Factors associated with data quality in the routine health information system of Benin

Abstract

BackgroundRoutine health information systems (RHIS) are crucial to the acquisition of data for health sector planning. In developing countries, the insufficient quality of the data produced by these systems limits their usefulness in regards to decision-making. The aim of this study was to identify the factors associated with poor data quality in the RHIS in Benin.MethodsThis cross-sectional descriptive and analytical study included health workers who were responsible for data collection in public and private health centers. The technique and tools used were an interview with a self-administered questionnaire. The dependent variable was the quality of the data. The independent variables were socio-demographic and work-related characteristics, personal and work-related resources, and the perception of the technical factors. The quality of the data was assessed using the Lot Quality Assurance Sampling method. We used survival analysis with univariate proportional hazards (PH) Cox models to derive hazards ratios (HR) and their 95% confidence intervals (95% CI). Focus group data were evaluated with a content analysis.ResultsA significant link was found between data quality and level of responsibility (p = 0.011), sector of employment (p = 0.007), RHIS training (p = 0.026), level of work engagement (p < 0.001), and the level of perceived self-efficacy (p = 0.03). The focus groups confirmed a positive relationship with organizational factors such as the availability of resources, supervision, and the perceived complexity of the technical factors.ConclusionThis exploratory study identified several factors associated with the quality of the data in the RHIS in Benin. The results could provide strategic decision support in improving the system’s performance.

Exploring the Information and ICT Skills of Health Professionals in Low- and Middle-Income Countries

“…These systems may be routine health information systems (RHIS) that "provide information at regular intervals of a year or less through mechanisms designed to meet predictable information needs" [24]. In Benin, a data quality assessment including a health worker survey (n=116) noted that the health professionals were inadequately qualified, as few had been trained on RHIS and the training received had not always been fit for purpose [25]. Kenya established their RHIS in 1984; however, 55% (67) of health workers had no knowledge about it [20].…”

Research gaps in routine health information system design barriers to data quality and use in low‐ and middle‐income countries: A literature review

“…Assessment in multiple countries showed that system design issues were related to complexity of systems, multiple forms to enter and use, and system responsiveness to user information needs . The people‐related problems were similar across different studies and included motivation to use data, data use training, and supervisory support …”

“…24 The people-related problems were similar across different studies and included motivation to use data, data use training, and supervisory support. 33,37,38 literature. Furthermore, review findings showed that individual training efforts primarily focus on imparting data analysis skills and neglect system design skills, such as requirement gathering, user-centered design, and usability evaluation, even though evidence shows that poor system design accounts for most of the issues that HIS users encounter.…”

Identification of the essential components of quality in the data collection process for public health information systems

“…A data collector collects or supplies data for the PHIS. Of the 45 articles, 23 assessed the performance of data collectors 14,[29][30][31]33,35,[38][39][40]44,46,47,49,51,55,57,59,60,62,66,[68][69][70] (see Table 1). Details of facilitators and barriers for data collector are shown in Supplemental Appendix Table 2. A data collector is a stakeholder with whom the data user should build up and nurture a relationship.…”