample of Iranian women's intention to first Pap test practice based on the protection motivation theory (PMT) variables. In this cross-sectional study, a total of 240 women referral to the 30 primary health care clinics were selected. They completed a developed scale based on PMT variables including intention, perceived vulnerability and severity, fear, response costs, response efficacy and self-efficacy. Path analysis was used to determine the association between predictive factors and intention. The results showed that PMT had goodness of fit with a χ2/df = 2.37, df = 28, P= 0.001 and RMSEA = 0.076. PMT explained 42% of the variance in women's intention to get first Pap smear test. Self-efficacy (b = 0.55, P< 0.001) and response efficacy (b = 0.19, P< 0.001) were found to be the predictors of intention. These findings may be used to develop tailored, theory-based educational interventions associated with Pap testing among women.

Determinants of Employment Status among People with Multiple Sclerosis.

ERIC Educational Resources Information Center

Roessler, Richard T.; Fitzgerald, Shawn M.; Rumrill, Phillip D.; Koch, Lynn C.

2001-01-01

Identifies factors predicting employment or lack thereof among adults with multiple sclerosis (MS). Results included the following variables as the best predictors of employment: symptom persistence, severity of symptoms, educational attainment, and presence of cognitive limitations. The relevance of the findings for rehabilitation assessment and…

The use of generalised additive models (GAM) in dentistry.

PubMed

Helfenstein, U; Steiner, M; Menghini, G

1997-12-01

Ordinary multiple regression and logistic multiple regression are widely applied statistical methods which allow a researcher to 'explain' or 'predict' a response variable from a set of explanatory variables or predictors. In these models it is usually assumed that quantitative predictors such as age enter linearly into the model. During recent years these methods have been further developed to allow more flexibility in the way explanatory variables 'act' on a response variable. The methods are called 'generalised additive models' (GAM). The rigid linear terms characterising the association between response and predictors are replaced in an optimal way by flexible curved functions of the predictors (the 'profiles'). Plotting the 'profiles' allows the researcher to visualise easily the shape by which predictors 'act' over the whole range of values. The method facilitates detection of particular shapes such as 'bumps', 'U-shapes', 'J-shapes, 'threshold values' etc. Information about the shape of the association is not revealed by traditional methods. The shapes of the profiles may be checked by performing a Monte Carlo simulation ('bootstrapping'). After the presentation of the GAM a relevant case study is presented in order to demonstrate application and use of the method. The dependence of caries in primary teeth on a set of explanatory variables is investigated. Since GAMs may not be easily accessible to dentists, this article presents them in an introductory condensed form. It was thought that a nonmathematical summary and a worked example might encourage readers to consider the methods described. GAMs may be of great value to dentists in allowing visualisation of the shape by which predictors 'act' and obtaining a better understanding of the complex relationships between predictors and response.

Recidivism in stalking and obsessional harassment.

PubMed

Rosenfeld, Barry

2003-06-01

Despite the rapidly growth of mental health attention focused on the phenomenon of stalking, no empirical research to date has attempted to assess the frequency of repeat offending or attempted to identify predictors of recidivism. A total of 148 stalking and harassment offenders who were court-ordered to undergo a mental health evaluation were followed for a period of 2.5-13 years in order to assess the frequency of repeat offenses and the variables that differentiated high versus low risk offenders. Recidivism data were obtained from a variety of sources, including criminal justice records, mental health records, and reports from probation officers and victims. A number of potential "predictor" variables were selected on the basis of the existing recidivism literature in other criminal justice populations. Frequency analysis were used to identify variables that significantly differentiated offenders who did and did not reoffened while survival analysis was used to analyze the impact of these covariates on time to reoffense. A total of 49% of the offenders reoffended during the follow-up period, 80% of whom reoffended during the first year. The strongest predictors of recidivism included the presence of a personality disorder, and in particular, a "Cluster B" personality disorder (i.e., antisocial, borderline, and/or narcissistic). In addition, those offenders with both a personality disorder and a history of substance abuse were significantly more likely to reoffened compared to either of these risk factors alone. Surprisingly, the presence of a delusional disorder (e.g., erotomania) was associated with a lower risk of reoffender. The findings are discussed in terms of the legal system and treatment implications.

Predictor variable resolution governs modeled soil types

USDA-ARS?s Scientific Manuscript database

Soil mapping identifies different soil types by compressing a unique suite of spatial patterns and processes across multiple spatial scales. It can be quite difficult to quantify spatial patterns of soil properties with remotely sensed predictor variables. More specifically, matching the right scale...

Predictors of CPAP compliance in different clinical settings: primary care versus sleep unit.

PubMed

Nadal, Núria; de Batlle, Jordi; Barbé, Ferran; Marsal, Josep Ramon; Sánchez-de-la-Torre, Alicia; Tarraubella, Nuria; Lavega, Merce; Sánchez-de-la-Torre, Manuel

2018-03-01

Good adherence to continuous positive airway pressure (CPAP) treatment improves the patient's quality of life and decreases the risk of cardiovascular disease. Previous studies that have analyzed the adherence to CPAP were performed in a sleep unit (SU) setting. The involvement of primary care (PC) in the management of obstructive sleep apnea (OSA) patients receiving CPAP treatment could introduce factors related to the adherence to treatment. The objective was to compare the baseline predictors of CPAP compliance in SU and PC settings. OSA patients treated with CPAP were followed for 6 months in SU or PC setting. We included baseline clinical and anthropometrical variables, the Epworth Sleep Scale (ESS) score, the quality of life index, and the Charlson index. A logistic regression was performed for each group to determine the CPAP compliance predictors. Discrimination and calibration were performed using the area under the curve and Hosmer-Lemeshow tests. We included 191 patients: 91 in the PC group and 100 in the SU group. In 74.9% of the patients, the compliance was ≥ 4 h per day, with 80% compliance in the SU setting and 69.2% compliance in the PC setting (p = 0.087). The predictors of CPAP compliance were different between SU and PC settings. Body mass index, ESS, and CPAP pressure were predictors in the SU setting, and ESS, gender, and waist circumference were predictors in the PC setting. The predictors of adequate CPAP compliance vary between SU and PC settings. Detecting compliance predictors could help in the planning of early interventions to improve CPAP adherence.

Empirical-statistical downscaling of reanalysis data to high-resolution air temperature and specific humidity above a glacier surface (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Hofer, Marlis; MöLg, Thomas; Marzeion, Ben; Kaser, Georg

2010-06-01

Recently initiated observation networks in the Cordillera Blanca (Peru) provide temporally high-resolution, yet short-term, atmospheric data. The aim of this study is to extend the existing time series into the past. We present an empirical-statistical downscaling (ESD) model that links 6-hourly National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data to air temperature and specific humidity, measured at the tropical glacier Artesonraju (northern Cordillera Blanca). The ESD modeling procedure includes combined empirical orthogonal function and multiple regression analyses and a double cross-validation scheme for model evaluation. Apart from the selection of predictor fields, the modeling procedure is automated and does not include subjective choices. We assess the ESD model sensitivity to the predictor choice using both single-field and mixed-field predictors. Statistical transfer functions are derived individually for different months and times of day. The forecast skill largely depends on month and time of day, ranging from 0 to 0.8. The mixed-field predictors perform better than the single-field predictors. The ESD model shows added value, at all time scales, against simpler reference models (e.g., the direct use of reanalysis grid point values). The ESD model forecast 1960-2008 clearly reflects interannual variability related to the El Niño/Southern Oscillation but is sensitive to the chosen predictor type.

Explanatory model of emotional-cognitive variables in school mathematics performance: a longitudinal study in primary school.

PubMed

Cerda, Gamal; Pérez, Carlos; Navarro, José I; Aguilar, Manuel; Casas, José A; Aragón, Estíbaliz

2015-01-01

This study tested a structural model of cognitive-emotional explanatory variables to explain performance in mathematics. The predictor variables assessed were related to students' level of development of early mathematical competencies (EMCs), specifically, relational and numerical competencies, predisposition toward mathematics, and the level of logical intelligence in a population of primary school Chilean students (n = 634). This longitudinal study also included the academic performance of the students during a period of 4 years as a variable. The sampled students were initially assessed by means of an Early Numeracy Test, and, subsequently, they were administered a Likert-type scale to measure their predisposition toward mathematics (EPMAT) and a basic test of logical intelligence. The results of these tests were used to analyse the interaction of all the aforementioned variables by means of a structural equations model. This combined interaction model was able to predict 64.3% of the variability of observed performance. Preschool students' performance in EMCs was a strong predictor for achievement in mathematics for students between 8 and 11 years of age. Therefore, this paper highlights the importance of EMCs and the modulating role of predisposition toward mathematics. Also, this paper discusses the educational role of these findings, as well as possible ways to improve negative predispositions toward mathematical tasks in the school domain.

Multivariable and Bayesian Network Analysis of Outcome Predictors in Acute Aneurysmal Subarachnoid Hemorrhage: Review of a Pure Surgical Series in the Post-International Subarachnoid Aneurysm Trial Era.

PubMed

Zador, Zsolt; Huang, Wendy; Sperrin, Matthew; Lawton, Michael T

2018-06-01

Following the International Subarachnoid Aneurysm Trial (ISAT), evolving treatment modalities for acute aneurysmal subarachnoid hemorrhage (aSAH) has changed the case mix of patients undergoing urgent surgical clipping. To update our knowledge on outcome predictors by analyzing admission parameters in a pure surgical series using variable importance ranking and machine learning. We reviewed a single surgeon's case series of 226 patients suffering from aSAH treated with urgent surgical clipping. Predictions were made using logistic regression models, and predictive performance was assessed using areas under the receiver operating curve (AUC). We established variable importance ranking using partial Nagelkerke R2 scores. Probabilistic associations between variables were depicted using Bayesian networks, a method of machine learning. Importance ranking showed that World Federation of Neurosurgical Societies (WFNS) grade and age were the most influential outcome prognosticators. Inclusion of only these 2 predictors was sufficient to maintain model performance compared to when all variables were considered (AUC = 0.8222, 95% confidence interval (CI): 0.7646-0.88 vs 0.8218, 95% CI: 0.7616-0.8821, respectively, DeLong's P = .992). Bayesian networks showed that age and WFNS grade were associated with several variables such as laboratory results and cardiorespiratory parameters. Our study is the first to report early outcomes and formal predictor importance ranking following aSAH in a post-ISAT surgical case series. Models showed good predictive power with fewer relevant predictors than in similar size series. Bayesian networks proved to be a powerful tool in visualizing the widespread association of the 2 key predictors with admission variables, explaining their importance and demonstrating the potential for hypothesis generation.

Early symptom burden predicts recovery after sport-related concussion

PubMed Central

Mannix, Rebekah; Monuteaux, Michael C.; Stein, Cynthia J.; Bachur, Richard G.

2014-01-01

Objective: To identify independent predictors of and use recursive partitioning to develop a multivariate regression tree predicting symptom duration greater than 28 days after a sport-related concussion. Methods: We conducted a prospective cohort study of patients in a sports concussion clinic. Participants completed questionnaires that included the Post-Concussion Symptom Scale (PCSS). Participants were asked to record the date on which they last experienced symptoms. Potential predictor variables included age, sex, score on symptom inventories, history of prior concussions, performance on computerized neurocognitive assessments, loss of consciousness and amnesia at the time of injury, history of prior medical treatment for headaches, history of migraines, and family history of concussion. We used recursive partitioning analysis to develop a multivariate prediction model for identifying athletes at risk for a prolonged recovery from concussion. Results: A total of 531 patients ranged in age from 7 to 26 years (mean 14.6 ± 2.9 years). The mean PCSS score at the initial visit was 26 ± 26; mean time to presentation was 12 ± 5 days. Only total score on symptom inventory was independently associated with symptoms lasting longer than 28 days (adjusted odds ratio 1.044; 95% confidence interval [CI] 1.034, 1.054 for PCSS). No other potential predictor variables were independently associated with symptom duration or useful in developing the optimal regression decision tree. Most participants (86%; 95% CI 80%, 90%) with an initial PCSS score of <13 had resolution of their symptoms within 28 days of injury. Conclusions: The only independent predictor of prolonged symptoms after sport-related concussion is overall symptom burden. PMID:25381296

Early symptom burden predicts recovery after sport-related concussion.

PubMed

Meehan, William P; Mannix, Rebekah; Monuteaux, Michael C; Stein, Cynthia J; Bachur, Richard G

2014-12-09

To identify independent predictors of and use recursive partitioning to develop a multivariate regression tree predicting symptom duration greater than 28 days after a sport-related concussion. We conducted a prospective cohort study of patients in a sports concussion clinic. Participants completed questionnaires that included the Post-Concussion Symptom Scale (PCSS). Participants were asked to record the date on which they last experienced symptoms. Potential predictor variables included age, sex, score on symptom inventories, history of prior concussions, performance on computerized neurocognitive assessments, loss of consciousness and amnesia at the time of injury, history of prior medical treatment for headaches, history of migraines, and family history of concussion. We used recursive partitioning analysis to develop a multivariate prediction model for identifying athletes at risk for a prolonged recovery from concussion. A total of 531 patients ranged in age from 7 to 26 years (mean 14.6 ± 2.9 years). The mean PCSS score at the initial visit was 26 ± 26; mean time to presentation was 12 ± 5 days. Only total score on symptom inventory was independently associated with symptoms lasting longer than 28 days (adjusted odds ratio 1.044; 95% confidence interval [CI] 1.034, 1.054 for PCSS). No other potential predictor variables were independently associated with symptom duration or useful in developing the optimal regression decision tree. Most participants (86%; 95% CI 80%, 90%) with an initial PCSS score of <13 had resolution of their symptoms within 28 days of injury. The only independent predictor of prolonged symptoms after sport-related concussion is overall symptom burden. © 2014 American Academy of Neurology.

Using genetic algorithms to achieve an automatic and global optimization of analogue methods for statistical downscaling of precipitation

NASA Astrophysics Data System (ADS)

Horton, Pascal; Weingartner, Rolf; Obled, Charles; Jaboyedoff, Michel

2017-04-01

Analogue methods (AMs) rely on the hypothesis that similar situations, in terms of atmospheric circulation, are likely to result in similar local or regional weather conditions. These methods consist of sampling a certain number of past situations, based on different synoptic-scale meteorological variables (predictors), in order to construct a probabilistic prediction for a local weather variable of interest (predictand). They are often used for daily precipitation prediction, either in the context of real-time forecasting, reconstruction of past weather conditions, or future climate impact studies. The relationship between predictors and predictands is defined by several parameters (predictor variable, spatial and temporal windows used for the comparison, analogy criteria, and number of analogues), which are often calibrated by means of a semi-automatic sequential procedure that has strong limitations. AMs may include several subsampling levels (e.g. first sorting a set of analogs in terms of circulation, then restricting to those with similar moisture status). The parameter space of the AMs can be very complex, with substantial co-dependencies between the parameters. Thus, global optimization techniques are likely to be necessary for calibrating most AM variants, as they can optimize all parameters of all analogy levels simultaneously. Genetic algorithms (GAs) were found to be successful in finding optimal values of AM parameters. They allow taking into account parameters inter-dependencies, and selecting objectively some parameters that were manually selected beforehand (such as the pressure levels and the temporal windows of the predictor variables), and thus obviate the need of assessing a high number of combinations. The performance scores of the optimized methods increased compared to reference methods, and this even to a greater extent for days with high precipitation totals. The resulting parameters were found to be relevant and spatially coherent. Moreover, they were obtained automatically and objectively, which reduces efforts invested in exploration attempts when adapting the method to a new region or for a new predictand. In addition, the approach allowed for new degrees of freedom, such as a weighting between the pressure levels, and non overlapping spatial windows. Genetic algorithms were then used further in order to automatically select predictor variables and analogy criteria. This resulted in interesting outputs, providing new predictor-criterion combinations. However, some limitations of the approach were encountered, and the need of the expert input is likely to remain necessary. Nevertheless, letting GAs exploring a dataset for the best predictor for a predictand of interest is certainly a useful tool, particularly when applied for a new predictand or a new region under different climatic characteristics.

[Intra-articular injections of triamcinolone hexacetonide in rheumatoid arthritis: short and long-term improvement predictors].

PubMed

Furtado, Rita Nely Vilar; Machado, Flavia Soares; Luz, Karine Rodrigues da; Santos, Marla Francisca dos; Konai, Monique Sayuri; Lopes, Roberta Vilela; Natour, Jamil

2015-01-01

Identify good response predictors to intra-articular injection (IAI) with triamcinolone hexacetonide (TH). This study was carried out in rheumatoid arthritis (RA) patients (American College of Rheumatology criteria) submitted to IAI (mono, pauci or polyarticular injection). A "blinded" observer prospectively evaluated joints at one week (T1), four weeks (T4), twelve weeks (T12) and 24 weeks (T24) after IAI. Outcome measurements included Visual Analogue Scale (0-10 cm) at rest, in movement and for swollen joints. Clinical, demographic and variables related to injection at baseline were analyzed according to IAI response. We studied 289 patients with RA (635 joints) with a mean age of 48.7 years (±10.68), 48.5% of them Caucasians, VAS for global pain=6.52 (±1.73). Under univariate analysis, the variables relating the best responses following IAI (improvement > 70%) were: "elbow and metacarpophalangeal (MCP) IAI, and functional class II". Under multivariate analysis, "males" and "non-whites" were the predictors with the best response to IAI at T4, while "elbow and MCP IAI", "polyarticular injection", "use of methotrexate" and "higher total dose of TH" obtained the best response at T24. Several predictors of good response to IAI in patients with RA were identified. The best-response predictors for TH IAI of long term were "apply elbow and MCP IAI" and "apply polyarticular injection". Copyright © 2014 Elsevier Editora Ltda. All rights reserved.

Attitudinal and behavioral characteristics predict high risk sexual activity in rural Tanzanian youth.

PubMed

Aichele, Stephen R; Borgerhoff Mulder, Monique; James, Susan; Grimm, Kevin

2014-01-01

The incidence of HIV infection in rural African youth remains high despite widespread knowledge of the disease within the region and increasing funds allocated to programs aimed at its prevention and treatment. This suggests that program efficacy requires a more nuanced understanding of the profiles of the most at-risk individuals. To evaluate the explanatory power of novel psychographic variables in relation to high-risk sexual behaviors, we conducted a survey to assess the effects of psychographic factors, both behavioral and attitudinal, controlling for standard predictors in 546 youth (12-26 years of age) across 8 villages in northern Tanzania. Indicators of high-risk sexual behavior included HIV testing, sexual history (i.e., virgin/non-virgin), age of first sexual activity, condom use, and number of lifetime sexual partners. Predictors in the statistical models included standard demographic variables, patterns of media consumption, HIV awareness, and six new psychographic features identified via factor analyses: personal vanity, family-building values, ambition for higher education, town recreation, perceived parental strictness, and spending preferences. In a series of hierarchical regression analyses, we find that models including psychographic factors contribute significant additional explanatory information when compared to models including only demographic and other conventional predictors. We propose that the psychographic approach used here, in so far as it identifies individual characteristics, aspirations, aspects of personal life style and spending preferences, can be used to target appropriate communities of youth within villages for leading and receiving outreach, and to build communities of like-minded youth who support new patterns of sexual behavior.

Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors.

PubMed

Heddam, Salim; Kisi, Ozgur

2017-07-01

In this paper, several extreme learning machine (ELM) models, including standard extreme learning machine with sigmoid activation function (S-ELM), extreme learning machine with radial basis activation function (R-ELM), online sequential extreme learning machine (OS-ELM), and optimally pruned extreme learning machine (OP-ELM), are newly applied for predicting dissolved oxygen concentration with and without water quality variables as predictors. Firstly, using data from eight United States Geological Survey (USGS) stations located in different rivers basins, USA, the S-ELM, R-ELM, OS-ELM, and OP-ELM were compared against the measured dissolved oxygen (DO) using four water quality variables, water temperature, specific conductance, turbidity, and pH, as predictors. For each station, we used data measured at an hourly time step for a period of 4 years. The dataset was divided into a training set (70%) and a validation set (30%). We selected several combinations of the water quality variables as inputs for each ELM model and six different scenarios were compared. Secondly, an attempt was made to predict DO concentration without water quality variables. To achieve this goal, we used the year numbers, 2008, 2009, etc., month numbers from (1) to (12), day numbers from (1) to (31) and hour numbers from (00:00) to (24:00) as predictors. Thirdly, the best ELM models were trained using validation dataset and tested with the training dataset. The performances of the four ELM models were evaluated using four statistical indices: the coefficient of correlation (R), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), and the mean absolute error (MAE). Results obtained from the eight stations indicated that: (i) the best results were obtained by the S-ELM, R-ELM, OS-ELM, and OP-ELM models having four water quality variables as predictors; (ii) out of eight stations, the OP-ELM performed better than the other three ELM models at seven stations while the R-ELM performed the best at one station. The OS-ELM models performed the worst and provided the lowest accuracy; (iii) for predicting DO without water quality variables, the R-ELM performed the best at seven stations followed by the S-ELM in the second place and the OP-ELM performed the worst with low accuracy; (iv) for the final application where training ELM models with validation dataset and testing with training dataset, the OP-ELM provided the best accuracy using water quality variables and the R-ELM performed the best at all eight stations without water quality variables. Fourthly, and finally, we compared the results obtained from different ELM models with those obtained using multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN). Results obtained using MLPNN and MLR models reveal that: (i) using water quality variables as predictors, the MLR performed the worst and provided the lowest accuracy in all stations; (ii) MLPNN was ranked in the second place at two stations, in the third place at four stations, and finally, in the fourth place at two stations, (iii) for predicting DO without water quality variables, MLPNN is ranked in the second place at five stations, and ranked in the third, fourth, and fifth places in the remaining three stations, while MLR was ranked in the last place with very low accuracy at all stations. Overall, the results suggest that the ELM is more effective than the MLPNN and MLR for modelling DO concentration in river ecosystems.

Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported.

PubMed

Whittle, Rebecca; Peat, George; Belcher, John; Collins, Gary S; Riley, Richard D

2018-05-18

Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use. A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risk. Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorised as high risk of error, however this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured. Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions. Copyright © 2018. Published by Elsevier Inc.

Body image flexibility: A predictor and moderator of outcome in transdiagnostic outpatient eating disorder treatment.

PubMed

Pellizzer, Mia L; Waller, Glenn; Wade, Tracey D

2018-04-01

Predictors of attrition and predictors and moderators of outcome were explored in a transdiagnostic sample of patients who received ten-session cognitive behavioral therapy (CBT-T) for nonunderweight eating disorders. Body image flexibility, a protective positive body image construct, was hypothesized to be a significant moderator. Data from two case series were combined to form a sample of 78 participants who received CBT-T. Baseline measures of body image, negative affect, personality, and motivation (readiness to change and self-efficacy) were included as potential predictors. Global eating disorder psychopathology at each assessment point (baseline, mid- and post-treatment, 1- and 3-month follow-up) was the outcome variable. Predictors of attrition were assessed using logistic regression, and multilevel modeling was applied for predictors and moderators of outcome. Body image flexibility emerged as the strongest predictor and moderator of global eating disorder psychopathology, followed by body image avoidance. Body checking, negative affect, personality beliefs, and self-efficacy were significant predictors of global eating disorder psychopathology. Higher body image flexibility predicted lower global eating disorder psychopathology at every assessment point. Further research is required to replicate findings and explore the benefit of focusing on positive body image in treatment. © 2018 Wiley Periodicals, Inc.

Psychosocial predictors of the onset of anxiety disorders in women: Results from a prospective 3-year longitudinal study

PubMed Central

Calkins, Amanda W.; Otto, Michael W.; Cohen, Lee S.; Soares, Claudio N.; Vitonis, Alison F.; Hearon, Bridget A.; Harlow, Bernard L.

2009-01-01

In a prospective, longitudinal, population-based study of 643 women participating in the Harvard Study of Moods and Cycles we examined whether psychosocial variables predicted a new or recurrent onset of an anxiety disorder. Presence of anxiety disorders was assessed every six months over three years via structured clinical interviews. Among individuals who had a new episode of anxiety, we confirmed previous findings that history of anxiety, increased anxiety sensitivity (the fear of anxiety related sensations), and increased neuroticism were significant predictors. We also found trend level support for assertiveness as a predictor of anxiety onset. However, of these variables, only history of anxiety and anxiety sensitivity provided unique prediction. We did not find evidence for negative life events as a predictor of onset of anxiety either alone or in interaction with other variables in a diathesis-stress model. These findings from a prospective longitudinal study are discussed in relation to the potential role of such predictors in primary or relapse prevention efforts. PMID:19699609

Incidence of workers compensation indemnity claims across socio-demographic and job characteristics.

PubMed

Du, Juan; Leigh, J Paul

2011-10-01

We hypothesized that low socioeconomic status, employer-provided health insurance, low wages, and overtime were predictors of reporting workers compensation indemnity claims. We also tested for gender and race disparities. Responses from 17,190 (person-years) Americans participating in the Panel Study of Income Dynamics, 1997-2005, were analyzed with logistic regressions. The dependent variable indicated whether the subject collected benefits from a claim. Odds ratios for men and African-Americans were relatively large and strongly significant predictors of claims; significance for Hispanics was moderate and confounded by education. Odds ratios for variables measuring education were the largest for all statistically significant covariates. Neither low wages nor employer-provided health insurance was a consistent predictor. Due to confounding from the "not salaried" variable, overtime was not a consistently significant predictor. Few studies use nationally representative longitudinal data to consider which demographic and job characteristics predict reporting workers compensation indemnity cases. This study did and tested some common hypotheses about predictors. Copyright © 2011 Wiley-Liss, Inc.

Short-term variability and predictors of urinary pentachlorophenol levels in Ohio preschool children

EPA Science Inventory

Pentachlorophenol (PCP) is a persistent and ubiquitous environmental contaminant. No published data exist on the temporal variability or important predictors of urinary PCP concentrations in young children. In this further analysis of study data, we have examined the associations...

The no-show patient in the model family practice unit.

PubMed

Dervin, J V; Stone, D L; Beck, C H

1978-12-01

Appointment breaking by patients causes problems for the physician's office. Patients who neither keep nor cancel their appointments are often referred to as "no shows." Twenty variables were identified as potential predictors of no-show behavior. These predictors were applied to 291 Family Practice Center patients during a one-month study in April 1977. A discriminant function and multiple regression procedure were utilized ascertain the predictability of the selected variables. Predictive accuracy of the variables was 67.4 percent compared to the presently utilized constant predictor technique, which is 73 percent accurate. Modification of appointment schedules based upon utilization of the variables studies as predictors of show/no-show behavior does not appear to be an effective strategy in the Family Practice Center of the Community Hospital of Sonoma County, Santa Rosa, due to the high proportion of patients who do, in fact, show. In clinics with lower show rates, the technique may prove to be an effective strategy.

A Longitudinal Study of Work After Retirement: Examining Predictors of Bridge Employment, Continued Career Employment, and Retirement.

PubMed

Bennett, Misty M; Beehr, Terry A; Lepisto, Lawrence R

2016-09-01

Older employees are increasingly accepting bridge employment, which occurs when older workers take employment for pay after they retire from their main career. This study examined predictors of workers' decisions to engage in bridge employment versus full retirement and career employment. A national sample of 482 older people in the United States was surveyed regarding various work-related and nonwork related predictors of retirement decisions, and their retirement status was measured 5 years later. In bivariate analyses, both work-related variables (career goal achievement and experienced pressure to retire) and nonwork-related variables (psychological distress and traditional gender role orientation) predicted taking bridge employment, but in multinomial logistic regression, only nonwork variables had unique effects. Few predictors differentiated the bridge employed and fully retired groups. Nonwork variables were salient in making the decision to retire, and bridge employment may be conceptually more similar to full retirement than to career employment. © The Author(s) 2016.

Physiological and behavioral indices of emotion dysregulation as predictors of outcome from cognitive behavioral therapy and acceptance and commitment therapy for anxiety.

PubMed

Davies, Carolyn D; Niles, Andrea N; Pittig, Andre; Arch, Joanna J; Craske, Michelle G

2015-03-01

Identifying for whom and under what conditions a treatment is most effective is an essential step toward personalized medicine. The current study examined pre-treatment physiological and behavioral variables as predictors and moderators of outcome in a randomized clinical trial comparing cognitive behavioral therapy (CBT) and acceptance and commitment therapy (ACT) for anxiety disorders. Sixty individuals with a DSM-IV defined principal anxiety disorder completed 12 sessions of either CBT or ACT. Baseline physiological and behavioral variables were measured prior to entering treatment. Self-reported anxiety symptoms were assessed at pre-treatment, post-treatment, and 6- and 12-month follow-up from baseline. Higher pre-treatment heart rate variability was associated with worse outcome across ACT and CBT. ACT outperformed CBT for individuals with high behavioral avoidance. Subjective anxiety levels during laboratory tasks did not predict or moderate treatment outcome. Due to small sample sizes of each disorder, disorder-specific predictors were not tested. Future research should examine these predictors in larger samples and across other outcome variables. Lower heart rate variability was identified as a prognostic indicator of overall outcome, whereas high behavioral avoidance was identified as a prescriptive indicator of superior outcome from ACT versus CBT. Investigation of pre-treatment physiological and behavioral variables as predictors and moderators of outcome may help guide future treatment-matching efforts. Copyright © 2014 Elsevier Ltd. All rights reserved.

The impact of sociodemographic factors vs. gender roles on female hospital workers' health: do we need to shift emphasis?

PubMed

Musshauser, Doris; Bader, Angelika; Wildt, Beatrice; Hochleitner, Margarethe

2006-09-01

The aim of the present study was to evaluate the physical and mental health status of female workers from five different occupational groups and to identify possible sociodemographic and gender-coded family-related factors as well as work characteristics influencing women's health. The identified predictors of health status were subjected to a gender-sensitive analysis and their relations to one another are discussed. A total of 1083 female hospital workers including medical doctors, technical and administrative personnel, nurses and a group mainly consisting of scientific personnel and psychologists completed a questionnaire measuring work- and family-related variables, sociodemographic data and the Short-form 36 Health Questionnaire (SF-36). Data were analysed by multivariate regression analyses. Female medical doctors reported highest scores for all physical health dimensions except General Health. Our study population showed general low mental health status among administrative personnel and the heterogeneous group, others, scored highest on all mental health component scores. A series of eight regression analyses were performed. Three variables contributed highly significantly to all SF-36 subscale scores: age, satisfaction with work schedule, and the unpaid work variable. Age had the strongest influence on all physical dimensions except General Health (beta=-0.17) and had no detectable influence on mental health scores. The unpaid work variable (beta=-0.23; p<0.001) exerted a stronger influence on General Health than did age. Nevertheless, these variables were limited predictors of physical and mental health status. In all occupational groups the amount of time spent daily on child care and household tasks, as a traditional gender-coded factor, and satisfaction with work schedule were the only contributors to mental health among working women in this study. Traditional sociodemographic data had no effect on mental health status. In addition to age, these factors were shown to be the only predictors of physical health status of female workers. Gender coded-factors matter. These findings underline the importance of including gender-coded family- and work-related variables in medical research over and above basic sociodemographic data in order to describe study populations more clearly.

Evaluation of Selected Recycling Curricula: Educating the Green Citizen.

ERIC Educational Resources Information Center

Boerschig, Sally; De Young, Raymond

1993-01-01

Solid waste curricula from various programs around the country were reviewed using eight variables identified as predictors of conservation behavior. Scores demonstrated that solid waste curricula focus mainly on knowledge and include, to a lesser extent, attitude change and action strategies. Lists the 14 programs evaluated in the study. (MDH)

Non-Traditional Predictors of Academic Success for Special Action Admissions.

ERIC Educational Resources Information Center

Tom, Alice K.

The use of nontraditional college admission variables in the prediction of academic success was assessed with 444 freshmen entering the University of California, Davis, under the Special Action process (wavering of admission requirements). For fall 1978, 1979, 1980 special entrants, attention was directed to college applications, including high…

Rape Myth Acceptance, Sexual Trauma History, and Posttraumatic Stress Disorder

ERIC Educational Resources Information Center

Baugher, Shannon N.; Elhai, Jon D.; Monroe, James R.; Gray, Matt J.

2010-01-01

The prediction of false rape-related beliefs (rape myth acceptance [RMA]) was examined using the Illinois Rape Myth Acceptance Scale (Payne, Lonsway, & Fitzgerald, 1999) among a nonclinical sample of 258 male and female college students. Predictor variables included measures of attitudes toward women, gender role identity (GRI), sexual trauma…

Analysis of Eighth Graders' Performance On Standardized Mathematics Tests.

ERIC Educational Resources Information Center

Meyinsse, Joseph; Tashakkori, Abbas

The main objective of this study was to show whether eighth graders' performance on standardized mathematics tests could be predicted from a variety of variables. These predictors included the students' race/ethnicity, gender, attitudes toward mathematics, students' time spent on homework, whether parents helped with homework assignments,…

Some Determinants of Journal Holding Patterns in Academic Libraries.

ERIC Educational Resources Information Center

McCain, Katherine W.

1992-01-01

Reports results of a study that examined academic library holdings for core journals in genetics and economics to identify quantitative and qualitative journal characteristics that are predictors of the number of libraries subscribing to the journal. Variables investigated include subject area, publisher, price, longevity, impact factor, prestige…

Career Decision Status as a Predictor of Resignation Behavior Five Years Later

ERIC Educational Resources Information Center

Earl, Joanne K.; Minbashian, Amirali; Sukijjakhamin, Aun; Bright, Jim E. H.

2011-01-01

This paper extends earlier research exploring the relationship between career decision status and work outcomes by examining resignation behavior in a group of new graduates five years after initial appointment. On appointment various measures were collected including career decision status variables. Earlier research identified a significant…

User Reaction to Videoconferencing. Which Students Cope Best?

ERIC Educational Resources Information Center

Wheeler, Steve

2000-01-01

Reviews a study conducted at the University of Plymouth (United Kingdom) to establish the psychological basis for user responses to digital videoconferencing. Discusses possible predictor variables, including left and right brain laterality and factors of age and gender; measurement of behavioral and affective responses in distance learners;…

Cultural Intelligence: An Examination of Predictive Relationships in a Study Abroad Population

ERIC Educational Resources Information Center

Banning, Bryan James

2010-01-01

This quantitative study examined the relationships between cultural intelligence (CQ) and four predictor variables: gender, degree level, major, and prior travel abroad, through a post-test only research design. Participants included undergraduate and graduate students enrolled in one of three large, public, research universities in the southeast…

School Quality and Learning Gains in Rural Guatemala

ERIC Educational Resources Information Center

Marshall, Jeffery H.

2009-01-01

I use unusually detailed data on schools, teachers and classrooms to explain student achievement growth in rural Guatemala. Several variables that have received little attention in previous studies--including the number of school days, teacher content knowledge and pedagogical methods--are robust predictors of achievement. A series of…

Predictors of Children's Prosocial Lie-Telling: Motivation, Socialization Variables, and Moral Understanding

ERIC Educational Resources Information Center

Popliger, Mina; Talwar, Victoria; Crossman, Angela

2011-01-01

Children tell prosocial lies for self- and other-oriented reasons. However, it is unclear how motivational and socialization factors affect their lying. Furthermore, it is unclear whether children's moral understanding and evaluations of prosocial lie scenarios (including perceptions of vignette characters' feelings) predict their actual prosocial…

Argentina wheat yield model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

Five models based on multiple regression were developed to estimate wheat yields for the five wheat growing provinces of Argentina. Meteorological data sets were obtained for each province by averaging data for stations within each province. Predictor variables for the models were derived from monthly total precipitation, average monthly mean temperature, and average monthly maximum temperature. Buenos Aires was the only province for which a trend variable was included because of increasing trend in yield due to technology from 1950 to 1963.

Argentina corn yield model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate corn yields for the country of Argentina. A meteorological data set was obtained for the country by averaging data for stations within the corn-growing area. Predictor variables for the model were derived from monthly total precipitation, average monthly mean temperature, and average monthly maximum temperature. A trend variable was included for the years 1965 to 1980 since an increasing trend in yields due to technology was observed between these years.

IRB Process Improvements: A Machine Learning Analysis.

