20th International AIDS Conference - Melbourne, Australia

Abstract

WEPDC0102 - Poster Discussion Abstract


Measuring and accounting for outcome-correlated recruitment and geographic recruitment bias in a respondent-driven sample of people who inject drugs in Tijuana, Mexico

Presented by Abby Rudolph (United States).

A. Rudolph1, T. Gaines2, R. Lozada3, A. Vera2, K. Brouwer2


1Pacific Institute for Research and Evaluation, Calverton, United States, 2University of California San Diego, San Diego, United States, 3Pro-COMUSIDA, Tijuana Baja California, United States

Background: Respondent-driven sampling (RDS) is a recruitment/analytic approach for hidden populations, including those at risk for HIV/AIDS, which aims to generate unbiased estimates of disease prevalence. Its widespread use combined with its reliance on untested assumptions creates a need for exploratory and diagnostic techniques for RDS data. We will assess two potential sources of bias: geographic recruitment bias and outcome-correlated recruitment.
Methods: Interviewer-administered surveys assessed demographics, drug/sex behaviors, residential coordinates, and recruiter-recruit ties among 1,048 people who inject drugs (PWID) recruited through RDS in Tijuana, Mexico. Blood samples were obtained through venipuncture. Simulations assessed geographic and network clustering of active syphilis (RPR titers>1:8). Gender-specific predicted probabilities were estimated using logistic regression with GEE (clustered on RDS chain) and robust standard errors.
Results: HIV prevalence was 4.4% (men=3.5%, women=9.7%). The unadjusted prevalence of active syphilis was 5.7% among men and 16.6% among women. RDS-weighted estimates were not significantly different. Syphilis clustered spatially in the Zona Norte, a neighborhood known for drug and sex markets. Network simulations revealed geographic recruitment bias; respondents living/working/injecting/buying drugs in the Zona Norte preferentially recruited others living/working/injecting/buying drugs there. Network simulations also identified non-random recruitment by syphilis; those with syphilis were 3 times more likely to recruit or be recruited by other syphilis-infected PWID (P< 0.0001). Gender-specific prevalence estimates accounting for clustering among RDS chains were highest among those directly/indirectly connected to syphilis cases and who lived/worked/injected/bought drugs in the Zona Norte (men:15.9%, women:25.6%) and lowest among those without network or neighborhood-level exposures (men:3.0%, women:6.1%). Prevalence among those with a network or neighborhood exposure were ~3% for men and 6-13% for women. Significant interaction between network and neighborhood covariates was observed only for men (P< 0.001).
Conclusions: Our findings underscore the importance of considering spatial and network dependencies and recruitment patterns when estimating disease prevalence in key populations. Understanding how measures may be biased and the limitations on the representativeness of estimates has important implications for accurately estimating the prevalence of HIV/STIs for disease surveillance and epidemiologic research. Estimates which do not account for these dependencies and recruitment biases could lead to inaccurate prevalence estimates with artificially narrow confidence intervals.


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