Maternal and child health data quality and the associated factors at public health facilities of the Sidama region, Ethiopia: A facility-based cross-sectional study.

Health data quality and the associated factors at public health facilities of Sidama region, Ethiopia

Abstract

Background: Data quality is a multi-dimensional term that includes accuracy, precision, completeness, timeliness, integrity, and confidentiality. Efforts have been made to improve maternal and child health (MCH) data quality to enhance decision-making processes regarding maternal and child morbidity and mortality. Despite these efforts, Ethiopia, including the Sidama regional state, still faces significant challenges in reducing MCH morbidity and mortality, which is often exacerbated by suboptimal data quality and utilization. However, the level of data quality remains significantly low, and there is a paucity of information on maternal and child data quality in Sidama regional state, Ethiopia. Therefore, this study aims to assess maternal and child health data quality and factors contributing to data quality in public health facilities of the study area.

Method: A facility-based cross-sectional study was conducted from February 06 to May 05, 2023. A total of 500 health professionals from 23 health centers,3 Hospitals, and 23 health posts were selected using a simple random sampling procedure. Data was gathered using a standardized checklist and self-administered questionnaires. The three common dimensions of data quality, accuracy, completeness, and timeliness, were used to determine the data quality level.   Epidata V4.4.1 was used to enter the data, and SPSS Version 25 was used for analysis. To determine the relationship between the variables, bivariate and multivariate logistic regressions were used. The association was reported using the adjusted odds ratio with the corresponding 95% confidence interval, and the significance level was set at a p-value less than 0.05.

Results: The quality of maternal and child health data in the region for accuracy, completeness, and timeliness was 73.00%, 88.80%, and 86.80% respectively. Overall, 79.4% (95%CI; 75.8% - 83.0%) of public health facilities in the Sidama region had Maternal and Child health data quality. Factors significantly associated with good MCH data quality include; a higher level of motivation (AOR = 2.04; 95% CI: 1.25-3.32), high level of self-efficacy/confidence (AOR = 3.43; 95% CI: 1.97-5.97), received written feedback (AOR = 1.81; 95%CI: 1.05-3.10) and having ability of conducting monthly LOT quality assurance sampling (AOR=2.47; 95% CI: 1.32-4.63).

Conclusion: According to the current study, the quality of maternal and child health data in the Sidama region was 79.4% and still requires targeted interventions. The findings highlight that improving data quality is strongly associated with enhancing staff motivation, improving self-efficacy, providing regular written feedback, and ensuring the ability to conduct monthly LOT Quality Assurance Sampling (LQAS). Therefore, strategic efforts focusing on these identified factors are crucial to strengthen MCH data quality, thereby enhancing evidence-based decision-making for reducing maternal and child morbidity and mortality in the region.