PubMed

Shoenbill, Kimberly; Song, Yiqiang; Cobb, Nichelle L; Drezner, Marc K; Mendonca, Eneida A

2017-06-01

Clinical research involving humans is critically important, but it is a lengthy and expensive process. Most studies require institutional review board (IRB) approval. Our objective is to identify predictors of delays or accelerations in the IRB review process and apply this knowledge to inform process change in an effort to improve IRB efficiency, transparency, consistency and communication. We analyzed timelines of protocol submissions to determine protocol or IRB characteristics associated with different processing times. Our evaluation included single variable analysis to identify significant predictors of IRB processing time and machine learning methods to predict processing times through the IRB review system. Based on initial identified predictors, changes to IRB workflow and staffing procedures were instituted and we repeated our analysis. Our analysis identified several predictors of delays in the IRB review process including type of IRB review to be conducted, whether a protocol falls under Veteran's Administration purview and specific staff in charge of a protocol's review. We have identified several predictors of delays in IRB protocol review processing times using statistical and machine learning methods. Application of this knowledge to process improvement efforts in two IRBs has led to increased efficiency in protocol review. The workflow and system enhancements that are being made support our four-part goal of improving IRB efficiency, consistency, transparency, and communication.

Miscarriage: A Special Type of Family Crisis.

ERIC Educational Resources Information Center

Day, Randal D.; Hooks, Daniel

1987-01-01

Surveyed 102 women about their experience with miscarriage. Found that family resource variables were a much stronger predictor of level of crisis and recovery than were personal or community resource variables. Adaptation and cohesion were significant predictors of speed or recovery and level of crisis, respectively. (Author/NB)

A comparison of acoustic and observed sediment classifications as predictor variables for modelling biotope distributions in Galway Bay, Ireland

NASA Astrophysics Data System (ADS)

O'Carroll, Jack P. J.; Kennedy, Robert; Ren, Lei; Nash, Stephen; Hartnett, Michael; Brown, Colin

2017-10-01

The INFOMAR (Integrated Mapping For the Sustainable Development of Ireland's Marine Resource) initiative has acoustically mapped and classified a significant proportion of Ireland's Exclusive Economic Zone (EEZ), and is likely to be an important tool in Ireland's efforts to meet the criteria of the MSFD. In this study, open source and relic data were used in combination with new grab survey data to model EUNIS level 4 biotope distributions in Galway Bay, Ireland. The correct prediction rates of two artificial neural networks (ANNs) were compared to assess the effectiveness of acoustic sediment classifications versus sediments that were visually classified by an expert in the field as predictor variables. To test for autocorrelation between predictor variables the RELATE routine with Spearman rank correlation method was used. Optimal models were derived by iteratively removing predictor variables and comparing the correct prediction rates of each model. The models with the highest correct prediction rates were chosen as optimal. The optimal models each used a combination of salinity (binary; 0 = polyhaline and 1 = euhaline), proximity to reef (binary; 0 = within 50 m and 1 = outside 50 m), depth (continuous; metres) and a sediment descriptor (acoustic or observed) as predictor variables. As the status of benthic habitats is required to be assessed under the MSFD the Ecological Status (ES) of the subtidal sediments of Galway Bay was also assessed using the Infaunal Quality Index. The ANN that used observed sediment classes as predictor variables could correctly predict the distribution of biotopes 67% of the time, compared to 63% for the ANN using acoustic sediment classes. Acoustic sediment ANN predictions were affected by local sediment heterogeneity, and the lack of a mixed sediment class. The all-round poor performance of ANNs is likely to be a result of the temporally variable and sparsely distributed data within the study area.

Prediction of Psilocybin Response in Healthy Volunteers

PubMed Central

Studerus, Erich; Gamma, Alex; Kometer, Michael; Vollenweider, Franz X.

2012-01-01

Responses to hallucinogenic drugs, such as psilocybin, are believed to be critically dependent on the user's personality, current mood state, drug pre-experiences, expectancies, and social and environmental variables. However, little is known about the order of importance of these variables and their effect sizes in comparison to drug dose. Hence, this study investigated the effects of 24 predictor variables, including age, sex, education, personality traits, drug pre-experience, mental state before drug intake, experimental setting, and drug dose on the acute response to psilocybin. The analysis was based on the pooled data of 23 controlled experimental studies involving 409 psilocybin administrations to 261 healthy volunteers. Multiple linear mixed effects models were fitted for each of 15 response variables. Although drug dose was clearly the most important predictor for all measured response variables, several non-pharmacological variables significantly contributed to the effects of psilocybin. Specifically, having a high score in the personality trait of Absorption, being in an emotionally excitable and active state immediately before drug intake, and having experienced few psychological problems in past weeks were most strongly associated with pleasant and mystical-type experiences, whereas high Emotional Excitability, low age, and an experimental setting involving positron emission tomography most strongly predicted unpleasant and/or anxious reactions to psilocybin. The results confirm that non-pharmacological variables play an important role in the effects of psilocybin. PMID:22363492

Prediction of psilocybin response in healthy volunteers.

PubMed

Studerus, Erich; Gamma, Alex; Kometer, Michael; Vollenweider, Franz X

2012-01-01

Responses to hallucinogenic drugs, such as psilocybin, are believed to be critically dependent on the user's personality, current mood state, drug pre-experiences, expectancies, and social and environmental variables. However, little is known about the order of importance of these variables and their effect sizes in comparison to drug dose. Hence, this study investigated the effects of 24 predictor variables, including age, sex, education, personality traits, drug pre-experience, mental state before drug intake, experimental setting, and drug dose on the acute response to psilocybin. The analysis was based on the pooled data of 23 controlled experimental studies involving 409 psilocybin administrations to 261 healthy volunteers. Multiple linear mixed effects models were fitted for each of 15 response variables. Although drug dose was clearly the most important predictor for all measured response variables, several non-pharmacological variables significantly contributed to the effects of psilocybin. Specifically, having a high score in the personality trait of Absorption, being in an emotionally excitable and active state immediately before drug intake, and having experienced few psychological problems in past weeks were most strongly associated with pleasant and mystical-type experiences, whereas high Emotional Excitability, low age, and an experimental setting involving positron emission tomography most strongly predicted unpleasant and/or anxious reactions to psilocybin. The results confirm that non-pharmacological variables play an important role in the effects of psilocybin.

Predictors of positive mental health among refugees: Results from Canada's General Social Survey.

PubMed

Beiser, Morton; Hou, Feng

2017-01-01

Do refugees have lower levels of positive mental health than other migrants? If so, to what extent is this attributable to post-migration experiences, including discrimination? How does gender affect the relationships between post-migration experience and positive mental health? To address these questions, the current study uses data from Statistics Canada's 2013 General Social Survey (GSS), a nationally representative household study that included 27,695 Canadians 15 years of age and older. The study compares self-reported positive mental health among 651 refugees, 309 economic immigrants, and 448 family class immigrants from 50 source countries. Immigration-related predictors of mental health were examined including sociodemographic characteristics, discrimination, acculturation variables, and experiences of reception. Separate analyses were carried out for women and men. Refugees had lower levels of positive mental health than other migrants. Affiliative feelings towards the source country jeopardized refugee, but not immigrant mental health. A sense of belonging to Canada was a significant predictor of mental health. Perceived discrimination explained refugee mental health disadvantage among men, but not women. Bridging social networks were a mental health asset, particularly for women. The implications of anti-refugee discrimination net of the effects of anti-immigrant and anti-visible minority antipathies are discussed, as well as possible reasons for gender differences in the salience of mental health predictors.

Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling

NASA Astrophysics Data System (ADS)

Bechtold, M.; Tiemeyer, B.; Laggner, A.; Leppelt, T.; Frahm, E.; Belting, S.

2014-04-01

Fluxes of the three main greenhouse gases (GHG) CO2, CH4 and N2O from peat and other organic soils are strongly controlled by water table depth. Information about the spatial distribution of water level is thus a crucial input parameter when upscaling GHG emissions to large scales. Here, we investigate the potential of statistical modeling for the regionalization of water levels in organic soils when data covers only a small fraction of the peatlands of the final map. Our study area is Germany. Phreatic water level data from 53 peatlands in Germany were compiled in a new dataset comprising 1094 dip wells and 7155 years of data. For each dip well, numerous possible predictor variables were determined using nationally available data sources, which included information about land cover, ditch network, protected areas, topography, peatland characteristics and climatic boundary conditions. We applied boosted regression trees to identify dependencies between predictor variables and dip well specific long-term annual mean water level (WL) as well as a transformed form of it (WLt). The latter was obtained by assuming a hypothetical GHG transfer function and is linearly related to GHG emissions. Our results demonstrate that model calibration on WLt is superior. It increases the explained variance of the water level in the sensitive range for GHG emissions and avoids model bias in subsequent GHG upscaling. The final model explained 45% of WLt variance and was built on nine predictor variables that are based on information about land cover, peatland characteristics, drainage network, topography and climatic boundary conditions. Their individual effects on WLt and the observed parameter interactions provide insights into natural and anthropogenic boundary conditions that control water levels in organic soils. Our study also demonstrates that a large fraction of the observed WLt variance cannot be explained by nationally available predictor variables and that predictors with stronger WLt indication, relying e.g. on detailed water management maps and remote sensing products, are needed to substantially improve model predictive performance.

Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling

NASA Astrophysics Data System (ADS)

Bechtold, M.; Tiemeyer, B.; Laggner, A.; Leppelt, T.; Frahm, E.; Belting, S.

2014-09-01

Fluxes of the three main greenhouse gases (GHG) CO2, CH4 and N2O from peat and other soils with high organic carbon contents are strongly controlled by water table depth. Information about the spatial distribution of water level is thus a crucial input parameter when upscaling GHG emissions to large scales. Here, we investigate the potential of statistical modeling for the regionalization of water levels in organic soils when data covers only a small fraction of the peatlands of the final map. Our study area is Germany. Phreatic water level data from 53 peatlands in Germany were compiled in a new data set comprising 1094 dip wells and 7155 years of data. For each dip well, numerous possible predictor variables were determined using nationally available data sources, which included information about land cover, ditch network, protected areas, topography, peatland characteristics and climatic boundary conditions. We applied boosted regression trees to identify dependencies between predictor variables and dip-well-specific long-term annual mean water level (WL) as well as a transformed form (WLt). The latter was obtained by assuming a hypothetical GHG transfer function and is linearly related to GHG emissions. Our results demonstrate that model calibration on WLt is superior. It increases the explained variance of the water level in the sensitive range for GHG emissions and avoids model bias in subsequent GHG upscaling. The final model explained 45% of WLt variance and was built on nine predictor variables that are based on information about land cover, peatland characteristics, drainage network, topography and climatic boundary conditions. Their individual effects on WLt and the observed parameter interactions provide insight into natural and anthropogenic boundary conditions that control water levels in organic soils. Our study also demonstrates that a large fraction of the observed WLt variance cannot be explained by nationally available predictor variables and that predictors with stronger WLt indication, relying, for example, on detailed water management maps and remote sensing products, are needed to substantially improve model predictive performance.

Pitfalls in statistical landslide susceptibility modelling

NASA Astrophysics Data System (ADS)

Schröder, Boris; Vorpahl, Peter; Märker, Michael; Elsenbeer, Helmut

2010-05-01

The use of statistical methods is a well-established approach to predict landslide occurrence probabilities and to assess landslide susceptibility. This is achieved by applying statistical methods relating historical landslide inventories to topographic indices as predictor variables. In our contribution, we compare several new and powerful methods developed in machine learning and well-established in landscape ecology and macroecology for predicting the distribution of shallow landslides in tropical mountain rainforests in southern Ecuador (among others: boosted regression trees, multivariate adaptive regression splines, maximum entropy). Although these methods are powerful, we think it is necessary to follow a basic set of guidelines to avoid some pitfalls regarding data sampling, predictor selection, and model quality assessment, especially if a comparison of different models is contemplated. We therefore suggest to apply a novel toolbox to evaluate approaches to the statistical modelling of landslide susceptibility. Additionally, we propose some methods to open the "black box" as an inherent part of machine learning methods in order to achieve further explanatory insights into preparatory factors that control landslides. Sampling of training data should be guided by hypotheses regarding processes that lead to slope failure taking into account their respective spatial scales. This approach leads to the selection of a set of candidate predictor variables considered on adequate spatial scales. This set should be checked for multicollinearity in order to facilitate model response curve interpretation. Model quality assesses how well a model is able to reproduce independent observations of its response variable. This includes criteria to evaluate different aspects of model performance, i.e. model discrimination, model calibration, and model refinement. In order to assess a possible violation of the assumption of independency in the training samples or a possible lack of explanatory information in the chosen set of predictor variables, the model residuals need to be checked for spatial auto¬correlation. Therefore, we calculate spline correlograms. In addition to this, we investigate partial dependency plots and bivariate interactions plots considering possible interactions between predictors to improve model interpretation. Aiming at presenting this toolbox for model quality assessment, we investigate the influence of strategies in the construction of training datasets for statistical models on model quality.

Variables that Predict Serve Efficacy in Elite Men’s Volleyball with Different Quality of Opposition Sets

PubMed Central

Valhondo, Álvaro; Fernández-Echeverría, Carmen; González-Silva, Jara; Claver, Fernando; Moreno, M. Perla

2018-01-01

Abstract The objective of this study was to determine the variables that predicted serve efficacy in elite men’s volleyball, in sets with different quality of opposition. 3292 serve actions were analysed, of which 2254 were carried out in high quality of opposition sets and 1038 actions were in low quality of opposition sets, corresponding to a total of 24 matches played during the Men’s European Volleyball Championships held in 2011. The independent variables considered in this study were the serve zone, serve type, serving player, serve direction, reception zone, receiving player and reception type; the dependent variable was serve efficacy and the situational variable was quality of opposition sets. The variables that acted as predictors in both high and low quality of opposition sets were the serving player, reception zone and reception type. The serve type variable only acted as a predictor in high quality of opposition sets, while the serve zone variable only acted as a predictor in low quality of opposition sets. These results may provide important guidance in men’s volleyball training processes. PMID:29599869

Conventional heart rate variability analysis of ambulatory electrocardiographic recordings fails to predict imminent ventricular fibrillation

NASA Technical Reports Server (NTRS)

Vybiral, T.; Glaeser, D. H.; Goldberger, A. L.; Rigney, D. R.; Hess, K. R.; Mietus, J.; Skinner, J. E.; Francis, M.; Pratt, C. M.

1993-01-01

OBJECTIVES. The purpose of this report was to study heart rate variability in Holter recordings of patients who experienced ventricular fibrillation during the recording. BACKGROUND. Decreased heart rate variability is recognized as a long-term predictor of overall and arrhythmic death after myocardial infarction. It was therefore postulated that heart rate variability would be lowest when measured immediately before ventricular fibrillation. METHODS. Conventional indexes of heart rate variability were calculated from Holter recordings of 24 patients with structural heart disease who had ventricular fibrillation during monitoring. The control group consisted of 19 patients with coronary artery disease, of comparable age and left ventricular ejection fraction, who had nonsustained ventricular tachycardia but no ventricular fibrillation. RESULTS. Heart rate variability did not differ between the two groups, and no consistent trends in heart rate variability were observed before ventricular fibrillation occurred. CONCLUSIONS. Although conventional heart rate variability is an independent long-term predictor of adverse outcome after myocardial infarction, its clinical utility as a short-term predictor of life-threatening arrhythmias remains to be elucidated.

Relationships between Speech Intelligibility and Word Articulation Scores in Children with Hearing Loss

PubMed Central

Ertmer, David J.

2012-01-01

Purpose This investigation sought to determine whether scores from a commonly used word-based articulation test are closely associated with speech intelligibility in children with hearing loss. If the scores are closely related, articulation testing results might be used to estimate intelligibility. If not, the importance of direct assessment of intelligibility would be reinforced. Methods Forty-four children with hearing losses produced words from the Goldman-Fristoe Test of Articulation-2 and sets of 10 short sentences. Correlation analyses were conducted between scores for seven word-based predictor variables and percent-intelligible scores derived from listener judgments of stimulus sentences. Results Six of seven predictor variables were significantly correlated with percent-intelligible scores. However, regression analysis revealed that no single predictor variable or multi- variable model accounted for more than 25% of the variability in intelligibility scores. Implications The findings confirm the importance of assessing connected speech intelligibility directly. PMID:20220022

Tree species distribution in temperate forests is more influenced by soil than by climate.

PubMed

Walthert, Lorenz; Meier, Eliane Seraina

2017-11-01

Knowledge of the ecological requirements determining tree species distributions is a precondition for sustainable forest management. At present, the abiotic requirements and the relative importance of the different abiotic factors are still unclear for many temperate tree species. We therefore investigated the relative importance of climatic and edaphic factors for the abundance of 12 temperate tree species along environmental gradients. Our investigations are based on data from 1,075 forest stands across Switzerland including the cold-induced tree line of all studied species and the drought-induced range boundaries of several species. Four climatic and four edaphic predictors represented the important growth factors temperature, water supply, nutrient availability, and soil aeration. The climatic predictors were derived from the meteorological network of MeteoSwiss, and the edaphic predictors were available from soil profiles. Species cover abundances were recorded in field surveys. The explanatory power of the predictors was assessed by variation partitioning analyses with generalized linear models. For six of the 12 species, edaphic predictors were more important than climatic predictors in shaping species distribution. Over all species, abundances depended mainly on nutrient availability, followed by temperature, water supply, and soil aeration. The often co-occurring species responded similar to these growth factors. Drought turned out to be a determinant of the lower range boundary for some species. We conclude that over all 12 studied tree species, soil properties were more important than climate variables in shaping tree species distribution. The inclusion of appropriate soil variables in species distribution models allowed to better explain species' ecological niches. Moreover, our study revealed that the ecological requirements of tree species assessed in local field studies and in experiments are valid at larger scales across Switzerland.

Diffusion of Impaired Driving Laws Among US States.

PubMed

Macinko, James; Silver, Diana

2015-09-01

We examined internal and external determinants of state's adoption of impaired driving laws. Data included 7 state-level, evidence-based public health laws collected from 1980 to 2010. We used event history analyses to identify predictors of first-time law adoption and subsequent adoption between state pairs. The independent variables were internal state factors, including the political environment, legislative professionalism, government capacity, state resources, legislative history, and policy-specific risk factors. The external factors were neighboring states' history of law adoption and changes in federal law. We found a strong secular trend toward an increased number of laws over time. The proportion of younger drivers and the presence of a neighboring state with similar laws were the strongest predictors of first-time law adoption. The predictors of subsequent law adoption included neighbor state adoption and previous legislative action. Alcohol laws were negatively associated with first-time adoption of impaired driving laws, suggesting substitution effects among policy choices. Organizations seeking to stimulate state policy changes may need to craft strategies that engage external actors, such as neighboring states, in addition to mobilizing within-state constituencies.

Diffusion of Impaired Driving Laws Among US States

PubMed Central

Silver, Diana

2015-01-01

Objectives. We examined internal and external determinants of state’s adoption of impaired driving laws. Methods. Data included 7 state-level, evidence-based public health laws collected from 1980 to 2010. We used event history analyses to identify predictors of first-time law adoption and subsequent adoption between state pairs. The independent variables were internal state factors, including the political environment, legislative professionalism, government capacity, state resources, legislative history, and policy-specific risk factors. The external factors were neighboring states’ history of law adoption and changes in federal law. Results. We found a strong secular trend toward an increased number of laws over time. The proportion of younger drivers and the presence of a neighboring state with similar laws were the strongest predictors of first-time law adoption. The predictors of subsequent law adoption included neighbor state adoption and previous legislative action. Alcohol laws were negatively associated with first-time adoption of impaired driving laws, suggesting substitution effects among policy choices. Conclusions. Organizations seeking to stimulate state policy changes may need to craft strategies that engage external actors, such as neighboring states, in addition to mobilizing within-state constituencies. PMID:26180969

Motor and cognitive outcomes in children after functional hemispherectomy.

PubMed

Samargia, Sharyl A; Kimberley, Teresa Jacobson

2009-01-01

Medically intractable epilepsy is a chronic recurrence of seizures that often requires surgery to reduce or eliminate them. Although a reduction of seizures is the primary goal of hemispherectomy, the effect of surgery on motor and cognitive skills is also of importance. This review will provide a discussion of (1) evidence regarding motor and cognitive outcomes, (2) predictors of these outcomes, and (3) neural mechanisms responsible for preservation of function after hemispherectomy. Motor and cognitive outcomes after hemispherectomy are variable and depend on many predictors including etiology and duration of seizure disorder, age at the time of surgery, premorbid status, and postsurgical seizure control. A refined ipsilateral pathway may explain the preservation of motor function in some children. A clear understanding of outcome predictors is important for planning effective rehabilitative programs after surgery.

ESS++: a C++ objected-oriented algorithm for Bayesian stochastic search model exploration

PubMed Central

Bottolo, Leonardo; Langley, Sarah R.; Petretto, Enrico; Tiret, Laurence; Tregouet, David; Richardson, Sylvia

2011-01-01

Summary: ESS++ is a C++ implementation of a fully Bayesian variable selection approach for single and multiple response linear regression. ESS++ works well both when the number of observations is larger than the number of predictors and in the ‘large p, small n’ case. In the current version, ESS++ can handle several hundred observations, thousands of predictors and a few responses simultaneously. The core engine of ESS++ for the selection of relevant predictors is based on Evolutionary Monte Carlo. Our implementation is open source, allowing community-based alterations and improvements. Availability: C++ source code and documentation including compilation instructions are available under GNU licence at http://bgx.org.uk/software/ESS.html. Contact: l.bottolo@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21233165

Predictors of First-Year Sultan Qaboos University Students' Grade Point Average

ERIC Educational Resources Information Center

Alkhausi, Hussain Ali; Al-Yahmadi, Hamad; Al-Kalbani, Muna; Clayton, David; Al-Barwani, Thuwayba; Al-Sulaimani, Humaira; Neisler, Otherine; Khan, Mohammad Athar

2015-01-01

This study investigated predictors of first-year university grade point average (GPA) using academic and nonacademic variables. Data were collected from 1511 Omani students selected conveniently from the population of students entering Sultan Qaboos University (SQU) in Fall 2010. Variables considered in the analysis were general education diploma…

Life Expectancy of Persons with Down Syndrome.

ERIC Educational Resources Information Center

Eyman, Richard K.; And Others

1991-01-01

Longevity of 12,543 Down's syndrome clients of the California Department of Developmental Services was examined. Findings indicated that predictors of survival were not different from mortality-related variables in the general population. Lack of mobility or poor feeding skills were better predictors of early death than variables associated with…

Centering Effects in HLM Level-1 Predictor Variables.

ERIC Educational Resources Information Center

Schumacker, Randall E.; Bembry, Karen

Research has suggested that important research questions can be addressed with meaningful interpretations using hierarchical linear modeling (HLM). The proper interpretation of results, however, is invariably linked to the choice of centering for the Level-1 predictor variables that produce the outcome measure for the Level-2 regression analysis.…

The effect of service satisfaction and spiritual well-being on the quality of life of patients with schizophrenia.

PubMed

Lanfredi, Mariangela; Candini, Valentina; Buizza, Chiara; Ferrari, Clarissa; Boero, Maria E; Giobbio, Gian M; Goldschmidt, Nicoletta; Greppo, Stefania; Iozzino, Laura; Maggi, Paolo; Melegari, Anna; Pasqualetti, Patrizio; Rossi, Giuseppe; de Girolamo, Giovanni

2014-05-15

Quality of life (QOL) has been considered an important outcome measure in psychiatric research and determinants of QOL have been widely investigated. We aimed at detecting predictors of QOL at baseline and at testing the longitudinal interrelations of the baseline predictors with QOL scores at a 1-year follow-up in a sample of patients living in Residential Facilities (RFs). Logistic regression models were adopted to evaluate the association between WHOQoL-Bref scores and potential determinants of QOL. In addition, all variables significantly associated with QOL domains in the final logistic regression model were included by using the Structural Equation Modeling (SEM). We included 139 patients with a diagnosis of schizophrenia spectrum. In the final logistic regression model level of activity, social support, age, service satisfaction, spiritual well-being and symptoms' severity were identified as predictors of QOL scores at baseline. Longitudinal analyses carried out by SEM showed that 40% of QOL follow-up variability was explained by QOL at baseline, and significant indirect effects toward QOL at follow-up were found for satisfaction with services and for social support. Rehabilitation plans for people with schizophrenia living in RFs should also consider mediators of change in subjective QOL such as satisfaction with mental health services. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Socio-ecological predictors of participation and dropout in organised sports during childhood.

PubMed

Vella, Stewart A; Cliff, Dylan P; Okely, Anthony D

2014-05-13

The purpose of this study was to explore the socio-ecological determinants of participation and dropout in organised sports in a nationally-representative sample of Australian children. Data were drawn from Waves 3 and 4 of the Longitudinal Study of Australian Children. In total, 4042 children aged 8.25 (SD = 0.44) years at baseline were included, with 24-months between Waves. Socio-ecological predictors were reported by parents and teachers, while cognitive and health measures were assessed by trained professionals. All predictors were assessed at age 8, and used to predict participation and dropout by age 10. Seven variables at age 8 were shown to positively predict participation in organised sports at age 10. These included: sex (boy); fewer people in household; higher household income; main language spoken at home (English); higher parental education; child taken to a sporting event; and, access to a specialist PE teacher during primary school. Four variables predicted dropout from organised sports by age 10: lower household income; main language spoken at home (non-English); lower parental education; and, child not taken to a sporting event. The interplay between child sex, socioeconomic indicators, and parental support is important in predicting children's participation in organised sports. Multilevel and multicomponent interventions to promote participation and prevent dropout should be underpinned by the Socio-Ecological Model and targeted to high risk populations using multiple levels of risk.

Predicting the In-Hospital Responsiveness to Treatment of Alcoholics. Social Factors as Predictors of Outcome. Brain Damage as a Factor in Treatment Outcome of Chronic Alcoholic Patients.

ERIC Educational Resources Information Center

Mascia, George V.; And Others

The authors attempt to locate predictor variables associated with the outcome of alcoholic treatment programs. Muscia's study focuses on the predictive potential of: (1) response to a GSR conditioning procedure; (2) several personality variables; and (3) age and IQ measures. Nine variables, reflecting diverse perspectives, were selected as a basis…

Predictors of outcome and methodological issues in children with acute lymphoblastic leukaemia in El Salvador.

PubMed

Bonilla, Miguel; Gupta, Sumit; Vasquez, Roberto; Fuentes, Soad L; deReyes, Gladis; Ribeiro, Raul; Sung, Lillian

2010-12-01

Most children with cancer live in low-income countries (LICs) where risk factors in paediatric acute lymphoblastic leukaemia (ALL) developed in high-income countries may not apply. We describe predictors of survival for children in El Salvador with ALL. We included patients <16 years diagnosed with ALL between January 2001 and July 2007 treated with the El Salvador-Guatemala-Honduras II protocol. Demographic, disease-related, socioeconomic and nutritional variables were examined as potential predictors of event-free survival (EFS) and overall survival (OS). 260/443 patients (58.7%) were classified as standard risk. Standard- and high-risk 5-year EFS were 56.3 ± 4.5% and 48.6 ± 5.5%; 5-year OS were 77.7 ± 3.8% and 61.9 ± 5.8%, respectively. Among standard-risk children, socioeconomic variables such as higher monthly income (hazard ratio [HR] per $100 = 0.84 [95% confidence interval (CI) 0.70-0.99; P=0.04]) and parental secondary education (HR = 0.49, 95% CI 0.29-0.84; P = 0.01) were associated with better EFS. Among high-risk children, higher initial white blood cell (HR per 10×10(9)/L = 1.03, 95% CI 1.02-1.05; P<0.001) predicted worse EFS; socioeconomic variables were not predictive. The difference in EFS and OS appeared related to overestimating OS secondary to poor follow-up after abandonment/relapse. Socioeconomic variables predicted worse EFS in standard-risk children while disease-related variables were predictive in high-risk patients. Further studies should delineate pathways through which socioeconomic status affects EFS in order to design effective interventions. EFS should be the primary outcome in LIC studies. Copyright © 2010 Elsevier Ltd. All rights reserved.

Correlates and predictors of sexual health among adolescent Latinas in the United States: A systematic review of the literature, 2004-2015.

PubMed

Morales-Alemán, Mercedes M; Scarinci, Isabel C

2016-06-01

Adolescent Latinas in the United States (US) are disproportionately affected by early pregnancy, sexually transmitted infections (STIs) and human immunodeficiency virus (HIV) in comparison to their non-Hispanic white counterparts. However, only a few studies have sought to understand the multi-level factors associated with sexual health in adolescent Latinas. Adhering to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, we conducted a systematic literature review to better understand the correlates and predictors of sexual health among adolescent Latinas in the US, identify gaps in the research, and suggest future directions for empirical studies and intervention efforts. Eleven studies were identified: five examined onset of sexual intercourse, nine examined determinants of sexual health/risk behaviors (e.g., number of sexual partners and condom use), and three examined determinants of a biological sexual health outcome (i.e., STIs or pregnancy). Two types of variables/factors emerged as important influences on sexual health outcomes: proximal context-level variables (i.e., variables pertaining to the individual's family, sexual/romantic partner or peer group) and individual-level variables (i.e., characteristics of the individual). A majority of the studies reviewed (n=9) examined some aspect of acculturation or Latino/a cultural values in relation to sexual health. Results varied widely between studies suggesting that the relationship between individual and proximal contextual variables (including acculturation) and sexual health may be more complex than previously conceived. This review integrates the findings on correlates and predictors of sexual health among adolescent Latinas, and supports the need for strengths-based theoretically guided research on the mechanisms driving these associations. Copyright © 2016 Elsevier Inc. All rights reserved.

Factors Associated with HIV Testing Among Participants from Substance Use Disorder Treatment Programs in the US: A Machine Learning Approach.

PubMed

Pan, Yue; Liu, Hongmei; Metsch, Lisa R; Feaster, Daniel J

2017-02-01

HIV testing is the foundation for consolidated HIV treatment and prevention. In this study, we aim to discover the most relevant variables for predicting HIV testing uptake among substance users in substance use disorder treatment programs by applying random forest (RF), a robust multivariate statistical learning method. We also provide a descriptive introduction to this method for those who are unfamiliar with it. We used data from the National Institute on Drug Abuse Clinical Trials Network HIV testing and counseling study (CTN-0032). A total of 1281 HIV-negative or status unknown participants from 12 US community-based substance use disorder treatment programs were included and were randomized into three HIV testing and counseling treatment groups. The a priori primary outcome was self-reported receipt of HIV test results. Classification accuracy of RF was compared to logistic regression, a standard statistical approach for binary outcomes. Variable importance measures for the RF model were used to select the most relevant variables. RF based models produced much higher classification accuracy than those based on logistic regression. Treatment group is the most important predictor among all covariates, with a variable importance index of 12.9%. RF variable importance revealed that several types of condomless sex behaviors, condom use self-efficacy and attitudes towards condom use, and level of depression are the most important predictors of receipt of HIV testing results. There is a non-linear negative relationship between count of condomless sex acts and the receipt of HIV testing. In conclusion, RF seems promising in discovering important factors related to HIV testing uptake among large numbers of predictors and should be encouraged in future HIV prevention and treatment research and intervention program evaluations.

Environmental filtering and land-use history drive patterns in biomass accumulation in a mediterranean-type landscape.

PubMed

Dahlin, Kyla M; Asner, Gregory P; Field, Christopher B

2012-01-01

Aboveground biomass (AGB) reflects multiple and often undetermined ecological and land-use processes, yet detailed landscape-level studies of AGB are uncommon due to the difficulty in making consistent measurements at ecologically relevant scales. Working in a protected mediterranean-type landscape (Jasper Ridge Biological Preserve, California, USA), we combined field measurements with remotely sensed data from the Carnegie Airborne Observatory's light detection and ranging (lidar) system to create a detailed AGB map. We then developed a predictive model using a maximum of 56 explanatory variables derived from geologic and historic-ownership maps, a digital elevation model, and geographic coordinates to evaluate possible controls over currently observed AGB patterns. We tested both ordinary least-squares regression (OLS) and autoregressive approaches. OLS explained 44% of the variation in AGB, and simultaneous autoregression with a 100-m neighborhood improved the fit to an r2 = 0.72, while reducing the number of significant predictor variables from 27 variables in the OLS model to 11 variables in the autoregressive model. We also compared the results from these approaches to a more typical field-derived data set; we randomly sampled 5% of the data 1000 times and used the same OLS approach each time. Environmental filters including incident solar radiation, substrate type, and topographic position were significant predictors of AGB in all models. Past ownership was a minor but significant predictor, despite the long history of conservation at the site. The weak predictive power of these environmental variables, and the significant improvement when spatial autocorrelation was incorporated, highlight the importance of land-use history, disturbance regime, and population dynamics as controllers of AGB.

Predicting academic performance of medical students: the first three years.

PubMed

Höschl, C; Kozený, J

1997-06-01

The purpose of this exploratory study was to identify a cluster of variables that would most economically explain variations in the grade point averages of medical students during the first 3 years of study. Data were derived from a study of 92 students admitted to the 3rd Faculty of Medicine in 1992-1993 academic year and who were still in the medical school at the end of the sixth semester (third year). Stepwise regression analysis was used to build models for predicting log-transformed changes in grade point average after six semesters of study-at the end of the first, second, and third years. Predictor variables were chosen from four domains: 1) high school grade point averages in physics, mathematics, and the Czech language over 4 years of study, 2) results of admission tests in biology, chemistry, and physics, 3) admission committee's assessment of the applicant's ability to reproduce a text, motivation to study medicine, and social maturity, and 4) scores on the sentimentality and attachment scales of the Tridimensional Personality Questionnaire. The regression model, which included performance in high school physics, results of the admission test in physics, assessment of the applicant's motivation to study medicine, and attachment scale score, accounted for 32% of the change in grade point average over six semesters of study. The regression models using the first-, second-, and third-year grade point averages as the dependent variables showed slightly decreasing amounts of explained variance toward the end of the third year of study and within domains, changing the structure of predictor variables. The results suggest that variables chosen from the assessment domains of high school performance, written entrance examination, admission interview, and personality traits may be significant predictors of academic success during the first 3 years of medical study.

Parent involvement in school: English speaking versus Spanish speaking families.

PubMed

Lee, Sang Min; Thorn, Antoinette; Bloomdahl, Susana Contreras; Ha, Jung Hee; Nam, Suk Kyung; Lee, Jayoung

2012-07-01

The purpose of the present study was to explore the relationships between three predictor variables (attitude toward school, parent-child communication, and school commitment action) and the criterion variable (parent involvement) in a representative sample and to examine if these relationships were consistent across three groups (English speaking Caucasian family, English speaking Latino family, and Spanish speaking Latino families). Using a national database (N = 9.841), multi-group SEM analyses were conducted to investigate the relationship between three predictor variables and the criterion variable in three family groups. While all three predictor variables significantly predicted parent involvement in English speaking Caucasian and Latino families, only two variables (parent-child communication and school commitment actions), significantly predicted parent involvement in Spanish speaking Latino families. The results of this study suggest that when administrators, teachers and counselors in school strive to share specific school-related information with Latino families, Spanish speaking families are more likely to become involved with schools.

Employment status and intimate partner violence among Mexican women.

PubMed

Terrazas-Carrillo, Elizabeth C; McWhirter, Paula T

2015-04-01

Exploring risk factors and profiles of intimate partner violence in other countries provides information about whether existing theories of this phenomenon hold consistent in different cultural settings. This study will present results of a regression analysis involving domestic violence among Mexican women (n = 83,159). Significant predictors of domestic violence among Mexican women included age, number of children in the household, income, education, self-esteem, family history of abuse, and controlling behavior of the husband. Women's employment status was not a significant predictor when all variables were included in the model; however, when controlling behavior of the husband was withdrawn from the model, women's employment status was a significant predictor of domestic violence toward women. Results from this research indicate that spousal controlling behavior may serve as a mediator of the predictive relationship between women's employment status and domestic violence among Mexican women. Findings provide support for continued exploration of the factors that mediate experiences of domestic violence among women worldwide. © The Author(s) 2014.

Independent predictors of delay in emergence from general anesthesia.

