1. Estimating population size, HIV prevalence and HIV incidence among men who have sex with men: a case example of synthesising multiple empirical data sources and methods in San Francisco.
- Author
-
Raymond, H. Fisher, Bereknyei, Sylvia, Berglas, Nancy, Hunter, Jennifer, Ojeda, Norah, and McFarland, Willi
- Subjects
DISEASE prevalence ,HIV prevention ,MEN who have sex with men ,AIDS prevention ,DISEASES - Abstract
Objective The number of persons living with HIV/AIDS, the number of new infections and the number of persons at risk for HIV infection are the foundations of evidence-based prevention, treatment and care planning. However, few jurisdictions have complete and accurate estimates of these indicators. HIV/AIDS case reporting, which includes only persons diagnosed with infection and reported to health departments, does not reflect all HIV/AIDS cases, thus underestimating the true size of the epidemic. Obtaining direct measures of HIV incidence is methodologically challenging. Moreover, no censuses exist for the number of persons at highest risk for infection, including men who have sex with men (MSM). Method We present an approach of triangulation that draws upon multiple empirical and overlapping sources of information through different methods to synthesise data-based estimates of the prevalence, incidence and denominator of MSM at risk for infection in San Francisco. We further use existing data to establish plausible upper and lower bounds for each estimate. Result We arrived at an overall population size of 66 487 of MSM in San Francisco as of 31 December 2010. The number of MSM living with HIV/AIDS was 15 873, corresponding to an HIV prevalence of 23.9%. We projected 806 new cases in 2010, translating to an incidence rate of 1.59% per year. Conclusions While not without limitations, our estimates provide useful information for the purpose of HIV/AIDS prevention and care planning, drawing from diverse sources that may be available in local health jurisdictions. We believe that our approach enhances the credibility of such estimates by mitigating bias from only one source of data or one methodological approach. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF