Skip to main content

Advertisement

Log in

Partnership Concurrency and Coital Frequency

  • Original Paper
  • Published:
AIDS and Behavior Aims and scope Submit manuscript

Abstract

National HIV prevalence estimates across sub-Saharan Africa range from less than 1 percent to over 25 percent. Recent research proposes several explanations for the observed variation, including prevalence of male circumcision, levels of condom use, presence of other sexually transmitted infections, and practice of multiple concurrent partnerships. However, the importance of partnership concurrency for HIV transmission may depend on how it affects coital frequency with each partner. The coital dilution hypothesis suggests that coital frequency within a partnership declines with the addition of concurrent partners. Using sexual behavior data from rural Malawi and urban Kenya, we investigate the relationship between partnership concurrency and coital frequency, and find partial support for the coital dilution hypothesis. We conclude the paper with a discussion of our findings in light of the current literature on concurrency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. A study of the determinants of partnership concurrency using the ULYKP data finds that coital frequency of the initial relationship is not predictive of entry into a second, concurrent partnership [18].

  2. We tested for a non-linear relationship with age, but the quadratic term was not significant. This specification of relationship duration is a restriction of the LNS data; the question asking about first sex in the LNS included response categories for within the last week, within the last month, within the last year, and more than 1 year ago. Inclusion of a linear and quadratic term for relationship duration in the ULYKP does not change the results.

  3. 12 % is for the ULYKP sample in Table 7.

  4. The same relationship may also be reported on by both partners and thus included twice in the sample.

  5. There is no mathematical requirement that levels of concurrency be the same for men and women. While the total number of sex acts must be the same (assuming only heterosexual sex), levels of concurrency may be different.

  6. Results are of comparable magnitude and significance for spousal (formal) and non-spousal (informal) relationships in the LNS. For ULYKP, there is a negative but non-significant association between concurrency and coital frequency for spousal relationships. There is a positive but non-significant association between concurrency and coital frequency for non-spousal relationships. See Online Appendix 4 for results.

  7. Even though the number of cases are not numerically important, it is worth noting that the UNAIDS definition forces us to right censor ongoing partnerships at the month of last sex if no sex was reported for the month before the survey.

  8. We cannot repeat the analysis for the 3 years and 1 year preceding the survey because of the small number of relationship-months in which individuals have concurrent partners.

  9. The assumption of a fixed number of partnerships implies that the number of men or women without a partner (i.e., the number of isolated nodes in a sexual network) increases as rates of partnership concurrency increase. In an empirical study of polygyny, for example, there is no evidence that young men’s access to sexual partners is restricted in populations with a higher prevalence of polygyny [17]. In other words, there is no evidence of an increase in the number of isolated nodes in populations where older polygynous men might be expected to crowd out the younger men from the partnerships market.

References

  1. Bongaarts J, Buettner T, Heilig G, Pelletier F. Has the HIV epidemic peaked? Popul Dev Rev. 2008;34(2):199–224.

    Article  Google Scholar 

  2. Cameron DW, D’Costa LJ, Maitha GM, Cheang M, Piot P, Simonsen JN, Ronald AR, Gakinya MN, Ndinya-Achola JO, Brunham RC. Female to male transmission of human immunodeficiency virus type 1: risk factors for seroconversion in men. Lancet. 1989;334(8660):403–7.

    Article  Google Scholar 

  3. Bongaarts J, Reining P, Way P, Conant F. The relationship between male circumcision and HIV infection in African populations. AIDS. 1989;3(6):373–7.

    Article  PubMed  CAS  Google Scholar 

  4. Moses S, Bradley JE, Jagelkerke NJD, Ronald AR, Ndinya-Achla JO, Plummer FA. Geographical patterns of male circumcision practices in Africa: association with HIV seroprevalence. Int J Epidemiol. 1990;19:693–7.

    Article  PubMed  CAS  Google Scholar 

  5. Chin J. The AIDS Pandemic: the collision of epidemiology with political correctness. Oxford: Radcliffe Publishing; 2007.

    Google Scholar 

  6. United Nations. Levels and trends of contraceptive use as Assessed in 2002. Population Division, Department of Economic and Social Affairs. New York: United Nations (ST/ESA/SER.A/239); 2006.

  7. Morris M, Kretzschmar M. Concurrent partnerships and the spread of HIV. AIDS. 1997;11(5):641–8.

    Article  PubMed  CAS  Google Scholar 

  8. Halperin DT, Epstein H. Concurrent sexual partnerships help to explain Africa’s high HIV prevalence: implications for prevention. Lancet. 2004;364(9428):4–6.

