Ensuring Data Quality

Reliable data is essential for program planning, implementation and performance management. Ensuring data quality implies having a system in place - including tools, processes and human resources – to assess the accuracy, reliability, precision, completeness and timeliness of data and to take remedial actions. The monitoring data collected from each Sub-recipient (SR) is consolidated by the Principal Recipient (PR).

The PR will diagnose systematic or procedural weaknesses at the SR level that lead to inaccurate, incomplete or delayed reporting to the PR and to the Global Fund which puts funding at risk. Data verification is therefore critical during implementation. The PR should ensure sufficient investments in HMIS components for data quality and data assurance: Revision of paper/digital tools, printing, training, formative supervision.

The Global Fund also supports routine data quality checks and audits (RDQA), as well as periodic Data Quality Reviews (DQR) at health facility and community levels. The WHO DQR framework is recommended as a harmonized and holistic approach to assess the quality of data collected from health facilities. This approach allows to quantify problems of data completeness, timeliness and accuracy according to program areas, identify weaknesses in the data management system and monitor performance of data quality over time. A national DQR can be implemented in collaboration with partners. The GF Country Team might also coordinate with the LFA the implementation of targeted DQRs. A data quality improvement plan should be developed based on the results of the DQR to address weaknesses in data.

At the Global Fund OIG audit of in-country data and data systems conducted in 2023, the OIG notes that Global Fund has developed detailed guidance and tools on monitoring programmatic data availability and quality at the country level. While there are well-designed guidelines and tools for monitoring and assurance, the OIG noted implementation challenges: “Regarding implementer monitoring of data quality, most issues were identified at the health facility level, where processes and controls over in-country HMIS are not always formalized and followed, and there are significant M&E staff capacity gaps impacting the robustness of monitoring of data quality. Outside health facilities, there are issues with monitoring, oversight, and supervision visits by national and regional entities. These reviews are often delayed, not performed, or do not result in improved data accuracy” (Source: Global Fund audit of In-country data and data systems, OIG, April 2023).

Since 2023, the Global Fund has been working with technical partners to develop the M&E System and Data Quality strategy that will provide an update on the data quality assurance mechanism.

GFPHST-led Data Quality Reviews

At the end of 2023 the GFPHST M&E team introduced Data Quality Reviews of aggregated grant-level results based on the comparison of PR-reported against LFA-verified data. Discrepancies exceeding a 5% threshold are explored by the grant M&E Specialist and PMU M&E Specialist, to understand the reasons. Most differences in reporting are attributable to reporting delays, revision of population estimates or reporting errors. The process of engagement between GFPHST and PMU contributes to better understanding of reporting requirements and reduction of reporting errors in subsequent reports. The outputs of this exercise feed into GFPHST information sessions on improved reporting.

During the debriefing conducted by the LFA following their verification of reported results, the PR is encouraged to clarify / discuss with the LFA any of the discrepancies identified between the reported and LFA-verified results, unless the reasons are obvious (e.g. improved reporting completeness between the PR reporting and LFA verification).