After adjusting for age, no running water (aPR: 1.82, 95% CI: 1.35 to 2.45), ≤2 household assets (aPR: 1.55, 95% CI: 1.16 to 2.07), pregnancy history (aPR: 1.77, 95% CI: 1.27 to 2.47), partner travel (aPR: 2.48, 95% CI: 1.34 to 4.61), partner concurrency (aPR: 2.33, 95% CI: 1.72 to 3.15), and transactional sex (aPR: 1.92, 95% CI: 1.37 to 2.67) were associated with high HIV risk perception (Table 3). These six variables remained significant predictors of risk perception when the analysis was restricted to participants who reported being HIV-uninfected.
Among participants who reported being HIV-negative or of unknown HIV status, 62% (n = 813) and 60% (n = 131) reported worrying about acquiring HIV, respectively. Nine of the 15 risk factors associated with HIV infection were associated with HIV worry (Table 2). Having more risk factors was associated with a higher probability of HIV worry (P < 0.001) (Fig. 2). However, 33% of those with ≥5 risk factors and 21% with >8 risk factors did not worry about acquiring HIV.
After adjusting for age, partner travel (aPR: 2.87, 95% CI: 1.43 to 5.77), partner concurrency (aPR: 1.98, 95% CI: 1.50 to 2.60), and an older partner (aPR: 2.96, 95% CI: 1.42 to 6.16) were each significant predictors of HIV worry (Table 3). These 3 variables remained significant predictors of worry after restricting the analysis to participants who reported being HIV-uninfected.
Our findings suggest that the context of sexual behavior may predict HIV prevalence more than individual behaviors themselves. Although number of sexual partners in the past year was predictive of HIV infection, 2 other key female sexual behaviors—age of debut and consistent condom use—each a focus of HIV prevention programs among young women,30,31 were not associated with HIV status in our study. Causal associations are difficult to infer using cross-sectional data alone. Condom use may have a strong reciprocal relationship with both perceived risk and risky sexual behavior, and it is possible that other factors such as the circumstances of sexual initiation and partner factors may be more salient than age at debut. We noted a strong association between HIV status and heavy alcohol use, a contextual behavior that may result in disinhibited sexual activity with high-risk partners.32–34 Similarly, no running water was associated with HIV status, suggesting that socioeconomic status may play a role in HIV risk. Although particular socioeconomic risk factors may vary across settings, many are modifiable through social protection approaches.35
In our study, more sources of vulnerability were associated with higher HIV prevalence, consistent with risk scores demonstrating that more risk factors are associated with higher risk of HIV acquisition17,19 and with observations that more sources of social protection are associated with fewer risky behaviors, better utilization of preventive strategies, and lower probability of unintended pregnancy among AGYW.36,37 Social protection, behavioral, and biomedical interventions that aim to mitigate vulnerabilities in combination could produce greater impacts on HIV prevention than single-domain HIV prevention.35
Although the majority of HIV-uninfected participants had at least one risk factor, few had >8, the level associated with the highest HIV prevalence. This observation suggests that identifying a small subset of women at highest risk of HIV infection is possible. Such identification is important because it would allow for PrEP targeting to those at highest risk. Replicating these findings in a larger cohort with an HIV incidence outcome is a critical next step.
Our study design presents several limitations. First, because of the cross-sectional nature of the data, we cannot infer causality between participant characteristics and HIV prevalence. Whereas in young women many infections are likely to be recent, a few of those infected may have been perinatally infected, such that the identified risk factors would not be relevant. However, based on staff interactions with participants, perinatal infection was uncommon in our study and most participants with HIV reported that their most recent diagnostic HIV test had been within the last 2 years. Second, these results are based on self report, and AGYW may have been HIV-infected, but not known it or reported it. Although a cross-sectional survey based on self report may have limited our risk factor analysis, it had less effect on our analyses of risk perception and worry because persons with unknown HIV status could still perceive themselves as at-risk. Third, interviewer-administered surveys can cause misreporting of HIV status, sexual behaviors, and socioeconomic markers because of social desirability or memory challenges. Finally, although single-item HIV risk perception assessments are common,52–55 multidimensional tools may elicit perceived risk more accurately.56,57
It has long been recognized that HIV incidence is disproportionately high among AGYW in SSA, but less clear which women are at highest risk and whether these women accurately perceive this risk. Our work shows that it is indeed possible to identify these women, but additional work is needed to help them appreciate their own risk and then seek and adhere to appropriate prevention strategies. Identifying the most vulnerable and developing strategies to enhance risk perception will bring us one step closer to reducing HIV incidence.
