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Study of effect on the obesity status using multilevel logistic regression analysis
Journal of the Korean Data & Information Science Society 2019;30:205-17
Published online January 31, 2019;  https://doi.org/10.7465/jkdi.2019.30.1.205
© 2019 Korean Data and Information Science Society.

Il-Su Park1 · Jun-Tae Han2

1Department of Health Management, Uiduk University
2Department of Government Grant, Korea Student Aid Foundation
Correspondence to: Team Manager, Government Grant Management Team, Department of Government Grant, Korea Student Aid Foundation, 125 Sinam-ro, dong-gu, Daegu, 41200, Korea. E-mail: hanjt@kosaf.go.kr
Received December 30, 2018; Revised January 11, 2019; Accepted January 11, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
This study was carried out to investigate the influence of various characteristics of individual and regional level on the obesity of individual. The data used for the analysis are Community Health Survey produced by Korea Centers for Disease Control and Prevention in 2016, as well as a large sample survey conducted by government agencies. We analyzed the factors affecting obesity by multiple correspondence analysis and multi-level logistic regression model according to overall and gender. As a result, it was found that a goodness of fit of model improved as the factors of each level were considered in the analysis model, and it was confirmed that both individual and regional levels were important factors for the individual’s obesity. The overall model has a high risk of obesity, such as men, low education, service and sales people, poor health status, attempt to control weight, subjective stress recognition, high local government financial independence, high crude divorce rates, so and on. However, gender-based models differed in the level of risk between factors influencing obesity by age, education, health screening, and depression experience. This study was to identify the reliable risk factors of obesity by various characteristics of individuals and various environmental factors using large data on national scale.
Keywords : Community health survey, multi-level logistic regression, obesity.