Quality of Maternal & Newborns Health indicators in Western Province of Rwanda - Niyonkuru Mathieu; Rutayisire Erigene (Journal of Public Health International, 2022)
Data quality is defined as a measure of data status that fulfills the following elements: accuracy, completeness, consistency, reliability, and if the data is current. The World Health Organization (WHO) reported that only 40% of all countries have an adequate system to collect information on birth and deaths. Even though the system is there, vital registration systems are inaccurate and incomplete in developing countries. In Rwanda, maternal health related data was over-reported more than other indicators. These are the main reasons for conducting the study to investigate the data quality of four maternal and newborn health indicators reported by Rwandan Western Province health centers. This concurrent-mixed method study included 61 data managers and 12 key informants. Routine data quality assessment tool and structured interview guide were used to collect data. Descriptive statistics were used to get proportion of respondents’ socio-demographic characteristics. The analysis was done for assessing median of data quality index. The results show that 55.7% of data managers were male while 58.3% of responsible of maternity were female. Majority (58.9%) of participants was in age’s category from 33-42, 61.6% have A1 education level and 53.4% have experience less than five years. Data quality index of one out of four (25%) MNH indicators was found below 95% accepted by WHO. The main reasons for insufiscient quality of data are lack of data validation meetings (57.5%) and incompleteness of reporting tools (36.4%). Monthly data validation meetings chaired by HC leaders are important to contribute to high-quality data in healthcare settings. Supportive supervisions done in data quality and management have to be organized in a supportive, and educative way.
Keywords: Data Quality