HIV transmission risk through anal intercourse: systematic review, meta-analysis and implications for HIV prevention
01 Aug 2010-International Journal of Epidemiology (Oxford University Press)-Vol. 39, Iss: 4, pp 1048-1063
TL;DR: It was demonstrated that it would require unreasonably low numbers of AI HIV exposures per partnership to reconcile the summary per-act and per-partner estimates, suggesting considerable variability in AI infectiousness between and within partnerships over time.
Abstract: Background The human immunodeficiency virus (HIV) infectiousness of anal intercourse (AI) has not been systematically reviewed, despite its role driving HIV epidemics among men who have sex with men (MSM) and its potential contribution to heterosexual spread. We assessed the per-act and per-partner HIV transmission risk from AI exposure for heterosexuals and MSM and its implications for HIV prevention.
Methods Systematic review and meta-analysis of the literature on HIV-1 infectiousness through AI was conducted. PubMed was searched to September 2008. A binomial model explored the individual risk of HIV infection with and without highly active antiretroviral therapy (HAART).
Results A total of 62 643 titles were searched; four publications reporting per-act and 12 reporting per-partner transmission estimates were included. Overall, random effects model summary estimates were 1.4% [95% confidence interval (CI) 0.2–2.5)] and 40.4% (95% CI 6.0–74.9) for per-act and per-partner unprotected receptive AI (URAI), respectively. There was no significant difference between per-act risks of URAI for heterosexuals and MSM. Per-partner unprotected insertive AI (UIAI) and combined URAI–UIAI risk were 21.7% (95% CI 0.2–43.3) and 39.9% (95% CI 22.5–57.4), respectively, with no available per-act estimates. Per-partner combined URAI–UIAI summary estimates, which adjusted for additional exposures other than AI with a ‘main’ partner [7.9% (95% CI 1.2–14.5)], were lower than crude (unadjusted) estimates [48.1% (95% CI 35.3–60.8)]. Our modelling demonstrated that it would require unreasonably low numbers of AI HIV exposures per partnership to reconcile the summary per-act and per-partner estimates, suggesting considerable variability in AI infectiousness between and within partnerships over time. AI may substantially increase HIV transmission risk even if the infected partner is receiving HAART; however, predictions are highly sensitive to infectiousness assumptions based on viral load.
Conclusions Unprotected AI is a high-risk practice for HIV transmission, probably with substantial variation in infectiousness. The significant heterogeneity between infectiousness estimates means that pooled AI HIV transmission probabilities should be used with caution. Recent reported rises in AI among heterosexuals suggest a greater understanding of the role AI plays in heterosexual sex lives may be increasingly important for HIV prevention.
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TL;DR: Though consistent lubricant use is common among this Thai MSM cohort, further promotion is needed with MSM engaging in risky sexual practices.
Abstract: This analysis measures prevalence and correlates of consistent lubricant use among a cohort of Thai men who have sex with men (MSM). Lubricant use was queried at the 12-month follow-up visit. Consistent lubricant use was evaluated with logistic regression. Consistent lubricant use was reported by 77.0% of men and was associated with consistent condom use with casual partners, while binge drinking, payment for sex, and inconsistent condom use with casual, and steady, partners were negatively associated. Though consistent lubricant use is common among this Thai MSM cohort, further promotion is needed with MSM engaging in risky sexual practices.
4 citations
TL;DR: Sensitivity analyses showed that the optimal allocation shifted when there were differences in transmission category proportions and progress along the HIV care continuum, suggesting that most health departments can use these analyses to guide the investment of CDC HIV prevention funds into strategies to prevent the most new cases.
Abstract: Objective
To estimate the optimal allocation of Centers for Disease Control and Prevention (CDC) HIV prevention funds for health departments in 52 jurisdictions, incorporating Health Resources and Services Administration (HRSA) Ryan White HIV/AIDS Program funds, to improve outcomes along the HIV care continuum and prevent infections.
Methods
Using surveillance data from 2010 to 2012 and budgetary data from 2012, we divided the 52 health departments into 5 groups varying by number of persons living with diagnosed HIV (PLWDH), median annual CDC HIV prevention budget, and median annual HRSA expenditures supporting linkage to care, retention in care, and adherence to antiretroviral therapy. Using an optimization and a Bernoulli process model, we solved for the optimal CDC prevention budget allocation for each health department group. The optimal allocation distributed the funds across prevention interventions and populations at risk for HIV to prevent the greatest number of new HIV cases annually.
