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Characterising patterns in routinely reported longitudinal HIV data in South Africa using a Bayesian multiplicative
Bareng A S Nonyane1, Laura Steiner2, Kate Shearer2
1Department of International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.
BMJ Public Health
|February 28, 2025
Summary
Discrepancies between HIV treatment databases were analyzed using a Bayesian model. The study found minor differences in ART initiation data, with a few facilities showing significant data gaps.
Area of Science:
- Health Informatics
- Epidemiology
- Biostatistics
Background:
- Longitudinal aggregated healthcare data from multiple facilities require robust analytical methods.
- Identifying discrepancies between routinely collected health data sources is crucial for data quality.
Purpose of the Study:
- To characterize patterns and identify discrepancies in longitudinal aggregated healthcare data between two database systems.
- To quantify the time effect and facility-specific variations in antiretroviral treatment (ART) initiations.
Main Methods:
- Utilized routinely collected data on ART initiations in 69 South African facilities in 2019 from Tier.net and DHIS.
- Employed a Bayesian multiplicative interaction model to analyze heterogeneous facility-specific slopes and database discrepancies.
Main Results:
- Average trends showed seasonal dips in ART initiations, particularly in December.
- Facility-specific slopes revealed distinct fluctuation patterns over time.
- Median difference in monthly ART initiations between databases was 1.6, with 3 facilities showing discrepancies of over 10 initiations.
Conclusions:
- Bayesian multiplicative interaction models effectively quantify trends and discrepancies in multi-facility healthcare data.
- The Bayesian framework efficiently estimates parameters for numerous facilities with heterogeneous time slopes.
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