Quality of routine health and nutrition data in Ethiopia: A systematic review - Taddese Alemu Zerfu; Tirsit Genye; Amare Abera Tareke (PLoS ONE, 2025)
Background High-quality data are vital for informed decision-making, enhancing population health, and achieving comprehensive insights. However, there is limited understanding of the consistency and reliability of routine Health Management Information System (HMIS) including nutrition data across diverse regions in Ethiopia. This study systematically reviewed the existing literature to address these knowledge gaps. Methods We systematically searched PubMed, HINARI, and Google Scholar for studies published from 2015 onwards to assess HMIS, including nutrition data quality in Ethiopia. The evaluations focused on completeness, consistency, and timeliness metrics defined by the WHO. We included diverse regional studies without indicator restrictions, prioritized data quality metrics as primary outcomes, and explored qualitative reasons for poor data quality as secondary outcomes. Results Of the 1790 papers screened, 25 met the inclusion criteria. The completeness of reporting varied widely among studies (50%–100%), with only 21% (4 out of 19) exceeding 90%. The consistency ranged from 38.9% to 90.5%, with only 6% of studies reporting internal consistency above 90%. Other consistency issues included lack of external consistency, indicator discrepancies, and outliers. Timeliness ranged from 41.9% to 93.7%, with 54% of studies reporting below 80%. In addition to the lack of studies addressing nutrition data, the quality was no better than other components of HMIS. The major factors contributing to poor data quality were human resource shortages, insufficient capacity building, behavioural influences, and infrastructural deficits. Conclusion The HMIS including nutrition data in Ethiopia, exhibited deficiencies in completeness, consistency, and timeliness, which were largely, attributed to capacity and resource constraints. Interventions should prioritize resource allocation, staff training, supervision, and feedback mechanisms to enhance data quality, thereby improving decision-making processes and population health outcomes.