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Estimating neighbourhood-level prevalence of adult obesity by socio-economic, behavioural and built environment factors in New York City

Yeran Sun, S. Wang, X. Sun

Public Health, Volume: 186, Pages: 57 - 62

Swansea University Author: Yeran Sun

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DOI (Published version): 10.1016/j.puhe.2020.05.003

Abstract

ObjectivesSome studies reveal that socio-economic status, behavioural factors, accessibility to supermarket or food store, are associated with the prevalence of obesity and overweight. In this study, we aimed to examine to what extent socio-economic, behavioural and built environment characteristics...

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Published in: Public Health
ISSN: 0033-3506
Published: Elsevier BV 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa56128
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2021-02-12T14:21:19.2493991</datestamp><bib-version>v2</bib-version><id>56128</id><entry>2021-01-25</entry><title>Estimating neighbourhood-level prevalence of adult obesity by socio-economic, behavioural and built environment factors in New York City</title><swanseaauthors><author><sid>10382520ce790248e1be61a6a9003717</sid><firstname>Yeran</firstname><surname>Sun</surname><name>Yeran Sun</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2021-01-25</date><abstract>ObjectivesSome studies reveal that socio-economic status, behavioural factors, accessibility to supermarket or food store, are associated with the prevalence of obesity and overweight. In this study, we aimed to examine to what extent socio-economic, behavioural and built environment characteristics can contribute to spatial disparities in adult obesity.Study designThe spatial analysis was undertaken to understand the association of spatial disparities in adult obesity and spatial disparities in socio-economic, behavioural and built environment characteristics.MethodsA spatial regression model which can remove the impact of auto-correlation in the residuals of conventionally regression models was applied to modelling local-scale rate of adult obesity (N = 59).ResultsOwing to the presence of residual spatial auto-correlation in the non-spatial regression model estimated, a spatial regression model was set up successfully to model local-scale rate of adult obesity across New York City (R2 = 0.8353, N = 59). Compared with socio-economic and built environment factors, behavioural factors make statistically significant contributions to spatial disparities in the prevalence of adult obesity (POAO). Particularly, two behavioural factors (&#x2018;sugary drinks consumption&#x2019; and &#x2018;fruits and vegetable consumption&#x2019;) can explain more than 70% of the variance of POAO (adjusted R2 = 0.7323, N = 59). Surprisingly, physical activity prevalence (percent of physically active adults) makes no statistically significant contributions.ConclusionsThe results further suggest that the reduction of adult obesity prevalence could benefit more from decreasing intake of sugary drinks than increasing physical activity. The local government and policy are advised to prioritise decreasing exposure of residents to sugary drinks through restricting advertising or increasing taxes rather than increasing neighbourhoods&#x2019; walkability through urban planning.</abstract><type>Journal Article</type><journal>Public Health</journal><volume>186</volume><journalNumber/><paginationStart>57</paginationStart><paginationEnd>62</paginationEnd><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0033-3506</issnPrint><issnElectronic/><keywords>Adult obesity; Spatial regression model; Behavioural factors; Socio-economic factors; Built environment</keywords><publishedDay>1</publishedDay><publishedMonth>9</publishedMonth><publishedYear>2020</publishedYear><publishedDate>2020-09-01</publishedDate><doi>10.1016/j.puhe.2020.05.003</doi><url/><notes/><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><apcterm/><lastEdited>2021-02-12T14:21:19.2493991</lastEdited><Created>2021-01-25T14:07:43.0469551</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Biosciences, Geography and Physics - Geography</level></path><authors><author><firstname>Yeran</firstname><surname>Sun</surname><order>1</order></author><author><firstname>S.</firstname><surname>Wang</surname><order>2</order></author><author><firstname>X.</firstname><surname>Sun</surname><order>3</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling 2021-02-12T14:21:19.2493991 v2 56128 2021-01-25 Estimating neighbourhood-level prevalence of adult obesity by socio-economic, behavioural and built environment factors in New York City 10382520ce790248e1be61a6a9003717 Yeran Sun Yeran Sun true false 2021-01-25 ObjectivesSome studies reveal that socio-economic status, behavioural factors, accessibility to supermarket or food store, are associated with the prevalence of obesity and overweight. In this study, we aimed to examine to what extent socio-economic, behavioural and built environment characteristics can contribute to spatial disparities in adult obesity.Study designThe spatial analysis was undertaken to understand the association of spatial disparities in adult obesity and spatial disparities in socio-economic, behavioural and built environment characteristics.MethodsA spatial regression model which can remove the