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. 2023 Sep 14;1(1):e000070.
doi: 10.1136/bmjph-2023-000070. eCollection 2023 Nov.

Characterising patterns in routinely reported longitudinal HIV data in South Africa using a Bayesian multiplicative interaction model

Bareng A S Nonyane  1 , Laura Steiner  2 , Kate Shearer  2 , Leisha Genade  3 , Neil Martinson  3 , Christopher J Hoffmann  2 , Jonathan E Golub  2 , Limakatso Lebina  3
Affiliations

Characterising patterns in routinely reported longitudinal HIV data in South Africa using a Bayesian multiplicative interaction model

Bareng A S Nonyane et al. BMJ Public Health. .

Abstract

Introduction: We consider an analytical problem of characterising patterns and identifying discrepancies between database systems for longitudinal aggregated healthcare data involving multiple facilities.

Methods: We used routinely collected data on the registered number of people living with HIV who initiated antiretroviral treatment (ART) in 69 South African facilities in 2019; reported in the Three Interlinked Electronic register (Tier.net) and the District Health Information System. A Bayesian multiplicative interaction model quantified the average time effect as realised through the heterogeneous facility-specific slopes and quantified discrepancies between the two database sources.

Results: The estimated average trends showed a slight dip in June and a large dip in December. The estimated slopes identified clusters of facilities based on their ranges of fluctuations over time. The differences in average monthly ART initiations between the two database sources had a median of 1.6 (IQR 0.8-3.3), while 3 outlying facilities differed by at least 10 ART initiations between the 2 sources.

Conclusion: Multiplicative interaction models are a powerful tool for quantifying average trends over time and for evaluating discrepancies between reporting systems for multiple facilities with heterogeneous time slopes. The Bayesian framework enables efficient estimation for a very large number of parameters.

Keywords: Epidemiology; Public Health; Statistics as Topic; trends.

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Conflict of interest statement

None declared.

Figures

Figure 1
Figure 1. Plots of the reported antiretroviral treatment (ART) initiation in source 1 (solid line) and source 2 (dashed line).
Figure 2
Figure 2. Monthly reported counts (solid lines) and predicted posterior means (dashed lines) for 16 facilities reported in source 1. The labels above the plots indicate the facility number, the estimated mean counts and their SD, and the facility-specific slopes βk. ART, antiretroviral treatment.
Figure 3
Figure 3. Posterior means of the estimated latent time effects θj, and the corresponding 95% highest posterior density region (dashed lines).
Figure 4
Figure 4. Facility-specific slopes βk versus within facility variance σk.
Figure 5
Figure 5. Differences in posterior mean intercepts from the two sources.
Figure 6
Figure 6. Observed data for facilities 24, 69, 6 and 42 as reported in the two data sources.
Figure 7
Figure 7. Posterior mean estimates of the multiplicative slope (βk) from the two sources, text labelling refers to clinic numbers.
See this image and copyright information in PMC

References

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