Men who have sex with men (MSM) remain disproportionately burdened by the HIV epidemic. In the United States, an estimated 25 000–37 000 MSM were newly infected with HIV in 2006  primarily through receptive anal intercourse [2–4]. Alcohol consumption among MSM is well characterized  and has been implicated as a risk factor for HIV infection . However, existing epidemiologic evidence of an effect of alcohol consumption on HIV seroconversion has been mixed [7,8], and therefore inconclusive for supporting population-level alcohol interventions as a strategy for HIV prevention .
In particular, recent studies using multiple time-dependent measures of alcohol consumption and adjusting for potential time-dependent confounders provide mixed results with one  supporting an earlier finding of a harmful association  and two not supporting a harmful association [12,13]. However, the standard statistical methods (e.g. regression, stratification) used in past studies may have failed to provide consistent estimates of the hypothesized detrimental effect of alcohol consumption because of inadequate control of time-dependent confounders [14,15]. Recent work among injection drug users suggests that standard adjustment for time-dependent confounders (e.g. illicit drug use) may block indirect effects of alcohol consumption acting through such confounders and lead to biased estimates .
Here we applied marginal structural models to estimate the association between alcohol consumption and HIV seroconversion. Using this approach, we can account for measured time-dependent confounders affected by prior alcohol consumption. We analyzed data from MSM at risk for HIV seroconversion using prospective data from the Multicenter AIDS Cohort Study (MACS) collected between 1984 and 2008. Specifically, we examined the joint effects of alcohol consumption and unprotected receptive anal intercourse on the risk of HIV seroconversion. We hypothesized that both high levels of alcohol consumption and unprotected receptive anal intercourse increase the hazard of HIV seroconversion, and that this joint effect is greater than multiplicative.
The study sample consisted of a subset of participants enrolled in the MACS, an ongoing study of the natural history of HIV infection among MSM in the US metropolitan areas of Baltimore, Maryland/Washington DC; Chicago, Illinois; Los Angeles, California; and Pittsburgh, Pennsylvania . Enrolled were 6972 men: 5622 in 1984–1992 and 1350 in 2001. Of these 6972 men, 4029 were sexually active, but HIV-seronegative at their baseline visit and were therefore eligible for this study. We analyzed data on the 3725 HIV-seronegative men who completed at least one follow-up visit.
Participants are followed semiannually at study visits that involve a physical examination, blood draws, and standardized questionnaires including the Center for Epidemiologic Studies Depression (CES-D) Scale . The risk behavior portions of the questionnaire were interviewer administered initially and were converted to audio computer-assisted self-interview for newly enrolled participants beginning in October 2001 and at follow-up visits for all participants in October 2002. Institutional review boards approved protocols and written informed consent forms completed by all study participants. MACS design details and questionnaires are available at http://www.statepi.jhsph.edu.
Ascertainment of HIV seroconversion
Participants were followed from their baseline visit until HIV seroconversion, death, loss to follow-up, or administrative censoring. HIV status was determined from blood specimens tested by enzyme-linked immunosorbent assay and was confirmed by western blot. The midpoint between dates of the last seronegative and the first seropositive test was taken as the estimated date of HIV seroconversion; when this date was more than 1 year after the last seronegative test date (n = 33), participants were classified as lost to follow-up. Follow-up practices in the MACS cohort have been described previously . Briefly, death information was obtained from death certificates, the Social Security Death Index, the National Death Index, autopsy records, and other notification sources. We censored follow-up for participants who failed to attend study visits for more than 1 year at the minimum of their date of death (if applicable), 1 year after their last visit, or 1 January 2008 (date of administrative censoring). All remaining seronegative participants seen after 1 January 2007 were administratively censored on 1 January 2008.
