Using data for impact: Improving newborn programming through revised indicators on the customizable and adjustable DHIS2 platform

This abstract has been accepted at the 2024 DHIS2 Annual Conference


Using data for impact: Improving newborn programming through revised indicators on the customizable and adjustable DHIS2 platform

Data for impact: Improving newborn health programming through revised indicators on the customizable and adjustable DHIS2 platform Background Indicators are critical for monitoring health program performance, but poorly formulated ones can lead to inaccurate and unreliable data collection, potentially identifying a problem that is not there or failing to identify a problem that is there. Zambia Ministry of Health (MoH) deployed the DHIS2 data collection, management, visualization, and analysis. However, some of the newborn health indicators on the DHIS2 platform, including “Resuscitate upon birth” and “Still births due to Asphyxia,” were poorly formulated and subject to user interpretation, which resulted in inaccurate and unreliable data reporting. Ultimately, this hindered effective monitoring of the newborn health program. To address the challenge of unsatisfactory newborn health data, leveraging the DHIS2 platform’s customizable and adjustable features, support was provided to MoH to revise and update newborn health indicators. Methods During the November 2022 program review meeting, a team of program managers and experts from selected hospitals and health centres was constituted to review the ambiguous newborn health indicators and make appropriate recommendations. Based on the recommendations and experiences from the implementation of the PeaCe Health program and to align the newborn indicators to the 2022 – 2026 National Health Strategic Plan (NHSP), in February 2023, CHAI advocated for the revision of newborn health indicators. To ensure common understanding, trainings on the revised indicators were conducted at both national and sub-national levels. Results Revisions to the newborn health indicators resulted in clear and concise indicators and more accurate and reliable data collection. For instance, prior to the revisions of the newborn health indicators, the DHIS2 data for 2021 and 2022 indicated that, respectively, 279.1% and 233.9% of asphyxiated newborns were successfully resuscitated. This was not only challenging to interpret but also inappropriate to informing programming. However, following the revisions in 2023, the quality and reliability of newborn health data improved, with the DHIS2 data for 2023 indicating that 81.3% of asphyxiated newborns were successfully resuscitated. Newborn health indicators producing accurate and reliable information resulted in improved programming, with implementation of interventions such as training of 242 staff in advanced newborn care training, targeted neonatal care mentorship, and technical supportive supervision. Conclusion If applied well, the customizable and adjustable DHIS2 platform can enhance the quality and reliability of newborn health data and lead to better programming, and ultimately contribute to reducing neonatal mortalities.

Primary Author: Fredrick Mumba


Keywords:
Newborn DHIS2 Customizable Adjustable Quality Reliability Inaccurate Ambiguous Programming

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