The authors thank the staff of Girl Power for their hard work and dedication to this project. The authors also thank each participant for her invaluable contribution to this research.
1. Joint United Nations Programme on HIV/AIDS. The Gap Report; 2014. Available at: http://http://www.unaids.org
/sites/default/files/media_asset/UNAIDS_Gap_report_en.pdf. Accessed March 14, 2016.
2. Pettifor AE, van der Straten A, Dunbar MS, et al. Early age of first sex: a risk factor for HIV infection among women in Zimbabwe. AIDS. 2004;18:1435–1442.
3. Clark S, Bruce J, Dude A. Protecting young women
from HIV/AIDS: the case against child and adolescent marriage. Int Fam Plan Perspect. 2006;32:79–88.
4. Jewkes RK, Dunkle K, Nduna M, et al. Intimate partner violence, relationship power inequity, and incidence of HIV infection in young women
in South Africa: a cohort study. Lancet. 2010;376:41–48.
5. Chen L, Jha P, Stirling B, et al. Sexual risk factors
for HIV infection in early and advanced HIV epidemics in sub-Saharan Africa: systematic overview of 68 epidemiological studies. PLoS One. 2007;2:e1001.
6. Ramjee G, Wand H. Geographical clustering of high risk sexual behaviors in “hot-spots” for HIV and sexually transmitted infections in Kwazulu-Natal, South Africa. AIDS Behav. 2014;18:317–322.
7. Polis CB, Curtis KM, Hannaford PC, et al. An updated systematic review of epidemiological evidence on hormonal contraceptive methods and HIV acquisition in women, 2016. AIDS. 2016;30:2665–2683.
8. Mugo NR, Heffron R, Donnell D, et al. Increased risk of HIV-1 transmission in pregnancy: a prospective study among African HIV-1-serodiscordant couples. AIDS. 2011;25:1887–1895.
9. Pettifor AE, Measham DM, Rees HV, et al. Sexual power and HIV risk, South Africa. Emerg Infect Dis. 2004;10:1996–2004.
10. Idele P, Gillespie A, Porth T, et al. Epidemiology of HIV and AIDS among adolescents: current status, inequities, and data gaps. J Acquir Immune Defic Syndr. 2014;66(suppl 2):S144–S153.
11. Joint United Nations Programme on HIV/AIDS. HIV Prevention
Among Adolescent Girls
and Young Women
; 2016. Available at: http://http://www.unaids.org
/sites/default/files/media_asset/UNAIDS_HIV_prevention_among_adolescent_girls_and_young_women.pdf. Accessed March 2, 2017.
12. Abdool Karim Q, Kharsany AB, Frohlich JA, et al. HIV incidence in young girls in KwaZulu-Natal, South Africa–public health imperative for their inclusion in HIV biomedical intervention trials. AIDS Behav. 2012;16:1870–1876.
13. National Statistical Office (NSO) and ICF Macro. Malawi
Demographic and Health Survey 2015–2016. Zomba, Malawi
, Rockville, MD: National Statistical Office (NSO) and ICF Macro; 2017.
14. National Statistical Office (NSO) and ICF Macro. Malawi
Demographic and Health Survey 2010. Zomba, Malawi
, Rockville, MD: National Statistical Office (NSO) and ICF Macro; 2011.
15. World Health Organization. Consolidated Guidelines on the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection: Recommendations for a Public Health Approach; 2015. Available at: http://http://www.who.int
/hiv/pub/guidelines/arv2013/download/en/index.html. Accessed January 25, 2016.
16. Shisana O, Rehle T, Simbayi L, et al. South African National HIV Prevalence, Incidence and Behaviour Survey, 2012. Cape Town, South Africa: HSRC Press; 2014.
17. Balkus JE, Brown E, Palanee T, et al. An Empiric HIV risk scoring tool to predict HIV-1 acquisition in African women. J Acquir Immune Defic Syndr. 2016;72:333–343.
18. Pintye J, Drake AL, Kinuthia J, et al. A risk assessment tool for identifying pregnant and postpartum women who may benefit from preexposure prophylaxis. Clin Infect Dis. 2017;64:751–758.
