How do we know? An assessment of integrated community case management data quality in four districts of Malawi.

Abstract

The World Health Organization contracted annual data quality assessments of Rapid Access Expansion (RAcE) projects to review integrated community case management (iCCM) data quality and the monitoring and evaluation (M&E) system for iCCM, and to suggest ways to improve data quality. The first RAcE data quality assessment was conducted in Malawi in January 2014 and we present findings pertaining to data from the health management information system at the community, facility and other sub-national levels because RAcE grantees rely on that for most of their monitoring data. We randomly selected 10 health facilities (10% of eligible facilities) from the four RAcE project districts, and collected quantitative data with an adapted and comprehensive tool that included an assessment of Malawi's M&E system for iCCM data and a data verification exercise that traced selected indicators through the reporting system. We rated the iCCM M&E system across five function areas based on interviews and observations, and calculated verification ratios for each data reporting level. We also conducted key informant interviews with Health Surveillance Assistants and facility, district and central Ministry of Health staff. Scores show a high-functioning M&E system for iCCM with some deficiencies in data management processes. The system lacks quality controls, including data entry verification, a protocol for addressing errors, and written procedures for data collection, entry, analysis and management. Data availability was generally high except for supervision data. The data verification process identified gaps in completeness and consistency, particularly in Health Surveillance Assistants' record keeping. Staff at all levels would like more training in data management. This data quality assessment illuminates where an otherwise strong M&E system for iCCM fails to ensure some aspects of data quality. Prioritizing data management with documented protocols, additional training and approaches to create efficient supervision practices may improve iCCM data quality.