title: Spatial vulnerability of fine particulate matter relative to the prevalence of diabetes in the United States.
abstract: Recent research supports a link between diabetes and fine particulate matter (≤ 2.5μg in diameter; PM2.5) in both laboratory and epidemiology studies. However, research investigating the potential relationship of the spatial vulnerability of diabetes to concomitant PM2.5 levels is still sparse, and the level of diabetes geographic disparities attributed to PM2.5 levels has yet to be evaluated. We conducted a Bayesian structured additive regression modeling approach to determine whether long-term exposure to PM2.5 is spatially associated with diabetes prevalence after adjusting for the socioeconomic status of county residents. This study utilizes the following data sources from 2004 to 2010: the Behavioral Risk Factor Surveillance System, the American Community Survey, and the Environmental Protection Agency. We also conducted spatial comparisons with low, median-low, median-high, and high levels of PM2.5 concentrations. When PM2.5 concentrations increased 1 μg/m(3), the increase in the relative risk percentage for diabetes ranged from -5.47% (95% credible interval = -6.14, -4.77) to 2.34% (95% CI = 2.01, 2.70), where 1323 of 3109 counties (42.55%) displayed diabetes vulnerability with significantly positive relative risk percentages. These vulnerable counties are more likely located in the Southeast, Central, and South Regions of the U.S. A similar spatial vulnerability pattern for concentrations of low PM2.5 levels was also present in these same three regions. A clear cluster of vulnerable counties at median-high PM2.5 level was found in Michigan. This study identifies the spatial vulnerability of diabetes prevalence associated with PM2.5, and thereby provides the evidence needed to prompt and establish enhanced surveillance that can monitor diabetes vulnerability in areas with low PM2.5 pollution.
authors.affiliation: University of Texas School of Public Health at San Antonio Regional Campus, Department of Biostatistics, San Antonio, TX, USA; University of Texas Health Science Center at San Antonio, Research to Advance Community Health Center, San Antonio, TX, USA. Electronic address: Lung-Chang.Chien@uth.tmc.edu.
authors.forename: Lung-Chang
authors.initials: LC
authors.lastname: Chien
authors.affiliation: University of Texas School of Public Health at San Antonio Regional Campus, Department of Epidemiology, Human, Genetics & Environmental Sciences, San Antonio, TX, USA.
authors.forename: Hasanat
authors.initials: H
authors.lastname: Alamgir
authors.affiliation: National Taiwan University, Department of Bioenvironmental Systems Engineering, Taipei, Taiwan.
authors.forename: Hwa-Lung
authors.initials: HL
authors.lastname: Yu
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000706:statistics & numerical data*; D003920:Diabetes Mellitus / Q000453:epidemiology*; D004781:Environmental Exposure / Q000706:statistics & numerical data*; D006801:Humans; D052638:Particulate Matter / Q000032:analysis*; D014481:United States / Q000453:epidemiology
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter
keywords: Diabetes prevalence; PM(2.5); Spatial vulnerability; Spatiotemporal model