{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Salerno PRVO"],"funding":["NIMHD NIH HHS"],"pagination":["1766-1774"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11447680"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["26(5)"],"pubmed_abstract":["<h4>Aims</h4>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.<h4>Materials and methods</h4>We performed a cross-sectional analysis of county-level adult obesity prevalence (body mass index ≥30 kg/m<sup>2</sup>) 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.<h4>Results</h4>Overall, we analysed 2752 counties in the United States, identifying a nationa"],"journal":["Diabetes, obesity & metabolism"],"pubmed_title":["County-level socio-environmental factors and obesity prevalence in the United States."],"pmcid":["PMC11447680"],"funding_grant_id":["P50 MD017351"],"pubmed_authors":["Al-Kindi S","Dong W","Rajagopalan S","Nasir K","Qian A","Deo S","Salerno PRVO"],"additional_accession":[]},"is_claimable":false,"name":"County-level socio-environmental factors and obesity prevalence in the United States.","description":"<h4>Aims</h4>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.<h4>Materials and methods</h4>We performed a cross-sectional analysis of county-level adult obesity prevalence (body mass index ≥30 kg/m<sup>2</sup>) 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.<h4>Results</h4>Overall, we analysed 2752 counties in the United States, identifying a nationa","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 May","modification":"2025-04-05T08:58:25.695Z","creation":"2025-04-05T08:58:25.695Z"},"accession":"S-EPMC11447680","cross_references":{"pubmed":["38356053"],"doi":["10.1111/dom.15488"]}}