Earlier diagnoses and faster treatment of HIV in the District of Columbia: HIV surveillance analysis, 2006-2016.

To address high HIV prevalence, the District of Columbia (DC) Department of Health has implemented multiple strategies to increase routine HIV testing since 2006. Examination of delayed HIV diagnosis over time can demonstrate population-level impact of public health strategies to promote HIV testing. Using HIV surveillance data, we examined delayed HIV diagnosis in DC (stage 3 within 90 days of diagnosis), CD4 count at HIV diagnosis, linkage to HIV care, and time to viral suppression among DC residents age 13 and above who were diagnosed from 2006 to 2016.  We used the Cochran-Armitage test of trend, Cuzick's test of trend, and Chi-square for univariate analyses, and we examined factors associated with delayed HIV diagnosis using a log-binomial multivariate model. 7,937 DC residents were diagnosed with HIV and had available data.  Between 2006 and 2016, delayed HIV diagnoses declined from 36.4% to 25.5%, median CD4 count increased from 190 cells/µl to 426 cells/µl, and median time from HIV diagnosis to viral suppression declined from 1,136 days to 84 days.  Women, youth ages 13-29, and men who have sex with men had lower proportions with delayed HIV diagnosis. In the multivariate models, racial/ethnic disparities in delayed HIV diagnoses were apparent during 2006-2008 but not during 2009-2016. Continued efforts around earlier HIV testing are needed in DC.

Planning area-specific prevention and intervention programs for HIV using spatial regression analysis.

OBJECTIVE: The study was conducted to inform area-based prevention intervention programs and plan resource allocation to reduce new infections in the District of Columbia (DC), United States of America. STUDY DESIGN: The analysis used spatial regression to evaluate the spatial heterogeneity of the new HIV rate and its association with sexually transmitted infection repeaters (STIREPs) and socio-economic as well as demographic characteristics. The HIV and STIREP data were obtained from the DC Department of Health surveillance data (2010-2016). Other covariates were obtained from the American Community Survey, 2016. METHODS: Ordinary least squares (OLS) and geographically weighted regression (GWR) were used to compare global and local relationships. GWR-computed robust results were compared with other spatial regression methods such as spatial lag or spatial error methods. RESULTS: For the OLS model, age, high school dropouts (NHSD), and the black population had an association with new HIV diagnoses (HIVDVi). The results from the GWR model demonstrate spatial variations of association of STIREPs; mean age of each block group; and percentage of female population, NHSD, unemployment, and poverty with HIVDVi. Akaike information criterion (AICc) value for the global model was 2770.99, and R2 was 0.54 (54%). The R2 and AICc of the GWR model was 0.81 (81%) and 2580.84, respectively, where the latter showed a 0.27 (27%) increase in R2 and a decreased AICc. CONCLUSION: These results will assist in planning HIV prevention and intervention strategies. These results will also be used for targeted testing, planning pre-exposure prophylaxis, and access to health care. The results will help plan resource allocation to community-based providers for prevention intervention programs and fund public health programs such as condom distribution, mobile vans, and youth-based sex education.