Temporal Variation in One-Time Partnership Rates Among Young Men Who Have Sex With Men and Transgender Women.
Journal
Journal of acquired immune deficiency syndromes (1999)
ISSN: 1944-7884
Titre abrégé: J Acquir Immune Defic Syndr
Pays: United States
ID NLM: 100892005
Informations de publication
Date de publication:
01 07 2021
01 07 2021
Historique:
received:
16
10
2020
accepted:
16
02
2021
pubmed:
7
3
2021
medline:
8
10
2021
entrez:
6
3
2021
Statut:
ppublish
Résumé
Volatility in sexual contact rates has been recognized as an important factor influencing HIV transmission dynamics. One-time partnerships may be particularly important given the potential to quickly accumulate large number of contacts. Yet, empirical data documenting individual variation in contact rates remain rare. This study provides much needed data on temporal variation in one-time partners to better understand behavioral dynamics and improve the accuracy of transmission models. Data for this study were obtained from a longitudinal cohort study of young men who have sex with men and transgender women in Chicago. Participants provided sexual network data every 6 months for 2 years. A series of random effects models examined variation in one-time partnership rates and disaggregated within and between associations of exposure variables. Exposure variables included prior number of one-time partners, number of casual partners, and having a main partner. Results indicated substantial between-person and within-person variation in one-time partners. Casual partnerships were positively associated and main partnerships negatively associated with one-time partnership rates. There remained a small positive association between prior one-time partnerships and the current number of one-time partnerships. Despite the preponderance of a low number of one-time partners, substantial variation in one-time partnership rates exists among young men who have sex with men and transgender women. Accordingly, focusing on high contact rate individuals alone may be insufficient to identify periods of highest risk. Future studies should use these estimates to more accurately model how volatility impacts HIV transmission and better understand how this variation influences intervention effectiveness.
Sections du résumé
BACKGROUND
Volatility in sexual contact rates has been recognized as an important factor influencing HIV transmission dynamics. One-time partnerships may be particularly important given the potential to quickly accumulate large number of contacts. Yet, empirical data documenting individual variation in contact rates remain rare. This study provides much needed data on temporal variation in one-time partners to better understand behavioral dynamics and improve the accuracy of transmission models.
METHODS
Data for this study were obtained from a longitudinal cohort study of young men who have sex with men and transgender women in Chicago. Participants provided sexual network data every 6 months for 2 years. A series of random effects models examined variation in one-time partnership rates and disaggregated within and between associations of exposure variables. Exposure variables included prior number of one-time partners, number of casual partners, and having a main partner.
RESULTS
Results indicated substantial between-person and within-person variation in one-time partners. Casual partnerships were positively associated and main partnerships negatively associated with one-time partnership rates. There remained a small positive association between prior one-time partnerships and the current number of one-time partnerships.
CONCLUSIONS
Despite the preponderance of a low number of one-time partners, substantial variation in one-time partnership rates exists among young men who have sex with men and transgender women. Accordingly, focusing on high contact rate individuals alone may be insufficient to identify periods of highest risk. Future studies should use these estimates to more accurately model how volatility impacts HIV transmission and better understand how this variation influences intervention effectiveness.
Identifiants
pubmed: 33675616
doi: 10.1097/QAI.0000000000002679
pii: 00126334-202107010-00006
pmc: PMC8192435
mid: NIHMS1677119
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e214-e221Subventions
Organisme : NIDA NIH HHS
ID : K08 DA037825
Pays : United States
Organisme : NIAID NIH HHS
ID : P30 AI027757
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI138783
Pays : United States
Organisme : NIDA NIH HHS
ID : U01 DA036939
Pays : United States
Informations de copyright
Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
The authors have no conflicts of interest to disclose.
Références
Matthews DD, Herrick AL, Coulter RW, et al.; POWER Study Team. Running backwards: consequences of current HIV incidence rates for the next generation of black MSM in the United States. AIDS Behav. 2016;20:7–16.
Sidibé M, Loures L, Samb B. The UNAIDS 90-90-90 target: a clear choice for ending AIDS and for sustainable health and development. J Int AIDS Soc. 2016;19:21133.
Fauci AS, Redfield RR, Sigounas G, et al. Ending the HIV epidemic: a plan for the United States. JAMA. 2019;321:844–845.
Crepaz N, Hess KL, Purcell DW, et al. Estimating national rates of HIV infection among MSM, persons who inject drugs, and heterosexuals in the United States. AIDS. 2019;33:701–708.
Delva W, Leventhal GE, Helleringer S. Connecting the dots: network data and models in HIV epidemiology. AIDS. 2016;30:2009–2020.
Pellis L, Ball F, Bansal S, et al. Eight challenges for network epidemic models. Epidemics. 2015;10:58–62.
Millett GA, Flores SA, Peterson JL, et al. Explaining disparities in HIV infection among black and white men who have sex with men: a meta-analysis of HIV risk behaviors. AIDS. 2007;21:2083–2091.
Millett GA, Peterson JL, Flores SA, et al. Comparisons of disparities and risks of HIV infection in black and other men who have sex with men in Canada, UK, and USA: a meta-analysis. The Lancet. 2012;380:341–348.