19. Wand H, Reddy T, Naidoo S, et al. A simple risk prediction algorithm for HIV transmission: results from HIV prevention
trials in KwaZulu Natal, South Africa (2002–2012). AIDS Behav. 2017. doi: .
20. Baeten JM, Heffron R, Kidoguchi L, et al. Integrated delivery of antiretroviral treatment and pre-exposure prophylaxis to HIV-1-serodiscordant couples: a prospective implementation study in Kenya and Uganda. PLoS Med. 2016;13:e1002099.
21. Baeten JM, Palanee-Phillips T, Brown ER, et al. Use of a vaginal ring containing dapivirine for HIV-1 prevention in women. N Engl J Med. 2016;375:2121–2132.
22. Bekker LG, Grant R, Hughes J, et al. HPTN 067/ADAPT Cape Town: A Comparison of Daily and Nondaily PrEP Dosing in African Women. Conference on Retroviruses and Opportunistic Infections; February 23–26, 2015; Seattle, WA.
23. Celum CL, Delany-Moretlwe S, McConnell M, et al. Rethinking HIV prevention
to prepare for oral PrEP implementation for young African women. J Int AIDS Soc. 2015;18(4 suppl 3):20227.
24. Hargreaves JR, Delany-Moretlwe S, Hallett TB, et al. The HIV prevention
cascade: integrating theories of epidemiological, behavioural, and social science into programme design and monitoring. Lancet HIV. 2016;3:e318–e322.
25. Smith KP, Watkins SC. Perceptions of risk and strategies for prevention: responses to HIV/AIDS in rural Malawi
. Soc Sci Med. 2005;60:649–660.
26. Kaler A. AIDS-talk in everyday life: the presence of HIV/AIDS in men's informal conversation in Southern Malawi
. Soc Sci Med. 2004;59:285–297.
27. Angotti N, Frye M, Kaler A, et al. Popular moralities and institutional rationalities in Malawi
's struggle against AIDS. Popul Dev Rev. 2014;40:447–473.
28. Brunette W, Sundt M, Dell N, et al. Open Data Kit 2.0: Expanding and Refining Information Services for Developing Regions; 2013. Proceedings of the 14th Workshop on Mobile Computing Systems and Applications, p. 10 ACM, New York, NY.
29. National Institute on Alcohol Abuse and Alcoholism. Rethinking Drinking: Alcohol and Your Health. Bethesda, MD: NIH Publication No. 15-3770; 2010.
30. Harrison A, Colvin CJ, Kuo C, et al. Sustained high HIV incidence in young women
in southern Africa: social, behavioral, and structural factors and emerging intervention approaches. Curr HIV/AIDS Rep. 2015;12:207–215.
31. Mavedzenge SN, Luecke E, Ross DA. Effective approaches for programming to reduce adolescent vulnerability to HIV infection, HIV risk, and HIV-related morbidity and mortality: a systematic review of systematic reviews. J Acquir Immune Defic Syndr. 2014;66(suppl 2):S154–S169.
32. Rosenberg M, Pettifor A, Van Rie A, et al. The relationship between alcohol outlets, HIV risk behavior, and HSV-2 infection among South African young women
: a cross-sectional study. PLoS One. 2015;10:e0125510.
33. Fisher JC, Bang H, Kapiga SH. The association between HIV infection and alcohol use: a systematic review and meta-analysis of African studies. Sex Transm Dis. 2007;34:856–863.
34. Scott-Sheldon LA, Carey KB, Cunningham K, et al. Alcohol use predicts sexual decision-making: a systematic review and meta-analysis of the experimental literature. AIDS Behav. 2016;20(suppl 1):S19–S39.
35. Cluver LD, Orkin FM, Yakubovich AR, et al. Combination social protection for reducing HIV-risk behavior among adolescents in South Africa. J Acquir Immune Defic Syndr. 2016;72:96–104.
36. Cluver L, Boyes M, Orkin M, et al. Child-focused state cash transfers and adolescent risk of HIV infection in South Africa: a propensity-score-matched case-control study. Lancet Glob Health. 2013;1:e362–e370.
37. Cluver LD, Orkin FM, Boyes ME, et al. Cash plus care: social protection cumulatively mitigates HIV-risk behaviour among adolescents in South Africa. AIDS. 2014;28(suppl 3):S389–S397.
