Routine Data Quality Assessment Tool – User ManualDownload Document: ms-17-117-1.pdf (3 MB)Abstract: Strong, robust systems for capturing health program data are essential to tracking progress toward health objectives, such as the Millennium Development Goals, and will be central to supporting data-informed decisions as part of the new Sustainable Development Goals. The data quality assessment tools were originally developed as part of global efforts to combat AIDS, malaria, and tuberculosis. Ambitious plans for national programs and donor-funded projects were in the works to reduce the burden of disease in countries around the world. Measuring the success and improving the management of these initiatives is predicated on strong monitoring and evaluation (M&E) systems that produce good-quality data related to program implementation. In the spirit of the Three Ones, the Stop TB Strategy, and the Roll Back Malaria Global Strategic Plan, a number of multilateral and bilateral organizations collaborated to develop the Data Quality Audit (DQA) Tool. This tool captures high-priority indicators from HIV and AIDS, tuberculosis, and malaria programs and offers a common approach to assessing and improving overall data quality. Having a single tool helps to ensure that standards are harmonized and allows for joint implementation by partners and national programs. Implementing the DQA tool revealed the need for a capacity-building and self-assessment version. To that end, MEASURE Evaluation (funded by the U.S. Agency for International Development), the World Health Organization, the U.S. Presidents Emergency Plan for AIDS Relief, and the Global Fund to Fight AIDS, Tuberculosis and Malaria worked together to develop the Routine Data Quality Assessment (RDQA) Tool. We designed it to build the capacity of health programs to assess and improve the quality of their data. The tool has subsequently been applied many timesboth by individual health programs and by country health management information systems (HMIS). The RDQA tool verifies the quality of reported data and assesses the underlying data management and reporting systems for standard program-level output indicators.Shortname: ms-17-117Author(s): MEASURE EvaluationYear: 2017Language: EnglishRegion(s): GlobalFiled under: Data Quality, HIV
