Neonatal indicator data in Tanzania District Health Information System: evaluation of availability and quality of selected newborn indicators, 2015-2022.

Abstract

Background: The Every Newborn Action Plan (ENAP) indicators are essential in monitoring neonatal healthcare coverage and quality. The District Health Information System (DHIS2), an open-source platform in over 80 countries, supports health data collection and analysis, enabling progress tracking at national and subnational levels. This study evaluates the availability and quality of maternal and newborn health indicators, explicitly focusing on ENAP indicators within Tanzania's DHIS2.

Methods: Using the EN-MINI tool, we assessed data availability for 20 ENAP indicators by analysing their numerators and denominators in Tanzania's DHIS2 (2015-2022) across all healthcare levels. World Health Organization's (WHO) data quality framework was adapted to examine four dimensions: (a) availability of indicators, (b) completeness of indicator reporting, (c) internal consistency of related indicators, and (d) indicator plausibility by comparing DHIS2 data with population-based Demographic and Health Survey (DHS) data.

Results: Of the 20 ENAP indicators, 14 were available in Tanzania's DHIS2, with definitions, numerators and denominators aligned with WHO standards. Between 2015 and 2022, the number of facilities reporting at least one delivery annually increased by 19% from 5,898 to 7,016. During this period, 75% to 97% of facilities consistently reported data on skilled attendance at birth and early breastfeeding initiation. In contrast, 4% to 54% of facilities reported on maternal and newborn outcomes, including complications such as stillbirths and maternal mortality. Internal consistency was high (> 94%). However, neonatal mortality rates reported in DHIS2 were lower than those reported in Tanzania DHS for similar periods, even after a 20% adjustment to account for home births.

Conclusion: Tanzania's DHIS2 captures many ENAP indicators; however, notable variability in data quality persists, with substantial data gaps related to maternal and newborn outcomes and complications. To address these challenges, it is crucial to strengthen routine data review, implement robust quality checks, enhance validation processes, provide targeted training, deliver constructive feedback, and conduct supportive supervision. Placing greater emphasis on using DHIS2 data to monitor progress will help identify gaps and drive improvements in data quality, ultimately supporting better maternal and newborn health outcomes.