OUTIL D'ÉVALUATION DE LA QUALITE DES DONNEES DE ROUTINE (RDQA)

The meaning of data quality varies depending on how the data are going to be used, with some cases prioritizing data accuracy over precision and vice versa. Data quality dimensions present a list of measurements that can help evaluate the suitability of the data. Seven key dimensions are commonly used to ensure the quality of health data: accuracy or validity, reliability, precision, completeness, timeliness, integrity, and confidentiality. Accuracy refers to the correctness of data, minimizing errors to the point of making them negligible. Reliability is achieved when data are generated consistently, regardless of the person using them or the time and frequency of their use. Precision means that data are detailed, such as recording the number of people who received HIV counseling and testing by gender. Completeness refers to an information system being appropriately inclusive, representing the exhaustive list of eligible people or units. Timeliness is achieved when data are up-to-date and available in time, affected by factors such as the frequency of updates, the rate of change of actual activities, and the time when information is used or required. Integrity is ensured when the system used to generate data is protected from bias or deliberate manipulation. Confidentiality means that clients are assured that their data will be stored in accordance with national and/or international data standards, with personal data not disclosed and processed with an appropriate level of security. In Burundi, data quality dimensions are defined as follows: accuracy refers to correct data that minimize errors, reliability is achieved through consistent protocols and procedures, precision means detailed data, completeness ensures an exhaustive list of eligible people or units, timeliness is achieved when data are up-to-date and available in time, integrity is ensured through protection from bias or manipulation, and confidentiality means data are stored in accordance with national and/or international standards. To ensure the quality of health data, it is essential to consider the working definitions and practical questions related to each dimension. For accuracy or validity, questions to ask include what is done with information if there are data errors, whether correct formulas are applied consistently, what should be done when there are missing or incomplete data, and whether reported final figures are correct.