    Article  PubMed  Google Scholar 

  9. Mah TL, Halperin DT. Concurrent sexual partnerships and the HIV epidemics in Africa: evidence to move forward. AIDS Behav. 2010;14(1):11–6.

    Article  PubMed  Google Scholar 

  10. Moody J. The importance of relationship timing for diffusion. Soc Forces. 2002;81(1):25–56.

    Article  Google Scholar 

  11. Boily MC, Alary M, Baggaley RF. Neglected issues and hypotheses regarding the impact of sexual concurrency on HIV and sexually transmitted infections. AIDS Behav. 2012;16(2):304–11.

    Google Scholar 

  12. Wawer M, Gray RH, Sewankambo NK, Serwadda D, Li X, Laeyendecker O, Kiwanuka N, Kigozi G, Kiddugavu M, Lutalo T, Nalugoda F, Wabwire-Mangen F, Meehan MP, Quinn TC. Rates of HIV-1 transmission per coital act, by stage of HIV-1 infection, in Rakai, Uganda. J Infect Dis. 2005;191(9):1403–9.

    Article  PubMed  Google Scholar 

  13. Eaton J, Hallett T, Garnett GP. Concurrent sexual partnerships and primary HIV infection: a critical interaction. AIDS Behav. 2010;15(4):687–92.

    Article  Google Scholar 

  14. Goodreau SM, Cassels S, Kasprzyk D, Montano DE, Greek A, Morris M. Concurrent partnerships, acute infection, and HIV epidemic dynamics among young adults in Zimbabwe. AIDS Behav. 2012;16(2):312–22.

    Google Scholar 

  15. Reniers G, Watkins S. Polygyny and the spread of HIV in sub-Saharan Africa: a case of benign concurrency. AIDS. 2010;24(2):299–307.

    Article  PubMed  Google Scholar 

  16. Sawers L, Isaac AG, Stillwaggon E. HIV and concurrent sexual partnerships: modeling the role of coital dilution. J Int AIDS Soc. 2011;14:44.

    Article  PubMed  Google Scholar 

  17. Reniers G, Tfaily R. Polygyny, partnership concurrency and the spread of HIV in sub-Saharan Africa. Demography. 2012;49(3):1075–101. doi:10.1007/s13524-012-0114-z.

    Article  PubMed  Google Scholar 

  18. Xu H, Luke N, Zulu E. Concurrent sexual partnerships among youth in urban Kenya: prevalence and partnership effects. Pop Stud. 2010;64(3):247–61.

    Article  Google Scholar 

  19. Kaler A. My girlfriends could fill a Yanu–Yanu bus: Rural Malawian men’s claims about their own serostatus. Demogr Res. 2003; Special Collection 1(Article 11):349–72.

    Google Scholar 

  20. Nnko S, Boerma JT, Urassa M, Mwaluko G, Zaba B. Secretive females or swaggering males? an assessment of the quality of sexual partnership reporting in rural Tanzania. Soc Sci Med. 2004;59(2):299–310.

    Article  PubMed  Google Scholar 

  21. Curtis SL, Sutherland EG. Measuring sexual behavior in the era of HIV/AIDS: the experience of Demographic and Health Surveys and similar enquiries. Sex Transm Infect. 2004; 80(Supplement 2):ii1–ii7.

    Google Scholar 

  22. Helleringer S, Kohler H-P, Kalilani-Phiri L, Mkandawire J, Armbruster B. The reliability of sexual partnership histories: implications for the measurement of partnership concurrency during surveys. AIDS. 2011;25(4):503–11.

    Article  PubMed  Google Scholar 

  23. Cleland J, Boerma JT, Caraël M, Weir SS. Monitoring sexual behavior in general populations: a synthesis of lessons of the past decade. Sex Transm Infect. 2004; 80(Supplement 2): ii1–ii7.

    Google Scholar 

  24. Clark S, Kabiru C, Zulu E. Do men and women report their sexual partnerships differently? evidence from Kisumu, Kenya. Int Perspect Sex Reprod Health. 2011;37(4):181–90.

    Article  PubMed  Google Scholar 

  25. Luke N, Clark S, Zulu E. The relationship history calendar: improving the scope and quality of data on youth sexual behavior. Demography. 2011;48:1151–76.

    Article  PubMed  Google Scholar 

  26. Helleringer S, Kohler H-P. Sexual network structure and the spread of HIV in Africa: evidence from Likoma Island, Malawi. AIDS. 2007;21(17):2323–32.

    Article  PubMed  Google Scholar 

  27. Helleringer S, Kohler H-P, Chimbiri A, Chatonda P, Mkandawire J. The Likoma network study: context, data collection and initial results. Demogr Res. 2009;21:427–68.

