• Galicia-garcia U, Benito-vicente A, Jebari S, Larrea-sebal A. Pathophysiology of type 2 diabetes mellitus. Int J Mol Sci Rev. 2020;21(6275):1–34.


    Google Scholar
     

  • Thomas RL, Halim S, Gurudas S, Sivaprasad S, Owens DR. IDF diabetes atlas: a review of studies utilising retinal photography on the global prevalence of diabetes related retinopathy between 2015 and 2018. Diabetes Res Clin Pract. 2019;157:107840.


    Google Scholar
     

  • Loop MS, Howard G, de los Campos G, Al-Hamdan MZ, Safford MM, Levitan EB, et al. Heat maps of hypertension, diabetes mellitus, and smoking in the continental United States. Circ Cardiovasc Qual Outcomes. 2017;10(1):e003350.


    Google Scholar
     

  • Tran BX, Nguyen LH, Pham NM. Global mapping of interventions to improve quality of life of people with diabetes in 1990–2018. Int J Environ Res Public Health. 2020;17:1597.


    Google Scholar
     

  • Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract. 2010;87(1):4–14.


    Google Scholar
     

  • Burrows NR, Ali MK, Ch B, Rolka D, Williams DE, Ph D, et al. Changes in Diabetes-Related complications in the united States, 1990–2010. New Engl J Med Orig. 2014;370(2014):1514–23.


    Google Scholar
     

  • Kibirige D, Lumu W, Jones AG, Smeeth L, Hattersley AT, Nyirenda MJ. Understanding the manifestation of diabetes in sub-Saharan Africa to inform therapeutic approaches and preventive strategies: a narrative review. Clin Diabetes Endocrinol. 2019;5(1):1–8.


    Google Scholar
     

  • International Diabetes Federation IDF. International Diabetes Federation. [cited 2024 Oct 2]. Diabetes: Facts & figures. Available from: https://idf.org/about-diabetes/diabetes-facts-figures/

  • Benoit SR, Hora I, Albright AL, Gregg EW. New directions in incidence and prevalence of diagnosed diabetes in the USA. BMJ Open Diabetes Res Care. 2019;7(1):1–6.


    Google Scholar
     

  • CDC. Diabetes. 2024 [cited 2024 Oct 1]. National Diabetes Statistics Report. Available from: https://www.cdc.gov/diabetes/php/data-research/index.html

  • Centers for Disease Control and Prevention. National Diabetes Statistics Report, 2020: Estimates of diabetes and its burden in the United States. Atlanta, GA: U.S. Department of Health and Human Services. 2020.

  • Pearson-stuttard J, Blundell S, Harris T, Cook DG, Critchley J. Review Diabetes and infection: assessing the association with glycaemic control in population-based studies. 2015;8587(October 2018).

  • Baron AD, Steinberg HO, Chaker H, Leaming R, Johnson A, Brechtel G. Insulin-mediated skeletal muscle vasodilation contributes to both insulin sensitivity and responsiveness in lean humans. J Clin Invest. 1995;96(2):786–92.


    Google Scholar
     

  • Rajagopalan S, Brook RD. Air pollution and type 2 diabetes: mechanistic insights. Perspect DIABETES. 2012;61(12):3037–45.


    Google Scholar
     

  • Thiering E, Heinrich J. Epidemiology of air pollution and diabetes. Trends Endocrinol Metab. 2015;26(7):384–94.


    Google Scholar
     

  • CDC. Diabetes. 2024 [cited 2024 Oct 1]. A Report Card: Diabetes in the United States Infographic. Available from: https://www.cdc.gov/diabetes/communication-resources/diabetes-statistics.html

  • Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JLJ, et al. The seventh report of the joint National committee on Prevention, Detection, Evaluation, and treatment of high blood pressure: the JNC 7 report. JAMA. 2003;289(19):2560–72.


    Google Scholar
     

  • American Diabetes Association. Economic costs of diabetes In the U.S. In 2017. Diabetes Care. 2018;41(5):917–28.


    Google Scholar
     

  • Barker LE, Kirtland KA, Gregg EW, Geiss LS, Thompson TJ. Geographic distribution of diagnosed diabetes in the U.S.: a diabetes belt. Am J Prev Med. 2011;40(4):434–9.


    Google Scholar
     

  • Kianfar N, Mesgari MS. GIS-based spatio-temporal analysis and modeling of COVID-19 incidence rates in Europe. Spatial and Spatio-temporal Epidemiology. 2022;41:100498.


