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Published on: October 31, 2010
HIV surveillance based on routine testing data from antenatal clinics in Malawi (2011-2018): measuring and adjusting
Mathieu Maheu-Giroux1, Andreas Jahn, Thokozani Kalua
1aDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Canada bMinistry of Health, Lilongwe, Malawi cI-TECH, Department of Global Health, University of Washington, Seattle, USA dDepartment of Infectious Disease Epidemiology, Imperial College London, St Mary's Hospital, London, United Kingdom.
Objective:
The use of routinely collected data from prevention of mother-to-child transmission programs (ANC-RT) has been proposed to monitor HIV epidemic trends. This poses several challenges for surveillance, one of them being that women may opt-out of testing and/or test stock-outs may result in inconsistent service availability. In this study, we sought to empirically quantify the relationship between imperfect HIV testing coverage and HIV prevalence among pregnant women from ANC-RT data.
Design:
We used reports from the ANC Register of all antenatal care (ANC) sites in Malawi (2011-2018), including 49 244 monthly observations, from 764 facilities, totaling 4 375 777 women.
Methods:
Binomial logistic regression models with facility-level fixed effects and marginal standardization were used to assess the effect of testing coverage on HIV prevalence.
Results:
Testing coverage increased from 78 to 98% over 2011-2018. We estimated that, had testing coverage been perfect, prevalence would have been 0.4% point lower (95% CI 0.3-0.5%) than the 7.9% observed prevalence, a relative overestimation of 6%. Bias in HIV prevalence was the highest in 2012, when testing coverage was lowest (72%), resulting in a relative overestimation of HIV prevalence of 15% (95% CI 12-17%). Overall, adjustments for imperfect testing coverage led to a subtler decline in HIV prevalence over 2011--2018.
Conclusion:
Malawi achieved high coverage of routine HIV testing in recent years. Nevertheless, imperfect testing coverage can lead to overestimation of HIV prevalence among pregnant women when coverage is suboptimal. ANC-RT data should be carefully evaluated for changes in testing coverage and completeness when used to monitor epidemic trends.