PubMed

Maeda, Shigeru; Tomoyasu, Yumiko; Higuchi, Hitoshi; Ishii-Maruhama, Minako; Egusa, Masahiko; Miyawaki, Takuya

2015-01-01

Some patients with intellectual disabilities spend longer than others in emergence from ambulatory general anesthesia for dental treatment. Although antiepileptic drugs and anesthetics might be involved, an independent predictor for delay of the emergence remains unclear. Thus, a purpose of this study is to identify independent factors affecting the delay of emergence from general anesthesia. This was a retrospective cohort study in dental patients with intellectual disabilities. Patients in need of sedative premedication were removed from participants. The outcome was time until emergence from general anesthesia. Stepwise multivariate regression analysis was used to extract independent factors affecting the outcome. Antiepileptic drugs and anesthetic parameters were included as predictor variables. The study included 102 cases. Clobazam, clonazepam, and phenobarbital were shown to be independent determinants of emergence time. Parameters relating to anesthetics, patients' backgrounds, and dental treatment were not independent factors. Delay in emergence time in ambulatory general anesthesia is likely to be related to the antiepileptic drugs of benzodiazepine or barbiturates in patients with intellectual disability.

Independent Predictors of Delay in Emergence From General Anesthesia

PubMed Central

Maeda, Shigeru; Tomoyasu, Yumiko; Higuchi, Hitoshi; Ishii-Maruhama, Minako; Egusa, Masahiko; Miyawaki, Takuya

2015-01-01

Some patients with intellectual disabilities spend longer than others in emergence from ambulatory general anesthesia for dental treatment. Although antiepileptic drugs and anesthetics might be involved, an independent predictor for delay of the emergence remains unclear. Thus, a purpose of this study is to identify independent factors affecting the delay of emergence from general anesthesia. This was a retrospective cohort study in dental patients with intellectual disabilities. Patients in need of sedative premedication were removed from participants. The outcome was time until emergence from general anesthesia. Stepwise multivariate regression analysis was used to extract independent factors affecting the outcome. Antiepileptic drugs and anesthetic parameters were included as predictor variables. The study included 102 cases. Clobazam, clonazepam, and phenobarbital were shown to be independent determinants of emergence time. Parameters relating to anesthetics, patients' backgrounds, and dental treatment were not independent factors. Delay in emergence time in ambulatory general anesthesia is likely to be related to the antiepileptic drugs of benzodiazepine or barbiturates in patients with intellectual disability. PMID:25849468

Quantitatively measured tremor in hand-arm vibration-exposed workers.

PubMed

Edlund, Maria; Burström, Lage; Hagberg, Mats; Lundström, Ronnie; Nilsson, Tohr; Sandén, Helena; Wastensson, Gunilla

2015-04-01

The aim of the present study was to investigate the possible increase in hand tremor in relation to hand-arm vibration (HAV) exposure in a cohort of exposed and unexposed workers. Participants were 178 male workers with or without exposure to HAV. The study is cross-sectional regarding the outcome of tremor and has a longitudinal design with respect to exposure. The dose of HAV exposure was collected via questionnaires and measurements at several follow-ups. The CATSYS Tremor Pen(®) was used for measuring postural tremor. Multiple linear regression methods were used to analyze associations between different tremor variables and HAV exposure, along with predictor variables with biological relevance. There were no statistically significant associations between the different tremor variables and cumulative HAV or current exposure. Age was a statistically significant predictor of variation in tremor outcomes for three of the four tremor variables, whereas nicotine use was a statistically significant predictor of either left or right hand or both hands for all four tremor variables. In the present study, there was no evidence of an exposure-response association between HAV exposure and measured postural tremor. Increase in age and nicotine use appeared to be the strongest predictors of tremor.

Patient-reported outcomes and socioeconomic status as predictors of clinical outcomes following hematopoietic stem cell transplantation: A study from the BMT CTN 0902 trial

PubMed Central

Knight, Jennifer M; Syrjala, Karen L; Majhail, Navneet S; Martens, Michael; Le-Rademacher, Jennifer; Logan, Brent R; Lee, Stephanie J; Jacobsen, Paul B; Wood, William A; Jim, Heather SL; Wingard, John R; Horowitz, Mary M; Abidi, Muneer H; Fei, Mingwei; Rawls, Laura; Rizzo, J Douglas

2016-01-01

This secondary analysis of a large, multi-center Blood and Marrow Transplant Clinical Trials Network (BMT CTN) randomized trial assessed whether patient-reported outcomes (PROs) and socioeconomic status (SES) before hematopoietic stem cell transplantation (HCT) are associated with each other and predictive of clinical outcomes including time to hematopoietic recovery, acute graft-versus-host disease, hospitalization days, and overall survival (OS) among 646 allogeneic and autologous HCT recipients. Pre-transplant Cancer and Treatment Distress (CTXD), Pittsburgh Sleep Quality Index (PSQI), and mental and physical component scores (MCS and PCS) of the SF-36 were correlated with each other and with SES variables. PROs and SES variables were further evaluated as predictors of clinical outcomes, with the PSQI and CTXD evaluated as OS predictors (p<.01 considered significant given multiple testing). Lower attained education was associated with increased distress (p=.002); lower income was related to worse physical functioning (p=.005) and increased distress (p=.008); lack of employment pre-transplant was associated with worse physical functioning (p<.01); unmarried status was associated with worse sleep (p=.003). In this large heterogeneous cohort of HCT recipients, while PROs and SES variables were correlated at baseline, they were not associated with any clinical outcomes. Future research should focus on HCT recipients at greater psychosocial disadvantage. PMID:27565521

Impact of neonatal risk and temperament on behavioral problems in toddlers born preterm.

PubMed

Guilherme Monte Cassiano, Rafaela; Gaspardo, Claudia Maria; Cordaro Bucker Furini, Guilherme; Martinez, Francisco Eulogio; Martins Linhares, Maria Beatriz

2016-12-01

Children born preterm are at risk for later developmental disorders. The present study examined the predictive effects of neonatal, sociodemographic, and temperament characteristics on behavioral outcomes at toddlerhood, in children born preterm. The sample included 100 toddlers born preterm and with very-low-birth-weight, and their mothers. Neonatal characteristics were evaluated using medical records. The mothers were interviewed using the Early Childhood Behavior Questionnaire for temperament assessment, and the Child Behavior Checklist for behavioral assessment. Multiple linear regression analyses were performed. Predictors of 39% of the variability of the total behavioral problems in toddlers born prematurely were: temperament with more Negative Affectivity and less Effortful Control, lower family socioeconomic status, and younger mothers at childbirth. Temperament with more Negative Affectivity and less Effortful Control and lower family socioeconomic status were predictors of 23% of the variability of internalizing behavioral problems. Additionally, 37% of the variability of externalizing behavioral problems was explained by temperament with more Negative Affectivity and less Effortful Control, and younger mothers at childbirth. The neonatal characteristics and stressful events in the neonatal intensive care unit did not predict behavioral problems at toddlerhood. However, temperament was a consistent predictor of behavioral problems in toddlers born preterm. Preventive follow-up programs could assess dispositional traits of temperament to provide early identification of preterm infants at high-risk for behavioral problems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Dimensions and predictors of disability—A baseline study of patients entering somatic rehabilitation in secondary care

PubMed Central

2018-01-01

Purpose The purpose of this study was to investigate disability among patients who were accepted for admission to a Norwegian rehabilitation center and to identify predictors of disability. Materials and methods In a cross-sectional study including 967 adult participants, the World Health Organization Disability Assessment Schedule version 2.0 36-item version was used for assessing overall and domain-specific disability as outcome variables. Patients completed the Hospital Anxiety and Depression Scale (HADS), EuroQoL EQ-5D-5L and questions about multi-morbidity, smoking and perceived physical fitness. Additionally, the main health condition, sociodemographic and environmental variables obtained from referrals and public registers were used as predictor variables. Descriptive statistics and linear regression analyses were performed. Results The mean (standard error) overall disability score was 30.0 (0.5), domain scores ranged from 11.9 to 44.7. Neurological diseases, multi-morbidity, low education, impaired physical fitness, pain, and higher HADS depressive score increased the overall disability score. A low HADS depressive score predicted a lower disability score in all domains. Conclusions A moderate overall disability score was found among patients accepted for admission to a rehabilitation center but “life activities” and “participation in society” had the highest domain scores. This should be taken into account when rehabilitation strategies are developed. PMID:29499064

The relationship between socio-demographic variables, job stressors, burnout, and hardy personality in nurses: an exploratory study.

PubMed

Garrosa, Eva; Moreno-Jiménez, Bernardo; Liang, Youxin; González, José Luis

2008-03-01

Nursing is considered as a risk profession with high levels of stress and burnout, and these levels are probably increasing. A model of prediction of burnout in nursing that includes socio-demographic variables, job stressors, and personal vulnerability, or resistance, is proposed. A cross-sectional correlational design was used. A sample of 473 nurses and student nurses in practice from three General Hospitals in Madrid (Spain) completed the "Nursing Burnout Scale". The data were analysed using descriptive statistics, Pearson correlations, and hierarchical multiple regression. The proposed model is a good predictor of the diverse burnout sub-dimensions: emotional exhaustion, depersonalisation, and lack of personal accomplishment. Significant predictors of burnout included age, job status, job stressors (workload, experience with pain and death, conflictive interaction, and role ambiguity), and hardy personality (commitment, control, and challenge). Identifying an integrative process of burnout among nurses is an essential step to develop effective managerial strategies so as to reduce the burnout problem. Specifically, the present study suggests that intervention aimed at reducing the risk for burnout may achieve better results if it includes enhancement of workers' hardy personality rather than just decreasing environmental stressors.

Neuropsychological deficits in preschool as predictors of ADHD symptoms and academic achievement in late adolescence

PubMed Central

Sjöwall, Douglas; Bohlin, Gunilla; Rydell, Ann-Margret; Thorell, Lisa B

2017-01-01

High levels of ADHD symptoms are related to severe negative outcomes, which underscore the importance of identifying early markers of these behavior problems. The main aim of the present study was therefore to investigate whether neuropsychological deficits in preschool are related to later ADHD symptoms and academic achievement, over and above the influence of early ADHD symptom levels. The present study is unique because it includes a broader range of predictors compared to previous studies and the participants are followed over time for as long as 13 years (i.e., ages 5–18 years). Preschool data included measures of executive functioning and reaction time variability as well as emotional reactivity and emotion regulation of both positive and negative emotions. When controlling for early ADHD symptom levels, working memory, reaction time variability, and regulation of happiness/exuberance were significantly related to inattention whereas regulation of happiness/exuberance and anger reactivity were significantly related to hyperactivity/impulsivity. Furthermore, working memory and reaction time variability in preschool were significantly related to academic achievement in late adolescence beyond the influence of early ADHD symptoms. These findings could suggest that it is possible to screen for early neuropsychological deficits and thereby identify children who are at risk of negative outcomes. Furthermore, our results suggest that interventions need to look beyond executive functioning deficits in ADHD and also target the role of emotional functioning and reaction time variability. The importance of including both the positive and negative aspects of emotional functioning and distinguishing between emotion regulation and emotional reactivity was also demonstrated. PMID:26212755

Predictors of functional disability in mild cognitive impairment and dementia.

PubMed

van Rossum, M E; Koek, H L

2016-08-01

Knowledge about factors predicting functional disability in mild cognitive impairment (MCI) and dementia would help health care providers to identify those patients who are at high risk of functional disability. Previous research is scarce and focused on only a small number of possible predictors. The aim of this study was to identify predictors of functional disability in patients with MCI and dementia. Cross-sectional cohort study. Data from patients who visited a memory clinic between 2011 and 2015 were evaluated. The Disability Assessment for Dementia (DAD) was used to assess functional disability. Patients diagnosed with MCI or dementia and with a DAD score available were included. This led to the inclusion of 474 patients. Univariate analyses with a broad range of variables were performed to detect factors that had a significant relationship to the DAD score. Age, gender and variables with a p-value of 0.1 or lower in the univariate analyses were taken into a multivariable analysis. This multiple linear regression analysis was performed to determine which variables were independently associated with the DAD score. Our multivariable model explained 42% of the variance in the DAD score. Independent predictors of the DAD score were age (B=0.03, 95%CI=0.002-0.05), gender (B=-0.43, 95%CI=-0.78 to -0.07), score on the Clinical Dementia Rating scale (CDR) (B=1.53, 95%CI=1.07-1.99 for CDR 1, B=2.93, 95%CI=2.28-3.58 for CDR 2, B=3.96, 95%CI=2.65-5.27 for CDR 3) and level of physical activity (B=0.56, 95%CI=0.05-1.07). Older age, male gender, higher CDR score and lower levels of physical activity are independent predictors of functional disability in MCI and dementia. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Predictors of hydrocephalus as a complication of non-traumatic subarachnoid hemorrhage: a retrospective observational cohort study in 107 patients.

PubMed

Vinas Rios, Juan Manuel; Sanchez-Aguilar, Martin; Kretschmer, Thomas; Heinen, Christian; Medina Govea, Fatima Azucena; Jose Juan, Sanchez-Rodriguez; Schmidt, Thomas

2018-01-01

The predictors of shunt dependency such as amount of subarachnoid blood, acute hydrocephalus (HC), mode of aneurysm repair, clinical grade at admission and cerebro spinal fluid (CSF) drainage in excess of 1500 ml during the 1st week after the subarachnoid hemorrhage (SAH) have been identified as predictors of shunt dependency. Therefore our main objective is to identify predictors of CSF shunt dependency following non-traumatic subarachnoid hemorrhage. We performed a retrospective study including patients from January 1st 2012 to September 30th 2014 between 16 and 89 years old and had a non-traumatic subarachnoid hemorrhage in cranial computed tomography (CCT). We excluded patients with the following characteristics: Patients who died 3 days after admittance, lesions in brainstem, previous surgical treatment in another clinic, traumatic brain injury, pregnancy and disability prior to SAH.We performed a descriptive and comparative analysis as well as a logistic regression with the variables that showed a significant difference ( p  < 0.05). Hence we identified the variables concerning HC after non traumatic SAH and its correlation. One hundred and seven clinical files of patients with non-traumatic SAH were analyzed. Twenty one (48%) later underwent shunt treatment. Shunt patients had significantly clinical and corroborated with doppler ultrasonography vasospasmus ( p  = 0.015), OR = 5.2. The amount of subarachnoidal blood according to modified Fisher grade was ( p  = 0.008) OR = 10.9. Endovascularly treated patients were less often shunted as compared with those undergoing surgical aneurysm repair ( p  = 0.004). Vasospasmus and a large amount of ventricular blood seem to be a predictor concerning hydrocephalus after non-traumatic SAH. Hence according to our results the presence of these two variables could alert the treating physician in the decision whether an early shunt implantation < 7 days after SAH should be necessary.

A respiratory alert model for the Shenandoah Valley, Virginia, USA

NASA Astrophysics Data System (ADS)

Hondula, David M.; Davis, Robert E.; Knight, David B.; Sitka, Luke J.; Enfield, Kyle; Gawtry, Stephen B.; Stenger, Phillip J.; Deaton, Michael L.; Normile, Caroline P.; Lee, Temple R.

2013-01-01

Respiratory morbidity (particularly COPD and asthma) can be influenced by short-term weather fluctuations that affect air quality and lung function. We developed a model to evaluate meteorological conditions associated with respiratory hospital admissions in the Shenandoah Valley of Virginia, USA. We generated ensembles of classification trees based on six years of respiratory-related hospital admissions (64,620 cases) and a suite of 83 potential environmental predictor variables. As our goal was to identify short-term weather linkages to high admission periods, the dependent variable was formulated as a binary classification of five-day moving average respiratory admission departures from the seasonal mean value. Accounting for seasonality removed the long-term apparent inverse relationship between temperature and admissions. We generated eight total models specific to the northern and southern portions of the valley for each season. All eight models demonstrate predictive skill (mean odds ratio = 3.635) when evaluated using a randomization procedure. The predictor variables selected by the ensembling algorithm vary across models, and both meteorological and air quality variables are included. In general, the models indicate complex linkages between respiratory health and environmental conditions that may be difficult to identify using more traditional approaches.

Abiotic Factors Affecting Benthic Invertebrate Biomass and Community Structure in a Fourth-Order Rocky Mountain Watershed

NASA Astrophysics Data System (ADS)

Chanat, J. G.; Clements, W. H.; MacDonald, L. H.

2005-05-01

The potential ecological impact of excess streambed sediment resulting from forest management activities is a persistent concern for land managers. This study examined the relationship between streambed sediment, along with other site- and reach-scale abiotic factors, and benthic macroinvertebrate community structure in a 272 km2 basin in the Colorado Front Range. Physical habitat parameters and invertebrates were sampled in late summer at 68 sites located in sixteen stream reaches. Invertebrate data were used to formulate twenty indices of community structure. Multiple regression identified site-level substrate particle size as the most important predictor of six indices, including total density (R2 = 0.22), biomass (R2 = 0.17), and taxa richness (R2 = 0.32). All of the remaining fourteen indices were most strongly predicted by reach-level variables, including discharge (percent shredders, R2 = 0.24; Plecoptera density, R2 = 0.29), and elevation (percent collector-filterers, R2 = 0.28; Trichoptera density, R2 = 0.37). Although the sites represented a wide range of substrate composition and embeddedness, no physical variable associated with fine sediment appeared as a strong predictor of any of the twenty indices. Thus, sediment is not among the most important factors associated with site-to-site variability of benthic community structure in this relatively pristine watershed.

Prospective Predictors of Technology-Based Sexual Coercion by College Males

PubMed Central

Thompson, Martie P.; Morrison, Deidra J.

2013-01-01

Objective Technology-based coercive behavior (TBC) represents an emerging public health problem. This study contributes to the literature by identifying prospective individual-, social-, and community-level predictors of TBC. Method Data were collected from 800 males who participated in a prospective study on attitudes and behaviors regarding relationships with women. Variables across multiple ecological layers were used to predict TBC. Results Bivariate analyses indicated that 16 of the 17 risk variables significantly predicted TBC including anger, impulsivity, sexual compulsivity, hostility towards women, rape supportive beliefs, high-risk drinking, childhood sexual abuse, interparental conflict, peer pressure to engage in sex, peer approval of forced sex, number of sexual partners, perceived negative sanctions for sexual aggression, exposure to pornography, and participation in varsity sports, student government, and religious groups. Multivariate regression analyses indicated five variables uniquely accounted for TBC behaviors, including rape supportive beliefs, peer approval of forced sex, number of sexual partners, exposure to pornography, and participation in student government. Conclusions Our findings that TBC can be prospectively predicted by these risk factors suggest that computer-based technology interventions focusing on these factors through social network ads that promote reflection on healthy social and romantic relationship behaviors and attitudes could help prevent and reduce TBC. PMID:24073356

Factors Influencing Fluid Milk Waste in a Breakfast in the Classroom School Breakfast Program.

PubMed

Blondin, Stacy A; Goldberg, Jeanne P; Cash, Sean B; Griffin, Timothy S; Economos, Christina D

2018-04-01

To determine predictors of fluid milk waste in a Breakfast in the Classroom School Breakfast Program. Cross-sectional with 3 repeated measures/classroom. Elementary schools in a medium-sized, low-income, urban school district. Twenty third- through fourth-grade classrooms across 6 schools. Dependent variables include percentage of total and served milk wasted. Independent variables included observed daily menu offerings, program factors, and teacher and student behavior. Descriptive statistics were used to characterize variables across classrooms and schools. Multilevel mixed-effects models were used to test associations between predictors and outcomes of interest. P ≤ .05 was considered statistically significant. Total milk waste increased 12% when juice was offered and 3% for each additional carton of unserved milk. Teacher encouragement to take and/or consume breakfast was associated with a 5% and 9% increase in total and served milk waste, respectively. When students were engaged in other activities in addition to eating breakfast, total milk waste decreased 10%. Beverage offerings were predictive of greater total milk waste. Teacher and student behavior also appeared to influence milk consumption. Findings suggest that specific changes to School Breakfast Program implementation policies and practices could have an important role in waste mitigation. Copyright © 2018 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

Identifying Psychosocial Variables That Predict Safer Sex Intentions in Adolescents and Young Adults

PubMed Central

Brüll, Phil; Ruiter, Robert A. C.; Wiers, Reinout W.; Kok, Gerjo

2016-01-01

Young people are especially vulnerable to sexually transmitted infections (STIs). The triad of deliberate and effective safer sex behavior encompasses condom use, combined with additional information about a partner’s sexual health, and the kind of sex acts usually performed. To identify psychosocial predictors of young people’s intentions to have safer sex, as related to this triad, we conducted an online study with 211 sexually active participants aged between 18 and 24 years. Predictors [i.e., perceived behavioral control (PBC), subjective norms, and intention] taken from Fishbein and Ajzen’s Reasoned Action Approach (RAA), were combined with more distal variables (e.g., behavioral inhibition, sensation seeking, parental monitoring, and knowledge about STIs). Beyond the highly predictive power of RAA variables, additional variance was explained by the number of instances of unprotected sexual intercourse (SI) during the last 12 months and reasons for using barrier protection during first SI. In particular, past condom non-use behavior moderated PBC related to intended condom use. Further, various distal variables showed significant univariate associations with intentions related to the three behaviors of interest. It may, therefore, be helpful to include measures of past behavior as well as certain additional distal variables in future safer sex programs designed to promote health-sustaining sexual behavior. PMID:27148520

Youth’s Reactions to Disasters and the Factors That Influence Their Response

PubMed Central

Pfefferbaum, Betty; Houston, J. Brian; North, Carol S.; Regens, James L.

2009-01-01

Youth’s reactions to disasters include stress reactions, posttraumatic stress disorder (PTSD), and comorbid conditions. A number of factors contribute to outcome including characteristics of the event; the nature of the youth’s exposure; and individual, family, and social predictors. Demographic features may be less important than exposure and other individual variables like preexisting conditions and exposure to other trauma. While youth’s disaster reactions reflect their developmental status and thus may differ from those of adults, their reactions generally parallel those of their parents in degree. Family factors that appear to influence youth’s reactions include parental reactions and the quality of interactions within the family. Social factors have not been well examined. We describe these outcomes and predictors to prepare professionals who may work with youth in post-disaster situations. PMID:19953191

Personal and organizational predictors of workplace sexual harassment of women by men.

PubMed

Dekker, I; Barling, J

1998-01-01

The authors investigated the predictors of workplace sexual harassment in 278 male university faculty and staff (M age = 45 years). Workplace variables (perceptions of organizational sanctions against harassment and perceptions of a sexualized workplace) and personal variables (adversarial sexual beliefs, sexual harassment beliefs, perspective taking, and self-esteem) were studied as predictors of sexualized and gender harassment. Social desirability was controlled. Both organizational variables and beliefs about sexual harassment predicted gender harassment and sexualized harassment. Perspective taking, adversarial sexual beliefs, and sexual harassment beliefs moderated the effects of perceived organizational sanctions against harassment on sexualized harassment. Findings are discussed as they relate to organizational efforts to reduce or prevent sexual harassment.

Prevalence and predictors of anaemia in Romanian infants 6-23 months old.

PubMed

Stativa, E; Rus, A V; Stanescu, A; Pennings, J S; Parris, S R; Wenyika, R

2016-09-01

Anaemia is a public health problem that can lead to a variety of detrimental effects on physical and neurodevelopment in young children. The present study explored the epidemiology of anaemia among infants in Romania, identified risk factors and created a model for predicting it. Data from 1532 infants aged 6-24 months were selected from a larger nationally representative cross-sectional survey. Demographic predictor variables and haemoglobin concentration were extant variables in the data set. Multiple logistic regression was used to determine the best predictors of anaemia. Overall, 46% of 6-24 month olds in the sample had anaemia (Hb < 11.0 g/dl). A variety of risk factors were associated with significantly greater odds of anaemia, but a five-factor model best predicted it (67.9% accuracy). These predictors included being male, living in a rural area, being third born or later, being a Hungarian and living in the South, South-West or West region of Romania. While data indicate a modest decrease in anaemia from earlier Romanian studies, it remains a significant problem. Models like this one have the potential to improve identification and treatment of anaemia in young children. © The Author 2015. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Correlates of willingness to engage in residential gardening: implications for health optimization in ibadan, Nigeria.

PubMed

Motunrayo Ibrahim, Fausat

2013-01-01

Gardening is a worthwhile adventure which engenders health op-timization. Yet, a dearth of evidences that highlights motivations to engage in gardening exists. This study examined willingness to engage in gardening and its correlates, including some socio-psychological, health related and socio-demographic variables. In this cross-sectional survey, 508 copies of a structured questionnaire were randomly self administered among a group of civil servants of Oyo State, Nigeria. Multi-item measures were used to assess variables. Step wise multiple regression analysis was used to identify predictors of willingness to engage in gar-dening Results: Simple percentile analysis shows that 71.1% of respondents do not own a garden. Results of step wise multiple regression analysis indicate that descriptive norm of gardening is a good predictor, social support for gardening is better while gardening self efficacy is the best predictor of willingness to engage in gardening (P< 0.001). Health consciousness, gardening response efficacy, education and age are not predictors of this willingness (P> 0.05). Results of t-test and ANOVA respectively shows that gender is not associated with this willingness (P> 0.05), but marital status is (P< 0.05).  Socio-psychological characteristics and being married are very rele-vant in motivations to engage in gardening. The nexus between gardening and health optimization appears to be highly obscured in this population.

Correlates of Willingness to Engage in Residential Gardening: Implications for Health Optimization in Ibadan, Nigeria

PubMed Central

Motunrayo Ibrahim, Fausat

2013-01-01

Background: Gardening is a worthwhile adventure which engenders health op­timization. Yet, a dearth of evidences that highlights motivations to engage in gardening exists. This study examined willingness to engage in gardening and its correlates, including some socio-psychological, health related and socio-demographic variables. Methods: In this cross-sectional survey, 508 copies of a structured questionnaire were randomly self administered among a group of civil servants of Oyo State, Nigeria. Multi-item measures were used to assess variables. Step wise multiple regression analysis was used to identify predictors of willingness to engage in gar­dening Results: Simple percentile analysis shows that 71.1% of respondents do not own a garden. Results of step wise multiple regression analysis indicate that descriptive norm of gardening is a good predictor, social support for gardening is better while gardening self efficacy is the best predictor of willingness to engage in gardening (P< 0.001). Health consciousness, gardening response efficacy, education and age are not predictors of this willingness (P> 0.05). Results of t-test and ANOVA respectively shows that gender is not associated with this willingness (P> 0.05), but marital status is (P< 0.05).  Conclusion: Socio-psychological characteristics and being married are very rele­vant in motivations to engage in gardening. The nexus between gardening and health optimization appears to be highly obscured in this population. PMID:24688974

Disturbance metrics predict a wetland Vegetation Index of Biotic Integrity

USGS Publications Warehouse

Stapanian, Martin A.; Mack, John; Adams, Jean V.; Gara, Brian; Micacchion, Mick

2013-01-01

Indices of biological integrity of wetlands based on vascular plants (VIBIs) have been developed in many areas in the USA. Knowledge of the best predictors of VIBIs would enable management agencies to make better decisions regarding mitigation site selection and performance monitoring criteria. We use a novel statistical technique to develop predictive models for an established index of wetland vegetation integrity (Ohio VIBI), using as independent variables 20 indices and metrics of habitat quality, wetland disturbance, and buffer area land use from 149 wetlands in Ohio, USA. For emergent and forest wetlands, predictive models explained 61% and 54% of the variability, respectively, in Ohio VIBI scores. In both cases the most important predictor of Ohio VIBI score was a metric that assessed habitat alteration and development in the wetland. Of secondary importance as a predictor was a metric that assessed microtopography, interspersion, and quality of vegetation communities in the wetland. Metrics and indices assessing disturbance and land use of the buffer area were generally poor predictors of Ohio VIBI scores. Our results suggest that vegetation integrity of emergent and forest wetlands could be most directly enhanced by minimizing substrate and habitat disturbance within the wetland. Such efforts could include reducing or eliminating any practices that disturb the soil profile, such as nutrient enrichment from adjacent farm land, mowing, grazing, or cutting or removing woody plants.

Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant.

PubMed

Vanderhaeghe, F; Smolders, A J P; Roelofs, J G M; Hoffmann, M

2012-03-01

Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

Predictive Modeling of Response to Pregabalin for the Treatment of Neuropathic Pain Using 6-Week Observational Data: A Spectrum of Modern Analytics Applications.

PubMed

Emir, Birol; Johnson, Kjell; Kuhn, Max; Parsons, Bruce

2017-01-01

This post hoc analysis used 11 predictive models of data from a large observational study in Germany to evaluate potential predictors of achieving at least 50% pain reduction by week 6 after treatment initiation (50% pain response) with pregabalin (150-600 mg/d) in patients with neuropathic pain (NeP). The potential predictors evaluated included baseline demographic and clinical characteristics, such as patient-reported pain severity (0 [no pain] to 10 [worst possible pain]) and pain-related sleep disturbance scores (0 [sleep not impaired] to 10 [severely impaired sleep]) that were collected during clinic visits (baseline and weeks 1, 3, and 6). Baseline characteristics were also evaluated combined with pain change at week 1 or weeks 1 and 3 as potential predictors of end-of-treatment 50% pain response. The 11 predictive models were linear, nonlinear, and tree based, and all predictors in the training dataset were ranked according to their variable importance and normalized to 100%. The training dataset comprised 9187 patients, and the testing dataset had 6114 patients. To adjust for the high imbalance in the responder distribution (75% of patients were 50% responders), which can skew the parameter tuning process, the training set was balanced into sets of 1000 responders and 1000 nonresponders. The predictive modeling approaches that were used produced consistent results. Baseline characteristics alone had fair predictive value (accuracy range, 0.61-0.72; κ range, 0.17-0.30). Baseline predictors combined with pain change at week 1 had moderate predictive value (accuracy, 0.73-0.81; κ range, 0.37-0.49). Baseline predictors with pain change at weeks 1 and 3 had substantial predictive value (accuracy, 0.83-0.89; κ range, 0.54-0.71). When variable importance across the models was estimated, the best predictor of 50% responder status was pain change at week 3 (average importance 100.0%), followed by pain change at week 1 (48.1%), baseline pain score (14.1%), baseline depression (13.9%), and using pregabalin as a monotherapy (11.7%). The finding that pain changes by week 1 or weeks 1 and 3 are the best predictors of pregabalin response at 6 weeks suggests that adhering to a pregabalin medication regimen is important for an optimal end-of-treatment outcome. Regarding baseline predictors alone, considerable published evidence supports the importance of high baseline pain score and presence of depression as factors that can affect treatment response. Future research would be required to elucidate why using pregabalin as a monotherapy also had more than a 10% variable importance as a potential predictor. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Psychosocial predictors of human papillomavirus vaccination intentions for young women 18 to 26: religiosity, morality, promiscuity, and cancer worry.

PubMed

Krakow, Melinda M; Jensen, Jakob D; Carcioppolo, Nick; Weaver, Jeremy; Liu, Miao; Guntzviller, Lisa M

2015-01-01

To determine whether five psychosocial variables, namely, religiosity, morality, perceived promiscuity, cancer worry frequency, and cancer worry severity, predict young women's intentions to receive the human papillomavirus (HPV) vaccination. Female undergraduate students (n=408) completed an online survey. Questions pertaining to hypothesized predictors were analyzed through bivariate correlations and hierarchical regression equations. Regressions examined whether the five psychosocial variables of interest predicted intentions to vaccinate above and beyond controls. Proposed interactions among predictor variables were also tested. Study findings supported cancer worry as a direct predictor of HPV vaccination intention, and religiosity and sexual experience as moderators of the relationship between concerns of promiscuity reputation and intentions to vaccinate. One dimension of cancer worry (severity) emerged as a particularly robust predictor for this population. This study provides support for several important, yet understudied, factors contributing to HPV vaccination intentions among college-aged women: cancer worry severity and religiosity. Future research should continue to assess the predictive contributions of these variables and evaluate how messages and campaigns to increase HPV vaccination uptake can utilize religious involvement and worry about cancer to promote more effectively HPV vaccination as a cancer prevention strategy. Copyright © 2015 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.

Silent strain of caregiving: exploring the best predictors of distress in family carers of geriatric patients.

PubMed

Bień-Barkowska, Katarzyna; Doroszkiewicz, Halina; Bień, Barbara

2017-01-01

The aim of this article was to identify the best predictors of distress suffered by family carers (FCs) of geriatric patients. A cross-sectional study of 100 FC-geriatric patient dyads was conducted. The negative impact of care (NIoC) subscale of the COPE index was dichotomized to identify lower stress (score of ≤15 on the scale) and higher stress (score of ≥16 on the scale) exerted on FCs by the process of providing care. The set of explanatory variables comprised a wide range of sociodemographic and care-related attributes, including patient-related results from comprehensive geriatric assessments and disease profiles. The best combination of explanatory variables that provided the highest predictive power for distress among FCs in the multiple logistic regression (LR) model was determined according to statistical information criteria. The statistical robustness of the observed relationships and the discriminative power of the model were verified with the cross-validation method. The mean age of FCs was 57.2 (±10.6) years, whereas that of geriatric patients was 81.7 (±6.4) years. Despite the broad initial set of potential explanatory variables, only five predictors were jointly selected for the best statistical model. A higher level of distress was independently predicted by lower self-evaluation of health; worse self-appraisal of coping well as a caregiver; lower sense of general support; more hours of care per week; and the motor retardation of the cared-for person measured with the speed of the Timed Up and Go (TUG) test. Worse performance on the TUG test was only the patient-related predictor of distress among the variables examined as contributors to the higher NIoC. Enhancing the mobility of geriatric patients through suitably tailored kinesitherapeutic methods during their hospital stay may mitigate the burden endured by FCs.

Predictors of job satisfaction among academic family medicine faculty

PubMed Central

Krueger, Paul; White, David; Meaney, Christopher; Kwong, Jeffrey; Antao, Viola; Kim, Florence

2017-01-01

Abstract Objective To identify predictors of job satisfaction among academic family medicine faculty members. Design A comprehensive Web-based survey of all faculty members in an academic department of family medicine. Bivariate and multivariable analyses (logistic regression) were used to identify variables associated with job satisfaction. Setting The Department of Family and Community Medicine at the University of Toronto in Ontario and its 15 affiliated community teaching hospitals and community-based teaching practices. Participants All 1029 faculty members in the Department of Family and Community Medicine were invited to complete the survey. Main outcome measures Faculty members’ demographic and practice information; teaching, clinical, administration, and research activities; leadership roles; training needs and preferences; mentorship experiences; health status; stress levels; burnout levels; and job satisfaction. Faculty members’ perceptions about supports provided, recognition, communication, retention, workload, teamwork, respect, resource distribution, remuneration, and infrastructure support. Faculty members’ job satisfaction, which was the main outcome variable, was obtained from the question, “Overall, how satisfied are you with your job?” Results Of the 1029 faculty members, 687 (66.8%) responded to the survey. Bivariate analyses revealed 26 predictors as being statistically significantly associated with job satisfaction, including faculty members’ ratings of their local department and main practice setting, their ratings of leadership and mentorship experiences, health status variables, and demographic variables. The multivariable analyses identified the following 5 predictors of job satisfaction: the Maslach Burnout Inventory subscales of emotional exhaustion and personal accomplishment; being born in Canada; the overall quality of mentorship that was received being rated as very good or excellent; and teamwork being rated as very good or excellent. Conclusion The findings from this study show that job satisfaction among academic family medicine faculty members is a multi-dimensional construct. Future improvement in overall level of job satisfaction will therefore require multiple strategies. PMID:28292815

Predictors of job satisfaction among academic family medicine faculty: Findings from a faculty work-life and leadership survey.

PubMed

Krueger, Paul; White, David; Meaney, Christopher; Kwong, Jeffrey; Antao, Viola; Kim, Florence

2017-03-01

To identify predictors of job satisfaction among academic family medicine faculty members. A comprehensive Web-based survey of all faculty members in an academic department of family medicine. Bivariate and multivariable analyses (logistic regression) were used to identify variables associated with job satisfaction. The Department of Family and Community Medicine at the University of Toronto in Ontario and its 15 affiliated community teaching hospitals and community-based teaching practices. All 1029 faculty members in the Department of Family and Community Medicine were invited to complete the survey. Faculty members' demographic and practice information; teaching, clinical, administration, and research activities; leadership roles; training needs and preferences; mentorship experiences; health status; stress levels; burnout levels; and job satisfaction. Faculty members' perceptions about supports provided, recognition, communication, retention, workload, teamwork, respect, resource distribution, remuneration, and infrastructure support. Faculty members' job satisfaction, which was the main outcome variable, was obtained from the question, "Overall, how satisfied are you with your job?" Of the 1029 faculty members, 687 (66.8%) responded to the survey. Bivariate analyses revealed 26 predictors as being statistically significantly associated with job satisfaction, including faculty members' ratings of their local department and main practice setting, their ratings of leadership and mentorship experiences, health status variables, and demographic variables. The multivariable analyses identified the following 5 predictors of job satisfaction: the Maslach Burnout Inventory subscales of emotional exhaustion and personal accomplishment; being born in Canada; the overall quality of mentorship that was received being rated as very good or excellent; and teamwork being rated as very good or excellent. The findings from this study show that job satisfaction among academic family medicine faculty members is a multi-dimensional construct. Future improvement in overall level of job satisfaction will therefore require multiple strategies. Copyright© the College of Family Physicians of Canada.

Predictors of Success for Students Entering Graduate School on a Probationary Basis.

ERIC Educational Resources Information Center

Nelson, Jacquelyn S.; Nelson, C. Van

This study sought to determine which combination of criteria would accurately predict the success of students in graduate education who began their graduate studies on probationary admission status. Variables examined included grade point average (GPA) after 9 hours of graduate coursework, Graduate Record Examination (GRE) verbal, quantitative,…

Factors Related to the Academic Success and Failure of College Football Players: The Case of the Mental Dropout.

ERIC Educational Resources Information Center

Lang, Gale; And Others

1988-01-01

Examines variables used to predict the academic success or failure of college football players. Valid predictors include the following: (1) high school grades; (2) repeating a year in school; (3) feelings towards school; (4) discipline history; (5) mother's education; and (6) high school background. (FMW)

Predictors of College Readiness: An Analysis of the Student Readiness Inventory

ERIC Educational Resources Information Center

Wilson, James K., III

2012-01-01

The purpose of this study was to better predict how a first semester college freshman becomes prepared for college. The theoretical framework guiding this study is Vrooms' expectancy theory, motivation plays a key role in success. This study used a hierarchical multiple regression model. The independent variables of interest included high school…

Correlation Study of Physics Achievement, Learning Strategy, Attitude and Gender in an Introductory Physics Course

ERIC Educational Resources Information Center

Sezgin Selcuk, Gamze

2010-01-01

This study investigates the relationship between multiple predictors of physics achievement including reported use of four learning strategy clusters (elaboration, organization, comprehension monitoring and rehearsal), attitudes towards physics (sense of care and sense of interest) and a demographic variable (gender) in order to determine the…

The Dynamic between Knowledge Production and Faculty Evaluation: Perceptions of the Promotion and Tenure Process across Disciplines

ERIC Educational Resources Information Center

Jackson, J. Kasi; Latimer, Melissa; Stoiko, Rachel

2017-01-01

This study sought to understand predictors of faculty satisfaction with promotion and tenure processes and reasonableness of expectations in the context of a striving institution. The factors we investigated included discipline (high-consensus [science and math] vs. low-consensus [humanities and social sciences]); demographic variables; and…

Prediction of Vigilant Attention and Cognitive Performance Using Self-Reported Alertness, Circadian Phase, Hours since Awakening, and Accumulated Sleep Loss

PubMed Central

Bermudez, Eduardo B.; Klerman, Elizabeth B.; Czeisler, Charles A.; Cohen, Daniel A.; Wyatt, James K.; Phillips, Andrew J. K.

2016-01-01

Sleep restriction causes impaired cognitive performance that can result in adverse consequences in many occupational settings. Individuals may rely on self-perceived alertness to decide if they are able to adequately perform a task. It is therefore important to determine the relationship between an individual’s self-assessed alertness and their objective performance, and how this relationship depends on circadian phase, hours since awakening, and cumulative lost hours of sleep. Healthy young adults (aged 18–34) completed an inpatient schedule that included forced desynchrony of sleep/wake and circadian rhythms with twelve 42.85-hour “days” and either a 1:2 (n = 8) or 1:3.3 (n = 9) ratio of sleep-opportunity:enforced-wakefulness. We investigated whether subjective alertness (visual analog scale), circadian phase (melatonin), hours since awakening, and cumulative sleep loss could predict objective performance on the Psychomotor Vigilance Task (PVT), an Addition/Calculation Test (ADD) and the Digit Symbol Substitution Test (DSST). Mathematical models that allowed nonlinear interactions between explanatory variables were evaluated using the Akaike Information Criterion (AIC). Subjective alertness was the single best predictor of PVT, ADD, and DSST performance. Subjective alertness alone, however, was not an accurate predictor of PVT performance. The best AIC scores for PVT and DSST were achieved when all explanatory variables were included in the model. The best AIC score for ADD was achieved with circadian phase and subjective alertness variables. We conclude that subjective alertness alone is a weak predictor of objective vigilant or cognitive performance. Predictions can, however, be improved by knowing an individual’s circadian phase, current wake duration, and cumulative sleep loss. PMID:27019198

Predictors of professional and personal satisfaction with a career in psychiatry.

PubMed

Garfinkel, Paul E; Bagby, R Michael; Schuller, Deborah R; Dickens, Susan E; Schulte, Fiona S

2005-05-01

Many factors, including personal experience and personality traits, contribute to the emotional difficulties that psychiatrists experience in their professional work. The nature of the work itself also plays a significant role. To determine those personal and professional characteristics that predict satisfaction with the practice of psychiatry. We mailed a questionnaire that included items pertaining to aspects of personal and professional life to the entire population of psychiatrists in Ontario (N = 1574). Of the 1574, 52% (n = 802) responded. We conducted a series of regression analyses to determine factors related to career satisfaction or regret. A belief in the intrinsic value of psychiatry, a low perceived degree of emotional burden from patients, financial success, and satisfaction with psychotherapeutic work emerged consistently as significant predictors. A subsequent discriminant function analysis indicated that all 4 of these variables accurately predicted those psychiatrists with extreme satisfaction or dissatisfaction with work. These results reveal several variables associated with career satisfaction in the practice of psychiatry that might be useful to discuss with residents who are beginning their careers.

Parental engagement in preventive parenting programs for child mental health: a systematic review of predictors and strategies to increase engagement

PubMed Central

Finan, Samantha J.; Swierzbiolek, Brooke; Priest, Naomi; Warren, Narelle

2018-01-01

Background Child mental health problems are now recognised as a key public health concern. Parenting programs have been developed as one solution to reduce children’s risk of developing mental health problems. However, their potential for widespread dissemination is hindered by low parental engagement, which includes intent to enrol, enrolment, and attendance. To increase parental engagement in preventive parenting programs, we need a better understanding of the predictors of engagement, and the strategies that can be used to enhance engagement. Method Employing a PRISMA method, we conducted a systematic review of the predictors of parent engagement and engagement enhancement strategies in preventive parenting programs. Key inclusion criteria included: (1) the intervention is directed primarily at the parent, (2) parent age >18 years, the article is (3) written in English and (4) published between 2004–2016. Stouffer’s method of combining p-values was used to determine whether associations between variables were reliable. Results Twenty-three articles reported a variety of predictors of parental engagement and engagement enhancement strategies. Only one of eleven predictors (child mental health symptoms) demonstrated a reliable association with enrolment (Stouffer’s p < .01). Discussion There was a lack of consistent evidence for predictors of parental engagement. Nonetheless, preliminary evidence suggests that engagement enhancement strategies modelled on theories, such as the Health Belief Model and Theory of Planned Behaviour, may increase parents’ engagement. Systematic review registration PROSPERO CRD42014013664. PMID:29719737

Predictors of clinical outcome following lumbar disc surgery: the value of historical, physical examination, and muscle function variables.

PubMed

Hebert, Jeffrey J; Fritz, Julie M; Koppenhaver, Shane L; Thackeray, Anne; Kjaer, Per

2016-01-01

Explore the relationships between preoperative findings and clinical outcome following lumbar disc surgery, and investigate the prognostic value of physical examination findings after accounting for information acquired from the clinical history. We recruited 55 adult patients scheduled for first time, single-level lumbar discectomy. Participants underwent a standardized preoperative evaluation including real-time ultrasound imaging assessment of lumbar multifidus function, and an 8-week postoperative rehabilitation programme. Clinical outcome was defined by change in disability, and leg and low back pain (LBP) intensity at 10 weeks. Linear regression models were used to identify univariate and multivariate predictors of outcome. Univariate predictors of better outcome varied depending on the outcome measure. Clinical history predictors included a greater proportion of leg pain to LBP, pain medication use, greater time to surgery, and no history of previous physical or injection therapy. Physical examination predictors were a positive straight or cross straight leg raise test, diminished lower extremity strength, sensation or reflexes, and the presence of postural abnormality or pain peripheralization. Preoperative pain peripheralization remained a significant predictor of improved disability (p = 0.04) and LBP (p = 0.02) after accounting for information from the clinical history. Preoperative lumbar multifidus function was not associated with clinical outcome. Information gleaned from the clinical history and physical examination helps to identify patients more likely to succeed with lumbar disc surgery. While this study helps to inform clinical practice, additional research confirming these results is required prior to confident clinical implementation.

Developing and validating a predictive model for stroke progression.

PubMed

Craig, L E; Wu, O; Gilmour, H; Barber, M; Langhorne, P

2011-01-01

Progression is believed to be a common and important complication in acute stroke, and has been associated with increased mortality and morbidity. Reliable identification of predictors of early neurological deterioration could potentially benefit routine clinical care. The aim of this study was to identify predictors of early stroke progression using two independent patient cohorts. Two patient cohorts were used for this study - the first cohort formed the training data set, which included consecutive patients admitted to an urban teaching hospital between 2000 and 2002, and the second cohort formed the test data set, which included patients admitted to the same hospital between 2003 and 2004. A standard definition of stroke progression was used. The first cohort (n = 863) was used to develop the model. Variables that were statistically significant (p < 0.1) on univariate analysis were included in the multivariate model. Logistic regression was the technique employed using backward stepwise regression to drop the least significant variables (p > 0.1) in turn. The second cohort (n = 216) was used to test the performance of the model. The performance of the predictive model was assessed in terms of both calibration and discrimination. Multiple imputation methods were used for dealing with the missing values. Variables shown to be significant predictors of stroke progression were conscious level, history of coronary heart disease, presence of hyperosmolarity, CT lesion, living alone on admission, Oxfordshire Community Stroke Project classification, presence of pyrexia and smoking status. The model appears to have reasonable discriminative properties [the median receiver-operating characteristic curve value was 0.72 (range 0.72-0.73)] and to fit well with the observed data, which is indicated by the high goodness-of-fit p value [the median p value from the Hosmer-Lemeshow test was 0.90 (range 0.50-0.92)]. The predictive model developed in this study contains variables that can be easily collected in practice therefore increasing its usability in clinical practice. Using this analysis approach, the discrimination and calibration of the predictive model appear sufficiently high to provide accurate predictions. This study also offers some discussion around the validation of predictive models for wider use in clinical practice.

Evaluation of variable selection methods for random forests and omics data sets.

PubMed

Degenhardt, Frauke; Seifert, Stephan; Szymczak, Silke

2017-10-16

Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power. If building a prediction model is the main goal of a study, often a minimal set of variables with good prediction performance is selected. However, if the objective is the identification of involved variables to find active networks and pathways, approaches that aim to select all relevant variables should be preferred. We evaluated several variable selection procedures based on simulated data as well as publicly available experimental methylation and gene expression data. Our comparison included the Boruta algorithm, the Vita method, recurrent relative variable importance, a permutation approach and its parametric variant (Altmann) as well as recursive feature elimination (RFE). In our simulation studies, Boruta was the most powerful approach, followed closely by the Vita method. Both approaches demonstrated similar stability in variable selection, while Vita was the most robust approach under a pure null model without any predictor variables related to the outcome. In the analysis of the different experimental data sets, Vita demonstrated slightly better stability in variable selection and was less computationally intensive than Boruta.In conclusion, we recommend the Boruta and Vita approaches for the analysis of high-dimensional data sets. Vita is considerably faster than Boruta and thus more suitable for large data sets, but only Boruta can also be applied in low-dimensional settings. © The Author 2017. Published by Oxford University Press.

Ecological and personal predictors of science achievement in an urban center

NASA Astrophysics Data System (ADS)

Guidubaldi, John Michael

This study sought to examine selected personal and environmental factors that predict urban students' achievement test scores on the science subject area of the Ohio standardized test. Variables examined were in the general categories of teacher/classroom, student, and parent/home. It assumed that these clusters might add independent variance to a best predictor model, and that discovering relative strength of different predictors might lead to better selection of intervention strategies to improve student performance. This study was conducted in an urban school district and was comprised of teachers and students enrolled in ninth grade science in three of this district's high schools. Consenting teachers (9), students (196), and parents (196) received written surveys with questions designed to examine the predictive power of each variable cluster. Regression analyses were used to determine which factors best correlate with student scores and classroom science grades. Selected factors were then compiled into a best predictive model, predicting success on standardized science tests. Students t tests of gender and racial subgroups confirmed that there were racial differences in OPT scores, and both gender and racial differences in science grades. Additional examinations were therefore conducted for all 12 variables to determine whether gender and race had an impact on the strength of individual variable predictions and on the final best predictor model. Of the 15 original OPT and cluster variable hypotheses, eight showed significant positive relationships that occurred in the expected direction. However, when more broadly based end-of-the-year science class grade was used as a criterion, 13 of the 15 hypotheses showed significant relationships in the expected direction. With both criteria, significant gender and racial differences were observed in the strength of individual predictors and in the composition of best predictor models.

The Niño1+2 region and the Niño4 region predictability.

NASA Astrophysics Data System (ADS)

Miguel, Tasambay-Salazar; Jose, Ortizbevia Maria; Francisco Jose, Alvarez-Garcia; Antonio, Ruizdeelvira

2016-04-01

The El Niño-Southern Oscillation variability is monitored basically by the the Niño3.4 Index. In addition, the Niño1+2 and the Niño4 Indexes are also used to characterise ENSO variability, by reason of their relationships with some of the variability of the neighboring regions, like the air temperature in South America or Australia. However, with the increased length of the available instrumental ENSO records, the need of considering the two different ENSO types identified, Eastern Pacific (EP) or Central Pacific (CP), has become more evident. (Yu and Kim 2013). While the Nino3.4 Index is used to monitor the EP events, the CP events are currently identified by removing from the Niño4 Index the variability associated to the Niño1+2 Index (Kao and Yu 2009). Therefore there is a renewed interest on the predictability of both Indexes. In this study we focus on the predictability of the Niño1+2 region variability and those of the Niño4 region, in the recent post-satellital period. We develop a methodology to identify potential predictors among climate modes, represented by their respective indexes. Among the tropical predictors tested we include the most commonly used,like the Southern Oscillation Index or the Warm Water Volume in the equatorial Pacific (WWV) Index, but also some whose part in the ENSO generation and evolution has been pointed only recently, like the Pacific Meridional Mode (PMM) Index or the North Tropical Zonal Gradient and South Tropical Zonal Gradient Indexes.We also include in our study some other tropical Indexes outside the Pacific basin, like the Tropical North Atlantic, the Tropical South Atlantic and the Indian Ocean Dipole Indexes. We use a seasonal approach, based in a linear statistical relationship and focus on leads going from one season to one year. In the case of the Niño1+2 Index, the number of potential predictors is much higher in spring, followed by winter and summer and last of all autumn. The potential predictor most frequently selected is the WWV Index, tied up with persistence. The other predictors consistently selected are the Pacific Meridional Mode (PMM) Index and the Tropical South Atlantic (TSA) Index. The skill values scored by the Niño4 Index hindcast experiments have many features in common with those found for the Niño3.4 case, as for instance the seasonal dependence on the target month. Here also the WWV is the most frequently selected predictor. The PMM comes in the second place. References Kao HY, Yu JY (2009) Contrasting Eastern-Pacific and Central Pacific types of ENSO.J Clim 22:615-632. doi: 10.1175/2008JCLI. Lagos P, Silva Y, Nickl E, Mosquera K (2008) El Niño-related precipitation variability in Peru.Adv. Geosci., 14, 331-337. Yu JY, Kim ST (2013) Identifying the types of major El Niño Events since 1870. Int J Climatol 33:2105-2112. doi: 10.1002/joc.3575. Tasambay-Salazar, M.; Ortiz Beviá, M. J.; Alvarez-García, F. J.; RuizdeElvira Serra, A.. An estimation of ENSO predictability from its seasonal teleconnections. Theoretical and Applied Climatology. 2015, doi: 10.1007/s00704-015-1546-3

Maxillomandibular advancement as the initial treatment of obstructive sleep apnoea: Is the mandibular occlusal plane the key?

PubMed

Rubio-Bueno, P; Landete, P; Ardanza, B; Vázquez, L; Soriano, J B; Wix, R; Capote, A; Zamora, E; Ancochea, J; Naval-Gías, L

2017-11-01

Maxillomandibular advancement (MMA) can be effective for managing obstructive sleep apnoea (OSA); however, limited information is available on the predictor surgical variables. This study investigated whether normalization of the mandibular occlusal plane (MOP) was a determinant factor in curing OSA. Patients with moderate or severe OSA who underwent MMA were evaluated by preoperative and postoperative three-dimensional (3D) scans and polysomnograms. The postoperative value of MOP and the magnitude of skeletal advancement were the predictor variables; change in the apnoea-hypopnoea index (AHI) was the main outcome variable. Thirty-four subjects with a mean age of 41±14years and 58,8% female were analysed. The Epworth Sleepiness Scale (ESS) was 17.4±5.4 and AHI was 38.3±10.7 per hour before surgery. Postoperative AHI was 6.5±4.3 per hour (P<0.001) with 52.94% of the patients considered as cured, and 47.06% suffering from a mild residual OSA with ESS 0.8±1.4 (P<0.001). 3D changes revealed a volume increase of 106.3±38.8%. The mandible was advanced 10.4±3.9mm and maxilla 4.9±3.2mm. MOP postoperative value was concluded to be the best predictor variable. Treatment planning should include MOP normalization and a mandibular advancement between 6 and 10mm. The maxillary advancement would depend on the desired aesthetic changes and final occlusion. Copyright © 2017 International Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

PREDICTING ADHERENCE TO TREATMENT FOR METHAMPHETAMINE DEPENDENCE FROM NEUROPSYCHOLOGICAL AND DRUG USE VARIABLES*

PubMed Central

Dean, Andy C.; London, Edythe D.; Sugar, Catherine A.; Kitchen, Christina M. R.; Swanson, Aimee-Noelle; Heinzerling, Keith G.; Kalechstein, Ari D.; Shoptaw, Steven

2009-01-01

Although some individuals who abuse methamphetamine have considerable cognitive deficits, no prior studies have examined whether neurocognitive functioning is associated with outcome of treatment for methamphetamine dependence. In an outpatient clinical trial of bupropion combined with cognitive behavioral therapy and contingency management (Shoptaw et al., 2008), 60 methamphetamine-dependent adults completed three tests of reaction time and working memory at baseline. Other variables that were collected at baseline included measures of drug use, mood/psychiatric functioning, employment, social context, legal status, and medical status. We evaluated the relative predictive value of all baseline measures for treatment outcome using Classification and Regression Trees (CART; Breiman, 1984), a nonparametric statistical technique that produces easily interpretable decision rules for classifying subjects that are particularly useful in clinical settings. Outcome measures were whether or not a participant completed the trial and whether or not most urine tests showed abstinence from methamphetamine abuse. Urine-verified methamphetamine abuse at the beginning of the study was the strongest predictor of treatment outcome; two psychosocial measures (e.g., nicotine dependence and Global Assessment of Functioning) also offered some predictive value. A few reaction time and working memory variables were related to treatment outcome, but these cognitive measures did not significantly aid prediction after adjusting for methamphetamine usage at the beginning of the study. On the basis of these findings, we recommend that research groups seeking to identify new predictors of treatment outcome compare the predictors to methamphetamine usage variables to assure that unique predictive power is attained. PMID:19608354

Differentiating progress in a clinical group of fibromyalgia patients during and following a multicomponent treatment program.

PubMed

Van Den Houte, Maaike; Luyckx, Koen; Van Oudenhove, Lukas; Bogaerts, Katleen; Van Diest, Ilse; De Bie, Jozef; Van den Bergh, Omer

2017-07-01

Treatments including multiple nonpharmacological components have beneficial effects on the key symptoms of fibromyalgia, although effects are limited and often do not persist. In this study, we examined different patterns of clinical progress and the dynamic interplay between predictors and outcomes over time. Fibromyalgia patients (N=153; 135 women) followed a multidisciplinary group program spanning 12weeks, aimed at "regaining control over daily functioning". Anxiety, depression, pain coping and kinesiophobia were used as predictor variables. Outcome variables were pain severity, pain-related disability, physical functioning and functional interference. All variables were assessed at 3 moments: on the first and last day of treatment, and 12weeks after the last day of treatment. Overall treatment effects were analyzed using mixed model analyses. Latent class growth analysis identifying different treatment trajectory classes was used to investigate individual differences in treatment effects. Finally, cross-lagged structural equation models were used to investigate the dynamic interplay between predictors and outcomes over time. Only a fourth to a third of the total group showed improvement on the outcome variables. These patients had lower baseline anxiety, depression and kinesiophobia, and improved more on anxiety, depression and kinesiophobia. Physical well-being had a stronger effect on anxiety and depression than vice versa. Physical functioning predicted relative changes in kinesiophobia, while kinesiophobia predicted relative changes in pain-related disability. The results emphasize the importance of tailoring treatments to individual needs in order to improve overall effectiveness of treatment programs. Copyright © 2017. Published by Elsevier Inc.

Factors influencing teamwork and collaboration within a tertiary medical center

PubMed Central

Chien, Shu Feng; Wan, Thomas TH; Chen, Yu-Chih

2012-01-01

AIM: To understand how work climate and related factors influence teamwork and collaboration in a large medical center. METHODS: A survey of 3462 employees was conducted to generate responses to Sexton’s Safety Attitudes Questionnaire (SAQ) to assess perceptions of work environment via a series of five-point, Likert-scaled questions. Path analysis was performed, using teamwork (TW) and collaboration (CO) as endogenous variables. The exogenous variables are effective communication (EC), safety culture (SC), job satisfaction (JS), work pressure (PR), and work climate (WC). The measurement instruments for the variables or summated subscales are presented. Reliability of each sub-scale are calculated. Alpha Cronbach coefficients are relatively strong: TW (0.81), CO (0.76), EC (0.70), SC (0.83), JS (0.91), WP (0.85), and WC (0.78). Confirmatory factor analysis was performed for each of these constructs. RESULTS: Path analysis enables to identify statistically significant predictors of two endogenous variables, teamwork and intra-organizational collaboration. Significant amounts of variance in perceived teamwork (R2 = 0.59) and in collaboration (R2 = 0.75) are accounted for by the predictor variables. In the initial model, safety culture is the most important predictor of perceived teamwork, with a β weight of 0.51, and work climate is the most significant predictor of collaboration, with a β weight of 0.84. After eliminating statistically insignificant causal paths and allowing correlated predictors1, the revised model shows that work climate is the only predictor positively influencing both teamwork (β = 0.26) and collaboration (β = 0.88). A relatively weak positive (β = 0.14) but statistically significant relationship exists between teamwork and collaboration when the effects of other predictors are simultaneously controlled. CONCLUSION: Hospital executives who are interested in improving collaboration should assess the work climate to ensure that employees are operating in a setting conducive to intra-organizational collaboration. PMID:25237612

Job Satisfaction in Mexican Faculty: An Analysis of its Predictor Variables. ASHE Annual Meeting Paper.

ERIC Educational Resources Information Center

Galaz-Fontes, Jesus Francisco; Gil-Anton, Manuel

This study examined overall job satisfaction among college faculty in Mexico. The study used data from a 1992-93 Carnegie International Faculty Survey. Secondary multiple regression analysis identified predictor variables for several faculty subgroups. Results were interpreted by differentiating between work-related and intrinsic factors, as well…

Teacher and Child Predictors of Achieving IEP Goals of Children with Autism

ERIC Educational Resources Information Center

Ruble, Lisa; McGrew, John H.

2013-01-01

It is encouraging that children with autism show a strong response to early intervention, yet more research is needed for understanding the variability in responsiveness to specialized programs. Treatment predictor variables from 47 teachers and children who were randomized to receive the COMPASS intervention (Ruble et al. in "The…

On the Misconception of Multicollinearity in Detection of Moderating Effects: Multicollinearity Is Not Always Detrimental

ERIC Educational Resources Information Center

Shieh, Gwowen

2010-01-01

Due to its extensive applicability and computational ease, moderated multiple regression (MMR) has been widely employed to analyze interaction effects between 2 continuous predictor variables. Accordingly, considerable attention has been drawn toward the supposed multicollinearity problem between predictor variables and their cross-product term.…

Follow-Up Care for Older Women With Breast Cancer

DTIC Science & Technology

2000-05-01

better predictor of upper body mor therapy, all cause mortality, self -reported function and overall physical function than upper body function, and...outcomes, including primary tu- Major Analytic Variables mor therapy and all cause mortality, as well as self -reported upper body and overall physical ...comorbidity and their relation to a range of patient outcomes, including primary tumor therapy and mortality, self -reported upper body function, and overall

Motivation for change as a predictor of treatment response for dysthymia.

PubMed

Frías Ibáñez, Álvaro; González Vallespí, Laura; Palma Sevillano, Carol; Farriols Hernando, Núria

2016-05-01

Dysthymia constitutes a chronic, mild affective disorder characterized by heterogeneous treatment effects. Several predictors of clinical response and attendance have been postulated, although research on the role of the psychological variables involved in this mental disorder is still scarce. Fifty-four adult patients, who met criteria for dysthymia completed an ongoing naturalistic treatment based on the brief interpersonal psychotherapy (IPT-B), which was delivered bimonthly over 16 months. As potential predictor variables, the therapeutic alliance, coping strategies, perceived self-efficacy, and motivation for change were measured at baseline. Outcome variables were response to treatment (Clinical Global Impression and Beck’s Depression Inventory) and treatment attendance. Stepwise multiple linear regression analyses revealed that higher motivation for change predicted better response to treatment. Moreover, higher motivation for change also predicted treatment attendance. Therapeutic alliance was not a predictor variable of neither clinical response nor treatment attendance. These preliminary findings support the adjunctive use of motivational interviewing (MI) techniques in the treatment of dysthymia. Further research with larger sample size and follow-up assessment is warranted.

Relationship of Personality Disorders to the Course of Major Depressive Disorder in a Nationally Representative Sample

PubMed Central

Skodol, Andrew E.; Grilo, Carlos M.; Keyes, Katherine; Geier, Timothy; Grant, Bridget F.; Hasin, Deborah S.

2011-01-01

Objective The purpose of this study was to examine the effects of specific personality disorder co-morbidity on the course of major depressive disorder in a nationally-representative sample. Method Data were drawn from 1,996 participants in a national survey. Participants who met criteria for major depressive disorder at baseline in face-to-face interviews (2001–2002) were re-interviewed three years later (2004–2005) to determine persistence and recurrence. Predictors included all DSM-IV personality disorders. Control variables included demographic characteristics, other Axis I disorders, family and treatment histories, and previously established predictors of the course of major depressive disorder. Results 15.1% of participants had persistent major depressive disorder and 7.3% of those who remitted had a recurrence. Univariate analyses indicated that avoidant, borderline, histrionic, paranoid, schizoid, and schizotypal personality disorders all elevated the risk for persistence. With Axis I co-morbidity controlled, all but histrionic personality disorder remained significant. With all other personality disorders controlled, borderline and schizotypal remained significant predictors. In final, multivariate analyses that controlled for age at onset of major depressive disorder, number of previous episodes, duration of current episode, family history, and treatment, borderline personality disorder remained a robust predictor of major depressive disorder persistence. Neither personality disorders nor other clinical variables predicted recurrence. Conclusions In this nationally-representative sample of adults with major depressive disorder, borderline personality disorder robustly predicted persistence, a finding that converges with recent clinical studies. Personality psychopathology, particularly borderline personality disorder, should be assessed in all patients with major depressive disorder, considered in prognosis, and addressed in treatment. PMID:21245088

The Impact of a Sexual and Reproductive Health Intervention for American Indian Adolescents on Predictors of Condom Use Intention.

PubMed

Tingey, Lauren; Chambers, Rachel; Rosenstock, Summer; Lee, Angelita; Goklish, Novalene; Larzelere, Francene

2017-03-01

American Indian (AI) adolescents experience inequalities in sexual health, in particular, early sexual initiation. Condom use intention is an established predictor of condom use and is an important construct for evaluating interventions among adolescents who are not yet sexually active. This analysis evaluated the impact of Respecting the Circle of Life (RCL), a sexual and reproductive health intervention for AI adolescents, on predictors of condom use intention. We utilized a cluster randomized controlled trial design to evaluate RCL among 267 AIs ages 13-19. We examined baseline psychosocial and theoretical variables associated with condom use intention. Generalized estimating equation regression models determined which baseline variables predictive of condom use intention were impacted. Mean sample age was 15.1 years (standard deviation 1.7) and 56% were female; 22% had initiated sex. A larger proportion of RCL versus control participants had condom use intention post intervention (relative risk [RR] = 1.39, p = .008), especially younger (ages 13-15; RR = 1.42, p = .007) and sexually inexperienced adolescents (RR = 1.44, p = .01); these differences attenuated at additional follow-up. Baseline predictors of condom use intention included being sexually experienced, having condom use self-efficacy, as well as response efficacy and severity (both theoretical constructs). Of these, the RCL intervention significantly impacted condom use self-efficacy and response efficacy. Results demonstrate RCL intervention efficacy impacting variables predictive of condom use intention at baseline, with greater differences among younger, sexually inexperienced adolescents. To sustain intervention impact, future RCL implementation should reinforce education and training in condom use self-efficacy and response efficacy and recruit younger, sexually inexperienced AI adolescents. Copyright © 2016 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Predictors of short-term outcome to exercise and manual therapy for people with hip osteoarthritis.

PubMed

French, Helen P; Galvin, Rose; Cusack, Tara; McCarthy, Geraldine M

2014-01-01

Physical therapy for hip osteoarthritis (OA) has shown short-term effects but limited long-term benefit. There has been limited research, with inconsistent results, in identifying prognostic factors associated with a positive response to physical therapy. The purpose of this study was to identify potential predictors of response to physical therapy (exercise therapy [ET] with or without adjunctive manual therapy [MT]) for hip OA based on baseline patient-specific and clinical characteristics. A prognostic study was conducted. Secondary analysis of data from a multicenter randomized controlled trial (RCT) (N=131) that evaluated the effectiveness of ET and ET+MT for hip OA was undertaken. Treatment response was defined using OMERACT/OARSI responder criteria. Ten baseline measures were used as predictor variables. Regression analyses were undertaken to identify predictors of outcome. Discriminative ability (sensitivity, specificity, and likelihood ratios) of significant variables was calculated. The RCT results showed no significant difference in most outcomes between ET and ET+MT at 9 and 18 weeks posttreatment. Forty-six patients were classified as responders at 9 weeks, and 36 patients were classified as responders at 18 weeks. Four baseline variables were predictive of a positive outcome at 9 weeks: male sex, pain with activity (<6/10), Western Ontario and McMaster Universities Osteoarthritis Index physical function subscale score (<34/68), and psychological health (Hospital Anxiety and Depression Scale score <9/42). No predictor variables were identified at the 18-week follow-up. Prognostic accuracy was fair for all 4 variables (sensitivity=0.5-0.58, specificity=0.57-0.72, likelihood ratios=1.25-1.77), indicating fair discriminative ability at predicting treatment response. The short-term follow-up limits the interpretation of results, and the low number of identified responders may have resulted in possible overfitting of the predictor model. The authors were unable to identify baseline variables in patients with hip OA that indicate those most likely to respond to treatment due to low discriminative ability. Further validation studies are needed to definitively define the best predictors of response to physical therapy in people with hip OA.

Temporal predictors of health-related quality of life in elderly people with diabetes: results of a German cohort study.

PubMed

Maatouk, Imad; Wild, Beate; Wesche, Daniela; Herzog, Wolfgang; Raum, Elke; Müller, Heiko; Rothenbacher, Dietrich; Stegmaier, Christa; Schellberg, Dieter; Brenner, Hermann

2012-01-01

The aim of the study was to determine predictors that influence health-related quality of life (HRQOL) in a large cohort of elderly diabetes patients from primary care over a follow-up period of five years. At the baseline measurement of the ESTHER cohort study (2000-2002), 1375 out of 9953 participants suffered from diabetes (13.8%). 1057 of these diabetes patients responded to the second-follow up (2005-2007). HRQOL at baseline and follow-up was measured using the SF-12; mental component scores (MCS) and physical component scores (PCS) were calculated; multiple linear regression models were used to determine predictors of HRQOL at follow-up. As possible predictors for HRQOL, the following baseline variables were examined: treatment with insulin, glycated hemoglobin (HbA1c), number of diabetes related complications, number of comorbid diseases, Body-Mass-Index (BMI), depression and HRQOL. Regression analyses were adjusted for sociodemographic variables and smoking status. 1034 patients (97.8%) responded to the SF-12 both at baseline and after five years and were therefore included in the study. Regression analyses indicated that significant predictors of decreased MCS were a lower HRQOL, a higher number of diabetes related complications and a reported history of depression at baseline. Complications, BMI, smoking and HRQOL at baseline significantly predicted PCS at the five year follow-up. Our findings expand evidence from previous cross-sectional data indicating that in elderly diabetes patients, depression, diabetes related complications, smoking and BMI are temporally predictive for HRQOL.

Temporal Predictors of Health-Related Quality of Life in Elderly People with Diabetes: Results of a German Cohort Study

PubMed Central

Wesche, Daniela; Herzog, Wolfgang; Raum, Elke; Müller, Heiko; Rothenbacher, Dietrich; Stegmaier, Christa; Schellberg, Dieter; Brenner, Hermann

2012-01-01

Background The aim of the study was to determine predictors that influence health-related quality of life (HRQOL) in a large cohort of elderly diabetes patients from primary care over a follow-up period of five years. Methods and Results At the baseline measurement of the ESTHER cohort study (2000–2002), 1375 out of 9953 participants suffered from diabetes (13.8%). 1057 of these diabetes patients responded to the second-follow up (2005–2007). HRQOL at baseline and follow-up was measured using the SF-12; mental component scores (MCS) and physical component scores (PCS) were calculated; multiple linear regression models were used to determine predictors of HRQOL at follow-up. As possible predictors for HRQOL, the following baseline variables were examined: treatment with insulin, glycated hemoglobin (HbA1c), number of diabetes related complications, number of comorbid diseases, Body-Mass-Index (BMI), depression and HRQOL. Regression analyses were adjusted for sociodemographic variables and smoking status. 1034 patients (97.8%) responded to the SF-12 both at baseline and after five years and were therefore included in the study. Regression analyses indicated that significant predictors of decreased MCS were a lower HRQOL, a higher number of diabetes related complications and a reported history of depression at baseline. Complications, BMI, smoking and HRQOL at baseline significantly predicted PCS at the five year follow-up. Conclusions Our findings expand evidence from previous cross-sectional data indicating that in elderly diabetes patients, depression, diabetes related complications, smoking and BMI are temporally predictive for HRQOL. PMID:22292092

Explanatory model of emotional-cognitive variables in school mathematics performance: a longitudinal study in primary school

PubMed Central

Cerda, Gamal; Pérez, Carlos; Navarro, José I.; Aguilar, Manuel; Casas, José A.; Aragón, Estíbaliz

2015-01-01

This study tested a structural model of cognitive-emotional explanatory variables to explain performance in mathematics. The predictor variables assessed were related to students’ level of development of early mathematical competencies (EMCs), specifically, relational and numerical competencies, predisposition toward mathematics, and the level of logical intelligence in a population of primary school Chilean students (n = 634). This longitudinal study also included the academic performance of the students during a period of 4 years as a variable. The sampled students were initially assessed by means of an Early Numeracy Test, and, subsequently, they were administered a Likert-type scale to measure their predisposition toward mathematics (EPMAT) and a basic test of logical intelligence. The results of these tests were used to analyse the interaction of all the aforementioned variables by means of a structural equations model. This combined interaction model was able to predict 64.3% of the variability of observed performance. Preschool students’ performance in EMCs was a strong predictor for achievement in mathematics for students between 8 and 11 years of age. Therefore, this paper highlights the importance of EMCs and the modulating role of predisposition toward mathematics. Also, this paper discusses the educational role of these findings, as well as possible ways to improve negative predispositions toward mathematical tasks in the school domain. PMID:26441739

Why we should use simpler models if the data allow this: relevance for ANOVA designs in experimental biology.