    Article  PubMed  Google Scholar 

  28. Helleringer S, Kohler H-P, Kalilani-Phiri L. The association of HIV serodiscordance and partnership concurrency in Likoma Island, Malawi. AIDS. 2009;23(10):1285–7.

    Article  PubMed  Google Scholar 

  29. UNAIDS. 2010 Report on the Global AIDS Epidemic. Geneva, UNAIDS; 2010.

  30. Clark AL, Wallin P. The accuracy of husbands’ and wives’ reports of the frequency of marital coitus. Pop Stud. 1964;18(2):165–73.

    Google Scholar 

  31. Glynn JR, Dube A, Kayuni N, Floyd S, Molesworth A, Parrott F, French N, Crampin AC. Measuring concurrency: an empirical study of different methods in a large population-based survey and evaluation of the UNAIDS guidelines. AIDS. 2012;26(8):977–85.

    Article  PubMed  Google Scholar 

  32. Eaton J. HIV: consensus indicators are needed for concurrency. Lancet. 2010;375:621–2.

    Article  Google Scholar 

  33. Morris M, Epstein H, Wawer M. Timing is everything: international variations in historical sexual partnership concurrency and HIV prevalence. PLoS One. 2010;5(11):e14092.

    Article  PubMed  Google Scholar 

  34. Sawers L. Measuring and modeling concurrency. J Int AIDS Soc. 2013;16(1):17431.

    PubMed  Google Scholar 

  35. Eaton J, McGrath N, Newell M-L. Unpacking the recommended indicator for concurrent sexual partnerships. AIDS. 2012;26(8):1037–9.

    Article  PubMed  Google Scholar 

  36. Epstein H. The invisible cure. New York: Farrar, Straus and Giroux; 2007.

    Google Scholar 

  37. Hudson CP. Concurrent partnerships could cause AIDS epidemics. Int J STD AIDS. 1993;4(5):249–53.

    PubMed  CAS  Google Scholar 

  38. Watts CH, May RM. The influence of concurrent partnerships on the dynamics of HIV/AIDS. Math Biosci. 1992;108(1):89–104.

    Article  PubMed  CAS  Google Scholar 

  39. Lurie MN, Rosenthal S. Concurrent partnerships as the driver of the HIV epidemic in sub-Saharan Africa? The evidence is limited. AIDS Behav. 2010;14(1):17–24.

    Article  PubMed  Google Scholar 

  40. Sawers L, Stillwaggon E. Concurrent sexual partnerships do not explain the HIV epidemics in Africa: a systematic review of the evidence. J Int AIDS Soc. 2010;13(1):34.

    Article  PubMed  Google Scholar 

  41. Kretzschmar M, Caraël M. Is concurrency driving HIV transmission in sub-Saharan African sexual networks? the significance of sexual partnership typology. AIDS Behav. 2012;. doi:10.1007/s10461-012-0254-6.

    PubMed  Google Scholar 

  42. Blower SM, Boe C. Sex acts, sex partners, and sex budgets: implications for risk factor analysis and estimation of HIV transmission probabilities. J AIDS. 1993;6(12):1347–52.

    CAS  Google Scholar 

  43. Epstein H, Swidler A, Gray R, Reniers G, Parker W, Parkhurst J, Short R, Halperin D. Measuring concurrent partnerships. Lancet. 2010; 375(9729):1869; author reply 1870.

    Google Scholar 

Download references

Acknowledgments

The LNS received support through the National Institute of Child Health and Development (Grants No. RO1 HD044228 and RO1 HD/MH41713), National Institute on Aging (Grant No. P30 AG12836), the Boettner Center for Pensions and Retirement Security at the University of Pennsylvania, and the National Institute of Child Health and Development Population Research Infrastructure Program (Grant No. R24 HD-044964), all at the University of Pennsylvania; as well as through National Institute of Child Health and Development (Grant No. R03HD071122) to Columbia University. Partial support for this research was provided by a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (Grant #5R24HD047879) and the National Science Foundation Graduate Research Fellowship Program (Grant #2009085286). The paper benefitted greatly from the comments of the working group on HIV and Marriage at the Annual Meeting of the Population Association of America. We would also like to thank Nancy Luke and Shelley Clark for providing access to the ULYKP data. Finally, we would like to acknowledge the critical role played by the editor and two anonymous reviewers for AIBE in encouraging us to clarify and contextualize our findings.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lauren Gaydosh.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 22 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gaydosh, L., Reniers, G. & Helleringer, S. Partnership Concurrency and Coital Frequency. AIDS Behav 17, 2376–2386 (2013). https://doi.org/10.1007/s10461-013-0525-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10461-013-0525-x

Keywords

Navigation