County-level socio-environmental factors and obesity prevalence in the United States
© 2024 John Wiley & Sons Ltd..
AIMS: To investigate high-risk sociodemographic and environmental determinants of health (SEDH) potentially associated with adult obesity in counties in the United States using machine-learning techniques.
MATERIALS AND METHODS: We performed a cross-sectional analysis of county-level adult obesity prevalence (body mass index ≥30 kg/m2) in the United States using data from the Diabetes Surveillance System 2017. We harvested 49 county-level SEDH factors that were used in a classification and regression trees (CART) model to identify county-level clusters. The CART model was validated using a 'hold-out' set of counties and variable importance was evaluated using Random Forest.
RESULTS: Overall, we analysed 2752 counties in the United States, identifying a national median (interquartile range) obesity prevalence of 34.1% (30.2%, 37.7%). The CART method identified 11 clusters with a 60.8% relative increase in prevalence across the spectrum. Additionally, seven key SEDH variables were identified by CART to guide the categorization of clusters, including Physically Inactive (%), Diabetes (%), Severe Housing Problems (%), Food Insecurity (%), Uninsured (%), Population over 65 years (%) and Non-Hispanic Black (%).
CONCLUSION: There is significant county-level geographical variation in obesity prevalence in the United States, which can in part be explained by complex SEDH factors. The use of machine-learning techniques to analyse these factors can provide valuable insights into the importance of these upstream determinants of obesity and, therefore, aid in the development of geo-specific strategic interventions and optimize resource allocation to help battle the obesity pandemic.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:26 |
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| Enthalten in: |
Diabetes, obesity & metabolism - 26(2024), 5 vom: 14. Apr., Seite 1766-1774 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Salerno, Pedro R V O [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 09.04.2024 Date Revised 09.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1111/dom.15488 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368461548 |
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| 500 | |a Date Revised 09.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2024 John Wiley & Sons Ltd. | ||
| 520 | |a AIMS: To investigate high-risk sociodemographic and environmental determinants of health (SEDH) potentially associated with adult obesity in counties in the United States using machine-learning techniques | ||
| 520 | |a MATERIALS AND METHODS: We performed a cross-sectional analysis of county-level adult obesity prevalence (body mass index ≥30 kg/m2) in the United States using data from the Diabetes Surveillance System 2017. We harvested 49 county-level SEDH factors that were used in a classification and regression trees (CART) model to identify county-level clusters. The CART model was validated using a 'hold-out' set of counties and variable importance was evaluated using Random Forest | ||
| 520 | |a RESULTS: Overall, we analysed 2752 counties in the United States, identifying a national median (interquartile range) obesity prevalence of 34.1% (30.2%, 37.7%). The CART method identified 11 clusters with a 60.8% relative increase in prevalence across the spectrum. Additionally, seven key SEDH variables were identified by CART to guide the categorization of clusters, including Physically Inactive (%), Diabetes (%), Severe Housing Problems (%), Food Insecurity (%), Uninsured (%), Population over 65 years (%) and Non-Hispanic Black (%) | ||
| 520 | |a CONCLUSION: There is significant county-level geographical variation in obesity prevalence in the United States, which can in part be explained by complex SEDH factors. The use of machine-learning techniques to analyse these factors can provide valuable insights into the importance of these upstream determinants of obesity and, therefore, aid in the development of geo-specific strategic interventions and optimize resource allocation to help battle the obesity pandemic | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a obesity prevalence | |
| 650 | 4 | |a public health | |
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| 700 | 1 | |a Dong, Weichuan |e verfasserin |4 aut | |
| 700 | 1 | |a Deo, Salil |e verfasserin |4 aut | |
| 700 | 1 | |a Nasir, Khurram |e verfasserin |4 aut | |
| 700 | 1 | |a Rajagopalan, Sanjay |e verfasserin |4 aut | |
| 700 | 1 | |a Al-Kindi, Sadeer |e verfasserin |4 aut | |
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