Abstract
BackgroundRoutine health facility data are a critical source of local monitoring of progress and performance at the subnational level. Uganda has been using district health statistics from facility data for many years. We aimed to systematically assess data quality and examine different methods to obtain plausible subnational estimates of coverage for maternal, newborn and child health interventions.MethodsAnnual data from the Uganda routine health facility information system 2015–2019 for all 135 districts were used, as well as national surveys for external comparison and the identification of near-universal coverage interventions. The quality of reported data on antenatal and delivery care and child immunization was assessed through completeness of facility reporting, presence of extreme outliers and internal data consistencies. Adjustments were made when necessary. The denominators for the coverage indicators were derived from population projections and health facility data on near-universal coverage interventions. The coverage results with different denominators were compared with the results from household surveys.ResultsUganda’s completeness of reporting by facilities was near 100% and extreme outliers were rare. Inconsistencies in reported events, measured by annual fluctuations and between intervention consistency, were common and more among the 135 districts than the 15 subregions. The reported numbers of vaccinations were improbably high compared to the projected population of births or first antenatal visits – and especially so in 2015–2016. There were also inconsistencies between the population projections and the expected target population based on reported numbers of antenatal visits or immunizations. An alternative approach with denominators derived from facility data gave results that were more plausible and more consistent with survey results than based on population projections, although inconsistent results remained for substantive number of subregions and districts.ConclusionOur systematic assessment of the quality of routine reports of key events and denominators shows that computation of district health statistics is possible with transparent adjustments and methods, providing a general idea of levels and trends for most districts and subregions, but that improvements in data quality are essential to obtain more accurate monitoring.
Highlights
Routine health facility data are a critical source of local monitoring of progress and performance at the subnational level
Our systematic assessment of the quality of routine reports of key events and denominators shows that computation of district health statistics is possible with transparent adjustments and methods, providing a general idea of levels and trends for most districts and subregions, but that improvements in data quality are essential to obtain more accurate monitoring
Routine health facility data are potentially a useful source of more frequent estimates of service coverage at the local level, as has been shown in several countries [3,4,5,6]. Such facility-data derived estimates of coverage may focus on indicators of maternal, newborn and child health (MNCH), child immunization [7], malaria [8] and other infectious disease programs such as HIV and TB
Summary
Routine health facility data are a critical source of local monitoring of progress and performance at the subnational level. We aimed to systematically assess data quality and examine different methods to obtain plausible subnational estimates of coverage for maternal, newborn and child health interventions. Routine health facility data are potentially a useful source of more frequent estimates of service coverage at the local level, as has been shown in several countries [3,4,5,6]. Such facility-data derived estimates of coverage may focus on indicators of maternal, newborn and child health (MNCH), child immunization [7], malaria [8] and other infectious disease programs such as HIV and TB. WHO has published a set of methods and indicators to assess the quality of reported data on services which is available as a module within DHIS2, and can be used to detect and investigate outliers and inconsistencies [10, 15]
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