Predicting U.S. Adolescent Fruit and Vegetable Consumption: Which Socio-ecological Factors Matter?

2020 ◽  
Vol 31 (4) ◽  
Author(s):  
Mary Odum ◽  
Jeff Housman ◽  
Ronald Williams ◽  
James Bishop ◽  
Sheri Burson
Keyword(s):  
Regression Models ◽  
Food Preference ◽  
Vegetable Consumption ◽  
Ecological Factors ◽  
Binary Logistic Regression ◽  
Fruit And Vegetable ◽  
Logistic Regression Models ◽  
Individual Level ◽  
Nationally Representative ◽  
Environmental Level

Multiple socio-ecological levels of influences are acknowledged for adolescent fruit and vegetable (FV) consumption, but consensus is lacking regarding the strength of predictors. Binary logistic regression models were used to determine the relative influence of individual-, interpersonal-, and environmental-level variables on adolescent FV consumption among a nationally representative sample (n=1737). Individual-level factors were the strongest predictors: self-efficacy was strongest for fruit (Wald=17.702; p<0.001; OR=1.310) and vegetable consumption (Wald=25.267; p<.001; OR=1.381), while food preference was strongest for green salad consumption (Wald=80.362; p<.001; OR=1.590). Interpersonal and environmental-level factors, while still influential, had a smaller impact on adolescent FV consumption.

Fair-Wages for People With Disabilities: Barriers and Facilitators

2020 ◽  
Vol 31 (3) ◽  
pp. 152-163
Author(s):  
Carli Friedman ◽  
Mary C. Rizzolo
Keyword(s):  
Regression Models ◽  
People With Disabilities ◽  
Binary Logistic Regression ◽  
Service Organizations ◽  
Organizational Level ◽  
Logistic Regression Models ◽  
Individual Level ◽  
Problematic Issue ◽  
Fair Wages ◽  
The Individual

Subminimum wage is a prominent and problematic issue affecting the lives of many people with disabilities. For this reason, the aim of this study was to identify the correlates of fair-wages (at least minimum wage) for people with disabilities—which factors facilitate and hinder people with disabilities’ access to fair-wages. We utilized Personal Outcome Measures® interview data from approximately 1,500 people with disabilities to examine how individual, employment, and organizational-level factors correlate with people with disabilities’ access to fair-wages. Binary logistic regression models revealed at the individual-level support needs, guardianship, and residence type all significantly correlate with people with disabilities’ odds of receiving fair-wages. In addition, the ability to experience a number of employment options, as well as decide where to work, produce higher odds of having fair-wages. Finally, our findings also revealed the key role service organizations can play in facilitating people with disabilities’ access to fair-wages. Attention to the facilitators that promote access to fair-wages for people with disabilities, and the barriers that hinder this access is one of the first steps toward ending this discrimination against people with disabilities.

scholarly journals Sex Differences in the Association between Internet Usage and Overweight/Obesity: Evidence from a Nationally Representative Survey in Nepal

Sexes ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 132-143
Author(s):  
Juwel Rana ◽  
Md. Momin Islam ◽  
John Oldroyd ◽  
Nandeeta Samad ◽  
Rakibul Islam
Keyword(s):  
Odds Ratio ◽  
Regression Models ◽  
Internet Use ◽  
Adjusted Odds Ratio ◽  
Internet Usage ◽  
The Internet ◽  
Logistic Regression Models ◽  
Biological Sex ◽  
Obesity Interventions ◽  
Nationally Representative

Objective: To examine the associations between internet use and overweight/obesity in people aged 15–49 years in Nepal and the extent to which these associations differ by biological sex. Materials and methods: The study analyzed the nationally representative Nepal Demographic and Health Survey (NDHS) 2016 data. Multivariable ordinal logistic regression models were fitted to estimate the total effects of internet use (IU) in the last 12 months and frequency of internet use (FIU) in the last month on overweight/obesity adjusted for potential confounders. Results: Of the 10,380 participants, the prevalence of overweight/obesity by IU was 38% (95% confidence interval (CI): 35.9%, 40.1%) for males and 44.1% (95% CI: 41.6%, 46.6%) for female. The likelihood of overweight/obesity was significantly higher (adjusted odds ratio (aOR): 1.55; 95% CI: 1.40, 1.73; p < 0.001) among those participants who used the internet compared to the participants who did not use the internet in the last 12 months. Similar associations were observed when using the augmented measure of exposure-FIU in the last month. We observed the modification effect of sex in the associations of IU (p-difference < 0.001) and FIU (p-difference < 0.002) with overweight/obesity in Nepal. Conclusions: Our findings suggest that future overweight/obesity interventions in Nepal discourage unnecessary internet use, particularly among males.

