Assessment of quality of routine health information system data and associated factors among departments in public health facilities of Harari region, Ethiopia

Abstract

Background Despite the improvements in the knowledge and understanding of the role of health information in the global health system, the quality of data generated by a routine health information system is still very poor in low and middle-income countries. There is a paucity of studies as to what determines data quality in health facilities in the study area. Therefore, this study was aimed to assess the quality of routine health information system data and associated factors in public health facilities of Harari region, Ethiopia. Methods A cross-sectional study was conducted in all public health facilities in the Harari region of Ethiopia. The department-level data were collected from respective department heads through document reviews, interviews, and observation checklists. Descriptive statistics were used to data quality and multivariate logistic regression was run to identify factors influencing data quality. The level of significance was declared at P value < 0.05. Result The study found good quality data in 51.35% (95% CI 44.6–58.1) of the departments in public health facilities in the Harari Region. Departments found in the health centers were 2.5 times more likely to have good quality data as compared to those found in the health posts. The presence of trained staffs able to fill reporting formats (AOR = 2.474; 95% CI 1.124–5.445) and provisions of feedbacks (AOR = 3.083; 95% CI 1.549–6.135) were also significantly associated with data quality. Conclusion The level of good data quality in the public health facilities was less than the expected national level. Lack of trained personnel able to fill the reporting format and feedback were the factors that are found to be affecting data quality. Therefore, training should be provided to increase the knowledge and skills of the health workers. Regular supportive supervision and feedback should also be maintained.

Level of implementation of district health information system 2 at public health facilities in Eastern Ethiopia

“…Data quality was measured as the fitness of data use based on three dimensions: accuracy ≥80%, completeness ≥85%, and timeliness ≥85%. 26 …”

“…Data quality was measured as the fitness of data use based on three dimensions: accuracy ≥80%, completeness ≥85%, and timeliness ≥85%. 26 Availability of adequate amount of DHIS2 format. It refers to the presence of the following formats for at least the previous 3 months to the next 2 months: tally sheets, registers, and report forms.…”

Quality of routine health facility data used for newborn indicators in low- and middle-income countries: A systematic review

“…These findings are echoed by a case series in the literature comparing data on four common under-five children’s illnesses in outpatient registers and monthly reports in Tanzania that found low completeness and timeliness of reporting and over-reporting of diagnoses [ 82 ]. Similarly, an HMIS review in Ethiopia using PRISM tools found lower register completeness compared to facility report completeness, and low data accuracy comparing reports to registers [ 83 ]. One systematic review focused specifically on childhood vaccination data quality in LMIC, comparing health facility data with patient recall, home-based records, and serology, as well as different combinations of data sources with one another, finding that facility data generally had better agreement with serology than surveys or home-based records [ 84 ].…”

Infectious Disease Control and Management in Ethiopia: A Case Study of Cholera

“…Improved capacity of systematic and quality surveillance and data recording in health system is needed. There are also gaps in the quality of routine health information system data in public health facilities at regional level compared to national level ( 40 ). Lack of trained personnel compromise quality of surveillance and data reporting.…”

“…Lack of trained personnel compromise quality of surveillance and data reporting. Regular training programs for health workers at regional, woreda, and kebele levels with committed supervision and feedback are essential ( 40 ). To enhance surveillance and laboratory capacity at all levels for early detection and case confirmation by 2028 and reducing cholera-associated mortality by 100% in cholera hotspot woredas by 2028 with no local transmission ( 18 ), sufficient structured capacity building program for health system strengthening is warranted.…”