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Activity Number: 87 - Invited ePoster Session: a Statistical Smörgåsbord
Type: Invited
Date/Time: Sunday, July 29, 2018 : 8:30 PM to 10:30 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #330121
Title: Statistical Methods for Addressing Missing Data in HIV/AIDS Surveillance Systems
Author(s): Sahar Zangeneh* and Ying Qing Chen and Deborah Donnell
Companies: Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center and Fred Hutch
Keywords: HIV/AIDS ; Surveillance Data ; Missing Data ; Dual-System Estimator

Growing research supports that HIV transmission can be prevented by treating HIV-infected individuals with antiretroviral treatment (ART). The proportion of individuals with suppressed viral load (VL) measures the effectiveness of therapy after initiation of ART in a given jurisdiction. National HIV surveillance systems collect and analyze data on all persons living with HIV/AIDS. Such datasets typically consist of sparse measurements on several individuals. Case records are updated over time as additional information is submitted to health departments through laboratories and/or providers. In the absence of noncoverage and measurement error, missingness of VL measurements could be explained by lack of adherence. However, the dynamic nature of case reporting entails the presence of such additional errors. We develop statistical methodologies that consider missing VL measurements to be driven from a mixture of two phenomena: noncoverage and/or reporting lag in addition to lack of adherence. The proposed methods are illustrated using simulated, as well as real data from the enhanced HIV/AIDS reporting system and the medical monitoring project.

Authors who are presenting talks have a * after their name.

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