Department of Statistics and Center for AIDS Research, University of Washington, Seattle, WA email@example.com
Supported by the National Institutes of Health (R01 HD 068395, P30 AI 027757, R24 HD 042828).
Conflict of interest: None.
Ethical statement: This is not a human subject research.
Ethical statement: This is not a human subject research.
Received for publication August 3, 2012, and accepted September 5, 2012.
To the Editor
The study of McCreesh et al.1 is one of the first efforts to move concurrency modeling from basic science to intervention programming support (see also Enns et al.2). It is worth noting that this change in purpose raises a different set of questions, and the modeling strategies themselves need to change. In particular, this changes the goal of the counterfactual used to estimate intervention impact. Evidence from Zimbabwe3 and common sense suggest that concurrency reductions, in vivo, will be accompanied by reductions in numbers of partners. McCreesh et al. have chosen to preserve the in vitro assumption that partnerships will not be reduced by the intervention, just reallocated to others. Although this is an appropriate counterfactual for a laboratory study, it is not appropriate for an intervention simulation study. The impact of the assumption in this context is to bias the intervention impact estimate downward.
Despite this, the study of McCreesh et al. has much to offer when the implications are carefully drawn and the findings are placed in the right context:
1. The estimates of concurrency point prevalence used by McCreesh et al. are low, even in the maximum concurrency scenario. The estimates of concurrency from the 2009 to 2010 Masaka data used by McCreesh et al. are 9.6% for men, and women’s rates are arbitrarily set to be 25% of the men’s, at 2.4%. The authors use a sensitivity analysis to examine the potential impact of underreporting of concurrency by 50%: raising prevalence to 14.4% and 3.6% for men and women, respectively. These rates are comparable with those reported by African Americans and Hispanics in US surveys.4 Concurrency rates observed in populations with HIV hyperepidemics are much higher than the maximum scenario in McCreesh et al.:
* 32% for men in KwaZulu-Natal, South Africa,5
* 21% and 20% for men and women in Botswana,6
* 43.9% for men in Lesotho in 2009,7
* 18.6% for men and 2.2% for women in Manicaland, Zimbabwe in 1998 to 2000 before both HIV and concurrency prevalence declined by 50%,3 and
* 21.3% for men and 4.5% for women in Nyanza province, Kenya.8
This suggests that the study’s findings are limited to settings with relatively low rates of concurrency and more moderate generalized epidemics. The authors are careful to avoid overgeneralizing from their study findings, but the accompanying editorial by Go and Blower (G&B) is not.9 Go and Blower state that this and one other study “indicate that concurrency is not a major driver of HIV epidemics in sub-Saharan Africa,” and they “recommend ending the debate on the importance of concurrency as a driver of HIV epidemics in sub-Saharan Africa.” With some modifications, the model of McCreesh et al. could be used to investigate the potential impact of concurrency reduction in the higher-prevalence settings above, but until it is, G&B’s generalization to all sub-Saharan African epidemics is speculative and their call to end the debate on concurrency unwarranted.
2. The results presented by the authors do not represent the final incidence reductions that can be achieved by the intervention. The concurrency reduction is phased in from 2010 to 2020, and the simulation estimates of impact cover the same period. As the graphs in Figs. 1E and S2 indicate, incidence is still declining in 2020, before and after the introduction of the intervention. The longer-term intervention impact may therefore continue to rise.
3. Despite this, the results are actually pretty impressive for a single behavioral prevention intervention. The “best estimate” in the study is that shifting 50% of concurrent relationships to serial ones while maintaining the total number of relationships over all would lead to an 11.9% decline in incidence—and a 16.2% decline among women. McCreesh et al. describe this as “relatively moderate,” and G&B in the accompanying editorial as “fairly minimal.” For comparison, note that raising ART coverage from 0 to 92% in this simulation by itself only reduces prevalence by about 28% by 2020 (Fig. S2). Although this study was not designed to estimate the impact of ART, the results are similar to a recent study in Lancet by Schwartlander and others,10 which estimated that 50% ART coverage plus 10% more male circumcision plus 15% increased condom among the HIV infected would result in a drop of about 30% in incidence by 2020 (with no further declines). This does not lead them to advocate abandoning treatment as prevention; instead, they propose that we aim for an even more ambitious goal of 80% ART coverage. All of these estimates assume that ART coverage translates into the level of viral suppression that is needed to reduce infectivity.
