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Ethiopia has made significant progress in the last two decades in improving the availability and coverage of essential maternal and child health services including childhood immunizations. As Ethiopia keeps momentum towards achieving national immunization goals, methods must be developed to analyze routinely collected health facility data and generate localized coverage estimates. This study leverages the District Health Information Software (DHIS2) platform to estimate immunization coverage for the first dose of measles vaccine (MCV1) and the third dose of diphtheria-pertussis-tetanus-Hib-HepB vaccine (Penta3) across Ethiopian districts ("woredas"). Monthly reported numbers of administered MCV1 and Penta3 immunizations were extracted from public facilities from DHIS2 for 2017/2018-2021/2022 and corrected for quality based on completeness and consistency across time and districts. We then utilized three sources for the target population (infants) to compute administrative coverage estimates: Central Statistical Agency, DHIS2, and WorldPop. The Ethiopian Demographic and Health Surveys were used as benchmarks to which administrative estimates were adjusted at the regional level. Administrative vaccine coverage was estimated for all woredas, and, after adjustments, was bounded within 0-100%. In regions with the highest immunization coverage, MCV1 coverage would range from 83 to 100% and Penta3 coverage from 88 to 100% (Addis Ababa, 2021/2022); MCV1 from 8 to 100% and Penta3 from 4 to 100% (Tigray, 2019/2020). Nationally, the Gini index for MCV1 was 0.37, from 0.13 (Harari) to 0.37 (Somali); for Penta3, it was 0.36, from 0.16 (Harari) to 0.36 (Somali). The use of routine health information systems, such as DHIS2, combined with household surveys permits the generation of local health services coverage estimates. This enables the design of tailored health policies with the capacity to measure progress towards achieving national targets, especially in terms of inequality reductions.

Abstract Background During the COVID-19 pandemic, governments and researchers have used routine health data to estimate potential declines in the delivery and uptake of essential health services. This research relies on the data being high quality and, crucially, on the data quality not changing because of the pandemic. In this paper, we investigated those assumptions and assessed data quality before and during COVID-19. Methods We obtained routine health data from the DHIS2 platforms in Ethiopia, Haiti, Lao People’s Democratic Republic, Nepal, and South Africa (KwaZulu-Natal province) for a range of 40 indicators on essential health services and institutional deaths. We extracted data over 24 months (January 2019–December 2020) including pre-pandemic data and the first 9 months of the pandemic. We assessed four dimensions of data quality: reporting completeness, presence of outliers, internal consistency, and external consistency. Results We found high reporting completeness across countries and services and few declines in reporting at the onset of the pandemic. Positive outliers represented fewer than 1% of facility-month observations across services. Assessment of internal consistency across vaccine indicators found similar reporting of vaccines in all countries. Comparing cesarean section rates in the HMIS to those from population-representative surveys, we found high external consistency in all countries analyzed. Conclusions While efforts remain to improve the quality of these data, our results show that several indicators in the HMIS can be reliably used to monitor service provision over time in these five countries.

Abstract COVID-19 has prompted the use of readily available administrative data to track health system performance in times of crisis and to monitor disruptions in essential healthcare services. In this commentary we describe our experience working with these data and lessons learned across countries. Since April 2020, the Quality Evidence for Health System Transformation (QuEST) network has used administrative data and routine health information systems (RHIS) to assess health system performance during COVID-19 in Chile, Ethiopia, Ghana, Haiti, Lao People’s Democratic Republic, Mexico, Nepal, South Africa, Republic of Korea and Thailand. We compiled a large set of indicators related to common health conditions for the purpose of multicountry comparisons. The study compiled 73 indicators. A total of 43% of the indicators compiled pertained to reproductive, maternal, newborn and child health (RMNCH). Only 12% of the indicators were related to hypertension, diabetes or cancer care. We also found few indicators related to mental health services and outcomes within these data systems. Moreover, 72% of the indicators compiled were related to volume of services delivered, 18% to health outcomes and only 10% to the quality of processes of care. While several datasets were complete or near-complete censuses of all health facilities in the country, others excluded some facility types or population groups. In some countries, RHIS did not capture services delivered through non-visit or nonconventional care during COVID-19, such as telemedicine. We propose the following recommendations to improve the analysis of administrative and RHIS data to track health system performance in times of crisis: ensure the scope of health conditions covered is aligned with the burden of disease, increase the number of indicators related to quality of care and health outcomes; incorporate data on nonconventional care such as telehealth; continue improving data quality and expand reporting from private sector facilities; move towards collecting patient-level data through electronic health records to facilitate quality-of-care assessment and equity analyses; implement more resilient and standardized health information technologies; reduce delays and loosen restrictions for researchers to access the data; complement routine data with patient-reported data; and employ mixed methods to better understand the underlying causes of service disruptions.