@文章{信息:doi/10.2196/37039，作者=“Brakefield, Whitney S和Olusanya, Olufunto A和shabannejad, Arash”，标题=“田纳西州谢尔比县邻里因素与成人肥胖的关系:地理空间机器学习方法”，期刊=“JMIR公共卫生监测”，年=“2022”，月=“8”，日=“9”，卷=“8”，数=“8”，页=“e37039”，关键词=“肥胖”;肥胖的监测;疾病监测;机器学习;地理信息系统;健康的社会决定因素;SDOH;背景:肥胖是一种全球流行病，每年至少造成280万人死亡。这种复杂的疾病与严重的社会经济负担、工作生产力下降、失业和其他健康社会决定因素(SDOH)差异有关。目的:本研究的目的是利用地理空间机器学习方法，调查SDOH对美国田纳西州谢尔比县成年人肥胖患病率的影响。 Methods: Obesity prevalence was obtained from the publicly available 500 Cities database of Centers for Disease Control and Prevention, and SDOH indicators were extracted from the US census and the US Department of Agriculture. We examined the geographic distributions of obesity prevalence patterns, using Getis-Ord Gi* statistics and calibrated multiple models to study the association between SDOH and adult obesity. Unsupervised machine learning was used to conduct grouping analysis to investigate the distribution of obesity prevalence and associated SDOH indicators. Results: Results depicted a high percentage of neighborhoods experiencing high adult obesity prevalence within Shelby County. In the census tract, the median household income, as well as the percentage of individuals who were Black, home renters, living below the poverty level, 55 years or older, unmarried, and uninsured, had a significant association with adult obesity prevalence. The grouping analysis revealed disparities in obesity prevalence among disadvantaged neighborhoods. Conclusions: More research is needed to examine links between geographical location, SDOH, and chronic diseases. The findings of this study, which depict a significantly higher prevalence of obesity within disadvantaged neighborhoods, and other geospatial information can be leveraged to offer valuable insights, informing health decision-making and interventions that mitigate risk factors of increasing obesity prevalence. ", issn="2369-2960", doi="10.2196/37039", url="https://publichealth.www.mybigtv.com/2022/8/e37039", url="https://doi.org/10.2196/37039", url="http://www.ncbi.nlm.nih.gov/pubmed/359437" }