Rosenberg ES, Sullivan PS, DiNenno EA, et al. Number of casual male sexual partners and associated factors among men who have sex with men: results from the national HIV behavioral surveillance system. BMC Public Health. 2011;11:189.
Carlo Hojilla J, Koester KA, Cohen SE, et al. Sexual behavior, risk compensation, and HIV prevention strategies among participants in the san francisco PrEP demonstration project: a qualitative analysis of counseling notes. AIDS Behav. 2016;20:1461–1469.
Elsesser SA, Oldenburg CE, Biello KB, et al. Seasons of risk: anticipated behavior on vacation and interest in episodic antiretroviral pre-exposure prophylaxis (PrEP) among a large national sample of U.S. Men who have sex with men (MSM). AIDS Behav. 2016;20:1400–1407.
Liu AY, Vittinghoff E, Chillag K, et al. Sexual risk behavior among HIV-uninfected men who have sex with men participating in a tenofovir preexposure prophylaxis randomized trial in the United States. J Acquir Immune Defic Syndr. 2013;64:87–94.
Montaño MA, Dombrowski JC, Dasgupta S, et al. Changes in sexual behavior and STI diagnoses among MSM initiating PrEP in a clinic setting. AIDS Behav. 2019;23:548–555.
Lim SH, Christen CL, Marshal MP, et al. Middle-aged and older men who have sex with men exhibit multiple trajectories with respect to the number of sexual partners. AIDS Behav. 2012;16:590–598.
Romero-Severson EO, Volz E, Koopman JS, et al. Dynamic variation in sexual contact rates in a cohort of HIV-negative gay men. Am J Epidemiol. 2015;182:255–262.
Basten M, Heijne JC, Geskus R, et al. Sexual risk behaviour trajectories among MSM at risk for HIV in Amsterdam, The Netherlands. AIDS. 2018;32:1185–1192.
Swann G, Newcomb ME, Crosby S, et al. Historical and developmental changes in condom use among young men who have sex with men using a multiple-cohort, accelerated longitudinal design. Arch Sex Behav. 2019;48:1099–1110.
Rozhnova G, van der Loeff MF, Heijne JC, et al. Impact of heterogeneity in sexual behavior on effectiveness in reducing HIV transmission with test-and-treat strategy. PLoS Comput Biol. 2016;12:e1005012.
Romero-Severson EO, Alam SJ, Volz E, et al. Acute-stage transmission of HIV. Epidemiology. 2013;24:516–521.
Zhang X, Zhong L, Romero-Severson EO, et al. Episodic HIV risk behavior can greatly amplify HIV prevalence and the fraction of transmissions from acute HIV infection. Stat Commun Infect Dis. 2012;4:1041.
Rozhnova G, Heijne JC, Basten M, et al. Impact of sexual trajectories of men who have sex with men on the reduction in HIV transmission by pre-exposure prophylaxis. Epidemics. 2019;28:100337.
Romero-Severson EO, Alam SJ, Volz EM, et al. Heterogeneity in number and type of sexual contacts in a gay urban cohort. Stat Commun Infect Dis. 2012;4:4.
Mustanski B, Morgan E, D'aquila R. Individual and network factors associated with racial disparities in HIV among young men who have sex with men: results from the RADAR cohort study. J Acquir Immune Defic Syndr. 2019;80:24–30.
Duncan SC, Duncan TE, Hops H. Analysis of longitudinal data within accelerated longitudinal designs. Psychol Methods. 1996;1:236–248.
Mustanski B, Garofalo R, Emerson EM. Mental health disorders, psychological distress, and suicidality in a diverse sample of lesbian, gay, bisexual, and transgender youths. Am J Public Health. 2010;100:2426–2432.
Mustanski B, Johnson AK, Garofalo R, et al. Perceived likelihood of using HIV pre-exposure prophylaxis medications among young men who have sex with men. AIDS Behav. 2013;17:2173–2179.
Sullivan PS, Peterson J, Rosenberg ES, et al. Understanding racial HIV/STI disparities in black and white men who have sex with men: a multilevel approach. PLoS One. 2014;9:e90514.
Weiss KM, Goodreau SM, Morris M, et al. Egocentric sexual networks of men who have sex with men in the United States: results from the ARTnet study. Epidemics. 2019;24:100386.
Falkenström F, Finkel S, Sandell R, et al. Dynamic models of individual change in psychotherapy process research. J Consulting Clin Psychol. 2017;85:537–549.
Firebaugh G, Warner C, Massoglia M. Fixed effects, random effects, and hybrid models for causal analysis. In: Morgan SL, ed. Handbook of Causal Analysis for Social Research. Netherlands: Springer; 2013:113–132.
Nakagawa S, Johnson PC, Schielzeth H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J R Soc Interf. 2017;14:124.
Wilkins AS. To lag or not to lag?: re-evaluating the use of lagged dependent variables in regression analysis. Polit Sci Res Methods. 2018;6:393.
