The online version of this article (https://doi.org/10.1186/s12981-018-0189-8) contains supplementary material, which is available to authorized users.
Concurrent with the UNAIDS 90-90-90 and NHAS plans, the District of Columbia (DC) launched its 90/90/90/50 plan (Plan) in 2015. The Plan proposes that by 2020, 90% of all DC residents will know their HIV status; 90% of residents living with HIV will be in sustained treatment; 90% of those in treatment will reach “Viral Suppression” and DC will achieve 50% reduction of new HIV cases. To achieve these goals targeted prevention strategies are imperative for areas where the relative risk (RR) of not being linked to care (NL), not retained in any care (NRC) and low viral suppression (NVSP) are highest in the District. These outcomes are denoted in this study as poor outcomes of HIV care continuum. This study applies the Bayesian model for RR for area specific random effects to identify the census tracts with poor HIV care continuum outcomes for DC.
This analysis was conducted using cases diagnosed from 2010 to 2015 and reported to the surveillance system from the District of Columbia Department of Health (DC DOH), HIV/AIDS, Hepatitis, STD and TB Administration. The jurisdictions of the District of Columbia is divided into 179 census tracts. It is challenging to plot sparse data in ‘small’ local administrative areas, characteristically which may have a single-count datum for each geographic area. Bayesian methods overcome this problem by assimilating prior information to the underlying RR, making the predicted RR estimates robust.
The RR of NL is higher in 59 (33%) out of 179 census tracts in DC. The RR of NRC was high in 46 (26%) of the census tracts while 52 census tracts (29%) show a high risk of having NVSP among its residents. This study also identifies clear correlated heterogeneity or clustering is evident in the northern tracts of the district.
The study finds census tracts with higher RR of poor linkage to care outcomes in the District. These results will inform the Plan which aims to increase targeted testing leading to early initiation of antiretroviral therapy. The uniqueness of this study lies in its translational scope where surveillance data can be used to inform local public health programs and enhance the quality of health for the people with HIV.
Additional file 1: Figure S1. OpenBugs Code and prior distributions used in the model.
Centers for Disease Control and Prevention. Understanding the HIV Care Continuum. 2014;1–5.
District of Columbia Department of Health HIV/AIDS, Hepatitis, STD and TA (HAHSTA). Retained HIV Care and Ryan White Care Dynamics Data through December 2015. 2015.
Hall HI, Tang T, Westfall AO, Mugavero MJ. HIV care visits and time to viral suppression, 19 US jurisdictions, and implications for treatment, prevention and the National HIV/AIDS Strategy. PLoS ONE. 2013;8:1–7.
Van de Laar MJ, Pharris A. Treatment as prevention: will it work? Euro surveillance. 2011;16:1–3.
Rebeiro PF, Gange SJ, Horberg MA, Abraham AG, Napravnik S, Samji H, et al. Geographic variations in retention in care among HIV-infected adults in the United States. PLoS ONE. 2016;11:4–9. CrossRef
Eberhart MG, Yehia BR, Hillier A, Voytek CD, Blank MB, Frank I, et al. Behind the cascade: analyzing spatial patterns along the HIV care continuum. J Acquir Immune Defic Syndr (1999). 2013;64 Suppl 1:S42–51. http://www.ncbi.nlm.nih.gov/pubmed/24126447.
Castel AD, Kuo I, Mikre M, Young T, Haddix M, Das S, et al. Feasibility of using HIV care-continuum outcomes to identify geographic areas for targeted HIV testing. J Acquir Immune Defic Syndr. 2017;74:96–103. CrossRef
Carlin BP, Louis TA. Bayes and empirical bayes methods for data analysis. 2nd ed. London: Chapman and Hall; 2000. CrossRef
Clayton D, Kaldor J. Empirical bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics. 1987;43:671–81. http://www.jstor.org/stable/2532003.
Robertson C, Nelson TA, MacNab YC, Lawson AB. Review of methods for space-time disease surveillance. Spat Spatiotemporal Epidemiol. 2010;1:105–16. https://doi.org/10.1016/j.sste.2009.12.001. CrossRefPubMed
MacNab YC. On identification in Bayesian disease mapping and ecological-spatial regression models. Stat Methods Med Res. 2014;23:134–55. http://www.ncbi.nlm.nih.gov/pubmed/22573502.
Goswami ND, Hecker EJ, Vickery C, Ahearn MA, Cox GM, Holland DP, et al. Geographic information system-based screening for TB, HIV, and syphilis (GIS-THIS): a cross-sectional study. PLoS ONE. 2012;7:1–8.
Sturtevant L. The new district of columbia: what population growth and demographic change mean for the city. J Urban Aff. 2014;36:276–99. CrossRef
Lawson A. Bayesian disease mapping: hierarchical modeling in spatial epidemiology. Boca Raton: Chapman and Hall: Taylor and Francis; 2013.
Aregay M, Lawson AB, Faes C, Kirby R. Bayesian multiscale modeling for aggregated disease mapping data. Proceedings of the Third ACM SIGSPATIAL International Workshop on the Use of GIS in Public Health—HealthGIS’14. 2014;45–8. http://www.scopus.com/inward/record.url?eid=2-s2.0-84926369237&partnerID=tZOtx3y1.
Gelman A. Multilevel (hierarchical) modeling: what it can and cannot do. Technometrics. 2006;48:432–5. CrossRef
Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity anf Fit. J R Stat Soc. 2002;64:583–639. CrossRef
Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Stat Sci. 1995;7:354–63.
Lawson AB, Browne WJ, CLVR. Disease Mapping with WinBUGS and MLwiN. Hoboken: Wiley; 2004.
Unaids. 90-90-90. An ambitious treatment target to help end the AIDS epidemic. 2014;40. http://www.unaids.org/sites/default/files/media_asset/90-90-90_en.pdf.
HAHSTA DC DEPT OF HEALTH, DC Appleseed WA partnership. 90-90-90-50 Plan. 2016.
- Geographic patterns of poor HIV/AIDS care continuum in District of Columbia
- BioMed Central
Neu im Fachgebiet Innere Medizin
Meistgelesene Bücher aus der Inneren Medizin
e.Med Kampagnen-Visual, Mail Icon II