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

Maximilian Präger; Christoph Kurz; Rolf Holle; Werner Maier; Michael Laxy

doi:10.1186/s12874-023-01883-y

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

BMC Med Res Methodol. 2023 Mar 17;23(1):65. doi: 10.1186/s12874-023-01883-y.

Authors

Maximilian Präger^{1

2}, Christoph Kurz³, Rolf Holle⁴, Werner Maier⁴, Michael Laxy^{5

6}

Affiliations

¹ Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany. maximilian.praeger@tum.de.
² Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-University Munich, Munich, Germany. maximilian.praeger@tum.de.
³ Munich School of Management and Munich Center of Health Sciences, Ludwig-Maximilians-University Munich, Munich, Germany.
⁴ Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-University Munich, Munich, Germany.
⁵ Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany.
⁶ German Center for Diabetes Research, Neuherberg, Germany.

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 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.

Keywords: Kernel density estimation; Obesity; OpenStreetMap; Risk score; Spatial.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Diabetes Mellitus, Type 2*
Geographic Information Systems
Humans
Obesity / epidemiology
Risk Factors
Spatial Analysis