scholarly journals Can Cut-Off-Values for Tumor Size or Patient Age in Breast Ultrasound Reduce Unnecessary Biopsies or is it all About Bi-rads?– A Retrospective Analysis of 763 Biopsied T1-Sized Lesions

2017 ◽  
Vol 03 (03) ◽  
pp. E94-E98 ◽  
Author(s):  
Laura Holzer-Fruehwald ◽  
Matthias Meissnitzer ◽  
Michael Weber ◽  
Stephan Holzer ◽  
Klaus Hergan ◽  
...  
Keyword(s):  
Regression Models ◽  
Breast Lesion ◽  
Binary Logistic Regression ◽  
Lesion Size ◽  
Breast Lesions ◽  
Predictive Values ◽  
Logistic Regression Models ◽  
Independent Variables ◽  
Oval Shape ◽  
Histological Result

Abstract Aims and Objectives To assess whether it is possible to establish a size cut-off-value for sonographically visible breast lesions in a screening situation, under which it is justifiable to obviate a biopsy and to evaluate the grayscale characteristics of the identified lesions. Materials and Methods Images of sonographically visible and biopsied breast lesions of 684 patients were retrospectively reviewed and assessed for the following parameters: size, shape, margin, lesion boundary, vascularity, patient’s age, side of breast, histological result, and initial BI-RADS category. Statistical analyses (t-test for independent variables, ROC analyses, binary logistic regression models, cross-tabulations, positive/negative predictive values) were performed using IBM SPSS (Version 21.0). Results Of all 763 biopsied lesions, 223 (29.2%) showed a malignant histologic result, while 540 (70.8%) were benign. Although we did find a statistically significant correlation of malignancy and lesion size (p=0.031), it was not possible to define a cut-off value, under which it would be justifiable to obviate a biopsy in terms of sensitivity and specificity (AUC: 0.558) at any age. Lesions showing the characteristics of a round or oval shape, a sharp delineation and no echogenic rim (n=112) were benign with an NPV of 99.1%. Conclusion It is not possible to define a cut-off value for size or age, under which a biopsy of a sonographically visible breast lesion can be obviated in the screening situation. The combination of the 3 grayscale characteristics, shape (round or oval), margin (circumscribed) and no echogenic-rim sign, showed an NPV of 99.1%. Therefore, it seems appropriate to classify such lesions as BI-RADS 2.

scholarly journals Knowledge and Determinants of Fruit and Vegetable Consumption among Adults in Hohoe Municipality, Ghana

2017 ◽  
Vol 2 (1) ◽  
pp. 1 ◽  
Author(s):  
Thomas K. Awuni ◽  
Gideon Kye-Duodu ◽  
Charles Duodu ◽  
Francis B. Zotor ◽  
Basma Ellahi
Keyword(s):  
Disease Risk ◽  
Vegetable Consumption ◽  
Binary Logistic Regression ◽  
World Health ◽  
Easy Access ◽  
Fruit And Vegetable ◽  
Face To Face ◽  
Home Based ◽  
Risk Factor Surveillance ◽  
Health Organization

<p><em>The World Health Organization (WHO) recommends that a person consumes at least 400g of Fruit and Vegetable (FV) daily to prevent chronic disease risk. We assessed knowledge of current WHO guidelines and other determinants of FV intake among adults (? 18 years, n = 397) in Hohoe Municipality, Ghana. Face-to-face interviews using a questionnaire adopted from </em><em>WHO Risk Factor Surveillance System were undertaken. </em><em>Knowledge of FV daily servings and determinants of intake were evaluated by descriptive statistics and binary logistic regression. There was a 99.2% response rate with approximately 9</em><em>% </em><em>of participants correctly stating the WHO daily recommended amount (P </em><em>=</em><em> </em><em>.</em><em>296</em><em>)</em><em>. Most (54%) of respondents’ FV intake was affected by unavailability of desired choice (</em><em>P </em><em>=</em><em> .050)</em><em>. Odds of inadequate consumption for persons aware of adequate intake amount was 1.97 (95% CI: 0.64, 6.05, P = .234) higher than persons without awareness. Participants with problems accessing their desired choice of FV had 0.59 odds (95% CI: 0.36, 0.95, P = .030) of consuming inadequate amount compared to those with easy access. Adequate FV intake depends on availability of consumer prefered choice regardless of knowledge of recommendations. Individual home based FV cultivation is relevant for availability of preferred choice and adequate consumption for NCDs risk reductions among Ghanaians.</em></p>

