Overlapping spatial clusters of sugar-sweetened beverage intake and body mass index in Geneva state, Switzerland.

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Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_847205150CC4
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Overlapping spatial clusters of sugar-sweetened beverage intake and body mass index in Geneva state, Switzerland.
Journal
Nutrition & diabetes
Author(s)
Joost S., De Ridder D., Marques-Vidal P., Bacchilega B., Theler J.M., Gaspoz J.M., Guessous I.
ISSN
2044-4052 (Electronic)
ISSN-L
2044-4052
Publication state
Published
Issued date
14/11/2019
Peer-reviewed
Oui
Volume
9
Number
1
Pages
35
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
Obesity and obesity-related diseases represent a major public health concern. Recently, studies have substantiated the role of sugar-sweetened beverages (SSBs) consumption in the development of these diseases. The fine identification of populations and areas in need for public health intervention remains challenging. This study investigates the existence of spatial clustering of SSB intake frequency (SSB-IF) and body mass index (BMI), and their potential spatial overlap in a population of adults of the state of Geneva using a fine-scale geospatial approach.
We used data on self-reported SSB-IF and measured BMI from residents aged between 20 and 74 years of the state of Geneva (Switzerland) that participated in the Bus Santé cross-sectional population-based study (n = 15,423). Getis-Ord Gi spatial indices were used to identify spatial clusters of SSB-IF and BMI in unadjusted models and models adjusted for individual covariates (education level, gender, age, nationality, and neighborhood-level median income).
We identified a significant spatial clustering of BMI and SSB-IF. 13.2% (n = 2034) of the participants were within clusters of higher SSB-IF and 10.7% (n = 1651) were within clusters of lower SSB-IF. We identified overlapping clusters of SSB-IF and BMI in specific areas where 11.1% (n = 1719) of the participants resided. After adjustment, the identified clusters persisted and were only slightly attenuated indicating that additional neighborhood-level determinants influence the spatial distribution of SSB-IF and BMI.
Our fine-scale spatial approach allowed to identify specific populations and areas presenting higher SSB-IF and highlighted the existence of an overlap between populations and areas of higher SSB-IF associated with higher BMI. These findings could guide policymakers to develop locally tailored interventions such as targeted prevention campaigns and pave the way for precision public health delivery.
Pubmed
Web of science
Open Access
Yes
Create date
29/11/2019 20:45
Last modification date
30/04/2021 6:12
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