Results
Both the HIV prevention interventions funded by the optimal allocation of CDC HIV prevention funds and the proportions of the budget allocated were similar across health department groups, particularly those representing the large majority of PLWDH. Consistently funded interventions included testing, partner services and linkage to care and interventions for men who have sex with men (MSM). Sensitivity analyses showed that the optimal allocation shifted when there were differences in transmission category proportions and progress along the HIV care continuum.
Conclusion
The robustness of the results suggests that most health departments can use these analyses to guide the investment of CDC HIV prevention funds into strategies to prevent the most new cases of HIV.
4 citations
4 citations
TL;DR: The authors conducted in-depth interviews with 19 men who have sex with transgender women in Baltimore, Maryland and Atlanta, Georgia to explore perspectives on HIV prevention, and found that participants used several strategies to reduce HIV risk: condoms, frequent HIV testing, communication about HIV status with partners, and limiting the types of sex acts performed.
Abstract: Cisgender men who have sex with transgender women face elevated risk for HIV, yet are understudied in prevention research. We conducted in-depth interviews with 19 men who have sex with transgender women in Baltimore, Maryland and Atlanta, Georgia to explore perspectives on HIV prevention. Participants used several strategies to reduce HIV risk: condoms, frequent HIV testing, communication about HIV status with partners, and limiting the types of sex acts performed. While condom use was inconsistent, it was preferred over pre-exposure prophylaxis, in part due to medical distrust. HIV self-testing was generally viewed unfavorably. Although most participants were referred to the study by their transgender women partners, they recommended reaching other men who have sex with transgender women in bars, nightclubs, online, and through social media. HIV prevention interventions should be inclusive of the needs and experiences of men who have sex with transgender women.
4 citations
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References
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TL;DR: A new quantity is developed, I 2, which the authors believe gives a better measure of the consistency between trials in a meta-analysis, which is susceptible to the number of trials included in the meta- analysis.
Abstract: Cochrane Reviews have recently started including the quantity I 2 to help readers assess the consistency of the results of studies in meta-analyses. What does this new quantity mean, and why is assessment of heterogeneity so important to clinical practice?
Systematic reviews and meta-analyses can provide convincing and reliable evidence relevant to many aspects of medicine and health care.1 Their value is especially clear when the results of the studies they include show clinically important effects of similar magnitude. However, the conclusions are less clear when the included studies have differing results. In an attempt to establish whether studies are consistent, reports of meta-analyses commonly present a statistical test of heterogeneity. The test seeks to determine whether there are genuine differences underlying the results of the studies (heterogeneity), or whether the variation in findings is compatible with chance alone (homogeneity). However, the test is susceptible to the number of trials included in the meta-analysis. We have developed a new quantity, I 2, which we believe gives a better measure of the consistency between trials in a meta-analysis.
Assessment of the consistency of effects across studies is an essential part of meta-analysis. Unless we know how consistent the results of studies are, we cannot determine the generalisability of the findings of the meta-analysis. Indeed, several hierarchical systems for grading evidence state that the results of studies must be consistent or homogeneous to obtain the highest grading.2–4
Tests for heterogeneity are commonly used to decide on methods for combining studies and for concluding consistency or inconsistency of findings.5 6 But what does the test achieve in practice, and how should the resulting P values be interpreted?
A test for heterogeneity examines the null hypothesis that all studies are evaluating the same effect. The usual test statistic …
45,105 citations
TL;DR: A checklist contains specifications for reporting of meta-analyses of observational studies in epidemiology, including background, search strategy, methods, results, discussion, and conclusion should improve the usefulness ofMeta-an analyses for authors, reviewers, editors, readers, and decision makers.
