Abstract:
Background: Health data is gathered at many levels throughout Ethiopia. The four health extension packages, in particular, represent the majority of the population and must be accurate, dependable, and timely. As a result, in order to obtain this accurate, dependable, and timely health information, we must first identify the major barriers to obtaining quality data. Therefore; this study will have a greater input to program managers for designing programs, proper implementation, and evaluation of their contribution. It could serve as the baseline for further study.
Objective: To determine data management practice and associated factors among health extension workers in Illu Aba bor and Bunno Bedele zones, Oromia region, southwest, Ethiopia 2021, G.C.
Method: An institutional based cross-sectional study design was conducted among 461health extension workers selected by simple randomly sampling technique in Illu Aba bor and Bunno Bedele zones, south west Ethiopia administered and observational checklists. The data were entered using epi data version 3.5.1 and exported to SPSS version 20 for analysis. Descriptive statistic were computed, bivariate and multivariable logistic regression models were fitted. Odds ratio with 95% confidence interval was estimated to use the association between outcome and explanatory variable p-value less than 0.05 was considered as to declare association.
Results: More than three-quarters (78%) of the study participants had good data management practice. Knowledge of data management (AOR=5.11, 95%CI=(2.62, 9.94), having a frequent supportive supervision (AOR=3.49, 95% CI=(1.8, 6.8),adequate Reference materials (AOR= 2.31, 95% CI= (1.15, 4.75)Training of data management practice and community health information system (AOR=3.4 (95% CI: (1.5, 7.6) and not having workload out of routine (AOR=3.39, 95% CI= (1.78, 6.80)were factors significantly associated with good data management practice.
Conclusion: This study revealed that the overall data management practice was higher compared to the previous studies. Improving regular supportive supervision, reducing workload out of their routine, and training crucial to more improve health extension workers' data management practice.
