Child immunization data quality in Rwanda: an assessment of routine health information system data.

BACKGROUND

Documentation and reporting of routine data by health workers is the backbone of the childhood immunization program. Immunization data from health management information systems (HMIS) in low-and middle-income countries (LMICs) are often incomplete and unreliable. In Rwanda, the immunization e-Tracker, an individual-level health management information system (HMIS) built on DHIS2 open-source software, has been implemented and scaled nationwide since 2019. The aim of this study was to assess the quality of the routine HMIS immunization data over time.

METHOD

Data were derived from four HMIS sources for January to December 2020 from 24 health facilities from four districts: health facility registers (paper-based), district aggregated reports (paper-based), national HMIS reports (electronic), and e-Tracker reports (electronic). We then obtained e-Tracker reports and national HMIS reports from 2022 for the same facilities and assessed changes over time. Data quality assessments were conducted for four selected childhood immunization indicators: Bacille Calmette-Guérin (BCG), Pentavalent 3 (Penta 3) and Measles & Rubella 1 (MR1). We calculated frequencies and percentage differences. Accuracy ratios were computed for HMIS reports against facility registers for 2020 and e-Tracker for 2022.

RESULTS

In 2020, varying degrees of inconsistencies between facility registers and HMIS reports were observed, ranging from - 2.57 to 0.67% for BCG, -13.85% to -1.45% for Penta3, and - 8.30-2.00% for MR1. Only BCG data were entered in the e-Tracker in 2020. By 2022, e-Tracker completeness of Penta3 and MR1 had also increased substantially.

CONCLUSIONS

Data quality in the paper based HMIS was variable across districts and health facilities. Improvements in quality of e-Tracker data over time demonstrate increased uptake of e-Tracker use by health workers, possibly explained by the removal of paper documentation and reporting. Further improvements in data quality can be achieved by purposefully designed implementation strategies to support health workers with digital data entry.