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ABSTRACT Background Population-level estimates of disease prevalence and control are needed to assess the effectiveness of prevention and treatment strategies. However, available data are often subject to differential missingness. Consider population-level HIV viral suppression: proportion of all HIV-positive persons who are suppressing viral replication. Individuals with measured HIV status, and, among HIV-positive individuals, those with measured viral suppression are likely to differ from those without such measurements. Methods We discuss three sets of assumptions sufficient to identify population-level suppression over time in the intervention arm of the SEARCH Study (NCT01864603), a community randomized trial in rural Kenya and Uganda (2013-2017). Using data on nearly 100,000 participants, we compare estimates from an unadjusted approach assuming data are missing-completely-at-random (MCAR); stratification on age group, sex, and community; and, targeted maximum likelihood estimation (TMLE) with Super Learner to adjust for baseline and time-updated predictors of measurement. Results Despite high annual coverage of testing, estimates of population-level viral suppression varied by identification assumption. Unadjusted estimates were most optimistic: 50% of HIV-positive persons suppressed at baseline, 80% at Year 1, 85% at Year 2, and 85% at Year 3. Stratification on baseline predictors yielded slightly lower estimates, and full adjustment reduced estimates further: 42% of HIV-positive persons suppressed at baseline, 71% at Year 1, 76% at Year 2, and 79% at Year 3. Conclusions Estimation of population-level disease burden and treatment coverage require appropriate adjustment for missingness. Even in “Big Data” settings, estimates relying on the MCAR assumption or baseline stratification should be interpreted with caution. |
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