ArticleMalaria journal2025
Concordance of data on key malaria indicators between DHIS2 and source documents, and influencing factors at public primary health facilities in eastern Uganda: a mixed methods study.
Article in Malaria journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Malaria Morbidity and Its Association With Indoor Residual Spraying in Amolatar District, Uganda: A Retrospective Ecological Study Using Routine DHIS2 Data.Health science reports · 2026Article
- Hotspot Mapping, Spatiotemporal Clustering and Forecasting of Malaria Incidence in Uganda, 2014-2023: Strategies to Inform Targeted Public Health Interventions.Journal of epidemiology and global health · 2026Article
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15 authors.
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Abstract
backgroundEffective malaria surveillance is a key strategy for malaria control in sub-Saharan Africa. In 2012, Uganda rolled out the District Health Information System, version 2 (DHIS2), however, the quality of the DHIS2 malaria surveillance data is questionable. The primary objective of this study was to assess the level of concordance between the DHIS2 and facility source documents on selected malaria data indicators and influencing factors at selected primary health facilities in Mayuge district.
methods12 public health facilities were enrolled in a parallel convergent mixed-methods study. Data collection included a retrospective review of data on key malaria indicators in DHIS2 weekly reports from 2021 to 2022 and source documents at selected public health facilities. In-depth interviews were conducted with facility heads and records personnel. Data concordance was defined as the agreement between the DHIS2 data and the source documents. Modified Poisson regression with cluster robust standard errors was used to assess factors associated with data concordance on Test Positivity Rates (TPR).
resultsConcordance between DHIS2 data and OPD register data for suspected malaria cases was 36.7%, 95% confidence interval [CI] 25.2, 49.9; suspected cases tested was 53.6%, 95% CI; 41.7, 65.05; test positive cases was 55.3%, 95% CI; 43.0, 67.0; and TPR was 56.8%, 95% CI; 43.9, 68.8. The presence of a Health Management Information System (HMIS) personnel at the facility (adjusted prevalence ratio [aPR] 1.41, (95% CI; 1.20, 1.66)), timely reporting (aPR = 1.15, 95% CI; 1.00, 1.31) and stock out of malaria rapid diagnostic tests (RDTs) (aPR = 0.55, 95% CI; 0.35, 0.86) were significantly associated with data concordance. Qualitative data highlighted regular data verification and the perceived value of HMIS data by health workers as facilitators of data concordance, while insufficient training and rapid diagnostic test (RDT) stockouts were identified as barriers.
conclusionData concordance between DHIS2 and source documents was below the World Health Organisation (WHO) performance standard of ≥ 80% on key malaria indicators. Presence of data clerks, and timely reporting were identified as the factors that improved data concordance. To improve the quality and timeliness of the DHIS2, having trained data staff at public health facilities is key. Alternatively, electronic primary data capture may help in reducing errors that arise during data capturing and aggregation.
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