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Abstract
Objective
This study aimed to assess the effects of urban physical environment on individual
obesity using geographically aggregated health behavior surveillance data applying
a
geo-imputation method.
Introduction
‘Where we live’ affects ‘How we live’. Information about
‘how one lives’ collected from the public health surveillance data
such as the Behavioral Risk Factor Surveillance System (BRFSS). Neighborhood
environment surrounding individuals affects their health behavior or health status
are influenced as well as their own traits. Meanwhile, geographical information of
subjects recruited in the health behavior surveillance data is usually aggregated
at
administrative levels such as a county. Even if we do not know accurate addresses
of
individuals, we can allocate them to the random locations where is analogous to
their real home within a locality using a geo-imputation method. In this study, we
assess the association between obesity and built environment by applying random
property allocation (1).
Methods
Data from the Korean Community Health Survey (KCHS), which is the nationwide
community-based cross-sectional survey conducted by 253 community health centers in
South Korea, were used (2). More than 90000 subjects recruited in the capital city
Seoul from 2011 to 2014. They were selected by two-step stratified random sampling
(424 administrative communities with an average area of 1.16km2 and two
house types) in each 25 counties. We re-allocated them randomly on the nested
locality based on their community (administrative boundaries) and hose type
(land-use) using GIS program (Figure 1). Surrounding built environment elements such
as fast-food markets, driving roads, public transit and road-crosse were measured
within 500m buffer from randomly allocated locations as density or distance.
Variables associating obesity are measured by: 1) self-reported obesity
(self-reported body mass index(BMI) ≥ 25) (Figure 2), 2) perceived obesity,
3) intention to weight control. We implemented logistic regression models to
estimate the effect of physical environmental factors on obesity.
Results
The person who lives in a detached house, nearer fast food markets or with higher
driving road density was more likely to be obese. Those who live in a detached house
was less perceived their obesity. Those who live in a detached house, nearer fast
food markets or with higher driving road density was less likely to intend to
control their body weights. An association between intention to weight control and
accessibility to subway station showed marginal effect.
Conclusions
Urban environments influenced individual’s obesity, perception, and intention
to weight loss. Since we used cross-sectional survey data, we do not account
cumulative environmental influence. Moreover, individuals’ self-selection of
more healthier places were not accounted. Even though we did not measure the
environment at individuals’ real address, we can measure the effects of
neighborhood environment more efficiently by using random property allocation.
Figure 1
Land use map (up) and result of random allocation (down) in Daehakcong,
Gwanakgu, Seoul.png
Distribution of obese population in Seoul, South Korea
ISDS Annual Conference Proceedings 2018. This is an Open Access
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Attribution-Noncommercial 3.0 Unported License (
http://creativecommons.org/licenses/by-nc/3.0/), permitting all
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original work is properly cited.