Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight
American Journal of Theoretical and Applied Statistics
Volume 8, Issue 1, January 2019, Pages: 18-25
Received: Jan. 19, 2019;
Accepted: Feb. 20, 2019;
Published: Mar. 6, 2019
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Noora Shrestha, Department of Mathematics and Statistics, P. K. Multiple Campus, Tribhuvan University, Kathmandu, Nepal
This study attempts to assess the likelihood of overweight and associated factors among the young students by analyzing their physical measurements and physical activity index. This paper has classified four hundred and fifteen subjects and precisely estimated the likelihood of outcome overweight by combining body mass index and CUN-BAE calculated. Multicollinearity is tested with multiple regression analysis. Box-Tidwell Test is used to check the linearity of the continuous independent variables and their logit (log odds). The binary regression analysis was executed to determine the influences of gender, physical activity index, and physical measurements on the likelihood that the subjects fall in overweight category. The sensitivity and specificity described by the model are 55.9% and 96.9% respectively. The increase in the value of waist to height ratio and neck circumference and drop in physical activity index are associated with the increased likelihood of subjects falling to overweight group. The prevalence of overweight is higher (27.8%) in female than in male (14.7%) subjects. The odds ratio for gender reveals that the likelihood of subjects falling to overweight category is 2.6 times higher in female compared to male subjects.
Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight, American Journal of Theoretical and Applied Statistics.
Vol. 8, No. 1,
2019, pp. 18-25.
Popkin, B. (2009). The world is fat: the fads, trends, policies, and products that are fattening the human race. USA: Penguin Group.
WHO. (2013). Non-communicable disease risk factor survey Nepal 2013. Kathmandu: Nepal Health Research Council and World Health Organization.
Ministry of Health, New ERA, and ICF. (2017). Nepal demographic and health survey 2016. Nepal: Ministry of Health.
Lahav, Y., Yoram, E., Ron, K., and Haggai, S. (2018). A novel body circumferences based estimation of percentage body fat. British Journal of Nutrition, 119(6), 720-725.
WHO. (2017). WHO country cooperation strategy Nepal 2013-2017. India: World Health Organization.
Hu, F. (2008). Measurements of adiposity and body composition. In: Hu F, ed. Obesity Epidemiology, 53–83. New York: Oxford University Press.
WHO. (2000). Obesity: preventing and managing the global epidemic, Geneva: World Health Organization.
WHO. (2008). Waist circumference and waist-hip ratio: Report of a WHO expert consultation. Switzerland: World Health Organization.
Lancet. (2004). Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. WHO expert consultation Public Health, 363(9403), 157-163.
Hung, S. P., Chen, C. Y., Guo, F. R., Chang, C. I., and Jan, C. F. (2017). Combine body mass index and body fat percentage measures to improve the accuracy of obesity screening in young adults. Obesity Research and Clinical Practice, 11(1), 11-18.
Berrington, G. A., Hartge, P., Cerhan, J. R., et al. (2010). Body mass index and mortality among 1.46 million white adults. New England Journal of Medicine, 363: 2211-2219.
World Health Organization. (2018). What is Moderate-intensity and Vigorous-intensity Physical Activity? Available at www.who.int/dietphysicalactivity/physical_activity_intensity/en/.
Javier, G. A., Victor, V., et al. (2012). Clinical usefulness of a new equation for estimating body fat. Diabetes Care, 35: 383-388.
Swainson, M. G., Batterham, A. M., Tsakirides, C., Rutherford, Z. H., and Hind, K. (2017). Prediction of whole-body fat percentage and visceral adipose tissue mass from five anthropometric variables. PLoS ONE, 12(5): e0177175.
Ho-Pham, L. T., Campbell, L. V., and Nguyen, T. V. (2011). More on body fat cutoff points. Mayo Clinic Proceedings, 86(6), 584. http://doi.org/10.4065/mcp.2011.0097.
Li, Y., Wang, H., Wang, K., Wang, W., Dong, F., Qian, Y., … Shan, G. (2017). Optimal body fat percentage cut-off values for identifying cardiovascular risk factors in Mongolian and Han adults: a population-based cross-sectional study in Inner Mongolia, China. BMJ Open, 7(4), e014675.
Belinda, B. and Peat, J. (2014). Medical statistics: a guide to SPSS, data analysis, and critical appraisal (2nd ed.). UK: Wiley.
Sylvia, W. S. (2004). Biostatistics and Epidemiology: a primer for health and biomedical professional (3rd ed.). USA: Springer.
Kleinbaum, G. D., and Klein, M. (2010). Logistic regression: A self learning text (3rd ed.). New York: Springer.
Valeria, F. and Eduard, B. (2017). Features selection using LASSO. Research paper in Business Analytics, VU: Amsterdam.
Hosmer, D. W., Hosmer, T., Le Cessie, S., and Lemeshow, S. (1997). A comparison of goodness of fit test for the logistic regression model, Statistics in Medicine, 16, 965-980.
Cox, D. R., and Snell, E. J. (1989). Analysis of binary data (2nd ed.). London: Chapman and Hall.
Hosmer, D. W. and Lemeshow, S. (2000). Applied logistic regression (2nd ed.). New York: John Wiley & Sons.
Elliot, A. C, and Woodward, W. W. (2007). Statistical analysis: Quick reference guidebook with SPSS examples. USA: Sage Publications.