scholarly journals Differences in Sickness Allowance Receipt between Swedish Speakers and Finnish Speakers in Finland

2017 ◽  
Vol 52 ◽  
pp. 43-58 ◽  
Author(s):  
Kaarina S. Reini ◽  
Jan Saarela
Keyword(s):  
Regression Models ◽  
Health Condition ◽  
Logistic Regression Models ◽  
Individual Level ◽  
Objective Health ◽  
Language Groups ◽  
Health Measure ◽  
All Cause Mortality ◽  
The Difference ◽  
The Individual

Previous research has documented lower disability retirement and mortality rates of Swedish speakers as compared with Finnish speakers in Finland. This paper is the first to compare the two language groups with regard to the receipt of sickness allowance, which is an objective health measure that reflects a less severe poor health condition. Register-based data covering the years 1988-2011 are used. We estimate logistic regression models with generalized estimating equations to account for repeated observations at the individual level. We find that Swedish-speaking men have approximately 30 percent lower odds of receiving sickness allowance than Finnish-speaking men, whereas the difference in women is about 15 percent. In correspondence with previous research on all-cause mortality at working ages, we find no language-group difference in sickness allowance receipt in the socially most successful subgroup of the population.

Women’s Autonomy and Support for Wife Beating: Findings From a Population-Based Survey in Jordan

2010 ◽  
Vol 25 (3) ◽  
pp. 409-419 ◽  
Author(s):  
Natalia Linos ◽  
Marwan Khawaja ◽  
Mohannad Al-Nsour
Keyword(s):  
Regression Models ◽  
Binary Logistic Regression ◽  
Population Based ◽  
Married Women ◽  
Wife Beating ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Logistic Regression Models ◽  
Hypothetical Scenario ◽  
Population Based Survey

The aim of this study is to examine attitudes among married women toward wife beating and to investigate the hypothesis that female individual empowerment is associated with such attitudes within a broader context of societal patriarchy in Jordan. The study uses data from a cross-sectional survey of a representative sample of married women (n = 5,390) conducted in 2002. Associations between acceptance of wife beating and several women’s empowerment variables, including decision-making power, as well as other risk factors were assessed, using odds ratios from binary logistic regression models. The key finding is that the vast majority (87.5%) of Jordanian women believe that wife beating is justified in at least one hypothetical scenario, and justification is negatively associated with empowerment variables and some demographic, geographic, and socioeconomic factors.

scholarly journals Application of Logistic Regression Models for the Marketability of Cucumber Cultivars

Agronomy ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 17 ◽  
Author(s):  
Manuel Díaz-Pérez ◽  
Ángel Carreño-Ortega ◽  
José-Antonio Salinas-Andújar ◽  
Ángel-Jesús Callejón-Ferre
Keyword(s):  
Weight Loss ◽  
Logistic Regression ◽  
Shelf Life ◽  
Regression Models ◽  
Chilling Injury ◽  
Binary Logistic Regression ◽  
Fruit Weight ◽  
Binary Logistic Regression Analysis ◽  
Cucumis Sativus L ◽  
Logistic Regression Models

The aim of this study is to establish a binary logistic regression method to evaluate and select cucumber cultivars (Cucumis sativus L.) with a longer postharvest shelf life. Each sample was evaluated for commercial quality (fruit aging, weight loss, wilting, yellowing, chilling injury, and rotting) every 7 days of storage. Simple and multiple binary logistic regression models were applied in which the dependent variable was the probability of marketability and the independent variables were the days of storage, cultivars, fruit weight loss, and months of evaluation. The results showed that cucumber cultivars with a longer shelf life can be selected by a simple and multiple binary logistic regression analysis. Storage time was the main determinant of fruit marketability. Fruit weight loss strongly influenced the probability of marketability. The logistic model allowed us to determine the cucumber weight loss percentage over which a fruit would be rejected in the market.

