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Health facility data are a critical source of local and continuous health statistics. Countries have introduced web-based information systems that facilitate data management, analysis, use and visualisation of health facility data. Working with teams of Ministry of Health and country public health institutions analysts from 14 countries in Eastern and Southern Africa, we explored data quality using national-level and subnational-level (mostly district) data for the period 2013–2017. The focus was on endline analysis where reported health facility and other data are compiled, assessed and adjusted for data quality, primarily to inform planning and assessments of progress and performance. The analyses showed that although completeness of reporting was generally high, there were persistent data quality issues that were common across the 14 countries, especially at the subnational level. These included the presence of extreme outliers, lack of consistency of the reported data over time and between indicators (such as vaccination and antenatal care), and challenges related to projected target populations, which are used as denominators in the computation of coverage statistics. Continuous efforts to improve recording and reporting of events by health facilities, systematic examination and reporting of data quality issues, feedback and communication mechanisms between programme managers, care providers and data officers, and transparent corrections and adjustments will be critical to improve the quality of health statistics generated from health facility data.

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BackgroundOne in eight sub-Saharan Africans now lives in a city with a population greater than 750,000. Decision makers require additional evidence regarding the burden of malaria in these large cities. This paper presents results from analysis of existing data from nationwide household surveys measuring malaria parasitaemia by microscopy among children six to 59 months of age in 15 countries of sub-Saharan Africa.MethodsGeo-coordinates for each survey cluster were used to determine the distance from the cluster to the centre of each of 16 large cities with populations greater than 750,000. Geo-coordinates of each site within 25 km of the centre were entered into Google Earth to obtain a satellite image of the location and determine whether it was within the boundaries of the metropolis. In the case of two countries for which survey geo-coordinates were not available, clusters located in an additional four large cities were identified based upon their designated district. Data from all sites within city boundaries were pooled together and compared to data from all rural sites within 150 km of the city centre or in the same zone of malaria endemicity.ResultsOf the 20 large cities, only in Ouagadougou were more than 10% of children found to have a malaria infection. The prevalence was less than 5% for 16 of these cities. Apart from Antananarivo where both the large city and the comparison rural communities were parasite-free, the prevalence in each of the large cities was 0 to 40% of that found among children living in rural communities within 150 km of these cities or within the same zone of malaria endemicity. In 14 of the 20 large cities, all of the children living in 75% or more of the clusters were malaria parasite-free.ConclusionsExisting data from malaria indicator surveys can be used to document the substantially lower prevalence of malaria in specific large cities. These findings will help policy makers, public health programmers and clinical workers in each country to develop and promote malaria control strategies that are suited to large cities as well as to those living in smaller communities.

Background Coverage with the third dose of diphtheria-pertussis-tetanus-containing vaccine (DPT3) is a widely used measure of the performance of routine immunization systems. Since 2015, data reported by Ethiopia’s health facilities have suggested DPT3 coverage to be greater than 95%. Yet, Demographic and Health Surveys in 2016 and 2019 found DPT3 coverage to be 53 and 61% respectively for years during this period. This case study reviews the last 20 years of administrative (based on facility data), survey and United Nations (UN) estimates of Ethiopia’s nationwide immunization coverage to document long-standing discrepancies in these statistics. Methods Published estimates were compiled of Ethiopia’s nationwide DPT3 coverage from 1999 to 2018. These estimates come from the Joint Reporting Form submitted annually to WHO and UNICEF, a series of 8 population-based surveys and the annual reports of the WHO/UNICEF Estimates of National Immunization Coverage (WUENIC). Possible reasons for variation in survey findings were explored through secondary analysis of data from the 2012 immunization coverage survey. In addition, selected health officials involved with management of the immunization program were interviewed to obtain their perspectives on the reliability of various methods for estimation of immunization coverage. Findings Comparison of Ethiopia’s estimates for the same year from different sources shows major and persistent discrepancies between administrative, survey and WUENIC estimates. Moreover, the estimates from each of these sources have repeatedly shown erratic year-to-year fluctuations. Those who were interviewed expressed scepticism of Demographic and Health Survey (DHS) statistics. Officials of the national immunization programme have repeatedly shown a tendency to overlook all survey statistics when reporting on programme performance. Conclusions The present case study raises important questions, not only about the estimation methods of national and UN agencies, but about the reliability and comparability of widely trusted coverage surveys. Ethiopia provides an important example of a country where no data source provides a truly robust “gold standard” for estimation of immunization coverage. It is essential to identify and address the reasons for these discrepancies and arrive at a consensus on how to improve the reliability and acceptability of each data source and how best to “triangulate” between them.