impact of auto-correlation in the residuals of conventionally regression models was applied to modelling local-scale rate of adult obesity (N = 59).ResultsOwing to the presence of residual spatial auto-correlation in the non-spatial regression model estimated, a spatial regression model was set up successfully to model local-scale rate of adult obesity across New York City (R2 = 0.8353, N = 59). Compared with socio-economic and built environment factors, behavioural factors make statistically significant contributions to spatial disparities in the prevalence of adult obesity (POAO). Particularly, two behavioural factors (‘sugary drinks consumption’ and ‘fruits and vegetable consumption’) can explain more than 70% of the variance of POAO (adjusted R2 = 0.7323, N = 59). Surprisingly, physical activity prevalence (percent of physically active adults) makes no statistically significant contributions.ConclusionsThe results further suggest that the reduction of adult obesity prevalence could benefit more from decreasing intake of sugary drinks than increasing physical activity. The local government and policy are advised to prioritise decreasing exposure of residents to sugary drinks through restricting advertising or increasing taxes rather than increasing neighbourhoods’ walkability through urban planning. Journal Article Public Health 186 57 62 Elsevier BV 0033-3506 Adult obesity; Spatial regression model; Behavioural factors; Socio-economic factors; Built environment 1 9 2020 2020-09-01 10.1016/j.puhe.2020.05.003 COLLEGE NANME COLLEGE CODE Swansea University 2021-02-12T14:21:19.2493991 2021-01-25T14:07:43.0469551 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography Yeran Sun 1 S. Wang 2 X. Sun 3
title Estimating neighbourhood-level prevalence of adult obesity by socio-economic, behavioural and built environment factors in New York City
spellingShingle Estimating neighbourhood-level prevalence of adult obesity by socio-economic, behavioural and built environment factors in New York City
Yeran Sun
title_short Estimating neighbourhood-level prevalence of adult obesity by socio-economic, behavioural and built environment factors in New York City
title_full Estimating neighbourhood-level prevalence of adult obesity by socio-economic, behavioural and built environment factors in New York City
title_fullStr Estimating neighbourhood-level prevalence of adult obesity by socio-economic, behavioural and built environment factors in New York City
title_full_unstemmed Estimating neighbourhood-level prevalence of adult obesity by socio-economic, behavioural and built environment factors in New York City
title_sort Estimating neighbourhood-level prevalence of adult obesity by socio-economic, behavioural and built environment factors in New York City
author_id_str_mv 10382520ce790248e1be61a6a9003717
author_id_fullname_str_mv 10382520ce790248e1be61a6a9003717_***_Yeran Sun
author Yeran Sun
author2 Yeran Sun
S. Wang
X. Sun
format Journal article
container_title Public Health
container_volume 186
container_start_page 57
publishDate 2020
institution Swansea University
issn 0033-3506
doi_str_mv 10.1016/j.puhe.2020.05.003
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Geography{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Geography
document_store_str 0
active_str 0
description ObjectivesSome studies reveal that socio-economic status, behavioural factors, accessibility to supermarket or food store, are associated with the prevalence of obesity and overweight. In this study, we aimed to examine to what extent socio-economic, behavioural and built environment characteristics can contribute to spatial disparities in adult obesity.Study designThe spatial analysis was undertaken to understand the association of spatial disparities in adult obesity and spatial disparities in socio-economic, behavioural and built environment characteristics.MethodsA spatial regression model which can remove the impact of auto-correlation in the residuals of conventionally regression models was applied to modelling local-scale rate of adult obesity (N = 59).ResultsOwing to the presence of residual spatial auto-correlation in the non-spatial regression model estimated, a spatial regression model was set up successfully to model local-scale rate of adult obesity across New York City (R2 = 0.8353, N = 59). Compared with socio-economic and built environment factors, behavioural factors make statistically significant contributions to spatial disparities in the prevalence of adult obesity (POAO). Particularly, two behavioural factors (‘sugary drinks consumption’ and ‘fruits and vegetable consumption’) can explain more than 70% of the variance of POAO (adjusted R2 = 0.7323, N = 59). Surprisingly, physical activity prevalence (percent of physically active adults) makes no statistically significant contributions.ConclusionsThe results further suggest that the reduction of adult obesity prevalence could benefit more from decreasing intake of sugary drinks than increasing physical activity. The local government and policy are advised to prioritise decreasing exposure of residents to sugary drinks through restricting advertising or increasing taxes rather than increasing neighbourhoods’ walkability through urban planning.
published_date 2020-09-01T04:10:51Z
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score 11.036706

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