County-Level Spatiotemporal Patterns of New HIV Diagnoses and Pre-exposure Prophylaxis (PrEP) Use in Mississippi, 2014–2018: A Bayesian Analysis of Publicly Accessible Censored Data

References

• Agustí, C., N. Font-Casaseca, F. Belvis, M. Julià, N. Vives, A. Montoliu, J. M. Pericàs, J. Casabona, and J. Benach. 2020. The role of socio-demographic determinants in the geo-spatial distribution of newly diagnosed HIV infections in small areas of Catalonia (Spain). BMC Public Health 20 (1):1–10. doi: 10.1186/s12889-020-09603-7.
   Web of Science ®Google Scholar
• Amram, O., J. Shoveller, R. Hogg, L. Wang, P. Sereda, R. Barrios, J. Montaner, and V. Lima. 2018. Distance to HIV care and treatment adherence: Adjusting for socio-demographic and geographical heterogeneity. Spatial and Spatio-Temporal Epidemiology 27:29–35. doi: 10.1016/j.sste.2018.08.001.
   Web of Science ®Google Scholar
• An, Q., J. Prejean, K. M. D. Harrison, and X. Fang. 2013. Association between community socioeconomic position and HIV diagnosis rate among adults and adolescents in the United States, 2005 to 2009. American Journal of Public Health 103 (1):120–26. doi: 10.2105/AJPH.2012.300853.
   Web of Science ®Google Scholar
• Bartell, S. M., and T. A. Lewandowski. 2011. Administrative censoring in ecological analyses of autism and a Bayesian solution. Journal of Environmental and Public Health 2011:1–5. doi: 10.1155/2011/202783.
   Google Scholar
• Besag, B. J. 1974. Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society: Series B 36 (2):192–236.
   Google Scholar
• Besag, J., J. York, and A. Mollie. 1991. Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics 43 (1):1–20. doi: 10.1007/BF00116466.
   Web of Science ®Google Scholar
• Best, N., S. Richardson, and A. Thomson. 2005. A comparison of Bayesian spatial models for disease mapping. Statistical Methods in Medical Research 14 (1):35–59.
   PubMed Web of Science ®Google Scholar
• Blangiardo, M., A. Boulieri, P. Diggle, F. B. Piel, G. Shaddick, and P. Elliott. 2020. Advances in spatiotemporal models for non-communicable disease surveillance. International Journal of Epidemiology 49 (Suppl. 1):I26–I37. doi: 10.1093/ije/dyz181.
   Google Scholar
• Boulieri, A., J. E. Bennett, and M. Blangiardo. 2020. A Bayesian mixture modeling approach for public health surveillance. Biostatistics 21 (3):369–83. doi: 10.1093/biostatistics/kxy038.
   Web of Science ®Google Scholar
• Buot, M. L. G., J. P. Docena, B. K. Ratemo, M. J. Bittner, J. T. Burlew, A. R. Nuritdinov, and J. R. Robbins. 2014. Beyond race and place: Distal sociological determinants of HIV disparities. PLoS ONE.9 (4):e91711. doi: 10.1371/journal.pone.0091711.
   PubMed Web of Science ®Google Scholar
• Centers for Disease Control and Prevention (CDC). 2020. Estimated HIV incidence and prevalence in the United States, 2014–2018. HIV Surveillance Supplemental Report 25 (1). Accessed May 27, 2021. https://www.cdc.gov/hiv/pdf/library/reports/surveillance/cdc-hiv-surveillance-supplemental-report-vol-25-1.pdf.
   Google Scholar
• Corpas-Burgos, F., and M. A. Martinez-Beneito. 2020. On the use of adaptive spatial weight matrices from disease mapping multivariate analyses. Stochastic Environmental Research and Risk Assessment 34 (3–4):531–44. doi: 10.1007/s00477-020-01781-5.
   Web of Science ®Google Scholar
• Dong, G., J. Ma, D. Lee, M. Chen, G. Pryce, and Y. Chen. 2020. Developing a locally adaptive spatial multilevel logistic model to analyze ecological effects on health using individual census records. Annals of the American Association of Geographers 110 (3):739–57. doi: 10.1080/24694452.2019.1644990.
   Web of Science ®Google Scholar
• Duncan, E. W., N. M. White, and K. Mengersen. 2017. Spatial smoothing in Bayesian models: A comparison of weights matrix specifications and their impact on inference. International Journal of Health Geographics 16 (1):47. https://ij-healthgeographics.biomedcentral.com/articles/. doi: 10.1186/s12942-017-0120-x.
