Routine health management information system data in Ethiopia: consistency, trends, and challenges

Abstract

Background : Ethiopia is investing in the routine Health Management Information System. Improved routine data are needed for decision-making in the health sector. Objective : To analyse the quality of the routine Health Management Information System data and triangulate with other sources, such as the Demographic and Health Surveys. Methods : We analysed national Health Management Information System data on 19 indicators of maternal health, neonatal survival, immunization, child nutrition, malaria, and tuberculosis over the 2012–2018 time period. The analyses were conducted by 38 analysts from the Ministry of Health, Ethiopia, and two government agencies who participated in the Operational Research and Coaching for Analysts (ORCA) project between June 2018 and June 2020. Using a World Health Organization Data Quality Review toolkit, we assessed indicator definitions, completeness, internal consistency over time and between related indicators, and external consistency compared with other data sources. Results : Several services reported coverage of above 100%. For many indicators, denominators were based on poor-quality population data estimates. Data on individual vaccinations had relatively good internal consistency. In contrast, there was low external consistency for data on fully vaccinated children, with the routine Health Management Information System showing 89% coverage but the Demographic and Health Survey estimate at 39%. Maternal health indicators displayed increasing coverage over time. Indicators on child nutrition, malaria, and tuberculosis were less consistent. Data on neonatal mortality were incomplete and operationalised as mortality on day 0–6. Our comparisons with survey and population projections indicated that one in eight early neonatal deaths were reported in the routine Health Management Information System. Data quality varied between regions. Conclusions : The quality of routine data gathered in the health system needs further attention. We suggest regular triangulation with data from other sources. We recommend addressing the denominator issues, reducing the complexity of indicators, and aligning indicators to international definitions.

Using health management information system data: case study and verification of institutional deliveries in Ethiopia

“…There have been few validations of HMIS data in LMICs. [5][6][7][8][9][10][11][12] Many of these studies have focused on assessing whether intervention coverage indicators estimated using HMIS are consistent with those from household surveys (eg, the demographic and health survey (DHS)). 5 9 11 13 Intervention coverage indicators (eg, the proportion of women giving birth in health facilities or the proportion of fully immunised children) require two valid data elements: valid numerators (eg, number of women giving birth in facilities) and valid denominators (eg, total number of women giving birth in a catchment area).…”

“…HMIS quality has likely improved since, given the multitude of training initiatives and interventions to improve data quality that have recently taken place in Ethiopia. 5 22–24 The expansion of internet connectivity along with the adoption of DHIS2 in 2018 may have facilitated standardisation in reporting and further improved data quality in Ethiopia.…”

“…Adane and colleagues estimated that the proportion of fully vaccinated children in Ethiopia would be 89% in the HMIS but only 39% in the DHS. 5 The Ethiopian FMOH also routinely compares health intervention coverage using the HMIS to those from the DHS. 28 Coverage estimates from HMIS often differ from household survey estimates, and it is not uncommon to find coverage estimates that are greater than 100%.…”

“…3 Since then, several initiatives have aimed to improve the quality of the Ethiopian HMIS. [4][5][6][7] In 2018, Ethiopia adopted the District Health Information Software 2 (DHIS2), a web-based HMIS platform used in over 70 LMICs, and launched a…”

“… 5–12 Many of these studies have focused on assessing whether intervention coverage indicators estimated using HMIS are consistent with those from household surveys (eg, the demographic and health survey (DHS)). 5 9 11 13 Intervention coverage indicators (eg, the proportion of women giving birth in health facilities or the proportion of fully immunised children) require two valid data elements: valid numerators (eg, number of women giving birth in facilities) and valid denominators (eg, total number of women giving birth in a catchment area). In addition to estimating intervention coverage indicators, the HMIS should be used to track individual data elements such as the absolute number of services delivered, the number of consultations taking place, and headcounts, cases and deaths from particular conditions.…”