Rethinking the heterosexual infectivity of HIV-1: a systematic review and meta-analysis.

Rethinking the heterosexual infectivity of HIV-1: a systematic review and meta-analysis.
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DOI:
10.1016/s1473-3099(08)70156-7
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发表时间:
2008-09
影响因子:
56.3
通讯作者:
Cohen, Myron S.
Cohen, Myron S.
中科院分区:
医学1区
文献类型:
--
作者:
Powers, Kimberly A.;Poole, Charles;Pettifor, Audrey E.;Cohen, Myron S.

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Studies of cumulative HIV incidence suggest that co-factors such as genital ulcer disease (GUD), HIV disease stage, and circumcision influence HIV transmission; however, the heterosexual infectivity of HIV-1 is commonly cited as a fixed value (∼0·001, or 1 transmission per thousand contacts). We sought to estimate transmission co-factor effects on the heterosexual infectivity of HIV-1 and to quantify the extent to which study methods have affected infectivity estimates. We conducted a systematic search (through April 2008) of PubMed, Web of Science, and relevant bibliographies to identify articles estimating the heterosexual infectivity of HIV-1. We used meta-regression and stratified random-effects meta-analysis to assess differences in infectivity by co-factors and study methods. Infectivity estimates were extremely heterogeneous, ranging from zero transmissions after more than 100 penile-vaginal contacts in some sero-discordant couples to one transmission for every 3·1 episodes of heterosexual anal intercourse. Estimates were only weakly associated with study methods. Infectivity differences (95% confidence intervals), expressed as number of transmissions per 1000 contacts, were 8 (0-16) comparing uncircumcised to circumcised male susceptibles, 6 comparing susceptible individuals with and without GUD, 2 comparing late-stage to mid-stage index cases, and 3 (0-5) comparing early-stage to mid-stage index cases. A single value for the heterosexual infectivity of HIV-1 fails to reflect the variation associated with important co-factors. The commonly cited value of ∼0·001 was estimated among stable couples with low prevalences of high-risk co-factors, and represents a lower bound. Co-factor effects are important to include in epidemic models, policy considerations, and prevention messages.