   Google Scholar
• Elmore, K., E. L. P. Bradley, A. C. Lima, G. M. Khalil, E. Obi-Tabot, Z. Gant, H. D. Dean, and D. H. McCree. 2019. Trends in geographic rates of HIV diagnoses among black females in the United States, 2010–2015. Journal of Women’s Health 28 (3):410–17. doi: 10.1089/jwh.2017.6868.
   Web of Science ®Google Scholar
• Fauci, A. S., R. R. Redfield, G. Sigounas, M. D. Weahkee, and B. P. Giroir. 2019. Ending the HIV epidemic: A plan for the United States. JAMA 321 (9):844–45. doi: 10.1001/jama.2019.1343.
   PubMed Web of Science ®Google Scholar
• Fridley, B. L., and P. Dixon. 2007. Data augmentation for a Bayesian spatial model involving censored observations. Environmetrics 18 (2):107–23. doi: 10.1002/env.806.
   Web of Science ®Google Scholar
• Gant Z., M. Lomotey, H. I. Hall, X. Hu, X. Guo, and R. Song. 2012. A county-level examination of the relationship between HIV and social determinants of health: 40 states, 2006–2008. The Open AIDS Journal 6 (1):1–7. doi: 10.2174/1874613601206010001.
   PubMedGoogle Scholar
• Gelman, A. 2006. Prior distribution for variance parameters in hierarchical models. Bayesian Analysis 1 (3):515–33. doi: 10.1214/06-BA117A.
   Web of Science ®Google Scholar
• Gelman, A., J. B. Carlin, H. S. Stern, and D. B. Rubin. 2014. Bayesian data analysis. Boca Raton, FL: CRC Press.
   Google Scholar
• Gray, S. C., T. Massaro, I. Chen, C. J. Edholm, R. Grotheer, Y. Zheng, and H. H. Chang. 2016. A county-level analysis of persons living with HIV in the southern United States. AIDS Care 28 (2):266–72. doi: 10.1080/09540121.2015.1080793.
   PubMed Web of Science ®Google Scholar
• Haining, R., and G. Li. 2020. Modelling spatial and spatial-temporal data. Boca Raton, FL: CRC Press.
   Google Scholar
• Harel, O., E. M. Mitchell, N. J. Perkins, S. R. Cole, E. J. Tchetgen Tchetgen, B. Sun, and E. F. Schisterman. 2018. Multiple imputation for incomplete data in epidemiologic studies. American Journal of Epidemiology 187 (3):576–84. doi: 10.1093/aje/kwx349.
   Web of Science ®Google Scholar
• Hossain, M. M., and A. B. Lawson. 2010. Space-time Bayesian small area disease risk models: Development and evaluation with a focus on cluster detection. Environmental and Ecological Statistics 17 (1):73–95. doi: 10.1007/s10651-008-0102-z.
   Web of Science ®Google Scholar
• Jeffries, W. L., and K. D. Henny. 2019. From epidemiology to action: The case for addressing social determinants of health to end HIV in the Southern United States. AIDS and Behavior 23 (Suppl. 3):340–46. doi: 10.1007/s10461-019-02687-2.
   PubMed Web of Science ®Google Scholar
• Law, J., M. Quick, and P. W. Chan. 2015. Analyzing hotspots of crime using a Bayesian spatiotemporal modeling approach: A case study of violent crime in the greater Toronto area. Geographical Analysis 47 (1):1–19. doi: 10.1111/gean.12047.
   Web of Science ®Google Scholar
• Lee, D. 2011. A comparison of conditional autoregressive models used in Bayesian disease mapping. Spatial and Spatio-Temporal Epidemiology 2 (2):79–89. doi: 10.1016/j.sste.2011.03.001.
   PubMedGoogle Scholar
• Lee, D., and R. Mitchell. 2013. Locally adaptive spatial smoothing using conditional auto-regressive models. Journal of the Royal Statistical Society: Series C (Applied Statistics) 62 (4):593–608. doi: 10.1111/rssc.12009.
   Web of Science ®Google Scholar
• Leroux, B. G., X. Lei, and N. Breslow. 1999. Estimation of disease rates in small areas: A new mixed model for spatial dependence. In Statistical models in epidemiology, the environment and clinical trials, ed. M. E. Halloran and D. Berry, 179–91. New York: Springer-Verlag.