Abstract: ObjectiveBecause of the pressure for timely, informed decisions in public health
and clinical practice and the explosion of information in the scientific literature,
research results must be synthesized. Meta-analyses are increasingly used
to address this problem, and they often evaluate observational studies. A
workshop was held in Atlanta, Ga, in April 1997, to examine the reporting
of meta-analyses of observational studies and to make recommendations to aid
authors, reviewers, editors, and readers.ParticipantsTwenty-seven participants were selected by a steering committee, based
on expertise in clinical practice, trials, statistics, epidemiology, social
sciences, and biomedical editing. Deliberations of the workshop were open
to other interested scientists. Funding for this activity was provided by
the Centers for Disease Control and Prevention.EvidenceWe conducted a systematic review of the published literature on the
conduct and reporting of meta-analyses in observational studies using MEDLINE,
Educational Research Information Center (ERIC), PsycLIT, and the Current Index
to Statistics. We also examined reference lists of the 32 studies retrieved
and contacted experts in the field. Participants were assigned to small-group
discussions on the subjects of bias, searching and abstracting, heterogeneity,
study categorization, and statistical methods.Consensus ProcessFrom the material presented at the workshop, the authors developed a
checklist summarizing recommendations for reporting meta-analyses of observational
studies. The checklist and supporting evidence were circulated to all conference
attendees and additional experts. All suggestions for revisions were addressed.ConclusionsThe proposed checklist contains specifications for reporting of meta-analyses
of observational studies in epidemiology, including background, search strategy,
methods, results, discussion, and conclusion. Use of the checklist should
improve the usefulness of meta-analyses for authors, reviewers, editors, readers,
and decision makers. An evaluation plan is suggested and research areas are
explored.
17,663 citations
TL;DR: The problem of making a combined estimate has been discussed previously by Cochran and Yates and Cochran (1937) for agricultural experiments, and by Bliss (1952) for bioassays in different laboratories as discussed by the authors.
Abstract: When we are trying to make the best estimate of some quantity A that is available from the research conducted to date, the problem of combining results from different experiments is encountered. The problem is often troublesome, particularly if the individual estimates were made by different workers using different procedures. This paper discusses one of the simpler aspects of the problem, in which there is sufficient uniformity of experimental methods so that the ith experiment provides an estimate xi of u, and an estimate si of the standard error of xi . The experiments may be, for example, determinations of a physical or astronomical constant by different scientists, or bioassays carried out in different laboratories, or agricultural field experiments laid out in different parts of a region. The quantity xi may be a simple mean of the observations, as in a physical determination, or the difference between the means of two treatments, as in a comparative experiment, or a median lethal dose, or a regression coefficient. The problem of making a combined estimate has been discussed previously by Cochran (1937) and Yates and Cochran (1938) for agricultural experiments, and by Bliss (1952) for bioassays in different laboratories. The last two papers give recommendations for the practical worker. My purposes in treating the subject again are to discuss it in more general terms, to take account of some recent theoretical research, and, I hope, to bring the practical recommendations to the attention of some biologists who are not acquainted with the previous papers. The basic issue with which this paper deals is as follows. The simplest method of combining estimates made in a number of different experiments is to take the arithmetic mean of the estimates. If, however, the experiments vary in size, or appear to be of different precision, the investigator may wonder whether some kind of weighted meani would be more precise. This paper gives recommendations about the kinds of weighted mean that are appropriate, the situations in which they
4,335 citations
TL;DR: The viral load is the chief predictor of the risk of heterosexual transmission of HIV-1, and transmission is rare among persons with levels of less than 1500 copies of HIV -1 RNA per milliliter.
Abstract: Background and Methods We examined the influence of viral load in relation to other risk factors for the heterosexual transmission of human immunodeficiency virus type 1 (HIV-1). In a community-based study of 15,127 persons in a rural district of Uganda, we identified 415 couples in which one partner was HIV-1–positive and one was initially HIV-1–negative and followed them prospectively for up to 30 months. The incidence of HIV-1 infection per 100 person-years among the initially seronegative partners was examined in relation to behavioral and biologic variables. Results The male partner was HIV-1–positive in 228 couples, and the female partner was HIV-1–positive in 187 couples. Ninety of the 415 initially HIV-1–negative partners seroconverted (incidence, 11.8 per 100 person-years). The rate of male-to-female transmission was not significantly different from the rate of female-to-male transmission (12.0 per 100 person-years vs. 11.6 per 100 person-years). The incidence of seroconversion was highest among ...
2,897 citations
TL;DR: A theoretical strategy of universal voluntary HIV testing and immediate treatment with ART, combined with present prevention approaches, could have a major effect on severe generalised HIV/AIDS epidemics.
1,948 citations