Using Health Management Information System Data: Case Study And Verification Of Institutional Deliveries In Ethiopia

Health management information systems (HMIS) are a crucial source of timely health statistics and have the potential to improve reporting in low-income countries. However, concerns about data quality have hampered their widespread adoption in research and policy decisions. This article presents results from a data verification study undertaken to gain insights into the quality of HMIS data in Ethiopia. We also provide recommendations for working with HMIS data for research and policy translation. We linked the HMIS to the 2016 Emergency Obstetric and Newborn Care Assessment, a national census of all health facilities that provided maternal and newborn health services in Ethiopia. We compared the number of visits for deliveries and caesarean sections (C-sections) reported in the HMIS in 2015 (January-December) to those found in source documents (paper-based labour and delivery and operating theatre registers) in 2425 facilities across Ethiopia. We found that two-thirds of facilities had 'good' HMIS reporting for deliveries (defined as reporting within 10% of source documents) and half had 'very good' reporting (within 5% of source documents). Results were similar for reporting on C-section deliveries. We found that good reporting was more common in urban areas (OR: 1.30, 95% CI 1.06 to 1.59), public facilities (OR: 2.95, 95% CI 1.38 to 6.29) and in hospitals compared with health centres (OR: 1.71, 95% CI 1.13 to 2.61). Facilities in the Somali and Afar regions had the lowest odds of good reporting compared with Addis Ababa and were more likely to over-report deliveries in the HMIS. Further work remains to address remaining discrepancies in the Ethiopian HMIS. Nonetheless, our findings corroborate previous data verification exercises in Ethiopia and support greater use and uptake of HMIS data for research and policy decisions (particularly, greater use of HMIS data elements (eg, absolute number of services provided each month) rather than coverage indicators). Increased use of these data, combined with feedback mechanisms, is necessary to maintain data quality.