181443 Improving Measurement Accuracy through Data Quality Assurance

Tuesday, October 28, 2008: 10:48 AM

Karen Hardee, PhD , Research Department, Population Action International, Washington, DC
David Murdoch Boone, PhD, MPH , MEASURE Evaluation, John Snow, Inc, Arlington, VA
Ronald Tran BaHuy, MBA , Geneva Secretariat, The Global Fund for AIDS, TB and Malaria, Geneva, Switzerland
Cyril Pervilhac, Dr PH , World Health Organization, Geneva, Switzerland
Annie LaTour, MPH , Office of the Global AIDS Coordinator, Washington, DC
Silvia Alayon, MS , MEASURE Evaluation, University of North Carolina at Chapel Hill, Chapel Hill, NC
National Programs and donor funded projects are working together to achieve ambitious goals for the care and treatment of HIV and AIDS patients, and the prevention of new infections. Programs must show effectiveness to warrant continued investment and increasingly, funding is being tied to performance. The quality of data generated by monitoring and evaluation systems is imperative if program effectiveness is to be evaluated. Many national programs and implementing partners are new to reporting results and justifying expenditures. The quality of data being reported is uncertain. To address the need for good quality data from HIV/AIDS programs, The Global Fund for AIDS, TB and Malaria, along with MEASURE Evaluation, has developed the Routine Data Quality Assessment Tool (RDQA), a simplified version of the tool the Fund will use to audit grant recipients as part of its performance based funding policy. The RDQA can be used to rapidly verify the quality of reported data for key indicators at selected sites and the ability to collect, manage, and report quality data. The RDQA helps identify strengths and weaknesses in a reporting system and identify measures for systems strengthening. The RDQA is designed to be flexible serve multiple purposes: 1) Routine data quality checks as part of on-going supervision, 2) Initial and follow-up assessments of reporting systems to measure the effectiveness of capacity building efforts, 3) Preparation for a formal data quality audit, and 4) Ad hoc assessments by external partners or other stakeholders. The potential users of the RDQA include program managers, supervisors and M&E staff at National, sub-national, or service delivery levels, as well as donors and other stakeholders. The output of the MS Excel based RDQA includes automated graphics depicting a quantitative assessment of reporting performance in terms of accuracy, timeliness, completeness and the availability of data, as well as a qualitative assessment of 1) strengths and weaknesses among functional areas of a reporting system, and 2) the relative strength of the reporting system in terms of dimensions of data quality, such as precision, reliability, integrity and confidentiality. Finally, the RDQA output includes a costed action plan for system strengthening.

Learning Objectives:
Articulate the importance of high quality of data for decision making Describe methods for assessing and assuring data quality Articulate the appropriate application of the Routine Data Quality Assessment Tool

Keywords: Health Information Systems, Data/Surveillance

Presenting author's disclosure statement:

Not Answered