Generating statistics from health facility data: the state of routine health information systems in Eastern and Southern Africa

Abstract

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.

Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania

“…For many years, the HMIS in low-income countries have remained paper-based, which is cumbersome, and of uncertain reliability [10]. The DHIS2, recently introduced as a tool to aggregate and process routine facility-based data is expected to facilitate availability, standardization, quality, timely usage, and evidence-based decisions at different levels of the health system [2,24,30]. However, DHIS2 is not the magic bullet and will not solve underlying quality problems currently facing HMIS [24,31].…”

Operationalising health systems thinking: a pathway to high effective coverage

“…In addition, some elements pose more difficulty for measurement, such as provider motivation. Routine health information systems data are often weak [ 46 ] but can be complemented with other secondary data (e.g. national or subnational surveys) or primary data collection through key informant interviews or special studies.…”

Twenty Years of Integrated Disease Surveillance and Response in Sub-Saharan Africa: Challenges and Opportunities for Effective Management of Infectious Disease Epidemics

“…However, over the years, the Health Management Information System (HMIS) has remained the primary data source for IDSR. The routine health information system in several SSA countries is characterised by persistent incompleteness and other data quality issues [70,71,72]. A high level of mismatch between the entries in the HMIS registers, tally sheets and the electronic District Health Information System (DHIS2) database have also been reported [72,73].…”