PubMed

Lazic, Stanley E

2008-07-21

Analysis of variance (ANOVA) is a common statistical technique in physiological research, and often one or more of the independent/predictor variables such as dose, time, or age, can be treated as a continuous, rather than a categorical variable during analysis - even if subjects were randomly assigned to treatment groups. While this is not common, there are a number of advantages of such an approach, including greater statistical power due to increased precision, a simpler and more informative interpretation of the results, greater parsimony, and transformation of the predictor variable is possible. An example is given from an experiment where rats were randomly assigned to receive either 0, 60, 180, or 240 mg/L of fluoxetine in their drinking water, with performance on the forced swim test as the outcome measure. Dose was treated as either a categorical or continuous variable during analysis, with the latter analysis leading to a more powerful test (p = 0.021 vs. p = 0.159). This will be true in general, and the reasons for this are discussed. There are many advantages to treating variables as continuous numeric variables if the data allow this, and this should be employed more often in experimental biology. Failure to use the optimal analysis runs the risk of missing significant effects or relationships.

Development of European NO2 Land Use Regression Model for present and future exposure assessment: Implications for policy analysis.

PubMed

Vizcaino, Pilar; Lavalle, Carlo

2018-05-04

A new Land Use Regression model was built to develop pan-European 100 m resolution maps of NO 2 concentrations. The model was built using NO 2 concentrations from routine monitoring stations available in the Airbase database as dependent variable. Predictor variables included land use, road traffic proxies, population density, climatic and topographical variables, and distance to sea. In order to capture international and inter regional disparities not accounted for with the mentioned predictor variables, additional proxies of NO 2 concentrations, like levels of activity intensity and NO x emissions for specific sectors, were also included. The model was built using Random Forest techniques. Model performance was relatively good given the EU-wide scale (R 2  = 0.53). Output predictions of annual average concentrations of NO 2 were in line with other existing models in terms of spatial distribution and values of concentration. The model was validated for year 2015, comparing model predictions derived from updated values of independent variables, with concentrations in monitoring stations for that year. The algorithm was then used to model future concentrations up to the year 2030, considering different emission scenarios as well as changes in land use, population distribution and economic factors assuming the most likely socio-economic trends. Levels of exposure were derived from maps of concentration. The model proved to be a useful tool for the ex-ante evaluation of specific air pollution mitigation measures, and more broadly, for impact assessment of EU policies on territorial development. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Insight and other predictors of physical examination refusal in psychotic illness.

PubMed

Iwata, Kazuya; Strydom, Andre; Osborn, David

2011-08-01

Poor physical health in psychiatric patients is well recognized, yet factors contributing to physical examination noncompliance in psychotic illness have not been previously studied. To examine whether insight or any other variables were independent predictors of physical examination noncompliance. A case-note study (N = 200) of inpatient psychiatric patients in four hospitals in London, UK was conducted to examine the relationship between insight and physical examination noncompliance within 24  h of admission and over 2 weeks. Clinical variables including illness severity were also examined. Patients who were noncompliant with physical examinations offered within 24  h and over 2 weeks were associated with lack of insight, higher illness severity, female gender, longer history of illness, current compulsory admission, and previous history of detention. After adjusting for confounding factors, lack of insight, female gender, and previous history of detention were found to be independent predictors of physical examination noncompliance for 24  h and 2 weeks. Lack of insight is highly predictive of physical examination noncompliance for up to 2 weeks, indicating that mental incapacity to consenting to medical care may be common and that more proactive physical screening may be required for these patients.

A simulation study on Bayesian Ridge regression models for several collinearity levels

NASA Astrophysics Data System (ADS)

Efendi, Achmad; Effrihan

2017-12-01

When analyzing data with multiple regression model if there are collinearities, then one or several predictor variables are usually omitted from the model. However, there sometimes some reasons, for instance medical or economic reasons, the predictors are all important and should be included in the model. Ridge regression model is not uncommon in some researches to use to cope with collinearity. Through this modeling, weights for predictor variables are used for estimating parameters. The next estimation process could follow the concept of likelihood. Furthermore, for the estimation nowadays the Bayesian version could be an alternative. This estimation method does not match likelihood one in terms of popularity due to some difficulties; computation and so forth. Nevertheless, with the growing improvement of computational methodology recently, this caveat should not at the moment become a problem. This paper discusses about simulation process for evaluating the characteristic of Bayesian Ridge regression parameter estimates. There are several simulation settings based on variety of collinearity levels and sample sizes. The results show that Bayesian method gives better performance for relatively small sample sizes, and for other settings the method does perform relatively similar to the likelihood method.

Mental health treatment needs for medical students: a national longitudinal study.

PubMed

Midtgaard, Mirim; Ekeberg, Øivind; Vaglum, Per; Tyssen, Reidar

2008-10-01

We aimed to study the occurrence and predictors of medical students' mental health problems that required treatment. Medical students from all Norwegian universities (N=421) were surveyed in their first term (T1), and 3 (T2) and 6 (T3) years later. The dependent variable was "Mental health problems in need of treatment". Predictor variables included personality traits, medical school stress and negative life events. The lifetime prevalence of mental health problems was 15% at T1. At T2, of the 31% who reported problems during the first 3years, a majority had not sought help. At T3, 14% reported problems during the preceding year. Adjusted predictors of problems at T2 were previous mental health problems (p<.001), low level of intensity personality trait (extraversion) (p<.01), reality weakness personality trait (p<.01), perceived medical school stress (p<.05) and negative life events (p<.05). Mental health problems during the first 3years were predicted by previous problems, personality, medical school stress and negative life events. A third of the students reported mental health problems during the first 3years. Intervention should focus on both individual problems and contextual stress.

Predictors of sustained six months quitting success: efforts of smoking cessation in low intensity smoke-free workplaces.

PubMed

Yasin, Siti Munira; Retneswari, Masilamani; Moy, Foong Ming; Taib, Khairul Mizan; Ismail, Nurhuda

2013-08-01

This study aims to identify the predictors of a 6-month quitting success among employees involved in workplace smoking cessation with low-intensity smoke-free policy. A multicentre prospective cohort study was conducted among employees from 2 different public universities in Malaysia. Interventions include at least 2 sessions of behavioural therapy combined with free nicotine replacement therapy (NRT) for 8 weeks. Participants were followed up for 6 months. Independent variables assessed were on sociodemographic and environmental tobacco smoke. Their quit status were determined at 1 week, 3 months and 6 months. One hundred and eighty- five smokers volunteered to participate. Among the participants, 15% and 13% sustained quit at 3 months and 6 months respectively. Multivariate analysis revealed that at 6 months, attending all 3 behavioural sessions predicted success. None of the environmental tobacco exposure variables were predictive of sustained cessation. Individual predictors of success in intra-workplace smoking cessation programmes do not differ from the conventional clinic-based smoking cessation. Furthermore, environmental tobacco exposure in low intensity smoke-free workplaces has limited influence on smokers who succeeded in maintaining 6 months quitting.

Multivariate predictors of music perception and appraisal by adult cochlear implant users.

PubMed

Gfeller, Kate; Oleson, Jacob; Knutson, John F; Breheny, Patrick; Driscoll, Virginia; Olszewski, Carol

2008-02-01

The research examined whether performance by adult cochlear implant recipients on a variety of recognition and appraisal tests derived from real-world music could be predicted from technological, demographic, and life experience variables, as well as speech recognition scores. A representative sample of 209 adults implanted between 1985 and 2006 participated. Using multiple linear regression models and generalized linear mixed models, sets of optimal predictor variables were selected that effectively predicted performance on a test battery that assessed different aspects of music listening. These analyses established the importance of distinguishing between the accuracy of music perception and the appraisal of musical stimuli when using music listening as an index of implant success. Importantly, neither device type nor processing strategy predicted music perception or music appraisal. Speech recognition performance was not a strong predictor of music perception, and primarily predicted music perception when the test stimuli included lyrics. Additionally, limitations in the utility of speech perception in predicting musical perception and appraisal underscore the utility of music perception as an alternative outcome measure for evaluating implant outcomes. Music listening background, residual hearing (i.e., hearing aid use), cognitive factors, and some demographic factors predicted several indices of perceptual accuracy or appraisal of music.

Facebook Addiction: Onset Predictors.

PubMed

Biolcati, Roberta; Mancini, Giacomo; Pupi, Virginia; Mugheddu, Valeria

2018-05-23

Worldwide, Facebook is becoming increasingly widespread as a communication platform. Young people especially use this social networking site daily to maintain and establish relationships. Despite the Facebook expansion in the last few years and the widespread acceptance of this social network, research into Facebook Addiction (FA) is still in its infancy. Hence, the potential predictors of Facebook overuse represent an important matter for investigation. This study aimed to deepen the understanding of the relationship between personality traits, social and emotional loneliness, life satisfaction, and Facebook addiction. A total of 755 participants (80.3% female; n = 606) aged between 18 and 40 (mean = 25.17; SD = 4.18) completed the questionnaire packet including the Bergen Facebook Addiction Scale, the Big Five, the short version of Social and Emotional Loneliness Scale for Adults, and the Satisfaction with Life Scale. A regression analysis was used with personality traits, social, family, romantic loneliness, and life satisfaction as independent variables to explain variance in Facebook addiction. The findings showed that Conscientiousness, Extraversion, Neuroticism, and Loneliness (Social, Family, and Romantic) were strong significant predictors of FA. Age, Openness, Agreeableness, and Life Satisfaction, although FA-related variables, were not significant in predicting Facebook overuse. The risk profile of this peculiar behavioral addiction is also discussed.

Special Judo Fitness Test Level and Anthropometric Profile of Elite Spanish Judo Athletes.

PubMed

Casals, Cristina; Huertas, Jesús R; Franchini, Emerson; Sterkowicz-Przybycień, Katarzyna; Sterkowicz, Stanislaw; Gutiérrez-García, Carlos; Escobar-Molina, Raquel

2017-05-01

Casals, C, Huertas, JR, Franchini, E, Sterkowicz-Przybycień, K, Sterkowicz, S, Gutiérrez-García, C, and Escobar-Molina, R. Special judo fitness test level and anthropometric profile of elite spanish judo athletes. J Strength Cond Res 31(5): 1229-1235, 2017-The aim of this study was to determine the anthropometric variables that best predict Special Judo Fitness Test (SJFT) performance. In addition, anthropometric profiles of elite Spanish judo athletes were compared by sex and age category (seniors and juniors). In this cross-sectional study, a total of 51 (29 females) athletes from the Spanish National Judo Team were evaluated during a competitive period. All athletes performed the SJFT and underwent an anthropometric assessment through skinfold thickness measurements. Mann-Whitney comparisons by sex and age category showed that males had significantly higher muscle mass and lower fat mass than females (p < 0.001), whereas juniors and seniors exhibited few differences in body composition. Linear regression analyses (stepwise method) were performed to explore the relationships between anthropometric characteristics and SJFT variables. Model 1 included sex, age category, and body mass as predictors. Body mass and sex significantly predicted the SJFT index (R = 0.27, p < 0.001); thus, both criteria should be considered before interpreting the test. The predictors of model 2 were quick-assessment variables, including skinfolds, breadths, girths, and height. This regression model showed that the biceps skinfold significantly predicted the SJFT index in elite athletes (R = 0.31, p < 0.001). Model 3 included body compositions and somatotypes as predictors. Higher muscle and bone masses and lower ectomorphy were associated with better SJFT performance (R = 0.44, p < 0.001). Hence, training programs should attempt to increase the muscle mass percentage and reduce the upper arm fat, whereas the bone percentage could be considered in the selection of talented athletes in conjunction with other factors.

A variant of sparse partial least squares for variable selection and data exploration.

PubMed

Olson Hunt, Megan J; Weissfeld, Lisa; Boudreau, Robert M; Aizenstein, Howard; Newman, Anne B; Simonsick, Eleanor M; Van Domelen, Dane R; Thomas, Fridtjof; Yaffe, Kristine; Rosano, Caterina

2014-01-01

When data are sparse and/or predictors multicollinear, current implementation of sparse partial least squares (SPLS) does not give estimates for non-selected predictors nor provide a measure of inference. In response, an approach termed "all-possible" SPLS is proposed, which fits a SPLS model for all tuning parameter values across a set grid. Noted is the percentage of time a given predictor is chosen, as well as the average non-zero parameter estimate. Using a "large" number of multicollinear predictors, simulation confirmed variables not associated with the outcome were least likely to be chosen as sparsity increased across the grid of tuning parameters, while the opposite was true for those strongly associated. Lastly, variables with a weak association were chosen more often than those with no association, but less often than those with a strong relationship to the outcome. Similarly, predictors most strongly related to the outcome had the largest average parameter estimate magnitude, followed by those with a weak relationship, followed by those with no relationship. Across two independent studies regarding the relationship between volumetric MRI measures and a cognitive test score, this method confirmed a priori hypotheses about which brain regions would be selected most often and have the largest average parameter estimates. In conclusion, the percentage of time a predictor is chosen is a useful measure for ordering the strength of the relationship between the independent and dependent variables, serving as a form of inference. The average parameter estimates give further insight regarding the direction and strength of association. As a result, all-possible SPLS gives more information than the dichotomous output of traditional SPLS, making it useful when undertaking data exploration and hypothesis generation for a large number of potential predictors.

Analysis of job satisfaction, burnout, and intent of respiratory care practitioners to leave the field or the job.

PubMed

Shelledy, D C; Mikles, S P; May, D F; Youtsey, J W

1992-01-01

Increased stress, burnout, and lack of job satisfaction may contribute to a decline in work performance, absenteeism, and intent to leave one's job or field. We undertook to determine organizational, job-specific, and personal predictors of level of burnout among respiratory care practitioners (RCPs). We also examined the relationships among burnout, job satisfaction (JS), absenteeism, and RCPs' intent to leave their job or the field. A pilot-tested assessment instrument was mailed to all active NBRC-credentialed RCPs in Georgia (n = 788). There were 458 usable returns (58% response rate). A random sample of 10% of the nonrespondents (n = 33) was then surveyed by telephone, and the results were compared to those of the mail respondents. Variables were compared to burnout and JS scores by correlational analysis, which was followed by stepwise multiple regression analyses to determine the ability of the independent variables to predict burnout and JS scores when used in combination. There were no significant differences between respondents and sampled nonrespondents in burnout scores (p = 0.56) or JS (p = 0.24). Prediction of burnout: The coefficient of multiple correlation, R2, indicated that in combination the independent variables accounted for 61% of the variance in burnout scores. The strongest predictor of burnout was job stress. Other job-related predictors of burnout were size of department, satisfaction with work, satisfaction with co-workers and co-worker support, job independence and job control, recognition by nursing, and role clarity. Personal-variable predictors were age, number of previous jobs held, social support, and intent to leave the field of respiratory care. Prediction of job satisfaction: R2 indicated that, in combination, the independent variables accounted for 63% of the variance observed in satisfaction with work, 36% of the variance observed in satisfaction with pay, 36% of the variance in satisfaction with promotions, 62% of the variance in satisfaction with supervision, and 48% of the variance in satisfaction with co-workers. Predictors of work-satisfaction level were recognition by physicians and nursing, age, burn-out level, absenteeism, and intent to leave the field. Predictors of level of satisfaction with pay were actual salary, job independence, organizational climate, ease of obtaining time off, job stress, absenteeism, intent to leave the field, and number of dependent children. Predictors of level of satisfaction with promotions were recognition by nursing, participation in decision making, job stress, intent to leave the field, past turnover rates, and absenteeism. Predictors of level of satisfaction with supervision included supervisor support, role clarity, independence, and ease of obtaining time off. The strongest predictor of level of satisfaction with co-workers was co-worker support. As overall level of JS increased, level of burnout decreased significantly (r = -0.59, p less than 0.001). As burnout level increased, increases occurred in absenteeism (r = 0.22, p less than 0.001), intent to leave the job (r = 0.48, p less than 0.001), and intent to leave the field (r = 0.51, p less than 0.001). Reduced job stress, increased job independence and job control, improved role clarity, and higher levels of JS were all associated with lower levels of burnout. Managerial attention to these factors may improve patient care and reduce absenteeism and turnover among RCPs.

Latitude of residence and position in time zone are predictors of cancer incidence, cancer mortality, and life expectancy at birth.

PubMed

Borisenkov, Mikhail F

2011-03-01

According to the hypothesis of circadian disruption, external factors that disturb the function of the circadian system can raise the risk of malignant neoplasm and reduce life span. Recent work has shown that the functionality of the circadian system is dependent not only on latitude of residence but also on the region's position in the time zone. The purpose of the present research was to examine the influence of latitude and time zone on cancer incidence, cancer mortality, and life expectancy at birth. A stepwise multiple regression analysis was carried out on residents of 59 regions of the European part of the Russian Federation (EPRF) using age-standardized parameters (per 100,000) of cancer incidence (CI), cancer mortality (CM), and life expectancy at birth (LE, yrs) as dependent variables. The geographical coordinates (latitude and position in the time zone) of the regions were used as independent variables, controlling for the level of economic development in the regions. The same analysis was carried out for LE in 31 regions in China. Latitude was the strongest predictor of LE in the EPRF population; it explained 48% and 45% of the variability in LE of women and men, respectively. Position within the time zone accounted for an additional 4% and 3% variability of LE in women and men, respectively. The highest values for LE were observed in the southeast of the EPRF. In China, latitude was not a predictor of LE, whereas position in the time zone explained 15% and 18% of the LE variability in women and men, respectively. The highest values of LE were observed in the eastern regions of China. Both latitude and position within the time zone were predictors for CI and CM of the EPRF population. Latitude was the best predictor of stomach CI and CM; this predictor explained 46% and 50% of the variability, respectively. Position within the time zone was the best predictor of female breast CM; it explained 15% of the variability. In most cases, CI and CM increased with increasing latitude of residence, from the eastern to the western border of the time zone, and with increasing level of economic development within the region. The dependence of CI, CM, and LE on the geographical coordinates of residence is in agreement with the hypothesis of circadian disruption.

Pharmacy Residency School-wide Match Rates and Modifiable Predictors in ACPE-accredited Colleges and Schools of Pharmacy

PubMed Central

Whittaker, Alana; Shan, Guogen

2017-01-01

Objective. To analyze the modifiable predictors of institution-wide residency match rates. Methods. This was a retrospective analysis of colleges and schools of pharmacy data and school-wide PGY-1 pharmacy residency match rates for 2013 through 2015. Independent variables included NAPLEX passing rates, history of ACPE probation, NIH funding, academic health center affiliation, dual-degree availability, program length, admit-to-applicant ratio, class size, tuition, student-driven research, clinically focused academic tracks, residency affiliation, U.S. News & World Report rankings, and minority enrollment. Results. In a repeated measures model, predictors of match results were NAPLEX pass rate, class size, academic health center affiliation, admit-to-applicant ratio, U.S. News & World Report rankings, and minority enrollment. Conclusion. Indicators of student achievement, college/school reputation, affiliations, and class demographics were significant predictors of institution-wide residency match rates. Further research is needed to understand how changes in these factors may influence overall match rates. PMID:29367773

Prognostic predictors of patients with carcinoma of the gastric cardia.

PubMed

Zhang, Ming; Li, Zhigao; Ma, Yan; Zhu, Guanyu; Zhang, Hongfeng; Xue, Yingwei

2012-05-01

This study gives insight into survival predictors and clinicopathological features of carcinoma of the gastric cardia. The study included 233 patients who underwent operation for carcinoma of the gastric cardia. Clinicopathological prognostic variables were evaluated as predictors of long-term survival by univariate and multivariate analysis. Cox regression was used for multivariate analysis and survival curves were drawn by the Kaplan- Meier method. Carcinoma of the gastric cardia was characterized by positive lymph node metastasis (77.3%), serosal invasion (83.3%) and more stage III or IV tumors (72.5%). Overall 5-year survival rate was 21.9% and median survival period was 24 months. The 5-year survival rate was influenced by tumor size, depth on invasion, lymph node metastasis, extent of lymph node dissection, disease stage, operation methods and resection margin. The absent of serosal invasion and lymph node metastasis, curative resection should be considered to be the favourable predictors of long-term survival of patients with carcinoma of the gastric cardia.

Pharmacy Residency School-wide Match Rates and Modifiable Predictors in ACPE-accredited Colleges and Schools of Pharmacy.

PubMed

Whittaker, Alana; Smith, Katherine P; Shan, Guogen

2017-12-01

Objective. To analyze the modifiable predictors of institution-wide residency match rates. Methods. This was a retrospective analysis of colleges and schools of pharmacy data and school-wide PGY-1 pharmacy residency match rates for 2013 through 2015. Independent variables included NAPLEX passing rates, history of ACPE probation, NIH funding, academic health center affiliation, dual-degree availability, program length, admit-to-applicant ratio, class size, tuition, student-driven research, clinically focused academic tracks, residency affiliation, U.S. News & World Report rankings, and minority enrollment. Results. In a repeated measures model, predictors of match results were NAPLEX pass rate, class size, academic health center affiliation, admit-to-applicant ratio, U.S. News & World Report rankings, and minority enrollment. Conclusion. Indicators of student achievement, college/school reputation, affiliations, and class demographics were significant predictors of institution-wide residency match rates. Further research is needed to understand how changes in these factors may influence overall match rates.

Remote sensing-based predictors improve distribution models of rare, early successional and boradleaf tree species in Utah

Treesearch

N. E. Zimmermann; T. C. Edwards; G. G. Moisen; T. S. Frescino; J. A. Blackard

2007-01-01

Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species...

A systematic review of land use regression models for volatile organic compounds

NASA Astrophysics Data System (ADS)

Amini, Heresh; Yunesian, Masud; Hosseini, Vahid; Schindler, Christian; Henderson, Sarah B.; Künzli, Nino

2017-12-01

Various aspects of land use regression (LUR) models for volatile organic compounds (VOCs) were systematically reviewed. Sixteen studies were identified published between 2002 and 2017. Of these, six were conducted in Canada, five in the USA, two in Spain, and one each in Germany, Italy, and Iran. They were developed for 14 different individual VOCs or groupings: benzene; toluene; ethylbenzene; m-xylene; p-xylene; (m/p)-xylene; o-xylene; total BTEX; 1,3-butadiene; formaldehyde; n-hexane; total hydro carbons; styrene; and acrolein. The models were based on measurements ranging from 22 sites in El Paso (USA) to 179 sites in Tehran (Iran). Only four studies in Rome (Italy), Sabadell (Spain), Tehran, and Windsor (Canada) met the Cocheo's criterion of having at least one passive sampler per 3.4 km2 of study area. The range of R2 values across all models was from 0.26 for 1,3-butadiene in Dallas (USA) to 0.93 for benzene in El Paso. The average R2 values among two or more studies of the same VOCs were as follows: benzene (0.70); toluene (0.60); ethylbenzene (0.66); (m/p)-xylene (0.65); o-xylene (0.61); total BTEX (0.66); 1,3-butadiene (0.46); and formaldehyde (0.56). The common spatial predictors of studied VOC concentrations were dominated by traffic-related variables, but they also included proximity to ports in the USA, number of chimneys in Canada, altitude in Spain, northern latitudes in Italy, and proximity to sewage treatment plants and to gas filling stores in Iran. For the traffic-related variables, the review suggests that large buffers, up to 5,000 m, should be considered in large cities. Although most studies reported logical directions of association for predictors, some reported inconsistent results. Some studies included log-transformed predictors while others divided one variable by another. Only six studies provided the p-values of predictors. Future work may incorporate chemistry-transport models, satellite observations, meteorological variables, particularly temperature, consider specific sources of aromatic vs aliphatic compounds, or may develop hybrid models. Currently, only one national model has been developed for Canada, and there are no global LUR models for VOCs. Overall, studies from outside North America and Europe are critically needed to describe the wide range of exposures experienced by different populations.

Intending to stay: Positive images, attitudes, and classroom experiences as influences on students' intentions to persist in science and engineering majors

NASA Astrophysics Data System (ADS)

Wyer, Mary Beth

2000-10-01

Contemporary research on persistence in undergraduate education in science and engineering has focused primarily on identifying the structural, social, and psychological barriers to participation by students in underrepresented groups. As a result, there is a wealth of data to document why students leave their majors, but there is little direct empirical data to support prevailing presumptions about why students stay. Moreover, researchers have used widely differing definitions and measures of persistence, and they have seldom explored field differences. This study compared three ways of measuring persistence. These constituted three criterion variables: commitment to major, degree aspirations, and commitment to a science/engineering career. The study emphasized social factors that encourage students to persist, including four predictor variables---(1) positive images of scientists/engineers, (2) positive attitudes toward gender and racial equality, (3) positive classroom experiences, and (4) high levels of social integration. In addition, because researchers have repeatedly documented the degree to which women are more likely than men to drop out of science and engineering majors, the study examined the potential impact of gender in relation to these predictor variables. A survey was administered in the classroom to a total of 285 students enrolled in a required course for either a biological sciences and or an engineering major. Predictor variables were developed from standard scales, including the Images of Science/Scientists Scale, the Attitudes toward Women Scale, the Women in Science Scale, and the Perceptions of Prejudice Scale. Based on logistic regression models, results indicate that positive images of scientists and engineers was significantly related to improving the odds of students having a high commitment to major, high degree aspirations, and high commitment to career. There was also evidence that positive attitudes toward gender and racial equality as well as positive classroom experiences improved the odds of students' having high degree aspirations. There was limited evidence to suggest the significance of gender in interaction with the predictor variables. There was tentative evidence that field differences may play a critical role in persistence. The study concludes on two points. The first is that gender may be a more important factor in explaining why some students leave their science and engineering majors than in explaining why others stay. The second is that research directed at improving diversity in science would benefit from discussion about the measures of persistence.

Modeling the human development index and the percentage of poor people using quantile smoothing splines

NASA Astrophysics Data System (ADS)

Mulyani, Sri; Andriyana, Yudhie; Sudartianto

2017-03-01

Mean regression is a statistical method to explain the relationship between the response variable and the predictor variable based on the central tendency of the data (mean) of the response variable. The parameter estimation in mean regression (with Ordinary Least Square or OLS) generates a problem if we apply it to the data with a symmetric, fat-tailed, or containing outlier. Hence, an alternative method is necessary to be used to that kind of data, for example quantile regression method. The quantile regression is a robust technique to the outlier. This model can explain the relationship between the response variable and the predictor variable, not only on the central tendency of the data (median) but also on various quantile, in order to obtain complete information about that relationship. In this study, a quantile regression is developed with a nonparametric approach such as smoothing spline. Nonparametric approach is used if the prespecification model is difficult to determine, the relation between two variables follow the unknown function. We will apply that proposed method to poverty data. Here, we want to estimate the Percentage of Poor People as the response variable involving the Human Development Index (HDI) as the predictor variable.

Psychological predictors of weight loss after bariatric surgery: a review of the recent research.

PubMed

Wimmelmann, Cathrine L; Dela, Flemming; Mortensen, Erik L

2014-01-01

Morbid obesity is the fastest growing BMI group in the U.S. and the prevalence of morbid obesity worldwide has never been higher. Bariatric surgery is the most effective treatment for severe forms of obesity especially with regard to a sustained long-term weight loss. Psychological factors are thought to play an important role for maintaining the surgical weight loss. However, results from prior research examining preoperative psychological predictors of weight loss outcome are inconsistent. The aim of this article was to review more recent literature on psychological predictors of surgical weight loss. We searched PubMed, PsycInfo and Web of Science, for original prospective studies with a sample size >30 and at least one year follow-up, using a combination of search terms such as 'bariatric surgery', 'morbid obesity', 'psychological predictors', and 'weight loss'. Only studies published after 2003 were included. 19 eligible studies were identified. Psychological predictors of surgical weight loss investigated in the reviewed studies include cognitive function, personality, psychiatric disorder, and eating behaviour. In general, recent research remains inconsistent, but the findings suggest that pre-surgical cognitive function, personality, mental health, composite psychological variables and binge eating may predict post-surgical weight loss to the extent that these factors influence post-operative eating behaviour. Copyright © 2013 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.

Difficulties with Regression Analysis of Age-Adjusted Rates.

DTIC Science & Technology

1982-09-01

variables used in those analyses, such as death rates in various states, have been age adjusted, whereas the predictor variables have not been age adjusted...The use of crude state death rates as the outcome variable with crude covariates and age as predictors can avoid the problem, at least under some...should be regressed on age-adjusted exposure Z+B+ Although age-specific death rates , Yas+’ may be available, it is often difficult to obtain age

Student-Related Variables as Predictors of Academic Achievement among Some Undergraduate Psychology Students in Barbados

ERIC Educational Resources Information Center

Fayombo, Grace Adebisi

2011-01-01

This study examined some student-related variables (interest in higher education, psychological resilience and study habit) as predictors of academic achievement among 131 (M (mean) = 28.17, SD (standard deviation) = 1.61) first year psychology students in the Introduction to Developmental Psychology class in UWI (The University of the West…

Whistle-Blowing and the Code of Silence in Police Agencies: Policy and Structural Predictors

ERIC Educational Resources Information Center

Rothwell, Gary R.; Baldwin, J. Norman

2007-01-01

This article reports the findings from a study that investigates predictors of police willingness to blow the whistle and police frequency of blowing the whistle on seven forms of misconduct. It specifically investigates the capacity of nine policy and structural variables to predict whistle-blowing. The results indicate that two variables, a…

Item Structural Properties as Predictors of Item Difficulty and Item Association.

ERIC Educational Resources Information Center

Solano-Flores, Guillermo

1993-01-01

Studied the ability of logical test design (LTD) to predict student performance in reading Roman numerals for 211 sixth graders in Mexico City tested on Roman numeral items varying on LTD-related and non-LTD-related variables. The LTD-related variable item iterativity was found to be the best predictor of item difficulty. (SLD)

A Study of the Relationship between Parenting Stress and Spirituality among Mothers of Elementary Children in Selected Korean Churches

ERIC Educational Resources Information Center

Choi, Seong Ji

2012-01-01

Problem: The problem of this study was to determine the relationship between parenting stress and six specified predictor variables of spirituality among mothers of elementary children attending selected Korean Baptist churches located in the Dallas/Ft. Worth area. The specified predictor variables of spirituality were awareness, instability,…

Individualism-Collectivism, Social-Network Orientation, and Acculturation as Predictors of Attitudes toward Seeking Professional Psychological Help among Chinese Americans.

ERIC Educational Resources Information Center

Tata, Shiraz Piroshaw; Leong, Frederick T. L.

1994-01-01

Used several culturally based variables (individualism-collectivism, social support attitudes, acculturation) and gender to predict patterns of help-seeking attitudes among Chinese American college students (n=219). Each of the independent variables was found to be a significant predictor of attitudes toward seeking professional psychological…

Predicting Preservice Music Teachers' Performance Success in Instrumental Courses Using Self-Regulated Study Strategies and Predictor Variables

ERIC Educational Resources Information Center

Ersozlu, Zehra N.; Nietfeld, John L.; Huseynova, Lale

2017-01-01

The purpose of this study was to examine the extent to which self-regulated study strategies and predictor variables predict performance success in instrumental performance college courses. Preservice music teachers (N = 123) from a music education department in two state universities in Turkey completed the Music Self-Regulated Studying…

Psychosocial Variables as Predictors of School Adjustment of Gifted Students with Learning Disabilities in Nigeria

ERIC Educational Resources Information Center

Fakolade, O. A.; Oyedokun, S. O.

2015-01-01

The paper considered several psychosocial variables as predictors of school adjustment of 40 gifted students with learning disabilities in Junior Secondary School in Ikenne Local Government Council Area of Ogun State, Nigeria. Purposeful random sampling was employed to select four schools from 13 junior secondary schools in the area, six…

Resolution of Unwanted Pregnancy during Adolescence through Abortion versus Childbirth: Individual and Family Predictors and Psychological Consequences

ERIC Educational Resources Information Center

Coleman, Priscilla K.

2006-01-01

Using data from the National Longitudinal Study of Adolescent Health, various demographic, psychological, educational, and family variables were explored as predictors of pregnancy resolution. Only 2 of the 17 variables examined were significantly associated with pregnancy resolution (risk-taking and the desire to leave home). After controlling…

The effects of maternal psychosocial factors on parenting attitudes of low-income, single mothers with young children.

PubMed

Lutenbacher, M; Hall, L A

1998-01-01

Although recent evidence implies linkages among depression or depressive symptoms, self-esteem, history of childhood abuse, and parenting attitudes, the evidence does not clearly elucidate the relationships among these variables. To investigate the relationships among maternal psychosocial factors (history of childhood abuse, everyday stressors, self-esteem, and depressive symptoms) and parenting attitudes of low-income, single mothers who have young children. Secondary analyses of data from in-home interviews with 206 low-income, single mothers from a southeastern United States urban area were conducted. A variety of scales, including the Adult-Adolescent Parenting Inventory (AAPI), were used to measure maternal psychosocial factors. Using the AAPI, a Modified Parenting Attitudes Measure (MPAM), and subscales, a three-stage regression procedure was used to test the model. For stages 1 and 2, everyday stressors were the strongest predictor of self-esteem. Childhood sexual abuse, everyday stressors, low self-esteem, and control variables accounted for 58% of variance in depressive symptoms. In the third stage for the AAPI, only control variables were retained except in the Lack of Empathy subscale, where depressive symptoms and control variables accounted for 16% of the variance. The third stage for the MPAM yielded, by subscale: Only control variables predicted Corporal Punishment Beliefs; depressive symptoms were the strongest predictor for the total MPAM (19% of variance) and of the Inappropriate Emotional Expectations subscale (17%); and childhood physical abuse was the only predictor of Role Reversal. Depressive symptoms mediated the effects of childhood abuse, everyday stressors, and self-esteem and provided the linkage between these variables and at-risk parenting attitudes. Self-esteem decreased as everyday stressors increased but did not directly affect parenting attitudes. A relationship was not found between childhood abuse and low self-esteem. This study highlights the complexity of parenting and the need to identify other factors of at-risk parenting not accounted for in this study.

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches

PubMed Central

Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils’ carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms—including the model tuning and predictor selection—were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models’ predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction. PMID:27128736

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches.

PubMed

Ließ, Mareike; Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

Predictive value of the DASH tool for predicting return to work of injured workers with musculoskeletal disorders of the upper extremity.

PubMed

Armijo-Olivo, Susan; Woodhouse, Linda J; Steenstra, Ivan A; Gross, Douglas P

2016-12-01

To determine whether the Disabilities of the Arm, Shoulder, and Hand (DASH) tool added to the predictive ability of established prognostic factors, including patient demographic and clinical outcomes, to predict return to work (RTW) in injured workers with musculoskeletal (MSK) disorders of the upper extremity. A retrospective cohort study using a population-based database from the Workers' Compensation Board of Alberta (WCB-Alberta) that focused on claimants with upper extremity injuries was used. Besides the DASH, potential predictors included demographic, occupational, clinical and health usage variables. Outcome was receipt of compensation benefits after 3 months. To identify RTW predictors, a purposeful logistic modelling strategy was used. A series of receiver operating curve analyses were performed to determine which model provided the best discriminative ability. The sample included 3036 claimants with upper extremity injuries. The final model for predicting RTW included the total DASH score in addition to other established predictors. The area under the curve for this model was 0.77, which is interpreted as fair discrimination. This model was statistically significantly different than the model of established predictors alone (p<0.001). When comparing the DASH total score versus DASH item 23, a non-significant difference was obtained between the models (p=0.34). The DASH tool together with other established predictors significantly helped predict RTW after 3 months in participants with upper extremity MSK disorders. An appealing result for clinicians and busy researchers is that DASH item 23 has equal predictive ability to the total DASH score. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Metamodeling and mapping of nitrate flux in the unsaturated zone and groundwater, Wisconsin, USA

NASA Astrophysics Data System (ADS)

Nolan, Bernard T.; Green, Christopher T.; Juckem, Paul F.; Liao, Lixia; Reddy, James E.