scholarly journals The sensitivity of modeled snow accumulation and melt to precipitation phase methods across a climatic gradient

2019 ◽  
Vol 23 (9) ◽  
pp. 3765-3786 ◽  
Author(s):  
Keith S. Jennings ◽  
Noah P. Molotch
Keyword(s):  
Logistic Regression ◽  
Air Temperature ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Snow Accumulation ◽  
Phase Method ◽  
Logistic Regression Models ◽  
Temperature Thresholds ◽  
Snow Cover Duration ◽  
Precipitation Phase

Abstract. A critical component of hydrologic modeling in cold and temperate regions is partitioning precipitation into snow and rain, yet little is known about how uncertainty in precipitation phase propagates into variability in simulated snow accumulation and melt. Given the wide variety of methods for distinguishing between snow and rain, it is imperative to evaluate the sensitivity of snowpack model output to precipitation phase determination methods, especially considering the potential of snow-to-rain shifts associated with climate warming to fundamentally change the hydrology of snow-dominated areas. To address these needs we quantified the sensitivity of simulated snow accumulation and melt to rain–snow partitioning methods at sites in the western United States using the SNOWPACK model without the canopy module activated. The methods in this study included different permutations of air, wet bulb and dew point temperature thresholds, air temperature ranges, and binary logistic regression models. Compared to observations of snow depth and snow water equivalent (SWE), the binary logistic regression models produced the lowest mean biases, while high and low air temperature thresholds tended to overpredict and underpredict snow accumulation, respectively. Relative differences between the minimum and maximum annual snowfall fractions predicted by the different methods sometimes exceeded 100 % at elevations less than 2000 m in the Oregon Cascades and California's Sierra Nevada. This led to ranges in annual peak SWE typically greater than 200 mm, exceeding 400 mm in certain years. At the warmer sites, ranges in snowmelt timing predicted by the different methods were generally larger than 2 weeks, while ranges in snow cover duration approached 1 month and greater. Conversely, the three coldest sites in this work were relatively insensitive to the choice of a precipitation phase method, with average ranges in annual snowfall fraction, peak SWE, snowmelt timing, and snow cover duration of less than 18 %, 62 mm, 10 d, and 15 d, respectively. Average ranges in snowmelt rate were typically less than 4 mm d−1 and exhibited a small relationship to seasonal climate. Overall, sites with a greater proportion of precipitation falling at air temperatures between 0 and 4 ∘C exhibited the greatest sensitivity to method selection, suggesting that the identification and use of an optimal precipitation phase method is most important at the warmer fringes of the seasonal snow zone.

scholarly journals Predicting the Criminal Records of Male-on-Female UK Homicide Offenders From Crime Scene Behaviors

2019 ◽  
pp. 088626051988852
Author(s):  
Louise Almond ◽  
Elias Matin ◽  
Michelle McManus
Keyword(s):  
Regression Models ◽  
Statistical Approach ◽  
Binary Logistic Regression ◽  
Crime Scene ◽  
The Other ◽  
Background Characteristics ◽  
Criminal Records ◽  
Logistic Regression Models ◽  
Offender Profiling ◽  
Criminal Histories

Offender profiling follows the idea that if offenders’ crime scene actions can be empirically linked to their background characteristics, it will be possible to predict one from the other. There is a lack of research exploring whether homicide offenders’ crime scene actions are predictive of their criminal histories, despite the potential utility of such information. The current study addresses this gap in the literature. A sample of 213 adult male-on-female homicides with sexual or unknown motive was drawn from a U.K.-wide database. Relationships between 13 preconviction variables and 29 crime scene behaviors were explored using a bivariate statistical approach. Subsequently, binary logistic regression models were used to predict the presence, or absence, of specific preconvictions based on a combination of offense behaviors. Analyses highlighted 16 statistically significant associations between key offense behaviors and previous convictions, these associations were often “less likely” to result in previous conviction. The analysis failed to find any association for various other variables, most notably sexual preconvictions. Results indicate offenders’ criminal histories can be predicted from their offense behaviors, though not all preconvictions may be similarly suited. Implications for practice are discussed.

Sign in / Sign up

Export Citation Format

Share Document