38. Schatz E. “Take your mat and go!”: rural Malawian women's strategies in the HIV/AIDS era. Cult Health Sex. 2005;7:479–492.
39. Morrow KM, Fava JL, Rosen RK, et al. Willingness to use microbicides varies by race/ethnicity, experience with prevention products, and partner type. Health Psychol. 2007;26:777–786.
40. Watson-Jones D, Weiss HA, Rusizoka M, et al. Risk factors
for herpes simplex virus type 2 and HIV among women at high risk in northwestern Tanzania: preparing for an HSV-2 intervention trial. J Acquir Immune Defic Syndr. 2007;46:631–642.
41. Ao TTH, Sam NE, Masenga EJ, et al. Human immunodeficiency virus type 1 among bar and hotel workers in northern Tanzania: the role of alcohol, sexual behavior, and herpes simplex virus type 2. Sex Transm Dis. 2006;33:163–169.
42. Corneli A, Wang M, Agot K, et al. Perception of HIV risk and adherence to a daily, investigational pill for HIV prevention
in FEM-PrEP. J Acquir Immune Defic Syndr. 2014;67:555–563.
43. Corneli AL, McKenna K, Headley J, et al. A descriptive analysis of perceptions of HIV risk and worry about acquiring HIV among FEM-PrEP participants who seroconverted in Bondo, Kenya, and Pretoria, South Africa. J Int AIDS Soc. 2014;17(3 suppl 2):19152.
44. Haberer JE, Baeten JM, Campbell J, et al. Adherence to antiretroviral prophylaxis for HIV prevention
: a substudy cohort within a clinical trial of serodiscordant couples in East Africa. PLoS Med. 2013;10:e1001511.
45. Ware NC, Wyatt MA, Haberer JE, et al. What's love got to do with it? Explaining adherence to oral antiretroviral pre-exposure prophylaxis for HIV-serodiscordant couples. J Acquir Immune Defic Syndr. 2012;59:463–468.
46. Baeten JM, Donnell D, Ndase P, et al. Antiretroviral prophylaxis for HIV prevention
in heterosexual men and women. N Engl J Med. 2012;367:399–410.
47. Marrazzo JM, Ramjee G, Richardson BA, et al. Tenofovir-based preexposure prophylaxis for HIV infection among African women. N Engl J Med. 2015;372:509–518.
48. Thigpen MC, Kebaabetswe PM, Paxton LA, et al. Antiretroviral preexposure prophylaxis for heterosexual HIV transmission in Botswana. N Engl J Med. 2012;367:423–434.
49. Van Damme L, Corneli A, Ahmed K, et al. Preexposure prophylaxis for HIV infection among African women. N Engl J Med. 2012;367:411–422.
50. Fonner VA, Dalglish SL, Kennedy CE, et al. Effectiveness and safety of oral HIV preexposure prophylaxis for all populations. AIDS. 2016;30:1973–1983.
51. Dolcini MM, Catania JA, Choi KH, et al. Cognitive and emotional assessments of perceived risk for HIV among unmarried heterosexuals. AIDS Educ Prev. 1996;8:294–307.
52. Stringer EM, Sinkala M, Kumwenda R, et al. Personal risk perception
, HIV knowledge and risk avoidance behavior, and their relationships to actual HIV serostatus in an urban African obstetric population. J Acquir Immune Defic Syndr. 2004;35:60–66.
53. Tenkorang EY, Rajulton F, Maticka-Tyndale E. Perceived risks of HIV/AIDS and first sexual intercourse among youth in Cape Town, South Africa. AIDS Behav. 2009;13:234–245.
54. Johnston L, O'Bra H, Chopra M, et al. The associations of voluntary counseling and testing acceptance and the perceived likelihood of being HIV-infected among men with multiple sex partners in a South African township. AIDS Behav. 2010;14:922–931.
55. Garfinkel DB, Alexander KA, McDonald-Mosley R, et al. Predictors of HIV-related risk perception
and PrEP acceptability among young adult female family planning patients. AIDS Care. 2016;29:1–8.
56. Bradley H, Tsui A, Hindin M, et al. Developing scales to measure perceived HIV risk and vulnerability among Ethiopian women testing for HIV. AIDS Care. 2011;23:1043–1052.
57. Napper LE, Fisher DG, Reynolds GL. Development of the perceived risk of HIV scale. AIDS Behav. 2012;16:1075–1083.