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Far from MCAR: obtaining population-level estimates of HIV viral suppression
Dalsone Kwarisiima
James Ayieko
Maya Petersen
Gabriel Chamie
Edwin Charlebois
Moses Kamya
Mark van der Laan
Joshua Schwab
Diane Havlir
Laura B. Balzer
Novel Coronavirus
Acceso Abierto
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][1]), 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. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Trial NCT01864603 ### Funding Statement This work was supported by grant numbers U01AI099959, UM1AI068636, and R01AI074345-06A1 from National Institute of Allergy and Infectious Diseases at the National Institutes of Health; by the President’s Emergency Plan for AIDS Relief; and by Gilead Sciences, which provided Truvada®. ### Author Declarations All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript. Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Data sufficient to reproduce the study findings will be made available approximately one year after completion of the ongoing trial ([NCT01864603][1]). Further inquiries can be directed to the SEARCH Scientific Committee at [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT01864603&atom=%2Fmedrxiv%2Fearly%2F2019%2F11%2F22%2F19012781.atom
Cold Spring Harbor Laboratory Press
Appears in Collections:Artículos científicos

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