Assessment of alcohol consumption
The typical number of drinks per week consumed by each participant was calculated as the product of participant-reported average number of drinking-days per week and average number of drinks per drinking-day (range: 0–84 drinks/week). A drink was defined explicitly as one 12-ounce beer (∼355 ml), one 4–5-ounce glass of wine (∼120–150 ml), or one mixed drink with 1.5 ounces (∼44 ml) of 80-proof hard liquor. The few (<1%) reports of 10 or more drinks per drinking-day were classified as 12 drinks. In models, we considered three levels of drinking: nondrinkers, moderate drinkers (1–14 drinks/week), and heavy drinkers (>14 drinks/week) based on reports averaged over the prior two visits (approximately 1 year). This categorization reflects current public health recommendations that adult men consume no more than 2 drinks/day . The exposure window was chosen to maximize stability of the alcohol assessment. We considered the impact of this choice of exposure window on our results by considering a range of empirical induction periods (≥2 years prior) shown in Supplementary Table 1, http://links.lww.com/QAD/A293. Trends were generally insensitive to the exposure window chosen although, as expected, the magnitude of the observed effect decreased as length of the exposure window increased.
Assessment of sexual risk behavior
The number of partners with whom the participant was the receptive partner during unprotected anal intercourse (hereafter, partners) was previously identified as a strong predictor of HIV seroconversion in the MACS [13,21] and other cohorts ; we therefore consider this exposure to be a marker of overall sexual risk behavior. Participants self-report the number of partners they have had at each semiannual visit. The few (<1%) reports of more than six partners since the previous visit were reset to the median of those with more than six partners (10 partners). In models, we considered the number of partners as one or fewer partners or multiple partners. Similar to alcohol measures, we averaged the number of partners over the previous two visits. The reference group combined men with one partner and those who report no partners because participants with a single long-term partner may not be at increased risk of HIV seroconversion and MACS participants currently without a partner are not representative of MSM who do not have unprotected anal intercourse. The overall distribution of alcohol consumption and partner number is presented stratified by time in Supplementary Table 2, http://links.lww.com/QAD/A293.
Assessment of covariates
Based on previously identified determinants of alcohol consumption [22,23] and HIV risk factors , we considered several time-fixed and time-dependent covariates as confounders. The following variables were assessed at baseline: participant's race and ethnicity (white non-Hispanic, white Hispanic, or black), age, enrollment city, and education (college graduate or not). Data on time-dependent confounders were recorded at each semiannual visit and included depressive symptoms indicated by a CES-D score more than 16; self-report of either gonorrhea or chlamydial infection; cigarette smoking (current or not); and use of any of the following illicit drugs: cocaine, crack cocaine, marijuana/hash, or nitrite inhalants (i.e., poppers). Injection drug use was uncommon (<1%) as was methamphetamine use (4%), which furthermore was not captured consistently over follow-up. Just 7% of the cohort reported use of any other drugs, including heroin. We therefore considered use of cocaine, crack cocaine, marijuana/hash, or nitrite inhalants as confounders.
Baseline data on smoking, CES-D score, and number of partners were missing for 6, 6, and 7% of participants, respectively. Data on all other variables were missing for less than 2% of participants. For the few values missing at baseline, we imputed the mode. For missing values over follow-up, the value from the previous visit was carried forward (smoking, 6%; CES-D score, 8%; number of partners, 9%; all others, <4%).
We used a joint marginal structural Cox proportional hazards model to estimate the joint effects of alcohol consumption and partner number on HIV seroconversion . The marginal structural model provides asymptotically consistent estimates of contrasts in potential outcomes under the assumptions of consistency, exchangeability, positivity, and correct model specification for each exposure and censoring. Details of the estimation of the joint marginal structural model are provided in Appendix A, http://links.lww.com/QAD/A293 [25,26].
Cumulative incidence of HIV seroconversion curves accounting for time-dependent confounders are presented . Effects were quantified using hazard ratios, and precision was assessed through 95% confidence limits based on robust variances. Departure from additivity was assessed using the relative excess risk due to interaction (RERI) . We evaluated the contribution of the product term using a robust Wald χ 2 test. No evidence of departure from proportional hazards for the exposures was observed in models that included exposure by time (P = 0.86) or exposure by log-time (P = 0.70) product terms, which allowed us to assume that the hazards of seroconversion remained constant over the 24-year follow-up period.
Alongside our weighted results, we present observed counts of HIV seroconversions and corresponding incidence rates calculated as number of HIV seroconversions divided by the number of person-years of observation. We also present results from standard analyses, which adjust for the same time-dependent confounders by including lagged values of time-dependent covariates in a standard Cox model . Results were similar to those observed when concurrent values of time-dependent covariates were included in the model. All analyses were conducted with SAS version 9.2 (SAS Institute, Inc., Cary, North Carolina, USA).
Between 1984 and 2008, 3725 men were followed for a median of 10.5 years [interquartile range (IQR): 4.7–11.7], during which 529 HIV seroconversions were observed. Eighty-three (2%) participants died during follow-up, 311 (8%) were lost to follow-up, and 2802 (75%) were administratively censored.
Participants were mostly white non-Hispanic (82%) and college graduates (59%). At baseline, members of this sexually active population reported a median of 1 (IQR: 0–2) partner in the prior 2 years. Illicit drug use was common (77%), as was smoking (51%) and most participants consumed alcohol (9% nondrinkers) but at a generally moderate level: median 8 drinks/week (IQR: 2–16) (Table 1). Over follow-up, HIV risk behaviors were less prevalent: participants reported no partners at 78% of follow-up visits; illicit drug use was reported at 48% of follow-up visits, and median alcohol consumption was 4 drinks/week (IQR: 2–12) over follow-up, each measured in the prior 6 months. Additional description of the participants by seroconversion status and by alcohol consumption are provided in Supplementary Tables 3 and 4, http://links.lww.com/QAD/A293, respectively.
Reports of heavy drinking were most common from white, non-Hispanics (91%) compared with white Hispanics and black, illicit drug users (73%) compared with nonusers, and men reporting multiple partners (13%) compared with those reporting 0–1 partners. Men reporting 0–1 or more than 1 partner on average over the prior year experienced crude HIV incidence rates of 9 (95% confidence limits: 8, 10) and 76 (95% confidence limits: 66, 86) cases per 1000 person-years, respectively. Figure 1a depicts this cumulative incidence for these two groups in the weighted population. For nondrinkers, moderate drinkers, and heavy drinkers over the prior year the crude incidence of HIV seroconversion were 10 (95% confidence limits: 7, 13), 13 (95% confidence limits: 12, 15), and 26 (95% confidence limits: 22, 31) cases per 1000 person-years, respectively (Table 2). Heavy drinkers were most likely to HIV seroconvert over follow-up in the weighted population (χ 2 Wald trend test P < 0.001) (Fig. 1b).
Table 2 presents the effects of alcohol consumption on HIV seroconversion from models that average over partners. In unadjusted, adjusted, and weighted models, the hazard for moderate drinkers was similar to that for nondrinkers, whereas the hazard for heavy drinkers was elevated. Compared with the unadjusted hazard ratio for heavy drinkers of 1.52 (95% confidence limits: 1.07, 2.16), adjustment for age, race, ethnicity, study site, depression symptoms, college graduation, smoking, illicit drug use, number of partners, and sexually transmitted infection using standard methods produced an attenuated hazard ratio of 1.19 (95% confidence limits: 0.83, 1.70). After accounting for the same variables using marginal structural models, the hazard of HIV seroconversion for heavy drinkers in the past year was 1.61 (95% confidence limits: 1.12, 2.29) times that of nondrinkers.
Table 3 presents the joint effects of alcohol consumption and partners in the prior year on HIV seroconversion from models that include a product term. We represent these joint effects in two ways: by examining the effect of alcohol consumption within strata of partners, and by presenting hazard ratios relative to a common referent, that is, nondrinkers without multiple partners. In the weighted model, the association between heavy drinking and HIV seroconversion appeared stronger among men with multiple partners (hazard ratio = 1.96; 95% confidence limits: 1.03, 3.72) versus without (hazard ratio = 1.37; 95% confidence limits: 0.88, 2.16), although this difference was imprecise (P = 0.42). The observed hazard ratio for moderate drinkers with multiple partners compared with nondrinkers without multiple partners (hazard ratio = 4.48) was similar to those expected under a multiplicative model (expected hazard ratio = 4.04). The observed hazard ratio for heavy drinkers with multiple partners compared with nondrinkers without multiple partners was larger than expected under a multiplicative model: hazard ratio = 7.40 and expected hazard ratio = 5.18, although again this departure was imprecise (Table 3). With respect to interaction on the additive scale, the RERI suggests departure from additivity for heavy drinkers with multiple partners (RERI = 3.25; 95% confidence limits: 0.32, 6.17) but not moderate drinkers with multiple partners (RERI = 0.63; 95% confidence limits: −1.81, 3.07).
Again, compared with results from the standard Cox model, results from the weighted model suggested a stronger effect. For example, when heavy drinkers with multiple partners were compared with nondrinkers without multiple partners, the hazard ratio from the marginal structural model was 7.40 (95% confidence limits: 4.74, 11.54) and the analogous hazard ratio from the standard adjusted model was 4.97 (95% confidence limits: 3.18, 7.77) (Table 3). Similar to the weighted analysis, no statistically significant evidence was found for greater-than-multiplicative interaction in the standard analysis (P = 0.28) (Table 3).
We reported the effect of alcohol consumption and risky sexual behavior on HIV seroconversion in prospective data on 3725 MSM followed in 1984–2008. Heavy alcohol consumption was associated with 1.61 times the hazard of HIV seroconversion compared with no consumption. We furthermore presented results from models that use traditional adjustment approaches alongside results from weighted models showing that traditional approaches produced attenuated estimates, although confidence intervals did overlap. Without results from the weighted models, we might have erroneously concluded that there was no independent association between alcohol consumption and HIV seroconversion.
The unadjusted hazard ratio of 1.52 we reported for heavy alcohol consumption versus nondrinking is similar to unadjusted hazard ratios reported in previous studies of MSM populations [4,10–13,30], although it is noteworthy that these studies defined alcohol consumption differently and are therefore not directly comparable. We reported an attenuated hazard ratio of 1.19 when adjusted for time-dependent confounders using traditional adjustment. This attenuation mirrors that reported by the San Francisco Men's Health Study  and in an earlier report of MACS data . The latter study reported an unadjusted hazard ratio of 2.05 (95% confidence limits: 1.53, 2.74) for heavy versus less-than-heavy drinking but a hazard ratio of 1.13 (95% confidence limits: 0.81, 1.56) after adjustment for time-dependent confounders. A statistically significant adjusted association between heavy alcohol consumption and HIV seroconversion persisted in only the EXPLORE cohort (hazard ratio = 1.97; 95% confidence limits: 1.32, 2.96) . Our findings and the majority of previous studies suggest that standard adjustment removes part of the indirect effect of alcohol consumption acting through other time-dependent HIV risk factors for which authors have previously adjusted (e.g. illicit drug use).
Changes in behaviors and expectancy that accompany alcohol consumption may be responsible for increased sexual risk behaviors and for subsequent HIV seroconversion observed among heavy drinkers [31,32]. Alcohol consumption is associated with higher numbers of sexual partners, higher numbers of unprotected anal sex acts, and condom failure [33,34]. Acute alcohol consumption has also been linked to suppression of both the innate and adaptive immune response and increased susceptibility to numerous infections, including HIV [32,35–37].
A limitation of the present data is collection of alcohol consumption measures that require recall over a 6-month period. As researchers have stated previously, global measures of alcohol consumption do not allow investigation of specific contextual modifiers of the relationships between alcohol consumption and sexual risk behaviors such as partner type and partner's alcohol consumption [6,38,39]. These contextual factors in turn may explain why we did not see a dramatic departure from multiplicative combination between the effects of alcohol consumption and sexual risk behavior on HIV seroconversion. For example, researchers have found that MSM are more likely to use condoms with casual partners while drinking but less likely to use condoms with steady partners in the same setting [40–42].
Self-reported alcohol consumption may also be an inadequate proxy for alcohol-induced responses. The behavioral and physiologic effects of alcohol may be person-specific, dependent on genetic background, body mass composition, and diet. Future research applying biomarkers of alcohol consumption to evaluate the reliability or accuracy of self-report data is needed in studies of sexual health.
As with any analysis, the validity of our inferences is limited by the degree to which we met our assumptions. First, we assumed no unmeasured confounding and no informative censoring due to unmeasured factors. However, there are likely unmeasured behavioral factors confounding the observed association. Moreover, we acknowledge that measurement of the confounders we included is imperfect (e.g. self-reported as opposed to directly assessed sexually transmitted infections) and may result in residual confounding. Second, we assumed that heavy drinkers who seroconverted during the study period are representative of those who seroconverted prior to study entry. Men who seroconverted prior to study entry may have engaged in more concomitant high-risk behaviors than those whose seroconversions were observed, leading us to observe a weaker association between alcohol consumption and HIV seroconversion. Third, we assumed that mode of exposure is irrelevant to the observed outcome . However, the behavioral mechanisms described above suggest that ignoring type of alcohol consumed and timing of its consumption with respect to HIV exposure may not be reasonable and may limit our ability to prescribe generalizable interventions based on our findings. Nevertheless, the demonstrable effect of alcohol consumption, measured broadly, is valuable in that it supports research to identify the particular means of exposure relevant for interventions.
The present study has several important strengths. First, it used information from a large prospective cohort of sexually active MSM followed for over two decades. Second, a large number of HIV seroconversions were observed, including a sizable portion among men previously reporting heavy drinking. Finally, using state-of-the-art quantitative methods, this study more fully captures the direct and indirect effects of alcohol consumption on HIV seroconversion – specifically, both the direct effect of alcohol on HIV susceptibility and the indirect effects mediated through HIV risk behaviors, such as illicit drug use that are also affected by prior alcohol consumption.
The potential for linking alcohol interventions with HIV prevention activities was described more than a decade ago , and randomized interventions that explicitly address alcohol's contribution to HIV have been tested in Africa [45–47]. However, such interventions have lagged behind for US adult MSM . We have reported an effect of alcohol consumption on HIV seroconversion among MSM of similar magnitude to illicit drugs such as methamphetamine, cocaine, and ecstasy . Under the abovestated assumptions, our results support the conclusion that 16% of HIV seroconversions among heavy drinkers could be prevented if half of these drinkers reduce their drinking to moderate levels; 21% could be prevented if two-thirds reduce their drinking to moderate levels . If replicated, our findings renew the call for population-level HIV interventions among US MSM that explicitly address heavy alcohol consumption.
Author contributions: P.M.S. had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Individual authorship roles were as follows: study concept and design – P.M.S., S.R.C., L.P.J., D.G.O.; acquisition of data – S.R.C., L.P.J.; analysis and interpretation of data – P.M.S., S.R.C., R.D.S., J.J.E., S.N., B.N.G., L.P.J., L.H.J.-H., R.K.B., D.G.O.; drafting of the manuscript – P.M.S.; critical revision of the manuscript for important intellectual content – P.M.S., S.R.C., R.D.S., J.J.E., S.N., B.N.G., L.P.J., L.H.J.-H., R.K.B., D.G.O.; statistical analysis – P.M.S., S.R.C.; obtained funding – S.R.C., L.P.J., D.G.O.; administrative, technical, or material support – R.D.S., L.P.J., L.H.J.-H., R.K.B., D.G.O.; and supervision – S.R.C., J.J.E., S.N., B.N.G.
The authors thank Chanelle Howe, PhD, and Myron Cohen, MD, for expert advice and Ms Debby Anderson for editorial assistance.
This work was supported by the National Institute on Alcohol Abuse and Alcoholism (grant number R01-AA-01759 to S.R.C.). Data in this manuscript were collected by the MACS with centers (Principal Investigators) at The Johns Hopkins Bloomberg School of Public Health (Joseph B. Margolick, Lisa P. Jacobson), Howard Brown Health Center, Feinberg School of Medicine, Northwestern University, and Cook County Bureau of Health Services (John P. Phair, Steven M. Wolinsky), University of California, Los Angeles (Roger Detels), and University of Pittsburgh (Charles R. Rinaldo). The MACS is funded by the National Institute of Allergy and Infectious Diseases, with additional supplemental funding from the National Cancer Institute [grant numbers UO1-AI-35042, 5-MO1-RR-00052 (GCRC), UO1-AI-35043, UO1-AI-35039, UO1-AI-35040, UO1-AI-35041]. Website located at http://www.statepi.jhsph.edu/macs/macs.html.
Conflicts of interest
All authors declare no conflict of interest.
1. Hall HI, Song R, Rhodes P, Prejean J, An Q, Lee M, et al. Estimation of HIV incidence in the United States
2. Kingsley LA, Detels R, Kaslow R, Polk BF, Rinaldo CR, Chmiel J, et al. Risk factors for seroconversion to human immunodeficiency virus among male homosexuals. Results from the Multicenter AIDS Cohort Study
3. Detels R, English P, Visscher BR, Jacobson L, Kingsley LA, Chmiel JS, et al. Seroconversion, sexual activity, and condom use among 2915 HIV seronegative men followed for up to 2 years
. J Acquir Immune Defic Syndr
4. Page-Shafer K, Veugelers PJ, Moss AR, Strathdee S, Kaldor JM, van Griensven GJ. Sexual risk behavior and risk factors for HIV-1 seroconversion in homosexual men participating in the Tricontinental Seroconverter Study, 1982–1994
. Am J Epidemiol
5. Stall R, Paul JP, Greenwood G, Pollack LM, Bein E, Crosby GM, et al. Alcohol use, drug use and alcohol-related problems among men who have sex with men: the Urban Men's Health Study
6. Woolf SE, Maisto SA. Alcohol use and risk of HIV infection among men who have sex with men
. AIDS Behav
7. Howe CJ, Sander PM, Plankey MW, Cole SR. Effects of time-varying exposures adjusting for time-varying confounders: the case of alcohol consumption and risk of incident human immunodeficiency virus infection
. Int J Public Health
8. Baliunas D, Rehm J, Irving H, Shuper P. Alcohol consumption and risk of incident human immunodeficiency virus infection: a meta-analysis
. Int J Public Health
9. Shuper PA, Neuman M, Kanteres F, Baliunas D, Joharchi N, Rehm J. Causal considerations on alcohol and HIV/AIDS--a systematic review
. Alcohol Alcohol
10. Koblin BA, Husnik MJ, Colfax G, Huang Y, Madison M, Mayer K, et al. Risk factors for HIV infection among men who have sex with men
11. Penkower L, Dew MA, Kingsley L, Becker JT, Satz P, Schaerf FW, Sheridan K. Behavioral, health and psychosocial factors and risk for HIV infection among sexually active homosexual men: the Multicenter AIDS Cohort Study
. Am J Public Health
12. Chesney MA, Barrett DC, Stall R. Histories of substance use and risk behavior: precursors to HIV seroconversion in homosexual men
. Am J Public Health
13. Plankey MW, Ostrow DG, Stall R, Cox C, Li X, Peck JA, Jacobson LP. The relationship between methamphetamine and popper use and risk of HIV seroconversion in the Multicenter AIDS Cohort Study
. J Acquir Immune Defic Syndr
14. Hernán MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men
15. Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology
16. Howe CJ, Cole SR, Ostrow DG, Mehta SH, Kirk GD. A prospective study of alcohol consumption and HIV acquisition among injection drug users
17. Kaslow RA, Ostrow DG, Detels R, Phair JP, Polk BF, Rinaldo CR Jr. The Multicenter AIDS Cohort Study: rationale, organization, and selected characteristics of the participants
. Am J Epidemiol
18. Radloff L. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas
19. Dudley J, Jin S, Hoover D, Metz S, Thackeray R, Chmiel J. The Multicenter AIDS Cohort Study: retention after 9 1/2 years
. Am J Epidemiol
20. Department of Agriculture, Department of Health and Human Services (US). Dietary guidelines for Americans, 2010
. 7th ed. Washington, DC: USDA; 2011.
21. Ostrow DG, Plankey MW, Cox C, Li X, Shoptow S, Jacobson LP, Stall RC. Specific sex drug combinations contribute to the majority of recent HIV seroconversions among MSM in the MACS
. J Acquir Immune Defic Syndr
22. Sander PM, Cole SR, Ostrow DG, Johnson-Hill LM, Bolan RK, Stall R, Jacobson LP. Predictors of alcohol consumption among U.S. men who have sex with men
(abstract #607-S). 43rd Annual Meeting of the Society for Epidemiologic Research
. Vol. 171 (Suppl. 11). Seattle, WA: Am J Epidemiol 2010.
23. Sander PM, Cole SR, Ostrow DG, Mehta SH, Kirk GD. Determinants of alcohol consumption in HIV-uninfected injection drug users
. Drug Alcohol Depend
24. Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the joint causal effect of nonrandomized treatments
. J Am Stat Assoc
25. D’Agostino RB, Lee ML, Belanger AJ, Cupples LA, Anderson K, Kannel WB. Relation of pooled logistic regression to time dependent Cox regression analysis: the Framingham Heart Study
. Stat Med
26. Cole SR, Hernán MA. Constructing inverse probability weights for marginal structural models
. Am J Epidemiol
27. Cole SR, Hernan MA. Adjusted survival curves with inverse probability weights
. Comput Methods Programs Biomed
28. Li R, Chambless L. Test for additive interaction in proportional hazards models
. Ann Epidemiol
29. Cox DR. Regression models and life tables (with discussion)
. J R Stat Soc (B)
30. Kippax S, Campbell D, Van de Ven P, Crawford J, Prestage G, Knox S, et al. Cultures of sexual adventurism as markers of HIV seroconversion: a case control study in a cohort of Sydney gay men
. AIDS Care
31. Steele CM, Josephs RA. Alcohol myopia. Its prized and dangerous effects
. Am Psychol
32. Dingle GA, Oei TP. Is alcohol a cofactor of HIV and AIDS? Evidence from immunological and behavioral studies
. Psychol Bull
33. Greenwood GL, White EW, Page-Shafer K, Bein E, Osmond DH, Paul J, Stall RD. Correlates of heavy substance use among young gay and bisexual men: The San Francisco Young Men's Health Study
. Drug Alcohol Depend
34. Stone E, Heagerty P, Vittinghoff E, Douglas JM, Koblin BA, Mayer KH, et al. Correlates of condom failure in a sexually active cohort of men who have sex with men
. J Acquir Immune Defic Syndr Hum Retrovirol
35. Bagasra O, Kajdacsy-Balla A, Lischner HW, Pomerantz RJ. Alcohol intake increases human immunodeficiency virus type 1 replication in human peripheral blood mononuclear cells
. J Infect Dis
36. Cook RT. Alcohol abuse, alcoholism, and damage to the immune system--a review
. Alcohol Clin Exp Res
37. Balla AK, Lischner HW, Pomerantz RJ, Bagasra O. Human studies on alcohol and susceptibility to HIV infection
38. Leigh BC, Stall R. Substance use and risky sexual behavior for exposure to HIV. Issues in methodology, interpretation, and prevention. Am Psychol
39. Heath J, Lanoy A, Maisto SA. The role of alcohol and substance use in risky sexual behavior among older men who have sex with men: a review and critique of the current literature
. AIDS Behav
40. Vanable PA, McKirnan DJ, Buchbinder SP, Bartholow BN, Douglas JM, Judson FN, MacQueen KM. Alcohol use and high-risk sexual behavior among men who have sex with men: the effects of consumption level and partner type
. Health Psychol
41. Seage GR 3rd, Mayer KH, Wold C, Lenderking WR, Goldstein R, Cai B, et al. The social context of drinking, drug use, and unsafe sex in the Boston Young Men Study
. J Acquir Immune Defic Syndr Hum Retrovirol
42. Stueve A, O’Donnell L, Duran R, San Doval A, Geier J. Being high and taking sexual risks: findings from a multisite survey of urban young men who have sex with men
. AIDS Educ Prev
43. Cole SR, Frangakis CE. The consistency statement in causal inference: a definition or an assumption?
44. Shoptaw S, Frosch D. Substance abuse treatment as HIV prevention for men who have sex with men
. AIDS Behav
45. Kalichman SC, Simbayi LC, Vermaak R, Cain D, Jooste S, Peltzer K. HIV/AIDS risk reduction counseling for alcohol using sexually transmitted infections clinic patients in Cape Town, South Africa
. J Acquir Immune Defic Syndr
46. Kalichman SC, Simbayi LC, Vermaak R, Cain D, Smith G, Mthebu J, Jooste S. Randomized trial of a community-based alcohol-related HIV risk-reduction intervention for men and women in Cape Town South Africa
. Ann Behav Med
47. Stanton BF, Li X, Kahihuata J, Fitzerald AM, Neumbo S, Kanduuombe G, et al. Increased protected sex and abstinence among Namibian youth following a HIV risk-reduction intervention: a randomized, longitudinal study
48. Rothman KJ, Greenland S, Lash TL. Modern epidemiology
. 3rd ed.Philadelphia, PA:Wolters Kluwer Health/Lippincott Williams & Wilkins; 2008.
alcohol drinking; HIV seropositivity; men who have sex with men; prospective studies; sexual behavior
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