    Google Scholar
     

  • Kianfar N, Mesgari MS, Mollalo A, Kaveh M. Spatio-temporal modeling of COVID-19 prevalence and mortality using artificial neural network algorithms. Spatial and Spatio-temporal Epidemiology. 2022;40:100471.


    Google Scholar
     

  • Borhani NO. Changes and geographic distribution of mortality from cerebrovascular disease. Am J Public Health Nations Health. 1965;55(5):673–81.


    Google Scholar
     

  • Rue H, Martino S, Chopin N. Approximate bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J R Stat Soc Series B Stat Methodol. 2009;71(2):319–92.


    Google Scholar
     

  • Moraga P, Dean C, Inoue J, Morawiecki P, Noureen SR, Wang F. Bayesian spatial modelling of geostatistical data using INLA and SPDE methods: A case study predicting malaria risk in Mozambique. Spat Spatio-Temporal Epidemiol. 2021;39:100440.

  • Asmarian N, Ayatollahi SMT, Sharafi Z, Zare N. Bayesian spatial joint model for disease mapping of zero-inflated data with R-INLA: a simulation study and an application to male breast cancer in Iran. Int J Environ Res Public Health. 2019;16(22):1–13.


    Google Scholar
     

  • Salubi EA, Elliott SJ. Geospatial analysis of cholera patterns in Nigeria: findings from a cross-sectional study. BMC Infect Dis. 2021;21(1):202.


    Google Scholar
     

  • Willi C, Bodenmann P, Ghali WA, Faris PD, Cornuz J. Active smoking and the risk of type 2 diabetes: a systematic review and meta-analysis. JAMA. 2007;298(22):2654–64.


    Google Scholar
     

  • Pan A, Wang Y, Talaei M, Hu FB, Wu T. Relation of active, passive, and quitting smoking with incident diabetes: a meta-analysis and systematic review. Lancet Diabetes Endocrinol. 2015;3(12):958–67.


    Google Scholar
     

  • Maddatu J, Anderson-Baucum E, Evans-Molina C. Smoking and the risk of type 2 diabetes. Transl Res. 2017;184:101–7.


    Google Scholar
     

  • Ley SH, Ardisson Korat AV, Sun Q, Tobias DK, Zhang C, Qi L, et al. Contribution of the nurses’ health studies to uncovering risk factors for type 2 diabetes: diet, lifestyle, biomarkers, and genetics. Am J Public Health. 2016;106(9):1624–30.


    Google Scholar
     

  • Abdullah A, Peeters A, de Courten M, Stoelwinder J. The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies. Diabetes Res Clin Pract. 2010;89(3):309–19.


    Google Scholar
     

  • Abdullah A, Stoelwinder J, Shortreed S, Wolfe R, Stevenson C, Walls H, et al. The duration of obesity and the risk of type 2 diabetes. Public Health Nutr. 2011;14(1):119–26.


    Google Scholar
     

  • Lee J, Callaghan T, Ory M, Zhao H, Bolin JN. The impact of Medicaid expansion on diabetes management. Diabetes Care. 2020;43(5):1094–101.


    Google Scholar
     

  • Volaco A, Cavalcanti AM, Filho RP, Précoma DB. Socioeconomic status: the missing link between obesity and diabetes mellitus? Curr Diabetes Rev. 2018;14(4):321–6.


    Google Scholar
     

  • Bowe B, Xie Y, Li T, Yan Y, Xian H, Al-Aly Z. The 2016 global and national burden of diabetes mellitus attributable to PM2·5 air pollution. Lancet Planet Health. 2018;2(7):e301-12.


    Google Scholar
     

  • Ren Z, Yuan J, Luo Y, Wang J, Li Y. Association of air pollution and fine particulate matter (PM2.5) exposure with gestational diabetes: a systematic review and meta-analysis. Ann Transl Med. 2023;11(1):23.


    Google Scholar
     

  • Strak M, Janssen N, Beelen R, Schmitz O, Vaartjes I, Karssenberg D, et al. Long-term exposure to particulate matter, NO2, and the oxidative potential of particulates and diabetes prevalence in a large National health survey. Environ Int. 2017;108:228–36.


    Google Scholar
     

  • Hellack B, Sugiri D, Schins RPF, Schikowski T, Krämer U, Kuhlbusch TAJ, et al. Land use regression modeling of oxidative potential of fine particles, NO2, PM2.5 mass, and association to type two diabetes mellitus. Atmos Environ. 2017;171:181–90.


