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Author Notes:

Correspondence: Kevin Maloney, PhD, MPH, 140 Decatur St., Suite 463, Atlanta, GA, 30303, Phone: (404) 413-1148, kmaloney@gsu.edu

Acknowledgements: The authors would like to thank Timothy Lash for input on the design of the study and review of an earlier draft of the manuscript, and the EpiModel team including Emeli Anderson, Christina Chandra, Laura Mann, and Adrien Le Guillou, for input on the presentation of results.

Competing interests: None declared.

Subjects:

Research Funding:

This work was supported by grants R21 MH112449, R01 AI138783, and P30 AI050409 (Emory University Center for AIDS Research) from the National Institutes of Health.

Keywords:

  • HIV
  • men who have sex with men
  • statistical models
  • measurement error
  • systematic bias
  • sexual networks

Sexual mixing by HIV status and pre-exposure prophylaxis use among men who have sex with men: addressing information bias

Tools:

Journal Title:

Epidemiology

Volume:

Volume 33, Number 6

Publisher:

, Pages 808-816

Type of Work:

Article | Post-print: After Peer Review

Abstract:

Background: Population-level estimates of sexual network mixing for parameterizing prediction models of pre-exposure prophylaxis (PrEP) effectiveness are needed to inform prevention of HIV transmission among men who have sex with men (MSM). Estimates obtained by egocentric sampling are vulnerable to information bias due to incomplete respondent knowledge. Methods: We estimated patterns of serosorting and PrEP sorting among MSM in the United States using data from a 2017–2019 egocentric sexual network study. Respondents served as proxies to report the HIV status and PrEP use of recent sexual partners. We contrasted results from a complete-case analysis (unknown HIV and PrEP excluded) versus a bias analysis with respondent-reported data stochastically reclassified to simulate unobserved self-reported data from sexual partners. Results: We found strong evidence of preferential partnering across analytical approaches. The bias analysis showed concordance between sexual partners of HIV diagnosis and PrEP use statuses for MSM with diagnosed HIV (39%; 95% simulation interval: 31, 46), MSM who used PrEP (32%; 21, 37), and MSM who did not use PrEP (83%; 79, 87). The fraction of partners with diagnosed HIV was higher among MSM who used PrEP (11%; 9, 14) compared to MSM who did not use PrEP (4%; 3, 5). Comparatively, across all strata of respondents, the complete-case analysis overestimated the fractions of partners with diagnosed HIV or PrEP use. Conclusion: We found evidence consistent with HIV and PrEP sorting among MSM, which may decrease the population-level effectiveness of PrEP. Bias analyses can improve mixing estimates for parameterization of transmission models.

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