Data Quality
The goal of monitoring and evaluation (M&E) systems is to produce data that are used to document progress towards goals and objectives and to improve health programs. However, the data produced by these systems is often incomplete, inaccurate, and tardy, due to insufficient capacity in the health system, or inadequate system design.
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To learn how this work is continuing after the MEASURE Evaluation project, please visit Data for Impact, PMI Measure Malaria, and TB DIAH. |
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MEASURE Evaluation understood that data must be of high quality if they are to be relied upon for making good decisions on health policy, health programs, and allocation of scarce resources. Data give the picture of what is happening; bad data call the entire system into question. MEASURE Evaluation conducted data quality assessments and built capacity to generate and use high-quality data.
Our tools helped countries evaluate the capacity of their M&E systems to collect, compile, and report quality data for planning; measure the accuracy of reporting priority indicators; and troubleshoot data quality issues.
The Data Quality Review (DQR) toolkit represents a collaborative effort of the World Health Organization (WHO); The Global Fund; Gavi, The Vaccine Alliance; and the MEASURE Evaluation project, funded by the United States Agency for International Development (USAID) to promote a harmonized approach to assessing the quality of data reported, from the health facility and community levels to the national level.
Our Data Quality Audit (DQA) tool permits formal auditing of data quality for priority HIV/AIDS, tuberculosis (TB), and malaria indicators in programs or projects. It was developed for the United States President’s Emergency Plan for AIDS Relief (PEPFAR) and The Global Fund as an integral part of performance-based measures of data accuracy for selected indicators. MEASURE Evaluation also produced a capacity-building and self-assessment version of the tool for health programs, nongovernmental organizations (NGOs), and donor-funded projects (the Routine Data Quality Assurance [RDQA] tool).
Our training curriculum, centered on the RDQA tool, has facilitator and participant guides, exercises, and case studies for a three-day training course. MEASURE Evaluation conducted such trainings in Africa, Latin America, and Asia.
Finally, MEASURE Evaluation documented an effort to integrate data quality assurance activities into standard operating procedures (SOPs) for health management information systems (HMIS) at the country level.
Related Content
Malaria Routine Data Quality Assessment Tool: A Checklist to Assess the Quality of Malaria Program Data (and User Manual)
Tool to Assess the Quality of Data on the Number of People on Antiretroviral Therapy: User Guide
Measuring the Quality of HIV/AIDS Client-Level Data Using Lot Quality Assurance Sampling (LQAS)
Helpful Hints: Expedited Data Quality Assessment
Helpful Hints: Routine Data Quality Assessment
Data for Impact Starts with High-Quality Data
Routine Health Information System (RHIS) Curriculum
PRISM: Performance of Routine Information System Management
Data Quality Course from the Global Health eLearning Center
Scaling mHealth for Community-Based Health Information Systems – Lessons and Best Practices
A Menu of Tools for Data Quality Assessment and Review
Building Leadership for Data Demand and Use: A Facilitator's Guide
Tools for Data Demand and Use in the Health Sector: Quick Guide
Comparative Analysis of Data Quality Assessment Tools
GEND_GBV Rapid Data Quality Review Tool
Data Quality Assessment (DQA) for HIV Program Indicators in Burundi: Final Report