   Google Scholar
• Li, G., N. Best, A. L. Hansell, I. Ahmed, and S. Richardson. 2012. BaySTDetect: Detecting unusual temporal patterns in small area data via Bayesian model choice. Biostatistics 13 (4):695–710. doi: 10.1093/biostatistics/kxs005.
   Web of Science ®Google Scholar
• Lunn, D., C. Jackson, N. Best, A. Thomas, and D. Spiegelhalter. 2012. The BUGS book: A practical introduction to Bayesian analysis. Boca Raton, FL: CRC Press.
   Google Scholar
• McCormack, S., D. T. Dunn, M. Desai, D. I. Dolling, M. Gafos, R. Gilson, A. K. Sullivan, A. Clarke, I. Reeves, G. Schembri, et al. 2016. Pre-exposure prophylaxis to prevent the acquisition of HIV-1 infection (PROUD): Effectiveness results from the pilot phase of a pragmatic open-label randomised trial. The Lancet 387 (10013):53–60. doi: 10.1016/S0140-6736(15)00056-2.
   PubMed Web of Science ®Google Scholar
• Mississippi State Department of Health. 2021. Mississippi’s Ending the HIV Epidemic plan. https://msdh.ms.gov/msdhsite/index.cfm/14,5116,150,63,pdf/MS_EHE_Plan_rev_4_2021.pdf.
   Google Scholar
• Monge, S., I. Jarrín, A. Mocroft, C. A. Sabin, G. Touloumi, A. van Sighem, S. Abgrall, R. Dray-Spira, B. Spire, A. Castagna, et al. 2015. Mortality in migrants living with HIV in western Europe (1997–2013): A collaborative cohort study. The Lancet HIV 2 (12):e540–e549. doi: 10.1016/S2352-3018(15)00203-9.
   Web of Science ®Google Scholar
• Mouhanna, F., A. D. Castel, P. S. Sullivan, I. Kuo, H. J. Hoffman, A. J. Siegler, J. S. Jones, R. Mera Giler, P. McGuinness, and M. R. Kramer. 2020. Small-area spatial-temporal changes in pre-exposure prophylaxis (PrEP) use in the general population and among men who have sex with men in the United States between 2012 and 2018. Annals of Epidemiology 49:1–7. doi: 10.1016/j.annepidem.2020.07.001.
   PubMed Web of Science ®Google Scholar
• Quick, H. 2019. Estimating county-level mortality rates using highly censored data from CDC WONDER. Preventing Chronic Disease 16 (6):1–9. doi: 10.5888/pcd16.180441.
   Google Scholar
• Ransome, Y., L. M. Bogart, I. Kawachi, A. Kaplan, K. H. Mayer, and B. Ojikutu. 2020. Area-level HIV risk and socioeconomic factors associated with willingness to use PrEP among Black people in the U.S. South. Annals of Epidemiology 42:33–41. doi: 10.1016/j.annepidem.2019.11.002.
   PubMed Web of Science ®Google Scholar
• Ransome, Y., I. Kawachi, S. Braunstein, and D. Nash. 2016. Structural inequalities drive late HIV diagnosis: The role of black racial concentration, income inequality, socioeconomic deprivation, and HIV testing. Health & Place 42 (September):148–58. doi: 10.1016/j.healthplace.2016.09.004.
   Google Scholar
• Rosenberg, E. S., D. W. Purcell, J. A. Grey, A. Hankin-Wei, E. Hall, and P. S. Sullivan. 2018. Rates of prevalent and new HIV diagnoses by race and ethnicity among men who have sex with men, U.S. states, 2013–2014. Annals of Epidemiology 28 (12):865–73. doi: 10.1016/j.annepidem.2018.04.008.
   PubMed Web of Science ®Google Scholar
• Rubin, D. B. 1987. Multiple imputation for nonresponse in surveys. New York: Wiley.
   Google Scholar
• Siegler, A. J., A. Bratcher, and K. M. Weiss. 2019. Geographic access to pre-exposure prophylaxis clinics among men who have sex with men in the United States. American Journal of Public Health 109 (9):1216–23. doi: 10.2105/AJPH.2019.305172.
   PubMed Web of Science ®Google Scholar
• Siegler, A. J., A. Bratcher, K. M. Weiss, F. Mouhanna, L. Ahlschlager, and P. S. Sullivan. 2018. Location location location: An exploration of disparities in access to publicly listed pre-exposure prophylaxis clinics in the United States. Annals of Epidemiology 28 (12):858–64. doi: 10.1016/j.annepidem.2018.05.006.
