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Using kernel density estimation to understand the influence of neighbourhood destinations on BMI

OBJECTIVES: Little is known about how the distribution of destinations in the local neighbourhood is related to body mass index (BMI). Kernel density estimation (KDE) is a spatial analysis technique that accounts for the location of features relative to each other. Using KDE, this study investigated...

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Autores principales: King, Tania L, Bentley, Rebecca J, Thornton, Lukar E, Kavanagh, Anne M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4762106/
https://www.ncbi.nlm.nih.gov/pubmed/26883235
http://dx.doi.org/10.1136/bmjopen-2015-008878
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author King, Tania L
Bentley, Rebecca J
Thornton, Lukar E
Kavanagh, Anne M
author_facet King, Tania L
Bentley, Rebecca J
Thornton, Lukar E
Kavanagh, Anne M
author_sort King, Tania L
collection PubMed
description OBJECTIVES: Little is known about how the distribution of destinations in the local neighbourhood is related to body mass index (BMI). Kernel density estimation (KDE) is a spatial analysis technique that accounts for the location of features relative to each other. Using KDE, this study investigated whether individuals living near destinations (shops and service facilities) that are more intensely distributed rather than dispersed, have lower BMIs. STUDY DESIGN AND SETTING: A cross-sectional study of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia. METHODS: Destinations were geocoded, and kernel density estimates of destination intensity were created using kernels of 400, 800 and 1200 m. Using multilevel linear regression, the association between destination intensity (classified in quintiles Q1(least)–Q5(most)) and BMI was estimated in models that adjusted for the following confounders: age, sex, country of birth, education, dominant household occupation, household type, disability/injury and area disadvantage. Separate models included a physical activity variable. RESULTS: For kernels of 800 and 1200 m, there was an inverse relationship between BMI and more intensely distributed destinations (compared to areas with least destination intensity). Effects were significant at 1200 m: Q4, β −0.86, 95% CI −1.58 to −0.13, p=0.022; Q5, β −1.03 95% CI −1.65 to −0.41, p=0.001. Inclusion of physical activity in the models attenuated effects, although effects remained marginally significant for Q5 at 1200 m: β −0.77 95% CI −1.52, −0.02, p=0.045. CONCLUSIONS: This study conducted within urban Melbourne, Australia, found that participants living in areas of greater destination intensity within 1200 m of home had lower BMIs. Effects were partly explained by physical activity. The results suggest that increasing the intensity of destination distribution could reduce BMI levels by encouraging higher levels of physical activity.
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spelling pubmed-47621062016-02-25 Using kernel density estimation to understand the influence of neighbourhood destinations on BMI King, Tania L Bentley, Rebecca J Thornton, Lukar E Kavanagh, Anne M BMJ Open Public Health OBJECTIVES: Little is known about how the distribution of destinations in the local neighbourhood is related to body mass index (BMI). Kernel density estimation (KDE) is a spatial analysis technique that accounts for the location of features relative to each other. Using KDE, this study investigated whether individuals living near destinations (shops and service facilities) that are more intensely distributed rather than dispersed, have lower BMIs. STUDY DESIGN AND SETTING: A cross-sectional study of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia. METHODS: Destinations were geocoded, and kernel density estimates of destination intensity were created using kernels of 400, 800 and 1200 m. Using multilevel linear regression, the association between destination intensity (classified in quintiles Q1(least)–Q5(most)) and BMI was estimated in models that adjusted for the following confounders: age, sex, country of birth, education, dominant household occupation, household type, disability/injury and area disadvantage. Separate models included a physical activity variable. RESULTS: For kernels of 800 and 1200 m, there was an inverse relationship between BMI and more intensely distributed destinations (compared to areas with least destination intensity). Effects were significant at 1200 m: Q4, β −0.86, 95% CI −1.58 to −0.13, p=0.022; Q5, β −1.03 95% CI −1.65 to −0.41, p=0.001. Inclusion of physical activity in the models attenuated effects, although effects remained marginally significant for Q5 at 1200 m: β −0.77 95% CI −1.52, −0.02, p=0.045. CONCLUSIONS: This study conducted within urban Melbourne, Australia, found that participants living in areas of greater destination intensity within 1200 m of home had lower BMIs. Effects were partly explained by physical activity. The results suggest that increasing the intensity of destination distribution could reduce BMI levels by encouraging higher levels of physical activity. BMJ Publishing Group 2016-02-16 /pmc/articles/PMC4762106/ /pubmed/26883235 http://dx.doi.org/10.1136/bmjopen-2015-008878 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/ This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
spellingShingle Public Health
King, Tania L
Bentley, Rebecca J
Thornton, Lukar E
Kavanagh, Anne M
Using kernel density estimation to understand the influence of neighbourhood destinations on BMI
title Using kernel density estimation to understand the influence of neighbourhood destinations on BMI
title_full Using kernel density estimation to understand the influence of neighbourhood destinations on BMI
title_fullStr Using kernel density estimation to understand the influence of neighbourhood destinations on BMI
title_full_unstemmed Using kernel density estimation to understand the influence of neighbourhood destinations on BMI
title_short Using kernel density estimation to understand the influence of neighbourhood destinations on BMI
title_sort using kernel density estimation to understand the influence of neighbourhood destinations on bmi
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4762106/
https://www.ncbi.nlm.nih.gov/pubmed/26883235
http://dx.doi.org/10.1136/bmjopen-2015-008878
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