Sexually transmitted diseases
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Intervention effects estimated from nonrandomized intervention studies are plagued by biases, yet social or structural intervention studies are rarely randomized. There are underutilized statistical methods available to mitigate biases due to self-selection, missing data, and confounding in longitudinal, observational data permitting estimation of causal effects. We demonstrate the use of Inverse Probability Weighting (IPW) to evaluate the effect of participating in a combined clinical and social sexually transmitted infection/human immunodeficiency virus prevention intervention on reduction of incident chlamydia and gonorrhea infections among sex workers in Brazil. ⋯ After correcting for selection bias, loss-to-follow-up, and confounding, our analysis suggests a protective effect of participating in the intervention. Evaluations of behavioral, social, and multilevel interventions to prevent sexually transmitted infection can benefit by introduction of weighting methods such as IPW.