Data Quality and use in Primary Health Care: A Case Study of the Immunization Program in Ebonyi State, Nigeria

Abstract

ABSTRACT Background: Reliable immunization data are vital for optimal coverage, informed decision-making, and efficient program implementation. Aim: This study assessed the quality of immunization data generation and use in primary healthcare centers (PHCs) in Ebonyi State, Nigeria. Methods: A cross-sectional study was conducted in 244 health facilities (HFs), selected through cluster sampling, across six local government areas (LGAs) in Ebonyi State. Information on the accuracy, completeness, timeliness, and monitoring system of the immunization program was collected using a validated Data Quality Self-Assessment (DQS) questionnaire and analyzed with IBM Statistical Package for the Social Sciences (SPSS) statistical software, version 25. The quality index was determined by the proportion of scores for all questions answered, “Yes,” divided by the maximum scores that could be obtained and converted into percentages. The quality index was defined as good for a score of ≥80%. The Chi-square and multivariate logistic regression analyses were conducted. The statistical significance level was set at a P value of <0.05. Results: Accurate, complete, and timely data were recorded in 137 (56.1%), 133 (56.6%), and 81 (33.3%) HFs, respectively. Overall, quality data were observed in a minor proportion, 14 (5.7%) of HFs. The HFs with good-quality data on specific monitoring indices include archiving (109 (44.7%)), reporting (106 (43.4%)), demographic information (58 (23.8%)), evidence of use of data (45 (18.4%)), recording (40 (16.4%)), and core output (14 (6.7%)). Accuracy was predicted by good reporting (AOR = 35.714, CI = 13.260–96.196); completeness was predicted by good archiving (OR = 26.749, CI = 11.514–62.144). Conclusion: Data quality and use in PHC are suboptimal. Integrating the quality self-assessment concept into staff training and supportive performance supervision could improve immunization data quality and use.