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KMID : 0904520140360010157
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Health and Medical Sociology
2014 Volume.36 No. 1 p.157 ~ p.181
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Analysis of Factors associated with Geographic Variations in the Prevalence of Adult Obesity using Decision Tree
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Kim Yoo-Mi
Cho Dae-Gon
Kang Sung-Hong
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Abstract
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This study examines how health behavior types, comorbid diseases and socioeconomic factors affect the prevalence of adult obesity. To analyze, we first construct a rich and combined data set including Annual Community Health Survey, a Census on Population and Housing, Regional Statistics on Medical Use, and various socioeconomic regional attributes at the level of 247 small administrative district in South Korea in the period from 2009-2011. We then use empirical methods (regression analysis and decision tree analysis) of estimating common- and region-specific factors that would give an influence on the adult obesity rate. Our results from the stepwise regression model suggest that the adult obesity rate is positively correlated with high-risk drinking rate, the ratio of depression, current smoking rate, hypertension prevalence, diabetes cure rate and low walking practice rate, as we anticipated. Also, our results from the decision tree model support our findings from the regression analysis, and they suggest that additional variables, such as diabetes diagnosis and cure rates, the education level and the ratio of marital status, may also be significant attributes that are associated with the prevalence of adult obesity. As a result, our study classifies 21 different types of geographic variations of the adult obesity, on the basis of important health-related and socioeconomic factors. Our study may shed light on which attributes are more closely related to the obesity rate and, indeed, suggest that there would be sufficient variations across regions, at least in our sample. Thus, in order to decrease the obesity problems, our study emphasizes the necessity to implement customized health policies according to the region-specific characteristics.
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KEYWORD
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Obesity, Geographic Variation, Annual Community Health Survey, Decision Tree, Ecological Study
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