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

ABSTRACT:

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 visuali

SUBMITTER: Prager M 

PROVIDER: S-EPMC10021981 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature