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Research Letters

Hormonal contraception and HIV acquisition: reanalysis using marginal structural modeling

Morrison, Charles Sa; Chen, Pai-Lienb; Kwok, Cynthiab; Richardson, Barbra Ac; Chipato, Tsungaid; Mugerwa, Roye; Byamugisha, Josaphatf; Padian, Nancyg; Celentano, David Dh; Salata, Robert Ai

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doi: 10.1097/QAD.0b013e32833a2537
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Abstract

In 2007, we published the results of a large multicenter cohort study designed specifically to investigate whether hormonal contraceptive use increased HIV acquisition [1]. We found no significant increased overall risk of HIV acquisition for either depo-medroxyprogesterone acetate (DMPA) or combined oral contraceptives (COCs) [1]. In a prespecified subgroup analysis, we found that whereas herpes simplex virus (HSV)-2-positive women using hormonal contraception had no increased HIV risk, HSV-2-negative women who used either DMPA or COC were at increased HIV-acquisition risk.

Marginal structural modeling is a statistical method that accounts for measured time-dependent confounding and reduces corresponding selection bias that may be present with observational study designs [2]. The marginal structural modeling approach (MSMA) seeks to mimic the results that would be obtained from a randomized study [3,4]. Given the time-varying hormonal contraceptive exposure and potential time-dependent confounding in the Hormonal Contraception and HIV (HC-HIV) Study, we decided to replicate our original study analysis [1] using MSMA.

The analysis population consisted of participants with at least one follow-up visit with valid HIV-1 results [1]. The outcome was the number of days from baseline to the first positive HIV-1 result or last study contact. With two exceptions, all variable definitions from the original analysis [1] were replicated in the reanalysis. First, to apply MSMA, hormonal contraceptive exposure time was divided into equal monthly intervals (versus visit segments used in the original analysis). Second, we adjusted for ‘any condom use’ instead of the original ‘inconsistent condom use’ variable because only the former met the criteria for time-dependent confounding. Other covariates included in the original multivariate model [1] which we identified as time-dependent confounders included participant behavioral risk and primary partner risk.

Stabilized inverse probability of treatment weights were obtained using multinomial logistic regressions of hormonal contraceptive exposure versus the time-dependent confounders and other time-independent (baseline age, site, living with partner, education) and time-varying covariates (STI history, coital frequency, breastfeeding) specified in the original analysis model.

We used a weighted Cox proportional hazard model with robust sandwich approach to estimate the effect of hormonal contraceptive exposure on HIV acquisition and its 95% confidence intervals (CIs) [5]. We also tested two variables for effect modification of the hormonal contraception–HIV relationship; enrolment HSV-2 infection status and age, and report strata-specific results. Data analyses were conducted using SAS version 9.2 (SAS Institute Inc., Cary, North Carolina, USA).

In this reanalysis 4435 African participants contributed 7775 years of follow-up; we observed 213 incident HIV infections (incidence rate: 2.7 per 100 woman-years).

To ascertain the impact of the revised exposure definition, we compared adjusted hazard ratios for DMPA and COC use for HIV acquisition using the new data structure but without MSMA weighting. We found effect measures for DMPA [adjusted hazard ratio (AHR) 1.25, 95% CI 0.89–1.77] and COC use (AHR 1.05, 95% CI 0.73–1.52) that were very similar to the original published results. Incorporating the MSMA weightings, we found that DMPA (AHR 1.48, 95% CI 1.02–2.15) but not COC use (AHR 1.19, 95% CI 0.80–1.76) was significantly associated with HIV acquisition (Table 1).

T1-21
Table 1:
HIV incidence rates and adjusted hazard ratios for incident HIV infection overall and by age, HSV-2 infection status and contraceptive exposure group (marginal structural modeling reanalysis)a.

We found a significant interaction between hormonal contraceptive use and age (Table 1). Among young women (18–24 years) both DMPA (AHR 2.76, 95% CI 1.62–4.72) and COC use (AHR 2.02, 95% CI 1.15–3.55) were associated with increased HIV acquisition. Among older women, neither DMPA (AHR 0.81, 95% CI 0.48–1.39) nor COC use (AHR 0.73, 95% CI 0.42–1.26) was associated with HIV risk. We divided the younger age group into women aged 18–20 and 21–24 years. Among participants 18–20 years of age, we found a strong increased HIV risk for both DMPA (AHR 9.29, 95% CI 2.72–31.69) and COC users (AHR 3.68, 95% CI 0.88–15.31). Among women 21–24 years of age, the increased HIV risk remained (DMPA: AHR 1.95, 95% CI 1.06–3.58; COC: AHR 1.67, 95% CI 0.90–3.09) but the effect was mitigated.

HIV incidence was higher among women whose enrolment HSV-2 status was positive rather than negative (Table 1). We found a significant interaction between hormonal contraceptive use and HSV-2 status similar to the original study results. Among HSV-2-positive participants, neither DMPA (AHR 1.03, 95% CI 0.67–1.59) nor COC use (AHR 1.07, 95% CI 0.69–1.65) increased HIV risk. However, among HSV-2-negative participants, HIV acquisition risk was significantly higher for DMPA (AHR 4.49; 95% CI 1.98–10.17) but not COC users (AHR 2.06 95% CI 0.87–4.92) compared with the nonhormonal group.

Marginal structural modeling approach has become increasingly recognized as a preferred method to analyze longitudinal data subject to time-dependent confounding [2,6,7], especially in cohort studies of HIV infection and HAART use [6–8]. Nevertheless, like all statistical approaches, MSMA depends on certain assumptions. The validity of the reanalysis assumes that all confounders were measured and sufficient to adjust for confounding biases. This assumption, however, is also required for the use of more standard statistical methods if their parameters are used for causal interpretation [4].

We found that the association between hormonal contraceptive use and HIV risk was modified by age. Young women using DMPA and COCs were at increased HIV risk compared to young women not using hormonal contraception. A similar age interaction was found in a study of injectable contraception and HIV acquisition conducted in South Africa [9]. However, no age interaction was found in a study of Kenyan sex workers (J. Baeten, personal communication). Whereas the mechanism for this age interaction is not clear, it is plausible that physiologic differences between younger and older women including cervical ectopy [10–12] or differences in immune function [13–17] could interact with hormonal contraception. Alternatively, our ability to accurately measure risk behaviors and control for confounding could differ between the younger and older women.

In summary, our reanalysis of the HC-HIV Study data using marginal structural modeling should reduce (but may not eliminate) bias in the initial study findings. We found that DMPA but not COC use was associated with a marginally increased risk of HIV acquisition. Young and HSV-2-negative women using hormonal contraception, particularly DMPA, were at increased HIV risk. If these results are confirmed, young women in areas of high HIV incidence may need alternative, highly effective contraceptive options. In addition, promotion of condoms and monogamous relationships among hormonal contraceptive users and their partners needs to be actively promoted.

Acknowledgements

Disclaimer: The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services or FHI, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government.

Funding support: This project has been funded with federal funds from the National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH), Department of Health and Human Services through a contract with Family Health International (FHI) (Contract Number N01-HD-0-3310).

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