2018-04-01

Nitrate contamination of groundwater in agricultural areas poses a major challenge to the sustainability of water resources. Aquifer vulnerability models are useful tools that can help resource managers identify areas of concern, but quantifying nitrogen (N) inputs in such models is challenging, especially at large spatial scales. We sought to improve regional nitrate (NO3-) input functions by characterizing unsaturated zone NO3- transport to groundwater through use of surrogate, machine-learning metamodels of a process-based N flux model. The metamodels used boosted regression trees (BRTs) to relate mappable landscape variables to parameters and outputs of a previous "vertical flux method" (VFM) applied at sampled wells in the Fox, Wolf, and Peshtigo (FWP) river basins in northeastern Wisconsin. In this context, the metamodels upscaled the VFM results throughout the region, and the VFM parameters and outputs are the metamodel response variables. The study area encompassed the domain of a detailed numerical model that provided additional predictor variables, including groundwater recharge, to the metamodels. We used a statistical learning framework to test a range of model complexities to identify suitable hyperparameters of the six BRT metamodels corresponding to each response variable of interest: NO3- source concentration factor (which determines the local NO3- input concentration); unsaturated zone travel time; NO3- concentration at the water table in 1980, 2000, and 2020 (three separate metamodels); and NO3- "extinction depth", the eventual steady state depth of the NO3- front. The final metamodels were trained to 129 wells within the active numerical flow model area, and considered 58 mappable predictor variables compiled in a geographic information system (GIS). These metamodels had training and cross-validation testing R2 values of 0.52 - 0.86 and 0.22 - 0.38, respectively, and predictions were compiled as maps of the above response variables. Testing performance was reasonable, considering that we limited the metamodel predictor variables to mappable factors as opposed to using all available VFM input variables. Relationships between metamodel predictor variables and mapped outputs were generally consistent with expectations, e.g. with greater source concentrations and NO3- at the groundwater table in areas of intensive crop use and well drained soils. Shorter unsaturated zone travel times in poorly drained areas likely indicated preferential flow through clay soils, and a tendency for fine grained deposits to collocate with areas of shallower water table. Numerical estimates of groundwater recharge were important in the metamodels and may have been a proxy for N input and redox conditions in the northern FWP, which had shallow predicted NO3- extinction depth. The metamodel results provide proof-of-concept for regional characterization of unsaturated zone NO3- transport processes in a statistical framework based on readily mappable GIS input variables.

Improving observational study estimates of treatment effects using joint modeling of selection effects and outcomes: the case of AAA repair.

PubMed

O'Malley, A James; Cotterill, Philip; Schermerhorn, Marc L; Landon, Bruce E

2011-12-01

When 2 treatment approaches are available, there are likely to be unmeasured confounders that influence choice of procedure, which complicates estimation of the causal effect of treatment on outcomes using observational data. To estimate the effect of endovascular (endo) versus open surgical (open) repair, including possible modification by institutional volume, on survival after treatment for abdominal aortic aneurysm, accounting for observed and unobserved confounding variables. Observational study of data from the Medicare program using a joint model of treatment selection and survival given treatment to estimate the effects of type of surgery and institutional volume on survival. We studied 61,414 eligible repairs of intact abdominal aortic aneurysms during 2001 to 2004. The outcome, perioperative death, is defined as in-hospital death or death within 30 days of operation. The key predictors are use of endo, transformed endo and open volume, and endo-volume interactions. There is strong evidence of nonrandom selection of treatment with potential confounding variables including institutional volume and procedure date, variables not typically adjusted for in clinical trials. The best fitting model included heterogeneous transformations of endo volume for endo cases and open volume for open cases as predictors. Consistent with our hypothesis, accounting for unmeasured selection reduced the mortality benefit of endo. The effect of endo versus open surgery varies nonlinearly with endo and open volume. Accounting for institutional experience and unmeasured selection enables better decision-making by physicians making treatment referrals, investigators evaluating treatments, and policy makers.

Multiple regression for physiological data analysis: the problem of multicollinearity.

PubMed

Slinker, B K; Glantz, S A

1985-07-01

Multiple linear regression, in which several predictor variables are related to a response variable, is a powerful statistical tool for gaining quantitative insight into complex in vivo physiological systems. For these insights to be correct, all predictor variables must be uncorrelated. However, in many physiological experiments the predictor variables cannot be precisely controlled and thus change in parallel (i.e., they are highly correlated). There is a redundancy of information about the response, a situation called multicollinearity, that leads to numerical problems in estimating the parameters in regression equations; the parameters are often of incorrect magnitude or sign or have large standard errors. Although multicollinearity can be avoided with good experimental design, not all interesting physiological questions can be studied without encountering multicollinearity. In these cases various ad hoc procedures have been proposed to mitigate multicollinearity. Although many of these procedures are controversial, they can be helpful in applying multiple linear regression to some physiological problems.

Using social cognitive theory to explain discretionary, "leisure-time" physical exercise among high school students.

PubMed

Winters, Eric R; Petosa, Rick L; Charlton, Thomas E

2003-06-01

To examine whether knowledge of high school students' actions of self-regulation, and perceptions of self-efficacy to overcome exercise barriers, social situation, and outcome expectation will predict non-school related moderate and vigorous physical exercise. High school students enrolled in introductory Physical Education courses completed questionnaires that targeted selected Social Cognitive Theory variables. They also self-reported their typical "leisure-time" exercise participation using a standardized questionnaire. Bivariate correlation statistic and hierarchical regression were conducted on reports of moderate and vigorous exercise frequency. Each predictor variable was significantly associated with measures of moderate and vigorous exercise frequency. All predictor variables were significant in the final regression model used to explain vigorous exercise. After controlling for the effects of gender, the psychosocial variables explained 29% of variance in vigorous exercise frequency. Three of four predictor variables were significant in the final regression equation used to explain moderate exercise. The final regression equation accounted for 11% of variance in moderate exercise frequency. Professionals who attempt to increase the prevalence of physical exercise through educational methods should focus on the psychosocial variables utilized in this study.

Communicative participation restrictions in multiple sclerosis: associated variables and correlation with social functioning.

PubMed

Yorkston, Kathryn M; Baylor, Carolyn; Amtmann, Dagmar

2014-01-01

Individuals with multiple sclerosis (MS) are at risk for communication problems that may restrict their ability to take participation in important life roles such as maintenance of relationships, work, or household management. The aim of this project is to examine selected demographic and symptom-related variables that may contribute to participation restrictions. This examination is intended to aid clinicians in predicting who might be at risk for such restrictions and what variables may be targeted in interventions. Community-dwelling adults with MS (n=216) completed a survey either online or using paper forms. The survey included the 46-item version of the Communicative Participation Item Bank, demographics (age, sex, living situation, employment status, education, and time since onset of diagnosis of MS), and self-reported symptom-related variables (physical activity, emotional problems, fatigue, pain, speech severity, and cognitive/communication skills). In order to identify predictors of restrictions in communicative participation, these variables were entered into a backwards stepwise multiple linear regression analysis. Five variables (cognitive/communication skills, speech severity, speech usage, physical activity, and education) were statistically significant predictors of communication participation. In order to examine the relationship of communicative participation and social role variables, bivariate Spearman correlations were conducted. Results suggest only a fair to moderate relationship between communicative participation and measures of social roles. Communicative participation is a complex construct associated with a number of self-reported variables. Clinicians should be alert to risk factors for reduced communicative participation including reduced cognitive and speech skills, lower levels of speech usage, limitations in physical activities and higher levels of education. The reader will be able to: (a) describe the factors that may restrict participation in individuals with multiple sclerosis; (b) list measures of social functioning that may be pertinent in adults with multiple sclerosis; (c) discuss factors that can be used to predict communicative participation in multiple sclerosis. Copyright © 2014 Elsevier Inc. All rights reserved.

Modeling longitudinal data, I: principles of multivariate analysis.

PubMed

Ravani, Pietro; Barrett, Brendan; Parfrey, Patrick

2009-01-01

Statistical models are used to study the relationship between exposure and disease while accounting for the potential role of other factors' impact on outcomes. This adjustment is useful to obtain unbiased estimates of true effects or to predict future outcomes. Statistical models include a systematic component and an error component. The systematic component explains the variability of the response variable as a function of the predictors and is summarized in the effect estimates (model coefficients). The error element of the model represents the variability in the data unexplained by the model and is used to build measures of precision around the point estimates (confidence intervals).

Motivational Factors Underlying College Students' Decisions to Resume Their Educational Pursuits in the Aftermath of Hurricane Katrina

ERIC Educational Resources Information Center

Phillips, Theresa M.; Herlihy, Barbara

2009-01-01

This study explored college student persistence at a historically Black university affected by Hurricane Katrina. Predictor variables including sex, residence status, Pell Grant status, campus housing status, college grade point average, attendance before Hurricane Katrina, and attendance at the university by parents or another close relative were…

The Continuity of College Students' Autonomous Learning Motivation and Its Predictors: A Three-Year Longitudinal Study

ERIC Educational Resources Information Center

Pan, Yingqiu; Gauvain, Mary

2012-01-01

This study examined change in Chinese students' autonomous learning motivation in the first three years of college and how this change is accounted for by intra- and inter-individual variables. The sample included 633 (328 female) college freshmen. Results showed that students' autonomous learning motivation decreased over years in college.…

Stress and Coping as Predictors of Young Children's Development and Psychosocial Adjustment.

ERIC Educational Resources Information Center

Carson, David K.; Swanson, Dee M.

A total of 38 children of 5-6 years in one of four early childhood or kindergarten programs participated in a study of the predictive relationship of stress and coping to development and psychosocial adjustment. Measures of independent variables included the Life Events Scale for Children, Family Invulnerability Test, Hassles Scale for Children,…

Covitality Constructs as Predictors of Psychological Well-Being and Depression for Secondary School Students

ERIC Educational Resources Information Center

Pennell, Claire; Boman, Peter; Mergler, Amanda

2015-01-01

This study was an examination of the strength of relations among covitality, and its underlying constructs of belief in self, emotional competence, belief in others, and engaged living, and two outcome variables: subjective well-being and depression. Participants included 361 Australian secondary school students (75 males and 286 females) who…

Causal Attributions as Predictors of Academic Achievement in Father-Absent Children.

ERIC Educational Resources Information Center

Salzman, Stephanie A.

The purpose of this study was to examine the potential impact of maternal attributions and self-attributions on the academic achievement of father-absent children in comparison to commonly identified family interaction and demographic variables. Subjects included 33 male and 34 female father-absent sixth graders (mean age of 11.6 years) and their…

Prediction of Women's Utilization of Resistance Strategies in a Sexual Assault Situation: A Prospective Study

ERIC Educational Resources Information Center

Gidycz, Christine A.; Van Wynsberghe, Amy; Edwards, Katie M.

2008-01-01

The present study prospectively explored the predictors of resistance strategies to a sexual assault situation. Participants were assessed at the beginning of an academic quarter on a number of variables, including past history of sexual victimization, perceived risk of sexual victimization, and intentions to use specific types of resistance…

Factors in the Admissions Process Influencing Persistence in a Master's of Science Program in Marine Science

ERIC Educational Resources Information Center

Dore, Melissa L.

2017-01-01

This applied dissertation was conducted to provide the graduate program in marine sciences a valid predictor for success in the admissions scoring systems that include the general Graduate Record Exam. The dependent variable was persistence: successfully graduating from the marine sciences master's programs. This dissertation evaluated other…

Are Body Dissatisfaction, Eating Disturbance, and Body Mass Index Predictors of Suicidal Behavior in Adolescents? A Longitudinal Study

ERIC Educational Resources Information Center

Crow, Scott; Eisenberg, Marla E.; Story, Mary; Neumark-Sztainer, Dianne

2008-01-01

Disordered eating, body dissatisfaction, and obesity have been associated cross sectionally with suicidal behavior in adolescents. To determine the extent to which these variables predicted suicidal ideation and attempts, the authors examined these relationships in a longitudinal design. The study population included 2,516 older adolescents and…

Post-Adoption Contact, Adoption Communicative Openness, and Satisfaction with Contact as Predictors of Externalizing Behavior in Adolescence and Emerging Adulthood

ERIC Educational Resources Information Center

Grotevant, Harold D.; Rueter, Martha; Von Korff, Lynn; Gonzalez, Christopher

2011-01-01

Background: This study examined the relation between three variables related to adoptive family relationships (post-adoption contact between adoptive and birth family members, adoption communicative openness, and satisfaction with contact) and adoptee externalizing behavior in adolescence and emerging adulthood. Method: The study included 190…

Surrounding land cover types as predictors of palustrine wetland vegetation quality in conterminous USA

USGS Publications Warehouse

Stapanian, Martin A.; Gara, Brian; Schumacher, William

2018-01-01

The loss of wetland habitats and their often-unique biological communities is a major environmental concern. We examined vegetation data obtained from 380 wetlands sampled in a statistical survey of wetlands in the USA. Our goal was to identify which surrounding land cover types best predict two indices of vegetation quality in wetlands at the regional scale. We considered palustrine wetlands in four regions (Coastal Plains, North Central East, Interior Plains, and West) in which the dominant vegetation was emergent, forested, or scrub-shrub. For each wetland, we calculated weighted proportions of eight land cover types surrounding the area in which vegetation was assessed, in four zones radiating from the edge of the assessment area to 2 km. Using Akaike's Information Criterion, we determined the best 1-, 2- and 3-predictor models of the two indices, using the weighted proportions of the land cover types as potential predictors. Mean values of the two indices were generally higher in the North Central East and Coastal Plains than the other regions for forested and emergent wetlands. In nearly all cases, the best predictors of the indices were not the dominant surrounding land cover types. Overall, proportions of forest (positive effect) and agriculture (negative effect) surrounding the assessment area were the best predictors of the two indices. One or both of these variables were included as predictors in 65 of the 72 models supported by the data. Wetlands surrounding the assessment area had a positive effect on the indices, and ranked third (33%) among the predictors included in supported models. Development had a negative effect on the indices and was included in only 28% of supported models. These results can be used to develop regional management plans for wetlands, such as creating forest buffers around wetlands, or to conserve zones between wetlands to increase habitat connectivity.

Multivariate outcome prediction in traumatic brain injury with focus on laboratory values.

PubMed

Nelson, David W; Rudehill, Anders; MacCallum, Robert M; Holst, Anders; Wanecek, Michael; Weitzberg, Eddie; Bellander, Bo-Michael

2012-11-20

Traumatic brain injury (TBI) is a major cause of morbidity and mortality. Identifying factors relevant to outcome can provide a better understanding of TBI pathophysiology, in addition to aiding prognostication. Many common laboratory variables have been related to outcome but may not be independent predictors in a multivariate setting. In this study, 757 patients were identified in the Karolinska TBI database who had retrievable early laboratory variables. These were analyzed towards a dichotomized Glasgow Outcome Scale (GOS) with logistic regression and relevance vector machines, a non-linear machine learning method, univariately and controlled for the known important predictors in TBI outcome: age, Glasgow Coma Score (GCS), pupil response, and computed tomography (CT) score. Accuracy was assessed with Nagelkerke's pseudo R². Of the 18 investigated laboratory variables, 15 were found significant (p<0.05) towards outcome in univariate analyses. In contrast, when adjusting for other predictors, few remained significant. Creatinine was found an independent predictor of TBI outcome. Glucose, albumin, and osmolarity levels were also identified as predictors, depending on analysis method. A worse outcome related to increasing osmolarity may warrant further study. Importantly, hemoglobin was not found significant when adjusted for post-resuscitation GCS as opposed to an admission GCS, and timing of GCS can thus have a major impact on conclusions. In total, laboratory variables added an additional 1.3-4.4% to pseudo R².

Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure

PubMed Central

Craig, Marlies H; Sharp, Brian L; Mabaso, Musawenkosi LH; Kleinschmidt, Immo

2007-01-01

Background Several malaria risk maps have been developed in recent years, many from the prevalence of infection data collated by the MARA (Mapping Malaria Risk in Africa) project, and using various environmental data sets as predictors. Variable selection is a major obstacle due to analytical problems caused by over-fitting, confounding and non-independence in the data. Testing and comparing every combination of explanatory variables in a Bayesian spatial framework remains unfeasible for most researchers. The aim of this study was to develop a malaria risk map using a systematic and practicable variable selection process for spatial analysis and mapping of historical malaria risk in Botswana. Results Of 50 potential explanatory variables from eight environmental data themes, 42 were significantly associated with malaria prevalence in univariate logistic regression and were ranked by the Akaike Information Criterion. Those correlated with higher-ranking relatives of the same environmental theme, were temporarily excluded. The remaining 14 candidates were ranked by selection frequency after running automated step-wise selection procedures on 1000 bootstrap samples drawn from the data. A non-spatial multiple-variable model was developed through step-wise inclusion in order of selection frequency. Previously excluded variables were then re-evaluated for inclusion, using further step-wise bootstrap procedures, resulting in the exclusion of another variable. Finally a Bayesian geo-statistical model using Markov Chain Monte Carlo simulation was fitted to the data, resulting in a final model of three predictor variables, namely summer rainfall, mean annual temperature and altitude. Each was independently and significantly associated with malaria prevalence after allowing for spatial correlation. This model was used to predict malaria prevalence at unobserved locations, producing a smooth risk map for the whole country. Conclusion We have produced a highly plausible and parsimonious model of historical malaria risk for Botswana from point-referenced data from a 1961/2 prevalence survey of malaria infection in 1–14 year old children. After starting with a list of 50 potential variables we ended with three highly plausible predictors, by applying a systematic and repeatable staged variable selection procedure that included a spatial analysis, which has application for other environmentally determined infectious diseases. All this was accomplished using general-purpose statistical software. PMID:17892584

Multi-model blending

DOEpatents

Hamann, Hendrik F.; Hwang, Youngdeok; van Kessel, Theodore G.; Khabibrakhmanov, Ildar K.; Muralidhar, Ramachandran

2016-10-18

A method and a system to perform multi-model blending are described. The method includes obtaining one or more sets of predictions of historical conditions, the historical conditions corresponding with a time T that is historical in reference to current time, and the one or more sets of predictions of the historical conditions being output by one or more models. The method also includes obtaining actual historical conditions, the actual historical conditions being measured conditions at the time T, assembling a training data set including designating the two or more set of predictions of historical conditions as predictor variables and the actual historical conditions as response variables, and training a machine learning algorithm based on the training data set. The method further includes obtaining a blended model based on the machine learning algorithm.

Intentions to Maintain Adherence to Mammography

PubMed Central

Bowling, J. Michael; Brewer, Noel T.; Lipkus, Isaac M.; Skinner, Celette Sugg; Strigo, Tara S.; Rimer, Barbara K.

2008-01-01

Abstract Objective Recent attention has focused on moving women from having initial mammograms to maintaining adherence to regular mammography schedules. We examined behavioral intentions to maintain mammography adherence, which include the likelihood of performing a behavior, and implementation intentions, specific action plans to obtain mammograms. Potential predictors were Theory of Planned Behavior constructs, previous barriers, previous mammography maintenance, and age. Methods Respondents were 2062 currently adherent women due for their next mammograms in 3–4 months according to American Cancer Society recommendations for annual screening. Statistical models were used to examine predictors of behavioral and two implementation intentions, including having thought about where women would get their next mammograms and having thought about making appointments. Results With the exception of pros, cons, and subjective norms, all variables predicted behavioral intentions (p ≤ 0.05). Stronger perceived control, previous mammography maintenance, and one barrier (vs. none) predicted being more likely to have thought about where to get their next mammograms. Previous maintenance and no barriers (vs. two) predicted being more likely to have thought about making appointments. Conclusions Our findings suggest that among women currently adherent to mammography, volitional factors, such as barriers, may be better predictors of implementation intentions than motivational factors, such as attitudes. Implementation variables may be useful in understanding how women move from intentions to action. Future research should examine how such factors relate to mammography maintenance behaviors and can be integrated into behavior change interventions. PMID:18657041

Socio-ecological predictors of participation and dropout in organised sports during childhood

PubMed Central

2014-01-01

Background The purpose of this study was to explore the socio-ecological determinants of participation and dropout in organised sports in a nationally-representative sample of Australian children. Methods Data were drawn from Waves 3 and 4 of the Longitudinal Study of Australian Children. In total, 4042 children aged 8.25 (SD = 0.44) years at baseline were included, with 24-months between Waves. Socio-ecological predictors were reported by parents and teachers, while cognitive and health measures were assessed by trained professionals. All predictors were assessed at age 8, and used to predict participation and dropout by age 10. Results Seven variables at age 8 were shown to positively predict participation in organised sports at age 10. These included: sex (boy); fewer people in household; higher household income; main language spoken at home (English); higher parental education; child taken to a sporting event; and, access to a specialist PE teacher during primary school. Four variables predicted dropout from organised sports by age 10: lower household income; main language spoken at home (non-English); lower parental education; and, child not taken to a sporting event. Conclusions The interplay between child sex, socioeconomic indicators, and parental support is important in predicting children’s participation in organised sports. Multilevel and multicomponent interventions to promote participation and prevent dropout should be underpinned by the Socio-Ecological Model and targeted to high risk populations using multiple levels of risk. PMID:24885978

Crop weather models of corn and soybeans for Agrophysical Units (APU's) in Iowa using monthly meteorological predictors

NASA Technical Reports Server (NTRS)

Leduc, S. (Principal Investigator)

1982-01-01

Models based on multiple regression were developed to estimate corn and soybean yield from weather data for agrophysical units (APU) in Iowa. The predictor variables are derived from monthly average temperature and monthly total precipitation data at meteorological stations in the cooperative network. The models are similar in form to the previous models developed for crop reporting districts (CRD). The trends and derived variables were the same and the approach to select the significant predictors was similar to that used in developing the CRD models. The APU's were selected to be more homogeneous with respect crop to production than the CRDs. The APU models are quite similar to the CRD models, similar explained variation and number of predictor variables. The APU models are to be independently evaluated and compared to the previously evaluated CRD models. That comparison should indicate the preferred model area for this application, i.e., APU or CRD.

Predictors of cultural capital on science academic achievement at the 8th grade level

NASA Astrophysics Data System (ADS)

Misner, Johnathan Scott

The purpose of the study was to determine if students' cultural capital is a significant predictor of 8th grade science achievement test scores in urban locales. Cultural capital refers to the knowledge used and gained by the dominant class, which allows social and economic mobility. Cultural capital variables include magazines at home and parental education level. Other variables analyzed include socioeconomic status (SES), gender, and English language learners (ELL). This non-experimental study analyzed the results of the 2011 Eighth Grade Science National Assessment of Educational Progress (NAEP). The researcher analyzed the data using a multivariate stepwise regression analysis. The researcher concluded that the addition of cultural capital factors significantly increased the predictive power of the model where magazines in home, gender, student classified as ELL, parental education level, and SES were the independent variables and science achievement was the dependent variable. For alpha=0.05, the overall test for the model produced a R2 value of 0.232; therefore the model predicted 23.2% of variance in science achievement results. Other major findings include: higher measures of home resources predicted higher 2011 NAEP eighth grade science achievement; males were predicted to have higher 2011 NAEP 8 th grade science achievement; classified ELL students were predicted to score lower on the NAEP eight grade science achievement; higher parent education predicted higher NAEP eighth grade science achievement; lower measures of SES predicted lower 2011 NAEP eighth grade science achievement. This study contributed to the research in this field by identifying cultural capital factors that have been found to have statistical significance on predicting eighth grade science achievement results, which can lead to strategies to help improve science academic achievement among underserved populations.

First-Grade Cognitive Abilities as Long-Term Predictors of Reading Comprehension and Disability Status

PubMed Central

Fuchs, Douglas; Compton, Donald L.; Fuchs, Lynn S.; Bryant, V. Joan; Hamlett, Carol L.; Lambert, Warren

2012-01-01

In a sample of 195 first graders selected for poor reading performance, the authors explored four cognitive predictors of later reading comprehension and reading disability (RD) status. In fall of first grade, the authors measured the children’s phonological processing, rapid automatized naming (RAN), oral language comprehension, and nonverbal reasoning. Throughout first grade, they also modeled the students’ reading progress by means of weekly Word Identification Fluency (WIF) tests to derive December and May intercepts. The authors assessed their reading comprehension in the spring of Grades 1–5. With the four cognitive variables and the WIF December intercept as predictors, 50.3% of the variance in fifth-grade reading comprehension was explained: 52.1% of this 50.3% was unique to the cognitive variables, 13.1% to the WIF December intercept, and 34.8% was shared. All five predictors were statistically significant. The same four cognitive variables with the May (rather than December) WIF intercept produced a model that explained 62.1% of the variance. Of this amount, the cognitive variables and May WIF intercept accounted for 34.5% and 27.7%, respectively; they shared 37.8%. All predictors in this model were statistically significant except RAN. Logistic regression analyses indicated that the accuracy with which the cognitive variables predicted end-of-fifth-grade RD status was 73.9%. The May WIF intercept contributed reliably to this prediction; the December WIF intercept did not. Results are discussed in terms of a role for cognitive abilities in identifying, classifying, and instructing students with severe reading problems. PMID:22539057

First-grade cognitive abilities as long-term predictors of reading comprehension and disability status.

PubMed

Fuchs, Douglas; Compton, Donald L; Fuchs, Lynn S; Bryant, V Joan; Hamlett, Carol L; Lambert, Warren

2012-01-01

In a sample of 195 first graders selected for poor reading performance, the authors explored four cognitive predictors of later reading comprehension and reading disability (RD) status. In fall of first grade, the authors measured the children's phonological processing, rapid automatized naming (RAN), oral language comprehension, and nonverbal reasoning. Throughout first grade, they also modeled the students' reading progress by means of weekly Word Identification Fluency (WIF) tests to derive December and May intercepts. The authors assessed their reading comprehension in the spring of Grades 1-5. With the four cognitive variables and the WIF December intercept as predictors, 50.3% of the variance in fifth-grade reading comprehension was explained: 52.1% of this 50.3% was unique to the cognitive variables, 13.1% to the WIF December intercept, and 34.8% was shared. All five predictors were statistically significant. The same four cognitive variables with the May (rather than December) WIF intercept produced a model that explained 62.1% of the variance. Of this amount, the cognitive variables and May WIF intercept accounted for 34.5% and 27.7%, respectively; they shared 37.8%. All predictors in this model were statistically significant except RAN. Logistic regression analyses indicated that the accuracy with which the cognitive variables predicted end-of-fifth-grade RD status was 73.9%. The May WIF intercept contributed reliably to this prediction; the December WIF intercept did not. Results are discussed in terms of a role for cognitive abilities in identifying, classifying, and instructing students with severe reading problems.

Constrained Stochastic Extended Redundancy Analysis.

PubMed

DeSarbo, Wayne S; Hwang, Heungsun; Stadler Blank, Ashley; Kappe, Eelco

2015-06-01

We devise a new statistical methodology called constrained stochastic extended redundancy analysis (CSERA) to examine the comparative impact of various conceptual factors, or drivers, as well as the specific predictor variables that contribute to each driver on designated dependent variable(s). The technical details of the proposed methodology, the maximum likelihood estimation algorithm, and model selection heuristics are discussed. A sports marketing consumer psychology application is provided in a Major League Baseball (MLB) context where the effects of six conceptual drivers of game attendance and their defining predictor variables are estimated. Results compare favorably to those obtained using traditional extended redundancy analysis (ERA).

Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.

Discrimination, acculturation and other predictors of depression among pregnant Hispanic women.

PubMed

Walker, Janiece L; Ruiz, R Jeanne; Chinn, Juanita J; Marti, Nathan; Ricks, Tiffany N

2012-01-01

The purpose of our study was to examine the effects of socioeconomic status, acculturative stress, discrimination, and marginalization as predictors of depression in pregnant Hispanic women. A prospective observational design was used. Central and Gulf coast areas of Texas in obstetrical offices. A convenience sample of 515 pregnant, low income, low medical risk, and self-identified Hispanic women who were between 22-24 weeks gestation was used to collect data. The predictor variables were socioeconomic status, discrimination, acculturative stress, and marginalization. The outcome variable was depression. Education, frequency of discrimination, age, and Anglo marginality were significant predictors of depressive symptoms in a linear regression model, F (6, 458) = 8.36, P<.0001. Greater frequency of discrimination was the strongest positive predictor of increased depressive symptoms. It is important that health care providers further understand the impact that age and experiences of discrimination throughout the life course have on depressive symptoms during pregnancy.

Patient-Reported Outcomes and Socioeconomic Status as Predictors of Clinical Outcomes after Hematopoietic Stem Cell Transplantation: A Study from the Blood and Marrow Transplant Clinical Trials Network 0902 Trial.

PubMed

Knight, Jennifer M; Syrjala, Karen L; Majhail, Navneet S; Martens, Michael; Le-Rademacher, Jennifer; Logan, Brent R; Lee, Stephanie J; Jacobsen, Paul B; Wood, William A; Jim, Heather S L; Wingard, John R; Horowitz, Mary M; Abidi, Muneer H; Fei, Mingwei; Rawls, Laura; Rizzo, J Douglas

2016-12-01

This secondary analysis of a large, multicenter Blood and Marrow Transplant Clinical Trials Network randomized trial assessed whether patient-reported outcomes (PROs) and socioeconomic status (SES) before hematopoietic stem cell transplantation (HCT) are associated with each other and predictive of clinical outcomes, including time to hematopoietic recovery, acute graft-versus-host disease, hospitalization days, and overall survival (OS) among 646 allogeneic and autologous HCT recipients. Pretransplantation Cancer and Treatment Distress (CTXD), Pittsburgh Sleep Quality Index (PSQI), and mental and physical component scores of the Short-Form 36 were correlated with each other and with SES variables. PROs and SES variables were further evaluated as predictors of clinical outcomes, with the PSQI and CTXD evaluated as OS predictors (P < .01 considered significant given multiple testing). Lower attained education was associated with increased distress (P = .002), lower income was related to worse physical functioning (P = .005) and increased distress (P = .008), lack of employment before transplantation was associated with worse physical functioning (P < .01), and unmarried status was associated with worse sleep (P = .003). In this large heterogeneous cohort of HCT recipients, although PROs and SES variables were correlated at baseline, they were not associated with any clinical outcomes. Future research should focus on HCT recipients at greater psychosocial disadvantage. Copyright © 2016 The American Society for Blood and Marrow Transplantation. Published by Elsevier Inc. All rights reserved.

Testing a bioecological model to examine social support in postpartum adolescents.

PubMed

Logsdon, M Cynthia; Ziegler, Craig; Hertweck, Paige; Pinto-Foltz, Melissa

2008-01-01

The purpose was twofold and included examining a bioecological model as a framework to describe social support in postpartum adolescents. The second purpose was to determine the relationship between a comprehensive view of the context of social support and symptoms of depression. Cross-sectional design with convenience sampling (n=85) of adolescents at 4-6 weeks postpartum, recruited from two community hospitals. Approval was received from the university's IRB (institutional review board), each recruitment site, the adolescent mothers, and their parents or guardians. Data were collected by a research assistant during home visits using a battery of self-report instruments to measure macro, meso, and microsystems of social support. Demographics, exposure to community violence (macrosystem), social support, social network (mesosystem), and perceived stress, mastery, and self-esteem (microsystem) were predictor variables. Depressive symptoms were measured by using the Center for Epidemiologic Studies of Depression (CES-D) Scale. Variables from each system were significant predictors of depressive symptoms but perceived stress was the strongest predictor. Many postpartum adolescents reported that they had been victims of violence. Significant symptoms of depression were identified in 37% of the postpartum adolescents. Context is important to consider in comparing international studies of social support. Researchers and clinicians should investigate variables associated with the low incidence of treatment for depressive symptoms in postpartum adolescents. Feelings of high self-esteem and mastery should be fostered in nursing interventions with postpartum adolescents and routine screening for symptoms of depression should be considered in relevant healthcare settings.

Frontal lobe function in elderly patients with Alzheimer's disease and caregiver burden.

PubMed

Hashimoto, Akiko; Matsuoka, Kiwamu; Yasuno, Fumihiko; Takahashi, Masato; Iida, Junzo; Jikumaru, Kiyoko; Kishimoto, Toshifumi

2017-07-01

Understanding of the relationship between caregiver burden and the degree of behavioural deficits in patients with Alzheimer's disease (AD) is relatively limited. Therefore, it is worthwhile to examine the correlations between the various relevant factors to improve the efficacy of care for patients with AD. The aim of this study was to investigate the specific contributions of frontal lobe dysfunction in AD patients to caregiver burden, while controlling for other predictor variables. Participants included 30 pairs of caregivers and patients with AD. The Zarit Burden Interview and Frontal Assessment Battery were used to measure the caregiver burden and patients' frontal lobe function, respectively. To investigate the effects of frontal lobe dysfunction on caregiver burden, hierarchical regression equations with steps incorporating additional predictor variables were fitted. We also performed a correlation analysis between the individual subdomains of the Zarit Burden Interview and the predictor variables. Our study suggests that the degree of frontal lobe dysfunction in AD patients predicts their caregiver burden, when other factors of daily functional limitations and neuropsychiatric symptoms are controlled. Daily functional limitations and neuropsychiatric symptoms affected caregivers' psychosocial burden, whereas frontal lobe dysfunction affected caregivers' burden due to the increase in the dependency of the patients. Our findings indicate that to ameliorate the disabilities of patients and reduce caregiver burden, there is a need for interventions that focus on psychosocial burdens, as shown in previous studies, as well as on excessive dependency due to frontal lobe dysfunction. © 2017 Japanese Psychogeriatric Society.

Assessing the influence of watershed characteristics on chlorophyll a in waterbodies at global and regional scales

USGS Publications Warehouse

Woelmer, Whitney; Kao, Yu-Chun; Bunnell, David B.; Deines, Andrew M.; Bennion, David; Rogers, Mark W.; Brooks, Colin N.; Sayers, Michael J.; Banach, David M.; Grimm, Amanda G.; Shuchman, Robert A.

2016-01-01

Prediction of primary production of lentic water bodies (i.e., lakes and reservoirs) is valuable to researchers and resource managers alike, but is very rarely done at the global scale. With the development of remote sensing technologies, it is now feasible to gather large amounts of data across the world, including understudied and remote regions. To determine which factors were most important in explaining the variation of chlorophyll a (Chl-a), an indicator of primary production in water bodies, at global and regional scales, we first developed a geospatial database of 227 water bodies and watersheds with corresponding Chl-a, nutrient, hydrogeomorphic, and climate data. Then we used a generalized additive modeling approach and developed model selection criteria to select models that most parsimoniously related Chl-a to predictor variables for all 227 water bodies and for 51 lakes in the Laurentian Great Lakes region in the data set. Our best global model contained two hydrogeomorphic variables (water body surface area and the ratio of watershed to water body surface area) and a climate variable (average temperature in the warmest model selection criteria to select models that most parsimoniously related Chl-a to predictor variables quarter) and explained ~ 30% of variation in Chl-a. Our regional model contained one hydrogeomorphic variable (flow accumulation) and the same climate variable, but explained substantially more variation (58%). Our results indicate that a regional approach to watershed modeling may be more informative to predicting Chl-a, and that nearly a third of global variability in Chl-a may be explained using hydrogeomorphic and climate variables.

Predictors of Sustainability of Social Programs

ERIC Educational Resources Information Center

Savaya, Riki; Spiro, Shimon E.

2012-01-01

This article presents the findings of a large scale study that tested a comprehensive model of predictors of three manifestations of sustainability: continuation, institutionalization, and duration. Based on the literature the predictors were arrayed in four groups: variables pertaining to the project, the auspice organization, the community, and…

Estimating the Classification Efficiency of a Test Battery.

ERIC Educational Resources Information Center

De Corte, Wilfried

2000-01-01

Shows how a theorem proven by H. Brogden (1951, 1959) can be used to estimate the allocation average (a predictor based classification of a test battery) assuming that the predictor intercorrelations and validities are known and that the predictor variables have a joint multivariate normal distribution. (SLD)

Effects of Internship Predictors on Successful Field Experience.

ERIC Educational Resources Information Center

Beard, Fred; Morton, Linda

1999-01-01

Finds that a majority of advertising and public-relations interns found their internships successful. Indicates that successful internships depend on predictors given the least attention by school programs: quality of supervision was the most important single predictor variable, followed in importance by organizational practices/policies, positive…

Relations among Socioeconomic Status, Age, and Predictors of Phonological Awareness

ERIC Educational Resources Information Center

McDowell, Kimberly D.; Lonigan, Christopher J.; Goldstein, Howard

2007-01-01

Purpose: This study simultaneously examined predictors of phonological awareness within the framework of 2 theories: the phonological distinctness hypothesis and the lexical restructuring model. Additionally, age as a moderator of the relations between predictor variables and phonological awareness was examined. Method: This cross-sectional…

Greater refill adherence to adalimumab therapy for patients using specialty versus retail pharmacies.