Achen CH. Why lagged dependent variables can suppress the explanatory power of other independent variables. 2000. Annual Meeting of the Political Methodology Section of the American Political Science Association, ; July 20-22, 2000; Los Angeles, CA.
Keele L, Kelly NJ. Dynamic models for dynamic theories: the ins and outs of lagged dependent variables. Polit Anal Annu Publ Methodol Section Am Polit Sci Assoc. 2006;14:186–205.
Enders CK, Mistler SA, Keller BT. Multilevel multiple imputation: a review and evaluation of joint modeling and chained equations imputation. Psychol Methods. 2016;21:222–240.
van Buuren S, Groothuis-Oudshoorn K. Mice: multivariate imputation by chained equations in R. J Stat Softw. 2010;45:1–68.
Rubin DB. Multiple Imputation for Survey Nonresponse. New York, NY: Wiley; 1987.
Pines HA, Gorbach PM, Weiss RE, et al. Sexual risk trajectories among MSM in the United States: implications for pre-exposure prophylaxis delivery. J Acquir Immune Defic Syndr. 2014;65:579–586.
Wilkinson AL, El-Hayek C, Fairley CK, et al. Measuring transitions in sexual risk among men who have sex with men: the novel use of latent class and latent transition analysis in HIV sentinel surveillance. Am J Epidemiol. 2017;185:627–635.
Brunham RC. Core group theory: a central concept in STD epidemiology. Venereol. 1997;10:34.
Liljeros F, Edling CR, Nunes Amaral LA. Sexual networks: implications for the transmission of sexually transmitted infections. Microbes Infect Inst Pasteur. 2003;5:189–196.
Feinstein BA, Dellucci TV, Sullivan PS, et al. Characterizing sexual agreements with one's most recent sexual partner among young men who have sex with men. AIDS Educ Prev. 2018;30:335–349.
Underhill K, Guthrie KM, Colleran C, et al. Temporal fluctuations in behavior, perceived HIV risk, and willingness to use pre-exposure prophylaxis (PrEP). Arch Sex Behav. 2018;47:2109–2121.
Henry CJ, Koopman JS. Strong influence of behavioral dynamics on the ability of testing and treating HIV to stop transmission. Scientific Rep. 2015;5:9467.
Nosyk B, Zang X, Krebs E, et al.; Localized HIV Modeling Study Group. Ending the HIV epidemic in the USA: an economic modelling study in six cities. Lancet HIV. 2020;7:e491–e503.
Giguère K, Alary M. Targeting core groups for gonorrhoea control: feasibility and impact. Sex Transm Infections. 2015;91:241–244.
Beymer MR, Holloway IW, Pulsipher C, et al. Current and future PrEP medications and modalities: on-demand, injectables, and topicals. Curr HIV AIDS Rep. 2019;16:349–358.
Cornelisse VJ, Lal L, Price B, et al. Interest in switching to on-demand HIV pre-exposure prophylaxis (PrEP) among Australian users of daily PrEP: an online survey. Open Forum Infect Dis. 2019;6:ofz287.
Gafos M, Horne R, Nutland W, et al. The context of sexual risk behaviour among men who have sex with men seeking PrEP, and the impact of PrEP on sexual behaviour. AIDS Behav. 2019;23:1708–1720.
Saberi P, Scott HM. On-demand oral pre-exposure prophylaxis with tenofovir/emtricitabine: what every clinician needs to know. J Gen Intern Med. 2020;35:1285–1288.
Hamilton DT, Rosenberg ES, Jenness SM, et al. Modeling the joint effects of adolescent and adult PrEP for sexual minority males in the United States. PLoS One. 2019;14:e0217315.
Luo W, Katz DA, Hamilton DT, et al. Development of an agent-based model to investigate the impact of HIV self-testing programs on men who have sex with men in atlanta and seattle. JMIR Public Health Surveill. 2018;4:e58.
Jenness SM, Goodreau SM, Rosenberg E, et al. Impact of the centers for disease control's HIV preexposure prophylaxis guidelines for men who have sex with men in the United States. J Infect Dis. 2016;214:1800–1807.
Kelly SL, Martin-Hughes R, Stuart RM, et al. The global Optima HIV allocative efficiency model: targeting resources in efforts to end AIDS. Lancet HIV. 2018;5:e190–e198.
Elion RA, Kabiri M, Mayer KH, et al. Estimated impact of targeted pre-exposure prophylaxis: strategies for men who have sex with men in the United States. Int J Environ Res Public Health. 2019;16:1592.
Goodreau SM, Hamilton DT, Jenness SM, et al. Targeting human immunodeficiency virus pre-exposure prophylaxis to adolescent sexual minority males in higher prevalence areas of the United States: a modeling study. J Adolesc Health. 2018;62:311–319.
Harmon TM, Fisher KA, McGlynn MG, et al. Exploring the potential health impact and cost-effectiveness of AIDS vaccine within a comprehensive HIV/AIDS response in low- and middle-income countries. PLoS One. 2016;11:e0146387.
Zhang L, Peng P, Wu Y, et al. Modelling the epidemiological impact and cost-effectiveness of PrEP for HIV transmission in MSM in China. AIDS Behav. 2019;23:523–533.