Estimated HIV Incidence in California, 2006–2009

INTRODUCTION: Accurate estimates of HIV incidence are crucial for prioritizing, targeting, and evaluating HIV prevention efforts. Using the methodology the CDC used to estimate national HIV incidence, we estimated HIV incidence in Los Angeles County (LAC), San Francisco (SF), and California’s remaining counties. METHODS: We estimated new HIV infections in 2006–2009 among adults and adolescents in LAC, SF and the remaining California counties using the Serologic Testing Algorithm for Recent Seroconversion (STARHS). STARHS methodology uses the BED HIV-1 capture enzyme immunoassay to determine recent HIV infections by testing remnant serum from persons newly diagnosed with HIV. A population-based incidence estimate is calculated using HIV testing data from newly diagnosed cases and imputing for persons unaware of their HIV infection. RESULTS: For years 2007–2009, respectively, we estimated new infections in LAC to be 2426 (95% CI 1871–2982), 1669 (CI 1309–2029) and 1898 (CI 1452–2344) (p<0.01); in SF for 2006–2009, 492 (CI 327–657), 490 (CI 335–646), 458 (CI 342–574) and 367 (CI 261–473) (p = 0.14); and in the remaining California counties in 2008–2009, 2526 (CI 1688–3364) and 2993 (CI 2141–3846) respectively. HIV infection rates among men who have sex with men (MSM) in LAC were 100 times higher than other risk populations; the SF MSM rate was 3 to 18 times higher than other demographic groups. In LAC, incidence rates among African-Americans were twice those of whites and Latinos; persons 40 years or older had lower rates of infection than younger persons. DISCUSSION: We report the first HIV incidence estimates for California, highlighting geographic disparities in HIV incidence and confirming national findings that MSM and African-Americans are disproportionately impacted by HIV. HIV incidence estimates can and should be used to target prevention efforts towards populations at highest risk of acquiring new HIV infections, focusing on geographic, racial and risk group disparities.