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Which Green Space Metric Best Predicts a Lowered Odds of Type 2 Diabetes?

The choice of a green space metric may affect what relationship is found with health outcomes. In this research, we investigated the relationship between percent green space area, a novel metric developed by us (based on the average contiguous green space area a spatial buffer has contact with), in...

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Autores principales: Mazumdar, Soumya, Chong, Shanley, Astell-Burt, Thomas, Feng, Xiaoqi, Morgan, Geoffrey, Jalaludin, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8068830/
https://www.ncbi.nlm.nih.gov/pubmed/33924462
http://dx.doi.org/10.3390/ijerph18084088
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author Mazumdar, Soumya
Chong, Shanley
Astell-Burt, Thomas
Feng, Xiaoqi
Morgan, Geoffrey
Jalaludin, Bin
author_facet Mazumdar, Soumya
Chong, Shanley
Astell-Burt, Thomas
Feng, Xiaoqi
Morgan, Geoffrey
Jalaludin, Bin
author_sort Mazumdar, Soumya
collection PubMed
description The choice of a green space metric may affect what relationship is found with health outcomes. In this research, we investigated the relationship between percent green space area, a novel metric developed by us (based on the average contiguous green space area a spatial buffer has contact with), in three different types of buffers and type 2 diabetes (T2D). We obtained information about diagnosed T2D and relevant covariates at the individual level from the large and representative 45 and Up Study. Average contiguous green space and the percentage of green space within 500 m, 1 km, and 2 km of circular buffer, line-based road network (LBRN) buffers, and polygon-based road network (PBRN) buffers around participants’ residences were used as proxies for geographic access to green space. Generalized estimating equation regression models were used to determine associations between access to green space and T2D status of individuals. It was found that 30%–40% green space within 500 m LBRN or PBRN buffers, and 2 km PBRN buffers, but not within circular buffers, significantly reduced the risk of T2D. The novel average green space area metric did not appear to be particularly effective at measuring reductions in T2D. This study complements an existing research body on optimal buffers for green space measurement.
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spelling pubmed-80688302021-04-26 Which Green Space Metric Best Predicts a Lowered Odds of Type 2 Diabetes? Mazumdar, Soumya Chong, Shanley Astell-Burt, Thomas Feng, Xiaoqi Morgan, Geoffrey Jalaludin, Bin Int J Environ Res Public Health Article The choice of a green space metric may affect what relationship is found with health outcomes. In this research, we investigated the relationship between percent green space area, a novel metric developed by us (based on the average contiguous green space area a spatial buffer has contact with), in three different types of buffers and type 2 diabetes (T2D). We obtained information about diagnosed T2D and relevant covariates at the individual level from the large and representative 45 and Up Study. Average contiguous green space and the percentage of green space within 500 m, 1 km, and 2 km of circular buffer, line-based road network (LBRN) buffers, and polygon-based road network (PBRN) buffers around participants’ residences were used as proxies for geographic access to green space. Generalized estimating equation regression models were used to determine associations between access to green space and T2D status of individuals. It was found that 30%–40% green space within 500 m LBRN or PBRN buffers, and 2 km PBRN buffers, but not within circular buffers, significantly reduced the risk of T2D. The novel average green space area metric did not appear to be particularly effective at measuring reductions in T2D. This study complements an existing research body on optimal buffers for green space measurement. MDPI 2021-04-13 /pmc/articles/PMC8068830/ /pubmed/33924462 http://dx.doi.org/10.3390/ijerph18084088 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mazumdar, Soumya
Chong, Shanley
Astell-Burt, Thomas
Feng, Xiaoqi
Morgan, Geoffrey
Jalaludin, Bin
Which Green Space Metric Best Predicts a Lowered Odds of Type 2 Diabetes?
title Which Green Space Metric Best Predicts a Lowered Odds of Type 2 Diabetes?
title_full Which Green Space Metric Best Predicts a Lowered Odds of Type 2 Diabetes?
title_fullStr Which Green Space Metric Best Predicts a Lowered Odds of Type 2 Diabetes?
title_full_unstemmed Which Green Space Metric Best Predicts a Lowered Odds of Type 2 Diabetes?
title_short Which Green Space Metric Best Predicts a Lowered Odds of Type 2 Diabetes?
title_sort which green space metric best predicts a lowered odds of type 2 diabetes?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8068830/
https://www.ncbi.nlm.nih.gov/pubmed/33924462
http://dx.doi.org/10.3390/ijerph18084088
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