IAS 2015 V ANCOUVER, C ANADA J ULY 21, 2015 G ALANT A U C HAN A MY B ENNETT S USAN B USKIN D AVID K ATZ M ATTHEW G OLDEN

Background In the US, HIV disproportionately affects MSM. The proportion of cases occurring in black MSM is increasing. Life tables can be used to estimate the lifetime risk of HIV diagnosis within age cohorts.

Methods We constructed life tables for to estimate the cumulative risk of HIV diagnosis among MSM born in U.S. Census data to define size of male populations in King County, Washington 6% of male population in King County estimated to be MSM, reflecting net in-migration during ages Local surveillance data to define # of HIV diagnoses in MSM each year, by age and race

Cumulative Risk of HIV Diagnosis Among White MSM

Cumulative Risk of HIV Diagnosis Among Black MSM

Cumulative Risk of HIV Diagnosis Among MSM : A Comparison between Racial Categories

Birth cohort Absolute Difference Relative Difference %43.20% %83.70%

Summary Cumulative risk of HIV diagnosis progressed in 3 phases: Increased in MSM born 1940s – 1960s Highest risk in MSM born Declined approximately 65% in those born after the mid-1960s Risk plateaued in later birth cohorts, born after mid-1970s This trend occurred in both black and white MSM Significant racial disparities persist. The largest absolute decrease in risk occurred in black MSM Relative disparities have increased in later birth cohorts

Limitations Undiagnosed HIV infection not included Generalizability to other localities Estimation of MSM population imprecise, although imprecision would not affect trend estimates Changes in ascertainment of race in the US census

Conclusions Our findings suggest that King County may be aging out of the HIV epidemic. High rates of infection in young MSM in some areas of the US suggest that such a trend is not ongoing nationally. The absence of progress in young MSM presents a long-term problem, as high rates of infection early in the life course lead to high cumulative risk of HIV within a birth cohort.

Acknowledgments Matthew Golden David Katz Susan Buskin Amy Bennett Julia Dombrowski Christine Khosropour Funding: T32 Training grant AI

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