Putting this in context, in the United States, after 17 years of ART availability, only 19% to 29% of people living with HIV are virally suppressed.11,12 and HIV prevalence has remained fairly stable for the last decade. By comparison, in Zimbabwe, we have observed a 50% decline in concurrency, along with a large decline in HIV prevalence, all before ART became available.3
4. Finally, the sex implications of this study are critical. Concurrency reduction in this study had a much larger impact on lowering women’s incidence in every scenario because men’s higher rates of concurrency are one of the key behavioral mechanisms that puts women at risk. The most optimistic estimates showed reductions of 16% to 23% in women’s incidence, and women’s relative benefits were between 2 and 4 times greater than men’s. The higher the level of men’s concurrency, the more women benefit from the intervention. Women’s HIV prevalence in the generalized heterosexual epidemics in Southern and Eastern Africa is typically 30% higher than men’s, and men’s concurrency is a compelling hypothesis for this disparity. To date, we do not have a single prevention intervention that targets women’s prevalence specifically. It is disappointing to have this uniquely protective intervention for women so summarily dismissed in the accompanying editorial by G&B.
Martina Morris, PhD
Department of Statistics and Center
for AIDS Research
University of Washington
1. McCreesh N, O’Brien K, Nsubuga RN, et al.. Exploring the potential impact of a reduction in partnership concurrency on hiv incidence in rural Uganda: A modeling study. Sex Transm Dis 2012; 39: 407–413.
2. Enns EA, Brandeau ML, Igeme TK, et al.. Assessing effectiveness and cost-effectiveness of concurrency reduction for HIV prevention. Int J STD AIDS 22: 558–567.
3. Gregson S, Gonese E, Hallett TB, et al.. HIV decline in Zimbabwe due to reductions in risky sex? Evidence from a comprehensive epidemiological review. Int J Epidemiol 2010; 39: 1311–1323.
4. Morris M, Epstein H, Wawer M. Timing is everything: International variations in historical sexual partnership concurrency and HIV prevalence. Plos One 2010; 5: e14092. doi:14010.11371/journal.pone.0014092.
5. Tanser F, Till B, Hund L, et al.. Effect of concurrent sexual partnerships on rate of new HIV infections in a high-prevalence, rural South African population: A cohort study. Lancet 2011; 378: 247–255.
6. Gourvenec D, Taruberekera N, Mochaka O, et al.. Multiple Concurrent Partnerships Among Men and Women Aged 15–34 in Botswana: Baseline Study, December 2007. Gaborone, Botswana: Population Services International, 2007.
7. C-Change. A Baseline Survey of Multiple and Concurrent Sexual Partnerships Among Basotho Men in Lesotho. Washington, DC: AED; 2009.
8. Voeten H, Egesah OB, Habbema JDF. Sexual behavior is more risky in rural than in urban areas among young women in Nyanza Province, Kenya. Sex Transm Dis 2004; 31: 481–487.
9. Go MH, Blower S. What impact will reducing concurrency have on decreasing the incidence of hiv in heterosexual populations? Sex Transm Dis 2012; 39: 414–415.
10. Schwartlander B, Stover J, Hallett T, et al.. Towards an improved investment approach for an effective response to HIV/AIDS. Lancet 2011; 377: 2031–2041.
11. Burns D, Dieffenbach C, Vernund S. Rethinking prevention of HIV type 1 infection. Clin Infect Dis 2010; 51: 725–731.
12. Skarbinski J, Johnson C, Frazier E, . Nationally representative estimates of the number of HIV+ adults who received medical care, were prescribed ART, and achieved viral suppression—Medical Monitoring Project, 2009 to 2010: US. Poster presented at: Conference on Retroviruses and Opportunistic Infections; Seattle, WA; 2012.