PubMed

Liu, Yifei; Yang, Mei; Chao, Jingdong; Mulani, Parvez M

2010-08-01

Retail pharmacies provide regular prescription drugs and some specialty prescription drugs, whereas specialty pharmacies focus on distributing specialty prescription drugs, including tumor necrosis factor (TNF) antagonists. It is unknown whether pharmacy type impacts patients' adherence to anti-TNF therapy. The relationship between pharmacy type (specialty vs. retail) and refill adherence to therapy with the TNF antagonist adalimumab was examined. This was a retrospective analysis of dispensing records of patients in the United States who were prescribed a TNF antagonist (adalimumab 40 mg per 0.8-mL injection) during a dispensation period from January 2003 to August 2009. Patients treated with adalimumab were included in the analysis regardless of diagnosis. For each patient, medication refill adherence (MRA) was calculated as total days of supply divided by total number of days evaluated, multiplied by 100. A regression analysis was conducted in which the dependent variable was MRA and the independent variables included pharmacy type (specialty vs. retail pharmacy), reimbursement/payment type, copayment/payment amount per prescription, age, sex, ethnicity, and annual income. Of the 86,079 patients included, 70% obtained the medication from a specialty pharmacy, 92% were members of Blue Cross and Blue Shield plans, 67% were women, and 81% were white. The average MRA was 84, and the average age was 52 years. Significant predictors (P<0.05) of MRA included pharmacy type, reimbursement/payment type, copayment/payment amount per prescription, age, sex, and ethnicity; and pharmacy type was the strongest predictor. When other independent variables were controlled for, MRA was 16% less for patients who used a retail pharmacy vs. patients who used a specialty pharmacy. Patients who used a specialty pharmacy to fulfill prescriptions for a TNF antagonist had a greater refill adherence than did patients who used a retail pharmacy.

Cognitive and Affective Variables and Their Relationships to Performance in a Lotus 1-2-3 Class.

ERIC Educational Resources Information Center

Guster, Dennis; Batt, Richard

1989-01-01

Describes study of two-year college students that was conducted to determine whether variables that were predictors of success in a programing class were also predictors of success in a package-oriented computer class using Lotus 1-2-3. Diagraming skill, critical thinking ability, spatial discrimination, and test anxiety level were examined. (11…

Strategic Interviewing to Detect Deception: Cues to Deception across Repeated Interviews

PubMed Central

Masip, Jaume; Blandón-Gitlin, Iris; Martínez, Carmen; Herrero, Carmen; Ibabe, Izaskun

2016-01-01

Previous deception research on repeated interviews found that liars are not less consistent than truth tellers, presumably because liars use a “repeat strategy” to be consistent across interviews. The goal of this study was to design an interview procedure to overcome this strategy. Innocent participants (truth tellers) and guilty participants (liars) had to convince an interviewer that they had performed several innocent activities rather than committing a mock crime. The interview focused on the innocent activities (alibi), contained specific central and peripheral questions, and was repeated after 1 week without forewarning. Cognitive load was increased by asking participants to reply quickly. The liars’ answers in replying to both central and peripheral questions were significantly less accurate, less consistent, and more evasive than the truth tellers’ answers. Logistic regression analyses yielded classification rates ranging from around 70% (with consistency as the predictor variable), 85% (with evasive answers as the predictor variable), to over 90% (with an improved measure of consistency that incorporated evasive answers as the predictor variable, as well as with response accuracy as the predictor variable). These classification rates were higher than the interviewers’ accuracy rate (54%). PMID:27847493

Predictors of the Perception of Smoking Health Risks in Smokers With or Without Schizophrenia.

PubMed

Kowalczyk, William J; Wehring, Heidi J; Burton, George; Raley, Heather; Feldman, Stephanie; Heishman, Stephen J; Kelly, Deanna L

2017-01-01

This study sought to examine the predictors of health risk perception in smokers with or without schizophrenia. The health risk subscale from the Smoking Consequences Questionnaire was dichotomized and used to measure health risk perception in smokers with (n = 67) and without schizophrenia (n = 100). A backward stepwise logistic regression was conducted using variables associated at the bivariate level to determine multivariate predictors. Overall, 62.5% of smokers without schizophrenia and 40.3% of smokers with schizophrenia completely recognize the health risks of smoking (p ≤ .01). Multivariate predictors for smokers without schizophrenia included: sex (Exp (B) = .3; p < .05), Smoking Consequences Questionnaire state enhancement (Exp (B) = .69; p < .01), and craving relief (Exp (B) = 1.8; p < .01). Among smokers with schizophrenia, predictors were education (Exp (B) = .7; p < .05), nicotine dependence (Exp (B) = .5; p < .01), motivation to quit (Exp (B) = 1.8; p < .01), and Smoking Consequences Questionnaire craving relief (Exp (B) = 1.8; p < .01). There was overlap and differences between predictors in smokers with and without schizophrenia. Commonly used techniques for education on the health consequences of cigarettes may work in smokers with schizophrenia, but intervention efforts specifically tailored to smokers with schizophrenia might be more efficacious.

Predictors of the Perception of Smoking Health Risks in Smokers With or Without Schizophrenia

PubMed Central

Kowalczyk, William J.; Wehring, Heidi J.; Burton, George; Raley, Heather; Feldman, Stephanie; Heishman, Stephen J.; Kelly, Deanna L.

2017-01-01

Objective This study sought to examine the predictors of health risk perception in smokers with or without schizophrenia. Methods The health risk subscale from the Smoking Consequences Questionnaire was dichotomized and used to measure health risk perception in smokers with (n = 67) and without schizophrenia (n = 100). A backward stepwise logistic regression was conducted using variables associated at the bivariate level to determine multivariate predictors. Results Overall, 62.5% of smokers without schizophrenia and 40.3% of smokers with schizophrenia completely recognize the health risks of smoking (p ≤ .01). Multivariate predictors for smokers without schizophrenia included: sex (Exp (B) = .3; p < .05), Smoking Consequences Questionnaire state enhancement (Exp (B) = .69; p < .01), and craving relief (Exp (B) = 1.8; p < .01). Among smokers with schizophrenia, predictors were education (Exp (B) = .7; p < .05), nicotine dependence (Exp (B) = .5; p < .01), motivation to quit (Exp (B) = 1.8; p < .01), and Smoking Consequences Questionnaire craving relief (Exp (B) = 1.8; p < .01). Conclusions There was overlap and differences between predictors in smokers with and without schizophrenia. Commonly used techniques for education on the health consequences of cigarettes may work in smokers with schizophrenia, but intervention efforts specifically tailored to smokers with schizophrenia might be more efficacious. PMID:27858591

A Socioecological Predication Model of Posttraumatic Stress Disorder in Low-Income, High-Risk Prenatal Native Hawaiian/Pacific Islander Women.

PubMed

Dodgson, Joan E; Oneha, Mary Frances; Choi, Myunghan

2014-01-01

Only recently has perinatal posttraumatic stress disorder (PTSD) been researched in any depth; however, the causes and consequences of this serious illness remain unclear. Most commonly, childbirth trauma and interpersonal violence have been reported as contributing factors. However, not all Native Hawaiian/Pacific Islander (NHPI) women who experience these events experience PTSD. The factors affecting PTSD are many and complex, intertwining individual, family, and community contexts. Using a socioecological framework, 3 levels of contextual variables were incorporated in this study (individual, family, and social/community). The purpose of this study was to determine the socioecological predictors associated with prenatal PTSD among NHPI. A case-control design was used to collect retrospective data about socioecological variables from medical record data. The sample was low-income, high-risk NHPI women receiving perinatal health care at a rural community health center in Hawaii who screened positive (n = 55) or negative (n = 91) for PTSD. Hierarchical logistic regression was conducted to determine socioecological predictors of positive PTSD screening. Although the majority of women (66.4%) experienced some form of interpersonal violence, a constellation of significant predictor variables from all 3 levels of the model were identified: depression (individual level), lack of family support and family stress (family level), and violence (social/community level). Each of the predictor variables has been identified by other researchers as significantly affecting perinatal PTSD. However, it is because these variables occur together that a more complex picture emerges, suggesting the importance of considering multiple variables in context when identifying and caring for these women. Although additional research is needed, it is possible that the significant predictor variables could be useful in identifying women who are at higher risk for PTSD in other similar populations. © 2014 by the American College of Nurse‐Midwives.

Environmental Controls on Multi-Scale Soil Nutrient Variability in the Tropics: the Importance of Land-Cover Change

NASA Astrophysics Data System (ADS)

Holmes, K. W.; Kyriakidis, P. C.; Chadwick, O. A.; Matricardi, E.; Soares, J. V.; Roberts, D. A.

2003-12-01

The natural controls on soil variability and the spatial scales at which correlation exists among soil and environmental variables are critical information for evaluating the effects of deforestation. We detect different spatial scales of variability in soil nutrient levels over a large region (hundreds of thousands of km2) in the Amazon, analyze correlations among soil properties at these different scales, and evaluate scale-specific relationships among soil properties and the factors potentially driving soil development. Statistical relationships among physical drivers of soil formation, namely geology, precipitation, terrain attributes, classified soil types, and land cover derived from remote sensing, were included to determine which factors are related to soil biogeochemistry at each spatial scale. Surface and subsurface soil profile data from a 3000 sample database collected in Rond“nia, Brazil, were used to investigate patterns in pH, phosphorus, nitrogen, organic carbon, effective cation exchange capacity, calcium, magnesium, potassium, aluminum, sand, and clay in this environment grading from closed canopy tropical forest to savanna. We focus on pH in this presentation for simplicity, because pH is the single most important soil characteristic for determining the chemical environment of higher plants and soil microbial activity. We determined four spatial scales which characterize integrated patterns of soil chemistry: less than 3 km; 3 to 10 km; 10 to 68 km; and from 68 to 550 km (extent of study area). Although the finest observable scale was fixed by the field sampling density, the coarser scales were determined from relationships in the data through coregionalization modeling, rather than being imposed by the researcher. Processes which affect soils over short distances, such as land cover and terrain attributes, were good predictors of fine scale spatial components of nutrients; processes which affect soils over very large distances, such as precipitation and geology, were better predictors at coarse spatial scales. However, this result may be affected by the resolution of the available predictor maps. Land-cover change exerted a strong influence on soil chemistry at fine spatial scales, and had progressively less of an effect at coarser scales. It is important to note that land cover, and interactions among land cover and the other predictors, continued to be a significant predictor of soil chemistry at every spatial scale up to hundreds of thousands of kilometers.

Dietary tendencies as predictors of marathon time in novice marathoners.

PubMed

Wilson, Patrick B; Ingraham, Stacy J; Lundstrom, Chris; Rhodes, Gregory

2013-04-01

The effects of dietary factors such as carbohydrate (CHO) on endurance-running performance have been extensively studied under laboratory-based and simulated field conditions. Evidence from "real-life" events, however, is poorly characterized. The purpose of this observational study was to examine the associations between prerace and in-race nutrition tendencies and performance in a sample of novice marathoners. Forty-six college students (36 women and 10 men) age 21.3 ± 3.3 yr recorded diet for 3 d before, the morning of, and during a 26.2-mile marathon. Anthropometric, physiological, and performance measurements were assessed before the marathon so the associations between diet and marathon time could be included as part of a stepwise-regression model. Mean marathon time was 266 ± 42 min. A pre-marathon 2-mile time trial explained 73% of the variability in marathon time (adjusted R2 = .73, p < .001). Day-before + morning-of CHO (DBMC) was the only other significant predictor of marathon time, explaining an additional 4% of the variability in marathon time (adjusted R2 = .77, p = .006). Other factors such as age, body-mass index, gender, day-before + morning-of energy, and in-race CHO were not significant independent predictors of marathon time. In this sample of primarily novice marathoners, DBMC intake was associated with faster marathon time, independent of other known predictors. These results suggest that novice and recreational marathoners should consider consuming a moderate to high amount of CHO in the 24-36 hr before a marathon.

Gross motor function is an important predictor of daily physical activity in young people with bilateral spastic cerebral palsy.

PubMed

Bania, Theofani A; Taylor, Nicholas F; Baker, Richard J; Graham, H Kerr; Karimi, Leila; Dodd, Karen J

2014-12-01

The aim of the study was to describe daily physical activity levels of adolescents and young adults with bilateral spastic cerebral palsy (CP) and to identify factors that help predict these levels. Daily physical activity was measured using an accelerometer-based activity monitor in 45 young people with bilateral spastic CP (23 males, 22 females; mean age 18y 6mo [SD 2y 5mo] range 16y 1mo-20y 11mo); classified as Gross Motor Function Classification System (GMFCS) level II or III and with contractures of <20° at hip and knee. Predictor variables included demographic characteristics (age, sex, weight) and physical characteristics (gross motor function, lower limb muscle strength, 6min walk distance). Data were analyzed using the information-theoretic approach, using the Akaike information criterion (AIC) and linear regression. Daily activity levels were low compared with published norms. Gross Motor Function Measure Dimension-E (GMFM-E; walking, running, and jumping) was the only common predictor variable in models that best predicted energy expenditure, number of steps, and time spent sitting/lying. GMFM Dimension-D (standing) and bilateral reverse leg press strength contributed to the models that predicted daily physical activity. Adolescents and young adults with bilateral spastic CP and mild to moderate walking disabilities have low levels of daily activity. The GMFM-E was an important predictor of daily physical activity. © 2014 Mac Keith Press.

FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J

2011-03-01

We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.

Population health needs as predictors of variations in NHS practice payments: a cross-sectional study of English general practices in 2013–2014 and 2014–2015

PubMed Central

Levene, Louis S; Baker, Richard; Wilson, Andrew; Walker, Nicola; Boomla, Kambiz; Bankart, M John G

2017-01-01

Background NHS general practice payments in England include pay for performance elements and a weighted component designed to compensate for workload, but without measures of specific deprivation or ethnic groups. Aim To determine whether population factors related to health needs predicted variations in NHS payments to individual general practices in England. Design and setting Cross-sectional study of all practices in England, in financial years 2013–2014 and 2014–2015. Method Descriptive statistics, univariable analyses (examining correlations between payment and predictors), and multivariable analyses (undertaking multivariable linear regressions for each year, with logarithms of payments as the dependent variables, and with population, practice, and performance factors as independent variables) were undertaken. Results Several population variables predicted variations in adjusted total payments, but inconsistently. Higher payments were associated with increases in deprivation, patients of older age, African Caribbean ethnic group, and asthma prevalence. Lower payments were associated with an increase in smoking prevalence. Long-term health conditions, South Asian ethnic group, and diabetes prevalence were not predictive. The adjusted R2 values were 0.359 (2013–2014) and 0.374 (2014–2015). A slightly different set of variables predicted variations in the payment component designed to compensate for workload. Lower payments were associated with increases in deprivation, patients of older age, and diabetes prevalence. Smoking prevalence was not predictive. There was a geographical differential. Conclusion Population factors related to health needs were, overall, poor predictors of variations in adjusted total practice payments and in the payment component designed to compensate for workload. Revising the weighting formula and extending weighting to other payment components might better support practices to address these needs. PMID:27872085

The Sociophonetic and Acoustic Vowel Dynamics of Michigan's Upper Peninsula English

NASA Astrophysics Data System (ADS)

Rankinen, Wil A.

The present sociophonetic study examines the English variety in Michigan's Upper Peninsula (UP) based upon a 130-speaker sample from Marquette County. The linguistic variables of interest include seven monophthongs and four diphthongs: 1) front lax, 2) low back, and 3) high back monophthongs and 4) short and 5) long diphthongs. The sample is stratified by the predictor variables of heritage-location, bilingualism, age, sex and class. The aim of the thesis is two fold: 1) to determine the extent of potential substrate effects on a 71-speaker older-aged bilingual and monolingual subset of these UP English speakers focusing on the predictor variables of heritage-location and bilingualism, and 2) to determine the extent of potential exogenous influences on an 85-speaker subset of UP English monolingual speakers by focusing on the predictor variables of heritage-location, age, sex and class. All data were extracted from a reading passage task collected during a sociolinguistic interview and measured instrumentally. The findings of this apparent-time data reveal the presence of lingering effects from substrate sources and developing effects from exogenous sources based upon American and Canadian models of diffusion. The linguistic changes-in-progress from above, led by middle-class females, are taking shape in the speech of UP residents of whom are propagating linguistic phenomena typically associated with varieties of Canadian English (i.e., low-back merger, Canadian shift, and Canadian raising); however, the findings also report resistance of such norms by working-class females. Finally, the data also reveal substrate effects demonstrating cases of dialect leveling and maintenance. As a result, the speech spoken in Michigan's Upper Peninsula can presently be described as a unique variety of English comprised of lingering substrate effects as well as exogenous effects modeled from both American and Canadian English linguistic norms.

Spatial variability of biotic and abiotic tree establishment constraints across a treeline ecotone in the Alaska range.

PubMed

Stueve, Kirk M; Isaacs, Rachel E; Tyrrell, Lucy E; Densmore, Roseann V

2011-02-01

Throughout interior Alaska (U.S.A.), a gradual warming trend in mean monthly temperatures occurred over the last few decades (approximatlely 2-4 degrees C). The accompanying increases in woody vegetation at many alpine treeline (hereafter treeline) locations provided an opportunity to examine how biotic and abiotic local site conditions interact to control tree establishment patterns during warming. We devised a landscape ecological approach to investigate these relationships at an undisturbed treeline in the Alaska Range. We identified treeline changes between 1953 (aerial photography) and 2005 (satellite imagery) in a geographic information system (GIS) and linked them with corresponding local site conditions derived from digital terrain data, ancillary climate data, and distance to 1953 trees. Logistic regressions enabled us to rank the importance of local site conditions in controlling tree establishment. We discovered a spatial transition in the importance of tree establishment controls. The biotic variable (proximity to 1953 trees) was the most important tree establishment predictor below the upper tree limit, providing evidence of response lags with the abiotic setting and suggesting that tree establishment is rarely in equilibrium with the physical environment or responding directly to warming. Elevation and winter sun exposure were important predictors of tree establishment at the upper tree limit, but proximity to trees persisted as an important tertiary predictor, indicating that tree establishment may achieve equilibrium with the physical environment. However, even here, influences from the biotic variable may obscure unequivocal correlations with the abiotic setting (including temperature). Future treeline expansion will likely be patchy and challenging to predict without considering the spatial variability of influences from biotic and abiotic local site conditions.

Population health needs as predictors of variations in NHS practice payments: a cross-sectional study of English general practices in 2013-2014 and 2014-2015.

PubMed

Levene, Louis S; Baker, Richard; Wilson, Andrew; Walker, Nicola; Boomla, Kambiz; Bankart, M John G

2017-01-01

NHS general practice payments in England include pay for performance elements and a weighted component designed to compensate for workload, but without measures of specific deprivation or ethnic groups. To determine whether population factors related to health needs predicted variations in NHS payments to individual general practices in England. Cross-sectional study of all practices in England, in financial years 2013-2014 and 2014-2015. Descriptive statistics, univariable analyses (examining correlations between payment and predictors), and multivariable analyses (undertaking multivariable linear regressions for each year, with logarithms of payments as the dependent variables, and with population, practice, and performance factors as independent variables) were undertaken. Several population variables predicted variations in adjusted total payments, but inconsistently. Higher payments were associated with increases in deprivation, patients of older age, African Caribbean ethnic group, and asthma prevalence. Lower payments were associated with an increase in smoking prevalence. Long-term health conditions, South Asian ethnic group, and diabetes prevalence were not predictive. The adjusted R 2 values were 0.359 (2013-2014) and 0.374 (2014-2015). A slightly different set of variables predicted variations in the payment component designed to compensate for workload. Lower payments were associated with increases in deprivation, patients of older age, and diabetes prevalence. Smoking prevalence was not predictive. There was a geographical differential. Population factors related to health needs were, overall, poor predictors of variations in adjusted total practice payments and in the payment component designed to compensate for workload. Revising the weighting formula and extending weighting to other payment components might better support practices to address these needs. © British Journal of General Practice 2017.

Spatial variability of biotic and abiotic tree establishment constraints across a treeline ecotone in the Alaska Range

USGS Publications Warehouse

Stueve, K.M.; Isaacs, R.E.; Tyrrell, L.E.; Densmore, R.V.

2011-01-01

Throughout interior Alaska (USA), a gradual warming trend in mean monthly temperatures occurred over the last few decades (;2-48C). The accompanying increases in woody vegetation at many alpine treeline (hereafter treeline) locations provided an opportunity to examine how biotic and abiotic local site conditions interact to control tree establishment patterns during warming. We devised a landscape ecological approach to investigate these relationships at an undisturbed treeline in the Alaska Range. We identified treeline changes between 1953 (aerial photography) and 2005 (satellite imagery) in a geographic information system (GIS) and linked them with corresponding local site conditions derived from digital terrain data, ancillary climate data, and distance to 1953 trees. Logistic regressions enabled us to rank the importance of local site conditions in controlling tree establishment. We discovered a spatial transition in the importance of tree establishment controls. The biotic variable (proximity to 1953 trees) was the most important tree establishment predictor below the upper tree limit, providing evidence of response lags with the abiotic setting and suggesting that tree establishment is rarely in equilibrium with the physical environment or responding directly to warming. Elevation and winter sun exposure were important predictors of tree establishment at the upper tree limit, but proximity to trees persisted as an important tertiary predictor, indicating that tree establishment may achieve equilibrium with the physical environment. However, even here, influences from the biotic variable may obscure unequivocal correlations with the abiotic setting (including temperature). Future treeline expansion will likely be patchy and challenging to predict without considering the spatial variability of influences from biotic and abiotic local site conditions. ?? 2011 by the Ecological Society of America.

Determination of the optimal training principle and input variables in artificial neural network model for the biweekly chlorophyll-a prediction: a case study of the Yuqiao Reservoir, China.

PubMed

Liu, Yu; Xi, Du-Gang; Li, Zhao-Liang

2015-01-01

Predicting the levels of chlorophyll-a (Chl-a) is a vital component of water quality management, which ensures that urban drinking water is safe from harmful algal blooms. This study developed a model to predict Chl-a levels in the Yuqiao Reservoir (Tianjin, China) biweekly using water quality and meteorological data from 1999-2012. First, six artificial neural networks (ANNs) and two non-ANN methods (principal component analysis and the support vector regression model) were compared to determine the appropriate training principle. Subsequently, three predictors with different input variables were developed to examine the feasibility of incorporating meteorological factors into Chl-a prediction, which usually only uses water quality data. Finally, a sensitivity analysis was performed to examine how the Chl-a predictor reacts to changes in input variables. The results were as follows: first, ANN is a powerful predictive alternative to the traditional modeling techniques used for Chl-a prediction. The back program (BP) model yields slightly better results than all other ANNs, with the normalized mean square error (NMSE), the correlation coefficient (Corr), and the Nash-Sutcliffe coefficient of efficiency (NSE) at 0.003 mg/l, 0.880 and 0.754, respectively, in the testing period. Second, the incorporation of meteorological data greatly improved Chl-a prediction compared to models solely using water quality factors or meteorological data; the correlation coefficient increased from 0.574-0.686 to 0.880 when meteorological data were included. Finally, the Chl-a predictor is more sensitive to air pressure and pH compared to other water quality and meteorological variables.

Modelling the distribution of chickens, ducks, and geese in China

USGS Publications Warehouse

Prosser, Diann J.; Wu, Junxi; Ellis, Erie C.; Gale, Fred; Van Boeckel, Thomas P.; Wint, William; Robinson, Tim; Xiao, Xiangming; Gilbert, Marius

2011-01-01

Global concerns over the emergence of zoonotic pandemics emphasize the need for high-resolution population distribution mapping and spatial modelling. Ongoing efforts to model disease risk in China have been hindered by a lack of available species level distribution maps for poultry. The goal of this study was to develop 1 km resolution population density models for China's chickens, ducks, and geese. We used an information theoretic approach to predict poultry densities based on statistical relationships between poultry census data and high-resolution agro-ecological predictor variables. Model predictions were validated by comparing goodness of fit measures (root mean square error and correlation coefficient) for observed and predicted values for 1/4 of the sample data which were not used for model training. Final output included mean and coefficient of variation maps for each species. We tested the quality of models produced using three predictor datasets and 4 regional stratification methods. For predictor variables, a combination of traditional predictors for livestock mapping and land use predictors produced the best goodness of fit scores. Comparison of regional stratifications indicated that for chickens and ducks, a stratification based on livestock production systems produced the best results; for geese, an agro-ecological stratification produced best results. However, for all species, each method of regional stratification produced significantly better goodness of fit scores than the global model. Here we provide descriptive methods, analytical comparisons, and model output for China's first high resolution, species level poultry distribution maps. Output will be made available to the scientific and public community for use in a wide range of applications from epidemiological studies to livestock policy and management initiatives.

Mobility predicts change in older adults' health-related quality of life: evidence from a Vancouver falls prevention prospective cohort study.

PubMed

Davis, Jennifer C; Bryan, Stirling; Best, John R; Li, Linda C; Hsu, Chun Liang; Gomez, Caitlin; Vertes, Kelly A; Liu-Ambrose, Teresa

2015-07-15

Older adults with mobility impairments are prone to reduced health related quality of life (HRQoL) is highly associated with mobility impairments. The consequences of falls have detrimental impact on mobility. Hence, ascertaining factors explaining variation among individuals' quality of life is critical for promoting healthy ageing, particularly among older fallers. Hence, the primary objective of our study was to identify key factors that explain variation in HRQoL among community dwelling older adults at risk of falls. We conducted a longitudinal analysis of a 12-month prospective cohort study at the Vancouver Falls Prevention Clinic (n = 148 to 286 depending on the analysis). We constructed linear mixed models where assessment month (0, 6, 12) was entered as a within-subjects repeated measure, the intercept was specified as a random effect, and predictors and covariates were entered as between-subjects fixed effects. We also included the predictors by sex and predictor by sex by time interaction terms in order to investigate sex differences in the relations between the predictor variable and the outcome variable, the EQ-5D. Our primary analysis demonstrated a significant mobility (assessed using the Short Performance Physical Battery and the Timed Up and Go) by time interaction (p < 0.05) and mobility by time by sex interaction (p < 0.05). The sensitivity analyses demonstrated some heterogeneity of these findings using an imputed and a complete case analysis. Mobility may be an important predictor of changes in HRQoL over time. As such, mobility is a critical factor to target for future intervention strategies aimed at maintaining or improving HRQoL in late life.

Modelling the distribution of chickens, ducks, and geese in China

PubMed Central

Prosser, Diann J.; Wu, Junxi; Ellis, Erle C.; Gale, Fred; Van Boeckel, Thomas P.; Wint, William; Robinson, Tim; Xiao, Xiangming; Gilbert, Marius

2011-01-01

Global concerns over the emergence of zoonotic pandemics emphasize the need for high-resolution population distribution mapping and spatial modelling. Ongoing efforts to model disease risk in China have been hindered by a lack of available species level distribution maps for poultry. The goal of this study was to develop 1 km resolution population density models for China’s chickens, ducks, and geese. We used an information theoretic approach to predict poultry densities based on statistical relationships between poultry census data and high-resolution agro-ecological predictor variables. Model predictions were validated by comparing goodness of fit measures (root mean square error and correlation coefficient) for observed and predicted values for ¼ of the sample data which was not used for model training. Final output included mean and coefficient of variation maps for each species. We tested the quality of models produced using three predictor datasets and 4 regional stratification methods. For predictor variables, a combination of traditional predictors for livestock mapping and land use predictors produced the best goodness of fit scores. Comparison of regional stratifications indicated that for chickens and ducks, a stratification based on livestock production systems produced the best results; for geese, an agro-ecological stratification produced best results. However, for all species, each method of regional stratification produced significantly better goodness of fit scores than the global model. Here we provide descriptive methods, analytical comparisons, and model output for China’s first high resolution, species level poultry distribution maps. Output will be made available to the scientific and public community for use in a wide range of applications from epidemiological studies to livestock policy and management initiatives. PMID:21765567

[Psychiatry of the life span?--relevance of age in psychiatric research].

PubMed

Sikorski, Claudia; Motzek, Tom

2010-11-01

The aim of this study was to determine to what extent studies published in two German journals took the age of their sample into consideration. All publications of the two journals were viewed. Only empirical research papers were included. It was then assessed whether they included information on age of the sample and, if that was the case, the studies were further categorized as only giving descriptive sample information, reporting age-specific results of dependent variables or using age as a predictor in regression analyses. Furthermore, the age range covered was assessed. 88 % of all studies included information on age. Of those, about half only provided descriptive information on the age of the study sample, while more than one third used the age variable as a predictor in multivariate models. Few studies reported age-specific outcomes. Main focus of research was on adult populations aged 18 to 65. Only few studies concentrated on children and adolescents. In light of demographic change and age specificity of psychological disorders, it will be necessary to further differentiate and report age-specific results of psychiatric research. A change in what is considered normative aging and developmental tasks for certain age groups calls for further research in those age groups. © Georg Thieme Verlag KG Stuttgart · New York.

Patterns and predictors of ADHD persistence into adulthood: Results from the National Comorbidity Survey Replication

PubMed Central

Kessler, Ronald C.; Adler, Lenard A.; Barkley, Russell; Biederman, Joseph; Conners, C. Keith; Faraone, Stephen V.; Greenhill, Laurence L.; Jaeger, Savina; Secnik, Kristina; Spencer, Thomas; Üstün, T. Bedirhan; Zaslavsky, Alan M.

2010-01-01

BACKGROUND Despite growing interest in adult ADHD, little is known about predictors of persistence of childhood cases into adulthood. METHODS A retrospective assessment of childhood ADHD, childhood risk factors, and a screen for adult ADHD were included in a sample of 3197 18–44 year old respondents in the National Comorbidity Survey Replication (NCS-R). Blinded adult ADHD clinical reappraisal interviews were administered to a sub-sample of respondents. Multiple imputation (MI) was used to estimate adult persistence of childhood ADHD. Logistic regression was used to study retrospectively reported childhood predictors of persistence. Potential predictors included socio-demographics, childhood ADHD severity, childhood adversity, traumatic life experiences, and comorbid DSM-IV child-adolescent disorders (anxiety, mood, impulse-control, and substance disorders). RESULTS 36.3% of respondents with retrospectively assessed childhood ADHD were classified by blinded clinical interviews as meeting DSM-IV criteria for current ADHD. Childhood ADHD severity and childhood treatment significantly predicted persistence. Controlling for severity and excluding treatment, none of the other variables significantly predicted persistence even though they were significantly associated with childhood ADHD. CONCLUSIONS No modifiable risk factors were found for adult persistence of ADHD. Further research, ideally based on prospective general population samples, is needed to search for modifiable determinants of adult persistence of ADHD. PMID:15950019

Motivational and neurocognitive deficits are central to the prediction of longitudinal functional outcome in schizophrenia.

PubMed

Fervaha, G; Foussias, G; Agid, O; Remington, G

2014-10-01

Functional impairment is characteristic of most individuals with schizophrenia; however, the key variables that undermine community functioning are not well understood. This study evaluated the association between selected clinical variables and one-year longitudinal functional outcomes in patients with schizophrenia. The sample included 754 patients with schizophrenia who completed both baseline and one-year follow-up visits in the CATIE study. Patients were evaluated with a comprehensive battery of assessments capturing symptom severity and cognitive performance among other variables. The primary outcome variable was functional status one-year postbaseline measured using the Heinrichs-Carpenter Quality of Life Scale. Factor analysis of negative symptom items revealed two factors reflecting diminished expression and amotivation. Multivariate regression modeling revealed several significant independent predictors of longitudinal functioning scores. The strongest predictors were baseline amotivation and neurocognition. Both amotivation and neurocognition also had independent predictive value for each of the domains of functioning assessed (e.g., vocational). Both motivational and neurocognitive deficits independently contribute to longitudinal functional outcomes assessed 1 year later among patients with schizophrenia. Both of these domains of psychopathology impede functional recovery; hence, it follows that treatments ameliorating each of these symptoms should promote community functioning among individuals with schizophrenia. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology.

PubMed

Fox, Eric W; Hill, Ryan A; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H

2017-07-01

Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p = 212) of landscape features from the StreamCat data set as potential predictors. We compare two types of RF models: a full variable set model with all 212 predictors and a reduced variable set model selected using a backward elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substantial improvement in cross-validated accuracy as a result of variable reduction. Moreover, the backward elimination procedure tended to select too few variables and exhibited numerous issues such as upwardly biased out-of-bag accuracy estimates and instabilities in the spatial predictions. We use simulations to further support and generalize results from the analysis of real data. A main purpose of this work is to elucidate issues of model selection bias and instability to ecologists interested in using RF to develop predictive models with large environmental data sets.

How Binary Skills Obscure the Transition from Non-Mastery to Mastery

ERIC Educational Resources Information Center

Karelitz, Tzur M.

2008-01-01

What is the nature of latent predictors that facilitate diagnostic classification? Rupp and Templin (this issue) suggest that these predictors should be multidimensional, categorical variables that can be combined in various ways. Diagnostic Classification Models (DCM) typically use multiple categorical predictors to classify respondents into…

Examining Preservice Science Teacher Understanding of Nature of Science: Discriminating Variables on the Aspects of Nature of Science

NASA Astrophysics Data System (ADS)

Jones, William I.

This study examined the understanding of nature of science among participants in their final year of a 4-year undergraduate teacher education program at a Midwest liberal arts university. The Logic Model Process was used as an integrative framework to focus the collection, organization, analysis, and interpretation of the data for the purpose of (1) describing participant understanding of NOS and (2) to identify participant characteristics and teacher education program features related to those understandings. The Views of Nature of Science Questionnaire form C (VNOS-C) was used to survey participant understanding of 7 target aspects of Nature of Science (NOS). A rubric was developed from a review of the literature to categorize and score participant understanding of the target aspects of NOS. Participants' high school and college transcripts, planning guides for their respective teacher education program majors, and science content and science teaching methods course syllabi were examined to identify and categorize participant characteristics and teacher education program features. The R software (R Project for Statistical Computing, 2010) was used to conduct an exploratory analysis to determine correlations of the antecedent and transaction predictor variables with participants' scores on the 7 target aspects of NOS. Fourteen participant characteristics and teacher education program features were moderately and significantly ( p < .01) correlated with participant scores on the target aspects of NOS. The 6 antecedent predictor variables were entered into multiple regression analyses to determine the best-fit model of antecedent predictor variables for each target NOS aspect. The transaction predictor variables were entered into separate multiple regression analyses to determine the best-fit model of transaction predictor variables for each target NOS aspect. Variables from the best-fit antecedent and best-fit transaction models for each target aspect of NOS were then combined. A regression analysis for each of the combined models was conducted to determine the relative effect of these variables on the target aspects of NOS. Findings from the multiple regression analyses revealed that each of the fourteen predictor variables was present in the best-fit model for at least 1 of the 7 target aspects of NOS. However, not all of the predictor variables were statistically significant (p < .007) in the models and their effect (beta) varied. Participants in the teacher education program who had higher ACT Math scores, completed more high school science credits, and were enrolled either in the Middle Childhood with a science concentration program major or in the Adolescent/Young Adult Science Education program major were more likely to have an informed understanding on each of the 7 target aspects of NOS. Analyses of the planning guides and the course syllabi in each teacher education program major revealed differences between the program majors that may account for the results.

Model averaging and muddled multimodel inferences.

PubMed

Cade, Brian S

2015-09-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Model averaging and muddled multimodel inferences

USGS Publications Warehouse

Cade, Brian S.

2015-01-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the tstatistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Time-in-a-bottle (TIAB): a longitudinal, correlational study of patterns, potential predictors, and outcomes of immunosuppressive medication adherence in adult kidney transplant recipients.

