Implications of rapid population growth on survey design and HIV estimates in the Rakai Community Cohort Study (RCCS), Uganda

Objective Since rapid population growth challenges longitudinal population-based HIV cohorts in Africa to maintain coverage of their target populations, this study evaluated whether the exclusion of some residents due to growing population size biases key HIV metrics like prevalence and population-level viremia. Design, setting and participants Data were obtained from the Rakai Community Cohort Study (RCCS) in south central Uganda, an open population-based cohort which began excluding some residents of newly constructed household structures within its surveillance boundaries in 2008. The study includes adults aged 15-49 years who were censused from 2019 to 2020. Measures We fit ensemble machine learning models to RCCS census and survey data to predict HIV seroprevalence and viremia (prevalence of those with viral load >1000 copies/mL) in the excluded population and evaluated whether their inclusion would change overall estimates. Results Of the 24 729 census-eligible residents, 2920 (12%) residents were excluded from the RCCS because they were living in new households. The predicted seroprevalence for these excluded residents was 10.8% (95% CI: 9.6% to 11.8%)-somewhat lower than 11.7% (95% CI: 11.2% to 12.3%) in the observed sample. Predicted seroprevalence for younger excluded residents aged 15-24 years was 4.9% (95% CI: 3.6% to 6.1%)-significantly higher than that in the observed sample for the same age group (2.6% (95% CI: 2.2% to 3.1%)), while predicted seroprevalence for older excluded residents aged 25-49 years was 15.0% (95% CI: 13.3% to 16.4%)-significantly lower than their counterparts in the observed sample (17.2% (95% CI: 16.4% to 18.1%)). Over all ages, the predicted prevalence of viremia in excluded residents (3.7% (95% CI: 3.0% to 4.5%)) was significantly higher than that in the observed sample (1.7% (95% CI: 1.5% to 1.9%)), resulting in a higher overall population-level viremia estimate of 2.1% (95% CI: 1.8% to 2.4%). Conclusions Exclusion of residents in new households may modestly bias HIV viremia estimates and some age-specific seroprevalence estimates in the RCCS. Overall, HIV seroprevalence estimates were not significantly affected.

Automated summary

This study evaluated the impact of excluding some residents due to population growth on key HIV metrics in a longitudinal population-based HIV cohort study in Uganda. The results showed that excluding residents in new households may slightly bias HIV viremia estimates and some age-specific seroprevalence estimates, but overall HIV seroprevalence estimates were not significantly affected.