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 - Shivan Nuwasiima; Arthur Mpimbaza; Laban Muteebwa; Elizabeth Nagawa; Emmanuel Arinaitwe; Faizo Kiberu; David Livingstone Ejalu; Jovan Mugerwa; Charles Batte; John Mukisa; Bosco Agaba; David Mukunya; et al. (Malaria Journal)
Abstract Background Effective 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. Methods 12 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). Results Concordance 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. Conclusion Data 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.