PubMed

Russell, Cynthia L; Ashbaugh, Catherine; Peace, Leanne; Cetingok, Muammer; Hamburger, Karen Q; Owens, Sarah; Coffey, Deanna; Webb, Andrew W; Hathaway, Donna; Winsett, Rebecca P; Madsen, Richard; Wakefield, Mark R

2013-01-01

This study examined patterns, potential predictors, and outcomes of immunosuppressive medication adherence in a convenience sample of 121 kidney transplant recipients aged 21 yr or older from three kidney transplant centers using a theory-based, descriptive, correlational, longitudinal design. Electronic monitoring was conducted for 12 months using electronic monitoring. Participants were persistent in taking their immunosuppressive medications, but execution, which includes both taking and timing, was poor. Older age was the only demographic variable associated with medication adherence (r = 0.25; p = 0.005). Of the potential predictors examined, only medication self-efficacy was associated with medication non-adherence, explaining about 9% of the variance (r = 0.31, p = 0.0006). The few poor outcomes that occurred were not significantly associated with medication non-adherence, although the small number of poor outcomes may have limited our ability to detect a link. Future research should test fully powered, theory-based, experimental interventions that include a medication self-efficacy component. © 2013 John Wiley & Sons A/S.

Time-in-a-Bottle (TIAB): A Longitudinal, Correlational Study of Patterns, Potential Predictors, and Outcomes of Immunosuppressive Medication Adherence in Adult Kidney Transplant Recipients

PubMed Central

Russell, Cynthia L.; Ashbaugh, Catherine; Peace, Leanne; Cetingok, Muammer; Hamburger, Karen Q.; Owens, Sarah; Coffey, Deanna; Webb, Andrew; Hathaway, Donna; Winsett, Rebecca P.; Madsen, Richard; Wakefield, Mark R.

2013-01-01

This study examined patterns, potential predictors, and outcomes of immunosuppressive medication adherence in a convenience sample of 121 kidney transplant recipients aged 21 years or older from three kidney transplant centers using a theory-based, descriptive, correlational, longitudinal design. Electronic monitoring was conducted for 12 months using the Medication Event Monitoring System. Participants were persistent in taking their immunosuppressive medications, but execution, which includes both taking and timing, was poor. Older age was the only demographic variable associated with medication adherence (r = 0.25; p = 0.005). Of the potential predictors examined, only medication self-efficacy was associated with medication non-adherence, explaining about 9% of the variance (r = 0.31, p = 0.0006). The few poor outcomes that occurred were not significantly associated with medication non-adherence, although the small number of poor outcomes may have limited our ability to detect a link. Future research should test fully powered, theory-based, experimental interventions that include a medication self-efficacy component. PMID:24093614

Finding structure in data using multivariate tree boosting

PubMed Central

Miller, Patrick J.; Lubke, Gitta H.; McArtor, Daniel B.; Bergeman, C. S.

2016-01-01

Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles such as random forests (Strobl, Malley, & Tutz, 2009) are a useful tool for finding structure, but are difficult to interpret with multiple outcome variables which are often of interest in psychology. To find and interpret structure in data sets with multiple outcomes and many predictors (possibly exceeding the sample size), we introduce a multivariate extension to a decision tree ensemble method called gradient boosted regression trees (Friedman, 2001). Our extension, multivariate tree boosting, is a method for nonparametric regression that is useful for identifying important predictors, detecting predictors with nonlinear effects and interactions without specification of such effects, and for identifying predictors that cause two or more outcome variables to covary. We provide the R package ‘mvtboost’ to estimate, tune, and interpret the resulting model, which extends the implementation of univariate boosting in the R package ‘gbm’ (Ridgeway et al., 2015) to continuous, multivariate outcomes. To illustrate the approach, we analyze predictors of psychological well-being (Ryff & Keyes, 1995). Simulations verify that our approach identifies predictors with nonlinear effects and achieves high prediction accuracy, exceeding or matching the performance of (penalized) multivariate multiple regression and multivariate decision trees over a wide range of conditions. PMID:27918183

Contributing to Overall Life Satisfaction: Personality Traits Versus Life Satisfaction Variables Revisited—Is Replication Impossible?

PubMed Central

Lachmann, Bernd; Sariyska, Rayna; Kannen, Christopher; Błaszkiewicz, Konrad; Trendafilov, Boris; Andone, Ionut; Eibes, Mark; Markowetz, Alexander; Li, Mei; Kendrick, Keith M.

2017-01-01

Virtually everybody would agree that life satisfaction is of immense importance in everyday life. Thus, it is not surprising that a considerable amount of research using many different methodological approaches has investigated what the best predictors of life satisfaction are. In the present study, we have focused on several key potential influences on life satisfaction including bottom-up and top-down models, cross-cultural effects, and demographic variables. In four independent (large scale) surveys with sample sizes ranging from N = 488 to 40,297, we examined the associations between life satisfaction and various related variables. Our findings demonstrate that prediction of overall life satisfaction works best when including information about specific life satisfaction variables. From this perspective, satisfaction with leisure showed the highest impact on overall life satisfaction in our European samples. Personality was also robustly associated with life satisfaction, but only when life satisfaction variables were not included in the regression model. These findings could be replicated in all four independent samples, but it was also demonstrated that the relevance of life satisfaction variables changed under the influence of cross-cultural effects. PMID:29295529

Effects of demographic and health variables on Rasch scaled cognitive scores.

PubMed

Zelinski, Elizabeth M; Gilewski, Michael J

2003-08-01

To determine whether demographic and health variables interact to predict cognitive scores in Asset and Health Dynamics of the Oldest-Old (AHEAD), a representative survey of older Americans, as a test of the developmental discontinuity hypothesis. Rasch modeling procedures were used to rescale cognitive measures into interval scores, equating scales across measures, making it possible to compare predictor effects directly. Rasch scaling also reduces the likelihood of obtaining spurious interactions. Tasks included combined immediate and delayed recall, the Telephone Interview for Cognitive Status (TICS), Series 7, and an overall cognitive score. Demographic variables most strongly predicted performance on all scores, with health variables having smaller effects. Age interacted with both demographic and health variables, but patterns of effects varied. Demographic variables have strong effects on cognition. The developmental discontinuity hypothesis that health variables have stronger effects than demographic ones on cognition in older adults was not supported.

Response variability in rapid automatized naming predicts reading comprehension

PubMed Central

Li, James J.; Cutting, Laurie E.; Ryan, Matthew; Zilioli, Monica; Denckla, Martha B.; Mahone, E. Mark

2009-01-01

A total of 37 children ages 8 to 14 years, screened for word-reading difficulties (23 with attention-deficit/hyperactivity disorder, ADHD; 14 controls) completed oral reading and rapid automatized naming (RAN) tests. RAN trials were segmented into pause and articulation time and intraindividual variability. There were no group differences on reading or RAN variables. Color- and letter-naming pause times and number-naming articulation time were significant predictors of reading fluency. In contrast, number and letter pause variability were predictors of comprehension. Results support analysis of subcomponents of RAN and add to literature emphasizing intraindividual variability as a marker for response preparation, which has relevance to reading comprehension. PMID:19221923

Binary recursive partitioning: background, methods, and application to psychology.

PubMed

Merkle, Edgar C; Shaffer, Victoria A

2011-02-01

Binary recursive partitioning (BRP) is a computationally intensive statistical method that can be used in situations where linear models are often used. Instead of imposing many assumptions to arrive at a tractable statistical model, BRP simply seeks to accurately predict a response variable based on values of predictor variables. The method outputs a decision tree depicting the predictor variables that were related to the response variable, along with the nature of the variables' relationships. No significance tests are involved, and the tree's 'goodness' is judged based on its predictive accuracy. In this paper, we describe BRP methods in a detailed manner and illustrate their use in psychological research. We also provide R code for carrying out the methods.

Heart rate variability measured early in patients with evolving acute coronary syndrome and 1-year outcomes of rehospitalization and mortality.

PubMed

Harris, Patricia R E; Stein, Phyllis K; Fung, Gordon L; Drew, Barbara J

2014-01-01

This study sought to examine the prognostic value of heart rate variability (HRV) measurement initiated immediately after emergency department presentation for patients with acute coronary syndrome (ACS). Altered HRV has been associated with adverse outcomes in heart disease, but the value of HRV measured during the earliest phases of ACS related to risk of 1-year rehospitalization and death has not been established. Twenty-four-hour Holter recordings of 279 patients with ACS were initiated within 45 minutes of emergency department arrival; recordings with ≥18 hours of sinus rhythm were selected for HRV analysis (number [N] =193). Time domain, frequency domain, and nonlinear HRV were examined. Survival analysis was performed. During the 1-year follow-up, 94 patients were event-free, 82 were readmitted, and 17 died. HRV was altered in relation to outcomes. Predictors of rehospitalization included increased normalized high frequency power, decreased normalized low frequency power, and decreased low/high frequency ratio. Normalized high frequency >42 ms(2) predicted rehospitalization while controlling for clinical variables (hazard ratio [HR] =2.3; 95% confidence interval [CI] =1.4-3.8, P=0.001). Variables significantly associated with death included natural logs of total power and ultra low frequency power. A model with ultra low frequency power <8 ms(2) (HR =3.8; 95% CI =1.5-10.1; P=0.007) and troponin >0.3 ng/mL (HR =4.0; 95% CI =1.3-12.1; P=0.016) revealed that each contributed independently in predicting mortality. Nonlinear HRV variables were significant predictors of both outcomes. HRV measured close to the ACS onset may assist in risk stratification. HRV cut-points may provide additional, incremental prognostic information to established assessment guidelines, and may be worthy of additional study.

Predictors of pesticide poisoning.

PubMed

Ferguson, J A; Sellar, C; McGuigan, M A

1991-01-01

The analysis of 1,026 reports of suspected pesticide poisonings to the regional Poison Control Centre at the Hospital for Sick Children, Toronto consisted of 597 (58.2%) cases less than six years of age. Age was the strongest predictor: there was a risk of 3.1 that young children would encounter rodenticide poisoning compared to that of insecticides; a ten-fold risk of having symptoms from pesticide poisoning if the victim was over five years of age; an increased risk of 5.9 of exposure to moderate or large amounts of pesticide, compared to small quantities, for those over five years of age; and there was less treatment referral for young children, and a 5.7 risk of being referred if the victim was over the age of five years. Other significant predictor variables include the type of person making the inquiry (lay or physician/nurse), the calendar season of the event, and the location (metropolitan or nonmetropolitan) of the event.

Comparison of the Performance of Noise Metrics as Predictions of the Annoyance of Stage 2 and Stage 3 Aircraft Overflights

NASA Technical Reports Server (NTRS)

Pearsons, Karl S.; Howe, Richard R.; Sneddon, Matthew D.; Fidell, Sanford

1996-01-01

Thirty audiometrically screened test participants judged the relative annoyance of two comparison (variable level) and thirty-four standard (fixed level) signals in an adaptive paired comparison psychoacoustic study. The signal ensemble included both FAR Part 36 Stage 2 and 3 aircraft overflights, as well as synthesized aircraft noise signatures and other non-aircraft signals. All test signals were presented for judgment as heard indoors, in the presence of continuous background noise, under free-field listening conditions in an anechoic chamber. Analyses of the performance of 30 noise metrics as predictors of these annoyance judgments confirmed that the more complex metrics were generally more accurate and precise predictors than the simpler methods. EPNL was somewhat less accurate and precise as a predictor of the annoyance judgments than a duration-adjusted variant of Zwicker's Loudness Level.

Predicting students' perceptions of academic misconduct on the Hogan Personality Inventory Reliability Scale.

PubMed

Stone, Thomas H; Kisamore, Jennifer L; Jawahar, I M

2008-04-01

Interest and research on academic misconduct has become more salient in part due to recent publicized academic and organizational scandals. The current study investigated a possible interaction between perception of the university's academic culture and personality, conceptualized as Reliability, on students' perceptions of academic misconduct. A convenience sample of 217 university business students (91 men, 126 women), whose average age was 22.3 yr. (SD = 4.4) was tested. Reliability was measured with an occupational scale included in the Hogan Personality Inventory. Two hierarchical regression analyses were conducted using Cheating Intentions and Likelihood of Reporting Cheating as criteria. Age, Reliability, Integrity Culture, and the interaction between scores on Reliability and Integrity Culture were entered as predictors. Only Age and Reliability scores were significant predictors of Cheating Intentions, while all variables were significant predictors for Likelihood of Reporting Cheating. Suggestions for practice and research are provided.

The Caregiving Experience in a Racially Diverse Sample of Cancer Family Caregivers

PubMed Central

Siefert, Mary Lou; Williams, Anna-leila; Dowd, Michael F.; Chappel-Aiken, Lolita; McCorkle, Ruth

2009-01-01

The literature supports a variety of predictor variables to account for the psychological and stress burden experienced by cancer family caregivers. Missing among the predictor variables are the differences by or influence of race/ethnicity. The purpose of this study was to describe the sample, explore differences in outcomes by patient and family caregiver characteristics, and determine if any of the patient and family characteristics, including race/ethnicity, predicted outcomes. Cross-sectional surveys were used to determine sociodemographics, psychological and physical health, and burdens of caregiving among 54 caregivers. The analysis consisted of descriptive methods, including frequencies and t tests, and regression modeling. The sample was 35% African American or Hispanic. African American and Hispanic caregivers were younger than white caregivers and more often women, were rarely the spouse of the patient, and frequently had other dependents, including children and older parents. African American and Hispanic caregivers reported lower incomes and more burden related to finances and employment than did white caregivers. When controlling for sociodemographic factors, there was no difference by race/ethnicity on the outcome measures. The experience of caregiving may supersede race/ethnicity and may be its own cultural entity. Areas of concern include the interrelationship between socioeconomic status and race/ethnicity, the absence of cultural frameworks to direct caregiver research, and the question of cultural relevance of measurement tools. PMID:18772665

Digital mapping of soil properties in Canadian managed forests at 250 m of resolution using the k-nearest neighbor method

NASA Astrophysics Data System (ADS)

Mansuy, N. R.; Paré, D.; Thiffault, E.

2015-12-01

Large-scale mapping of soil properties is increasingly important for environmental resource management. Whileforested areas play critical environmental roles at local and global scales, forest soil maps are typically at lowresolution.The objective of this study was to generate continuous national maps of selected soil variables (C, N andsoil texture) for the Canadian managed forest landbase at 250 m resolution. We produced these maps using thekNN method with a training dataset of 538 ground-plots fromthe National Forest Inventory (NFI) across Canada,and 18 environmental predictor variables. The best predictor variables were selected (7 topographic and 5 climaticvariables) using the Least Absolute Shrinkage and Selection Operator method. On average, for all soil variables,topographic predictors explained 37% of the total variance versus 64% for the climatic predictors. Therelative root mean square error (RMSE%) calculated with the leave-one-out cross-validation method gave valuesranging between 22% and 99%, depending on the soil variables tested. RMSE values b 40% can be considered agood imputation in light of the low density of points used in this study. The study demonstrates strong capabilitiesfor mapping forest soil properties at 250m resolution, compared with the current Soil Landscape of CanadaSystem, which is largely oriented towards the agricultural landbase. The methodology used here can potentiallycontribute to the national and international need for spatially explicit soil information in resource managementscience.

Reliability, reference values and predictor variables of the ulnar sensory nerve in disease free adults.

PubMed

Ruediger, T M; Allison, S C; Moore, J M; Wainner, R S

2014-09-01

The purposes of this descriptive and exploratory study were to examine electrophysiological measures of ulnar sensory nerve function in disease free adults to determine reliability, determine reference values computed with appropriate statistical methods, and examine predictive ability of anthropometric variables. Antidromic sensory nerve conduction studies of the ulnar nerve using surface electrodes were performed on 100 volunteers. Reference values were computed from optimally transformed data. Reliability was computed from 30 subjects. Multiple linear regression models were constructed from four predictor variables. Reliability was greater than 0.85 for all paired measures. Responses were elicited in all subjects; reference values for sensory nerve action potential (SNAP) amplitude from above elbow stimulation are 3.3 μV and decrement across-elbow less than 46%. No single predictor variable accounted for more than 15% of the variance in the response. Electrophysiologic measures of the ulnar sensory nerve are reliable. Absent SNAP responses are inconsistent with disease free individuals. Reference values recommended in this report are based on appropriate transformations of non-normally distributed data. No strong statistical model of prediction could be derived from the limited set of predictor variables. Reliability analyses combined with relatively low level of measurement error suggest that ulnar sensory reference values may be used with confidence. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Predictors of First-Onset Substance Use Disorders During the Prospective Course of Bipolar Spectrum Disorders in Adolescents

PubMed Central

Goldstein, Benjamin I.; Strober, Michael; Axelson, David; Goldstein, Tina R.; Gill, Mary Kay; Hower, Heather; Dickstein, Daniel; Hunt, Jeffrey; Yen, Shirley; Kim, Eunice; Ha, Wonho; Liao, Fangzi; Fan, Jieyu; Iyengar, Satish; Ryan, Neal D.; Keller, Martin B.; Birmaher, Boris

2013-01-01

Objective Substance use disorders (SUD) are common and problematic in bipolar disorder (BP). We prospectively examined predictors of first-onset SUD among adolescents with BP. Method Adolescents (12–17 years old; N=167) in the Course and Outcome of Bipolar Youth (COBY) study fulfilling criteria for BP-I, BP-II, or operationalized BP not otherwise specified, without SUD at intake, were included. Baseline demographic, clinical, and family history variables, and clinical variables assessed during follow-up, were examined in relation to first-onset SUD. Participants were prospectively interviewed every 38.5±22.2 weeks for an average of 4.25±2.11 years. Results First-onset SUD developed among 32% of subjects, after a mean of 2.7±2.0 years from intake. Lifetime alcohol experimentation at intake most robustly predicted first-onset SUD. Lifetime oppositional defiant disorder and panic disorder, family history of SUD, low family cohesiveness, and absence of antidepressant treatment at intake were also associated with increased risk of SUD, whereas BP subtype was not. Risk of SUD increased with increasing number of these six predictors: 54.7% of subjects with ≥3 predictors developed SUD vs. 14.1% of those with <3 predictors (Hazard Ratio 5.41 95% CI 2.7–11.0 p<0.0001). Greater hypo/manic symptom severity in the preceding 12 weeks predicted greater likelihood of SUD onset. Lithium exposure in the preceding 12 weeks predicted lower likelihood of SUD. Conclusions This study identifies several predictors of first-onset SUD in the COBY sample which, if replicated, may suggest targets of preventive interventions for SUD among youth with BP. Treatment-related findings are inconclusive and must be interpreted tentatively given the limitations of observational naturalistic treatment data. There is a substantial window of opportunity between BP and SUD onset during which preventive strategies may be employed. PMID:24074469

Modeling continuous covariates with a "spike" at zero: Bivariate approaches.

PubMed

Jenkner, Carolin; Lorenz, Eva; Becher, Heiko; Sauerbrei, Willi

2016-07-01

In epidemiology and clinical research, predictors often take value zero for a large amount of observations while the distribution of the remaining observations is continuous. These predictors are called variables with a spike at zero. Examples include smoking or alcohol consumption. Recently, an extension of the fractional polynomial (FP) procedure, a technique for modeling nonlinear relationships, was proposed to deal with such situations. To indicate whether or not a value is zero, a binary variable is added to the model. In a two stage procedure, called FP-spike, the necessity of the binary variable and/or the continuous FP function for the positive part are assessed for a suitable fit. In univariate analyses, the FP-spike procedure usually leads to functional relationships that are easy to interpret. This paper introduces four approaches for dealing with two variables with a spike at zero (SAZ). The methods depend on the bivariate distribution of zero and nonzero values. Bi-Sep is the simplest of the four bivariate approaches. It uses the univariate FP-spike procedure separately for the two SAZ variables. In Bi-D3, Bi-D1, and Bi-Sub, proportions of zeros in both variables are considered simultaneously in the binary indicators. Therefore, these strategies can account for correlated variables. The methods can be used for arbitrary distributions of the covariates. For illustration and comparison of results, data from a case-control study on laryngeal cancer, with smoking and alcohol intake as two SAZ variables, is considered. In addition, a possible extension to three or more SAZ variables is outlined. A combination of log-linear models for the analysis of the correlation in combination with the bivariate approaches is proposed. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Self Efficacy and Some Demographic Variables as Predictors of Occupational Stress among Primary School Teachers in Delta State of Nigeria

ERIC Educational Resources Information Center

Akpochafo, G. O.

2014-01-01

This study investigated self efficacy and some demographic variables as predictors of occupational stress among primary school teachers in Delta State. Three hypotheses were formulated to guide the study. The study adopted a descriptive survey design that utilized an expost-facto research type. A sample of one hundred and twenty primary school…

Identification of Variables Associated with Group Separation in Descriptive Discriminant Analysis: Comparison of Methods for Interpreting Structure Coefficients

ERIC Educational Resources Information Center

Finch, Holmes

2010-01-01

Discriminant Analysis (DA) is a tool commonly used for differentiating among 2 or more groups based on 2 or more predictor variables. DA works by finding 1 or more linear combinations of the predictors that yield maximal difference among the groups. One common goal of researchers using DA is to characterize the nature of group difference by…

Influence of Selected Personal Characteristics and County Situational Factors on Time Allocated to Dairy Subjects by Extension Agents in Selected Tennessee Counties.

ERIC Educational Resources Information Center

Northcutt, Sherwin Dean; And Others

The study deals with various predictors of time spent on dairy subjects by Extension agents and predictors of contacts made by agents with dairy clientele. Purposes were to determine the relationships, if any, between various independent variables and groups of independent variables (agents' background and training, county dairy situation, agents'…

Contextual Influences on Sources of Academic Self-Efficacy: A Validation with Secondary School Students of Kerala

ERIC Educational Resources Information Center

Gafoor, K. Abdul; Ashraf, P. Muhammed

2012-01-01

This study investigates the theorized sources of Academic Self-Efficacy among the higher secondary school students of Kerala, India. Mastery Experience in the form of Academic Achievement, vicarious experience in the form of School Image and Social Persuasion in the form of Parental Encouragement are included as the predictor variables of Academic…

Predicting Performance on the Tennessee Comprehensive Assessment for Third Grade Reading Students Using Reading Curriculum Based Measures

ERIC Educational Resources Information Center

Kirkham, Scott; Lampley, James H.

2014-01-01

The purpose of this study was to investigate the relationship between three predictor variables (Fall R-CBM, Winter R-CBM, and Spring R-CBM) and the Tennessee Comprehensive Assessment Program third grade reading and language arts assessment. The population selected for this study included all third grade students from an East Tennessee school…

Predicting Success for College Students Enrolled in an Online, Lab-Based, Biology Course for Non-Majors

ERIC Educational Resources Information Center

Foster, Regina

2012-01-01

Online education has exploded in popularity. While there is ample research on predictors of traditional college student success, little research has been done on effective methods of predicting student success in online education. In this study, a number of demographic variables including GPA, ACT, gender, age and others were examined to determine…

Need for Affect, Need for Cognition, and the Intention-Fruit Consumption Relationship: An Action-Control Perspective

ERIC Educational Resources Information Center

de Bruijn, Gert-Jan; Keer, Mario; van den Putte, Bas; Neijens, Peter

2012-01-01

Objective: Predictors of action-control profiles are useful targets for health behaviour change interventions, but action-control research has not focused on fruit consumption and has not yet included need for affect and need for cognition, despite the demonstrated usefulness of these variables in a broad range of research. The role of these…

The independent relationship between triglycerides and coronary heart disease.

PubMed

Morrison, Alan; Hokanson, John E

2009-01-01

The aim was to review epidemiologic studies to reassess whether serum levels of triglycerides should be considered independently of high-density lipoprotein-cholesterol (HDL-C) as a predictor of coronary heart disease (CHD). We systematically reviewed population-based cohort studies in which baseline serum levels of triglycerides and HDL-C were included as explanatory variables in multivariate analyses with the development of CHD (coronary events or coronary death) as dependent variable. A total of 32 unique reports describing 38 cohorts were included. The independent association between elevated triglycerides and risk of CHD was statistically significant in 16 of 30 populations without pre-existing CHD. Among populations with diabetes mellitus or pre-existing CHD, or the elderly, triglycerides were not significantly independently associated with CHD in any of 8 cohorts. Triglycerides and HDL-C were mutually exclusive predictors of coronary events in 12 of 20 analyses of patients without pre-existing CHD. Epidemiologic studies provide evidence of an association between triglycerides and the development of primary CHD independently of HDL-C. Evidence of an inverse relationship between triglycerides and HDL-C suggests that both should be considered in CHD risk estimation and as targets for intervention.

Predicting outcome in severe traumatic brain injury using a simple prognostic model.

PubMed

Sobuwa, Simpiwe; Hartzenberg, Henry Benjamin; Geduld, Heike; Uys, Corrie

2014-06-17

Several studies have made it possible to predict outcome in severe traumatic brain injury (TBI) making it beneficial as an aid for clinical decision-making in the emergency setting. However, reliable predictive models are lacking for resource-limited prehospital settings such as those in developing countries like South Africa. To develop a simple predictive model for severe TBI using clinical variables in a South African prehospital setting. All consecutive patients admitted at two level-one centres in Cape Town, South Africa, for severe TBI were included. A binary logistic regression model was used, which included three predictor variables: oxygen saturation (SpO₂), Glasgow Coma Scale (GCS) and pupil reactivity. The Glasgow Outcome Scale was used to assess outcome on hospital discharge. A total of 74.4% of the outcomes were correctly predicted by the logistic regression model. The model demonstrated SpO₂ (p=0.019), GCS (p=0.001) and pupil reactivity (p=0.002) as independently significant predictors of outcome in severe TBI. Odds ratios of a good outcome were 3.148 (SpO₂ ≥ 90%), 5.108 (GCS 6 - 8) and 4.405 (pupils bilaterally reactive). This model is potentially useful for effective predictions of outcome in severe TBI.

Patterns and predictors of HIV risk among urban American Indians.

PubMed

Walters, K L; Simoni, J M; Harris, C

2000-01-01

A preliminary survey of HIV risk and service preferences among American Indians residing in the New York metropolitan area included 68 women and 32 men (M age=35.8 years). Overall, the sample was knowledgeable about the mechanisms of HIV transmission, and 58 percent reported having taken an HIV test. However, of the 63 percent who reported sexual activity in the last six months, 73 percent reported engaging in vaginal or anal sex without a condom with at least 1 partner, and 52 percent used condoms none of the time during vaginal and anal sex. Almost half (43 percent) reported alcohol or other drug (AOD) use for non-ceremonial purposes in the last six months. Alarmingly, 44 percent reported lifetime trauma, including domestic violence (20 percent) and physical (29 percent) or sexual (26 percent) assault by a family member or stranger. Bivariate and multivariate analyses indicated trauma and drug use were factors that may place respondents at risk for sexual transmission of HIV. Trauma variables were better predictors of HIV risk behaviors than social cognitive variables providing preliminary support for the use of a postcolonial framework in American Indian HIV studies.

The independent relationship between triglycerides and coronary heart disease

PubMed Central

Morrison, Alan; Hokanson, John E

2009-01-01

Aims: The aim was to review epidemiologic studies to reassess whether serum levels of triglycerides should be considered independently of high-density lipoprotein-cholesterol (HDL-C) as a predictor of coronary heart disease (CHD). Methods and results: We systematically reviewed population-based cohort studies in which baseline serum levels of triglycerides and HDL-C were included as explanatory variables in multivariate analyses with the development of CHD (coronary events or coronary death) as dependent variable. A total of 32 unique reports describing 38 cohorts were included. The independent association between elevated triglycerides and risk of CHD was statistically significant in 16 of 30 populations without pre-existing CHD. Among populations with diabetes mellitus or pre-existing CHD, or the elderly, triglycerides were not significantly independently associated with CHD in any of 8 cohorts. Triglycerides and HDL-C were mutually exclusive predictors of coronary events in 12 of 20 analyses of patients without pre-existing CHD. Conclusions: Epidemiologic studies provide evidence of an association between triglycerides and the development of primary CHD independently of HDL-C. Evidence of an inverse relationship between triglycerides and HDL-C suggests that both should be considered in CHD risk estimation and as targets for intervention. PMID:19436658

Generalized SAMPLE SIZE Determination Formulas for Investigating Contextual Effects by a Three-Level Random Intercept Model.

PubMed

Usami, Satoshi

2017-03-01

Behavioral and psychological researchers have shown strong interests in investigating contextual effects (i.e., the influences of combinations of individual- and group-level predictors on individual-level outcomes). The present research provides generalized formulas for determining the sample size needed in investigating contextual effects according to the desired level of statistical power as well as width of confidence interval. These formulas are derived within a three-level random intercept model that includes one predictor/contextual variable at each level to simultaneously cover various kinds of contextual effects that researchers can show interest. The relative influences of indices included in the formulas on the standard errors of contextual effects estimates are investigated with the aim of further simplifying sample size determination procedures. In addition, simulation studies are performed to investigate finite sample behavior of calculated statistical power, showing that estimated sample sizes based on derived formulas can be both positively and negatively biased due to complex effects of unreliability of contextual variables, multicollinearity, and violation of assumption regarding the known variances. Thus, it is advisable to compare estimated sample sizes under various specifications of indices and to evaluate its potential bias, as illustrated in the example.

Correlates and predictors of colorectal cancer screening among male automotive workers.

PubMed

McQueen, Amy; Vernon, Sally W; Myers, Ronald E; Watts, Beatty G; Lee, Eun Sul; Tilley, Barbara C

2007-03-01

Most studies examining factors associated with colorectal cancer (CRC) screening (CRCS) are cross-sectional and thus temporal relationships cannot be determined. Furthermore, less attention has been paid to psychosocial predictors of CRCS. We examined both cross-sectional correlates of prior CRCS and predictors of prospective CRCS initiation and maintenance during The Next Step Trial, a 2-year worksite behavioral intervention to promote regular CRCS and dietary change. The sample included 2,693 White male automotive workers at increased occupational risk for, but no history of, CRC who completed a baseline survey. Stratified analyses were conducted for three dependent variables (prior CRCS, CRCS initiation, and CRCS maintenance). We also assessed prior CRCS as a moderator in prospective analyses. Multivariable logistic regression analyses with generalized linear mixed models were used to adjust for cluster sampling. Except for education, cross-sectional correlates of prior CRCS including older age, family history of CRC or polyps, personal history of polyps, self-efficacy, family support, and intention were also significant prospective predictors of increased CRCS during the trial. Despite differences in the patterns of association for CRCS initiation and maintenance in stratified analyses, the only associations with prospective CRCS that were significantly moderated by prior CRCS were family history and CRCS availability. Correlates of prior CRCS that also were prospective predictors of CRCS may be suitable targets for intervention. Additionally, intervention messages addressing psychosocial constructs may be relevant for both CRCS initiation and maintenance. However, studies with more diverse samples are needed to replicate the results reported here.

Psychosocial variables and time to injury onset: a hurdle regression analysis model.

PubMed

Sibold, Jeremy; Zizzi, Samuel

2012-01-01

Psychological variables have been shown to be related to athletic injury and time missed from participation in sport. We are unaware of any empirical examination of the influence of psychological variables on time to onset of injury. To examine the influence of orthopaedic and psychosocial variables on time to injury in college athletes. One hundred seventy-seven (men 5 116, women 5 61; age 5 19.45 6 1.39 years) National Collegiate Athletic Association Division II athletes. Hurdle regression analysis (HRA) was used to determine the influence of predictor variables on days to first injury. Worry (z = 2.98, P = .003), concentration disruption (z = -3.95, P < .001), and negative life-event stress (z = 5.02, P < .001) were robust predictors of days to injury. Orthopaedic risk score was not a predictor (z = 1.28, P = .20). These findings support previous research on the stress-injury relationship, and our group is the first to use HRA in athletic injury data. These data support the addition of psychological screening as part of preseason health examinations for collegiate athletes.

Overall Memory Impairment Identification with Mathematical Modeling of the CVLT-II Learning Curve in Multiple Sclerosis

PubMed Central

Stepanov, Igor I.; Abramson, Charles I.; Hoogs, Marietta; Benedict, Ralph H. B.

2012-01-01

The CVLT-II provides standardized scores for each of the List A five learning trials, so that the clinician can compare the patient's raw trials 1–5 scores with standardized ones. However, frequently, a patient's raw scores fluctuate making a proper interpretation difficult. The CVLT-II does not offer any other methods for classifying a patient's learning and memory status on the background of the learning curve. The main objective of this research is to illustrate that discriminant analysis provides an accurate assessment of the learning curve, if suitable predictor variables are selected. Normal controls were ninety-eight healthy volunteers (78 females and 20 males). A group of MS patients included 365 patients (266 females and 99 males) with clinically defined multiple sclerosis. We show that the best predictor variables are coefficients B3 and B4 of our mathematical model B3 ∗ exp(−B2  ∗  (X − 1)) + B4  ∗  (1 − exp(−B2  ∗  (X − 1))) because discriminant functions, calculated separately for B3 and B4, allow nearly 100% correct classification. These predictors allow identification of separate impairment of readiness to learn or ability to learn, or both. PMID:22745911

An examination of hospital satisfaction with blood suppliers.

PubMed

Carden, Robert; DelliFraine, Jami L

2004-11-01

The purpose of this study was to identify factors that predict overall hospital satisfaction with blood suppliers. The data for this study came from a 2001 satisfaction survey of hospital blood bank managers conducted by the National Blood Data Resource Center. A total of 1325 blood-utilizing hospitals were included in the final study database. The measurement of hospital satisfaction with its blood supplier encompasses the five composites of the SERVQUAL model. The five composites are 1) tangibles, 2) reliability, 3) responsiveness, 4) assurance, and 5) empathy. Linear regression was performed with overall hospital satisfaction as the dependent variable and the five composites of the SERVQUAL model and control variables as predictors of overall hospital satisfaction with blood suppliers. Significant predictors of hospital satisfaction with blood suppliers are satisfaction with medical and clinical support provided by the blood center, satisfaction with the routine delivery schedule, and price (service fee) of red cells. Prior studies have demonstrated the importance of customer satisfaction to organizations. As organizations, blood centers can benefit from improved satisfaction from their hospital customers. Blood center strategies that focus on improving these three predictors of overall hospital satisfaction with primary blood suppliers will be the most likely to improve and/or maintain hospital customer satisfaction with primary blood suppliers.

Psychosocial correlates of fruit and vegetable consumption among African American men.

PubMed

Moser, Richard P; Green, Valerie; Weber, Deanne; Doyle, Colleen

2005-01-01

To determine the best predictors of fruit and vegetable consumption among African American men age 35 years and older. Data (n = 291) from a 2001 nationally representative mail survey commissioned by the American Cancer Society. 291 African American men age 35 years and older. (1) total fruits and vegetables without fried potatoes, (2) total fruit with juice, and (3) total vegetables without fried potatoes. Independent variables included 3 blocks of predictors: (1) demographics, (2) a set of psychosocial scales, and (3) intent to change variables based on a theoretical algorithm. Linear regression models; analysis of variance for the intent to change group. Alpha = .05. Regression model for total fruits and vegetables, significant psychosocial predictors: social norms, benefits, tangible rewards, and barriers-other. Total fruit with juice: social norms, benefits, tangible rewards. Total vegetables, no fried potatoes: tangible rewards, barriers-other interests. For African American men, fruit consumption appears to be motivated by perceived benefits and standards set by important people in their lives; vegetable consumption is a function of extrinsic rewards and preferences for high-calorie, fatty foods. The results suggest that communications to increase fruit and vegetable consumption should be crafted to reflect differences in sources of motivation for eating fruits versus eating vegetables.

Overall Memory Impairment Identification with Mathematical Modeling of the CVLT-II Learning Curve in Multiple Sclerosis.

PubMed

Stepanov, Igor I; Abramson, Charles I; Hoogs, Marietta; Benedict, Ralph H B

2012-01-01

The CVLT-II provides standardized scores for each of the List A five learning trials, so that the clinician can compare the patient's raw trials 1-5 scores with standardized ones. However, frequently, a patient's raw scores fluctuate making a proper interpretation difficult. The CVLT-II does not offer any other methods for classifying a patient's learning and memory status on the background of the learning curve. The main objective of this research is to illustrate that discriminant analysis provides an accurate assessment of the learning curve, if suitable predictor variables are selected. Normal controls were ninety-eight healthy volunteers (78 females and 20 males). A group of MS patients included 365 patients (266 females and 99 males) with clinically defined multiple sclerosis. We show that the best predictor variables are coefficients B3 and B4 of our mathematical model B3 ∗ exp(-B2  ∗  (X - 1)) + B4  ∗  (1 - exp(-B2  ∗  (X - 1))) because discriminant functions, calculated separately for B3 and B4, allow nearly 100% correct classification. These predictors allow identification of separate impairment of readiness to learn or ability to learn, or both.