Bias Adjustment Techniques Are Underutilized in HIV Sexual Risk Estimation: A Systematic Review

Abstract

Background: Valid measurement of determinants of HIV infection among men who have sex with men (MSM) is critical for intervention planning and resource allocation. However, sexual minority research concerning HIV risk often relies on proxy exposures of sexual behaviors such as sexual orientation and partner gender. Inferring high risk sexual behaviors (i.e., condomless anal intercourse) from these proxies inaccurately captures HIV risk, but few studies have attempted to correct for this bias. Methods: We performed a systematic review of methodological practices for estimating risk of HIV infection among MSM. Results: We identified 32 studies in which high risk sexual behavior was assessed: 82% (n = 26) measured and used sexual risk behaviors (e.g., condomless anal intercourse or sexual positioning) to assess risk of HIV infection; 9% (n = 3) used proxy measures; and 9% (n = 3) used both behavior and proxy variables. Various treatments of misclassification reported by investigators included the following: 82% (n = 26) discussed misclassification of sexual behavior as a potential limitation; however, among these studies, no attempts were made to correct misclassification; 12% (n = 4) did not report exposure misclassification, and 6% (n = 2) explicitly considered this information bias and conducted a Bayesian approach to correct for misclassification. Conclusions: Our systematic review indicates that a majority of studies engaging in collecting primary data have taken additional steps to acquire detailed information regarding sexual risk behaviors. However, reliance on population-based surveys may still lead to potentially biased estimates. Thus, bias analytic techniques are potential tools to control for any suspected biases.

Four Decades of Epidemiologic Science on HIV Infection and Disease, and Its Impact on Public Health Practice and Policy for Sexual and Gender Minority Persons

“…In addition, various key methodological issues remain a major concern for association studies. 60 , 61 We, therefore, highlight current challenges in studies assessing HIV risk among SGM and future directions in which epidemiologic methods can be used to address these issues.…”

“…Current analytical techniques such as sensitivity analysis or Bayesian analysis are quite robust in assessing and adjusting for such biases; however, such methods remain underutilized in studies estimating HIV risk among MSM and other SGM populations. 61 In addition, bias in sampling is often a problem because samples are often small and derived using non-probability sampling methods. 62 SGM samples are rarely ever representative of the population and more prone to selection bias.…”