title: Identifying counties vulnerable to diabetes from obesity prevalence in the United States: a spatiotemporal analysis.
abstract: Clinical and epidemiological research has reported a strong association between diabetes and obesity. However, whether increased diabetes prevalence is more likely to appear in areas with increased obesity prevalence has not been thoroughly investigated in the United States (US). The Bayesian structured additive regression model was applied to identify whether counties with higher obesity prevalence are more likely clustered in specific regions in 48 contiguous US states. Prevalence data adopted the small area estimate from the Behavioral Risk Factor Surveillance System. Confounding variables like socioeconomic status adopted data were from the American Community Survey. This study reveals that an increased percentage of relative risk of diabetes was more likely to appear in Southeast, Northeast, Central and South regions. Of counties vulnerable to diabetes, 36.8% had low obesity prevalence, and most of them were located in the Southeast, Central, and South regions. The geographic distribution of counties vulnerable to diabetes expanded to the Southwest, West and Northern regions when obesity prevalence increased. This study also discloses that 7.4% of counties had the largest average in predicted diabetes prevalence compared to the other counties. Their average diabetes prevalence escalated from 8.7% in 2004 to 11.2% in 2011. This study not only identifies counties vulnerable to diabetes due to obesity, but also distinguishes counties in terms of different levels of vulnerability to diabetes. The findings can provide the possibility of establishing targeted surveillance systems to raise awareness of diabetes in those counties.
authors.affiliation: Department of Biostatistics, University of Texas Health Science Center at Houston (UTHealth), School of Public Health, Houston, TX. Lung-Chang.Chien@uth.tmc.edu.
authors.forename: Xiao
authors.initials: X
authors.lastname: Li
authors.forename: Amanda
authors.initials: A
authors.lastname: Staudt
authors.forename: Lung-Chang
authors.initials: LC
authors.lastname: Chien
mesh_terms: D001499:Bayes Theorem; D003920:Diabetes Mellitus / Q000453:epidemiology*; D006801:Humans; D009765:Obesity / Q000453:epidemiology*; D015995:Prevalence; D062211:Spatio-Temporal Analysis; D014481:United States