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A spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development and parameter variation

BACKGROUND: Overweight and obesity are severe public health problems worldwide. Obesity can lead to chronic diseases such as type 2 diabetes mellitus. Environmental factors may affect lifestyle aspects and are therefore expected to influence people’s weight status. To assess environmental risks, sev...

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Autores principales: Präger, Maximilian, Kurz, Christoph, Holle, Rolf, Maier, Werner, Laxy, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021981/
https://www.ncbi.nlm.nih.gov/pubmed/36932344
http://dx.doi.org/10.1186/s12874-023-01883-y
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author Präger, Maximilian
Kurz, Christoph
Holle, Rolf
Maier, Werner
Laxy, Michael
author_facet Präger, Maximilian
Kurz, Christoph
Holle, Rolf
Maier, Werner
Laxy, Michael
author_sort Präger, Maximilian
collection PubMed
description BACKGROUND: Overweight and obesity are severe public health problems worldwide. Obesity can lead to chronic diseases such as type 2 diabetes mellitus. Environmental factors may affect lifestyle aspects and are therefore expected to influence people’s weight status. To assess environmental risks, several methods have been tested using geographic information systems. Freely available data from online geocoding services such as OpenStreetMap (OSM) can be used to determine the spatial distribution of these obesogenic factors. The aim of our study was to develop and test a spatial obesity risk score (SORS) based on data from OSM and using kernel density estimation (KDE). METHODS: Obesity-related factors were downloaded from OSM for two municipalities in Bavaria, Germany. We visualized obesogenic and protective risk factors on maps and tested the spatial heterogeneity via Ripley’s K function. Subsequently, we developed the SORS based on positive and negative KDE surfaces. Risk score values were estimated at 50 random spatial data points. We examined the bandwidth, edge correction, weighting, interpolation method, and numbers of grid points. To account for uncertainty, a spatial bootstrap (1000 samples) was integrated, which was used to evaluate the parameter selection via the ANOVA F statistic. RESULTS: We found significantly clustered patterns of the obesogenic and protective environmental factors according to Ripley’s K function. Separate density maps enabled ex ante visualization of the positive and negative density layers. Furthermore, visual inspection of the final risk score values made it possible to identify overall high- and low-risk areas within our two study areas. Parameter choice for the bandwidth and the edge correction had the highest impact on the SORS results. DISCUSSION: The SORS made it possible to visualize risk patterns across our study areas. Our score and parameter testing approach has been proven to be geographically scalable and can be applied to other geographic areas and in other contexts. Parameter choice played a major role in the score results and therefore needs careful consideration in future applications. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01883-y.
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spelling pubmed-100219812023-03-18 A spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development and parameter variation Präger, Maximilian Kurz, Christoph Holle, Rolf Maier, Werner Laxy, Michael BMC Med Res Methodol Research BACKGROUND: Overweight and obesity are severe public health problems worldwide. Obesity can lead to chronic diseases such as type 2 diabetes mellitus. Environmental factors may affect lifestyle aspects and are therefore expected to influence people’s weight status. To assess environmental risks, several methods have been tested using geographic information systems. Freely available data from online geocoding services such as OpenStreetMap (OSM) can be used to determine the spatial distribution of these obesogenic factors. The aim of our study was to develop and test a spatial obesity risk score (SORS) based on data from OSM and using kernel density estimation (KDE). METHODS: Obesity-related factors were downloaded from OSM for two municipalities in Bavaria, Germany. We visualized obesogenic and protective risk factors on maps and tested the spatial heterogeneity via Ripley’s K function. Subsequently, we developed the SORS based on positive and negative KDE surfaces. Risk score values were estimated at 50 random spatial data points. We examined the bandwidth, edge correction, weighting, interpolation method, and numbers of grid points. To account for uncertainty, a spatial bootstrap (1000 samples) was integrated, which was used to evaluate the parameter selection via the ANOVA F statistic. RESULTS: We found significantly clustered patterns of the obesogenic and protective environmental factors according to Ripley’s K function. Separate density maps enabled ex ante visualization of the positive and negative density layers. Furthermore, visual inspection of the final risk score values made it possible to identify overall high- and low-risk areas within our two study areas. Parameter choice for the bandwidth and the edge correction had the highest impact on the SORS results. DISCUSSION: The SORS made it possible to visualize risk patterns across our study areas. Our score and parameter testing approach has been proven to be geographically scalable and can be applied to other geographic areas and in other contexts. Parameter choice played a major role in the score results and therefore needs careful consideration in future applications. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01883-y. BioMed Central 2023-03-17 /pmc/articles/PMC10021981/ /pubmed/36932344 http://dx.doi.org/10.1186/s12874-023-01883-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Präger, Maximilian
Kurz, Christoph
Holle, Rolf
Maier, Werner
Laxy, Michael
A spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development and parameter variation
title A spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development and parameter variation
title_full A spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development and parameter variation
title_fullStr A spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development and parameter variation
title_full_unstemmed A spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development and parameter variation
title_short A spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development and parameter variation
title_sort spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development and parameter variation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021981/
https://www.ncbi.nlm.nih.gov/pubmed/36932344
http://dx.doi.org/10.1186/s12874-023-01883-y
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