Misreporting of Patient Outcomes in the South African National HIV Treatment Database: Consequences for Programme Planning, Monitoring, and Evaluation

Abstract

Background: Monitoring progress toward global treatment targets using HIV programme data in sub-Saharan Africa has proved challenging. Constraints in routine data collection and reporting can lead to biased estimates of treatment outcomes. In 2010, South Africa introduced an electronic patient monitoring system for HIV patient visits, TIER.Net. We compare treatment status and outcomes recorded in TIER.Net to outcomes ascertained through detailed record review and tracing in order to assess discrepancies and biases in retention and mortality rates. Methods: The Agincourt Health and Demographic Surveillance System (HDSS) in north-eastern South Africa is served by eight public primary healthcare facilities. Since 2014, HIV patient visits are logged electronically at these clinics, with patient records individually linked to their HDSS record. These data were used to generate a list of patients >90 days late for their last scheduled clinic visit and deemed lost to follow-up (LTFU). Patient outcomes were ascertained through a review of the TIER.Net database, physical patient files, registers kept by two non-government organisations that assist with patient tracing, cross-referencing with the HDSS records and supplementary physical tracing. Descriptive statistics were used to compare patient outcomes reported in TIER.Net to their outcome ascertained in the study. Results: Of 1074 patients that were eligible for this analysis, TIER.Net classified 533 (49.6%) as LTFU, 80 (7.4%) as deceased, and 186 (17.3%) as transferred out. TIER.Net misclassified 36% of patient outcomes, overestimating LTFU and underestimating mortality and transfers out. TIER.Net missed 40% of deaths and 43% of transfers out. Patients categorized as LTFU in TIER.Net were more likely to be misclassified than patients classified as deceased or transferred out. Discussion: Misclassification of patient outcomes in TIER.Net has consequences for programme forecasting, monitoring, and evaluation. Undocumented transfers accounted for the majority of misclassification, suggesting that the transfer process between clinics should be improved for more accurate reporting of patient outcomes. Processes that lead to the correct classification of patient status including patient tracing should be strengthened. Clinics could cross-check all available data sources before classifying patients as LTFU. Programme evaluators and modelers could consider using correction factors to improve estimates of outcomes from TIER.Net.

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