    Google Scholar
     

  • Blauw LL, Aziz NA, Tannemaat MR, Blauw CA, de Craen AJ, Pijl H, et al. Diabetes incidence and glucose intolerance prevalence increase with higher outdoor temperature. BMJ Open Diabetes Res Care. 2017;5(1):e000317.


    Google Scholar
     

  • US CDC UC. BRFSS Prevalence & Trends Data: Home | DPH | CDC [Internet]. 2023 [cited 2024 Oct 1]. Available from: https://www.cdc.gov/brfss/brfssprevalence/index.html

  • Bureau UC. Census.gov. [cited 2024 Oct 1]. Population and Housing Unit Estimates Tables. Available from: https://www.census.gov/programs-surveys/popest/data/tables.html

  • Xie H, Barker LE, Rolka DB, Incorporating design weights and historical data into model-based. Small-area estimation. [cited 2024 Oct 1]; Available from: https://stacks.cdc.gov/view/cdc/87428

  • 2023 County Health Rankings National Findings Report. | County Health Rankings & Roadmaps [Internet]. [cited 2024 Oct 1]. Available from: https://www.countyhealthrankings.org/findings-and-insights/2023-county-health-rankings-national-findings-report

  • Azur MJ, Stuart EA, Frangakis C, Leaf PJ. Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res. 2011;20(1):40–9.


    Google Scholar
     

  • Gilks WR, Richardson S, Spiegelhalter DJ, editors. Markov Chain Monte Carlo in Practice. 1st ed. New York: Chapman & Hall/CRC. 1995:512. https://doi.org/10.1201/b14835.

  • Lindgren F, Rue H. Bayesian spatial modelling with R-INLA. J Stat Softw. 2015;63(19):1–25.


    Google Scholar
     

  • Hodges JS, Reich BJ. Adding Spatially-Correlated errors can mess up the fixed effect you love. Am Stat. 2010;64(4):325–34.


    Google Scholar
     

  • RStudio. RStudio Desktop [Internet]. Posit. 2024 [cited 2024 Jun 24]. Available from: https://www.posit.co/

  • Wang L, Li X, Wang Z, Bancks MP, Carnethon MR, Greenland P, et al. Trends in prevalence of diabetes and control of risk factors in diabetes among US adults, 1999–2018. JAMA. 2021;326(8):704–16.


    Google Scholar
     

  • Qiao S, Li Z, Weissman S, Li X, Olatosi B, Davis C, et al. Disparity in HIV service interruption in the outbreak of COVID-19 in South Carolina. AIDS Behav. 2021;25(1):49–57.


    Google Scholar
     

  • Appel SJ, Harrell JS, Deng S. Racial and socioeconomic differences in risk factors for cardiovascular disease among Southern rural women. Nurs Res. 2002;51(3):140–7.


    Google Scholar
     

  • Mohai P, Lantz PM, Morenoff J, House JS, Mero RP. Racial and socioeconomic disparities in residential proximity to polluting industrial facilities: evidence from the Americans’ changing lives study. Am J Public Health. 2009;99(Suppl 3):649–56.


    Google Scholar
     

  • Rimm EB, Chan J, Stampfer MJ, Colditz GA, Willett WC. Prospective study of cigarette smoking, alcohol use, and the risk of diabetes in men. BMJ. 1995;310(6979):555–9.


    Google Scholar
     

  • Sullivan PW, Ghushchyan VH, Ben-Joseph R. The impact of obesity on diabetes, hyperlipidemia, and hypertension in the United States. Qual Life Res. 2008;17(8):1063–71.


    Google Scholar
     

  • He D, Wu S, Zhao H, Qiu H, Fu Y, Li X, et al. Association between particulate matter 2.5 and diabetes mellitus: a meta-analysis of cohort studies. J Diabetes Investig. 2017;8(5):687–96.


    Google Scholar
     

  • Park S, McGuire LC, Galuska DA. Regional differences in sugar-sweetened beverage intake among US adults. J Acad Nutr Diet. 2015;115(12):1996–2002.


    Google Scholar
     

  • Oya J, Vistisen D, Christensen DL, Faurholt-Jepsen D, Mohan V, Ramachandran A, et al. Geographic differences in the associations between impaired glucose regulation and cardiovascular risk factors among young adults. Diabet Med. 2015;32(4):497–504.


    Google Scholar
     

  • Moody A, Cowley G, Fat LN, Mindell JS. Social inequalities in prevalence of diagnosed and undiagnosed diabetes and impaired glucose regulation in participants in the health surveys for England series. BMJ Open. 2016;6(2):e010155.


    Google Scholar