   PubMed Web of Science ®Google Scholar
• Siegler, A. J., C. C. Mehta, F. Mouhanna, R. M. Giler, A. Castel, E. Pembleton, C. Jaggi, J. Jones, M. R. Kramer, P. McGuinness, et al. 2020. Policy- and county-level associations with HIV pre-exposure prophylaxis use, the United States, 2018. Annals of Epidemiology 45:24–31. doi: 10.1016/j.annepidem.2020.03.013.
   PubMed Web of Science ®Google Scholar
• Siegler, A. J., F. Mouhanna, R. M. Giler, K. Weiss, E. Pembleton, J. Guest, J. Jones, A. Castel, H. Yeung, M. Kramer, et al. 2018. The prevalence of pre-exposure prophylaxis use and the pre-exposureprophylaxis-to-need ratio in the fourth quarter of 2017, United States. Annals of Epidemiology 28 (12):841–49. doi: 10.1016/j.annepidem.2018.06.005.
   PubMed Web of Science ®Google Scholar
• Smith, D. K., P. S. Sullivan, B. Cadwell, L. A. Waller, A. Siddiqi, R. Mera-Giler, X. Hu, K. W. Hoover, N. S. Harris, and S. McCallister. 2020. Evidence of an association of increases in pre-exposure prophylaxis coverage with decreases in human immunodeficiency virus diagnosis rates in the United States, 2012–2016. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America 71 (12):3144–51. doi: 10.1093/cid/ciz1229.
   PubMed Web of Science ®Google Scholar
• Spinner, C. D., C. Boesecke, A. Zink, H. Jessen, H. J. Stellbrink, J. K. Rockstroh, and S. Esser. 2016. HIV pre-exposure prophylaxis (PrEP): A review of current knowledge of oral systemic HIV PrEP in humans. Infection 44 (2):151–58. doi: 10.1007/s15010-015-0850-2.
   PubMed Web of Science ®Google Scholar
• Stopka, T. J., L. Brinkley-Rubinstein, K. Johnson, P. A. Chan, M. Hutcheson, R. Crosby, D. Burke, L. Mena, and A. Nunn. 2018. HIV clustering in Mississippi: Spatial epidemiological study to inform implementation science in the deep south. Journal of Medical Internet Research 4(2):e35.
   Google Scholar
• Sullivan, P. S., R. M. Giler, F. Mouhanna, E. S. Pembleton, J. L. Guest, J. Jones, A. D. Castel, H. Yeung, M. Kramer, S. McCallister, et al. 2018. Trends in the use of oral emtricitabine/tenofovir disoproxil fumarate for pre-exposure prophylaxis against HIV infection, United States, 2012–2017. Annals of Epidemiology 28 (12):833–40. doi: 10.1016/j.annepidem.2018.06.009.
   PubMed Web of Science ®Google Scholar
• Sullivan, P. S., A. Satcher Johnson, E. S. Pembleton, R. Stephenson, A. C. Justice, K. N. Althoff, H. Bradley, A. D. Castel, A. M. Oster, E. S. Rosenberg, et al. 2021. Epidemiology of HIV in the USA: Epidemic burden, inequities, contexts, and responses. The Lancet 397 (10279):1095–1106. doi: 10.1016/S0140-6736(21)00395-0.
   PubMed Web of Science ®Google Scholar
• Sullivan, P. S., C. Woodyatt, C. Koski, E. Pembleton, P. McGuinness, J. Taussig, A. Ricca, N. Luisi, E. Mokotoff, N. Benbow, et al. 2020. A data visualization and dissemination resource to support HIV prevention and care at the local level: Analysis and uses of the AIDSVu public data resource. Journal of Medical Internet Research 22 (10):e23173. doi: 10.2196/23173.
   PubMed Web of Science ®Google Scholar
• U.S. Food and Drug Administration (FDA). 2019. FDA in brief: FDA continues to encourage ongoing education about the benefits and risks associated with PrEP, including additional steps to help reduce the risk of getting HIV. Accessed May 27, 2021. https://www.fda.gov/news-events/fda-brief/fda-brief-fda-continues-encourage-ongoing-education-about-benefits-and-risks-associated-prep
   Google Scholar
• Williams, L. D., U. Ibragimov, B. Tempalski, R. Stall, A. Satcher Johnson, G. Wang, H. L. F. Cooper, and S. R. Friedman. 2020. Trends over time in HIV prevalence among people who inject drugs in 89 large US metropolitan statistical areas, 1992–2013. Annals of Epidemiology 45:12–23. doi: 10.1016/j.annepidem.2020.03.011.
   Web of Science ®Google Scholar