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

Glèlè Ahanhanzo Y, Ouedraogo LT, Kpozèhouen A, Coppieters Y, Makoutodé M, Wilmet-Dramaix M - Arch Public Health (2014)

Bottom Line: A 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).This 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Epidemiology and Biostatistics Department, Public Health Regional Institute, University of Abomey-Calavi, Abomey-Calavi, Benin ; Center of research in Epidemiology, Biostatistics and Clinical Research, School of Public Health, Université Libre de Bruxelles, Bruxelles, Belgium.

ABSTRACT

Background: Routine 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.

Methods: This 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.

Results: A 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.

Conclusion: This 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.

No MeSH data available.


Batch rejection probability according to number of draws by sector of employment.
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Figure 2: Batch rejection probability according to number of draws by sector of employment.

Mentions: These findings are summarized in Table 2. The probability of batch rejection was lower among health workers who, in addition to their RHIS activities, were responsible for the health center; the health workers who were not responsible for the health center were significantly more at risk of rejection of their batch (HR: 1.52; p = 0.011). Likewise, the RHIS training or retraining in the last 12 months was a risk factor of batch rejection. Indeed, people who were not trained or retrained in RHIS in the previous 12 months were significantly more at risk of rejection of their batch compared to health workers who received training in RHIS in the previous 12 months (HR: 1.49; p = 0.026) (Table 2). This difference was also noted according to the level of the health worker’s engagement in RHIS activities (HR: 1.56; p < 0.01) (Figure 1) and the sector of employment (HR: 1.87; p < 0.001) (Figure 2). Therefore, a batch from the private sector is more at risk of rejection than a batch from the public sector, and a batch from people with a low level of work engagement is more at risk of rejection than a batch from a health worker with a high level of work engagement. There were no differences if the health workers received a financial incentive (p = 0.690), had the material resources (p = 0.078) or were supervised over the last six months (0.349). Likewise, the perception of the complexity of the technical factors was not significantly associated with the probability of the batch’s rejection (p = 0.466). This comparison, according to the two categories of perceived self-efficacy, was not statistically significant (p = 0.052).


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

Glèlè Ahanhanzo Y, Ouedraogo LT, Kpozèhouen A, Coppieters Y, Makoutodé M, Wilmet-Dramaix M - Arch Public Health (2014)

Batch rejection probability according to number of draws by sector of employment.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4128530&req=5

Figure 2: Batch rejection probability according to number of draws by sector of employment.
Mentions: These findings are summarized in Table 2. The probability of batch rejection was lower among health workers who, in addition to their RHIS activities, were responsible for the health center; the health workers who were not responsible for the health center were significantly more at risk of rejection of their batch (HR: 1.52; p = 0.011). Likewise, the RHIS training or retraining in the last 12 months was a risk factor of batch rejection. Indeed, people who were not trained or retrained in RHIS in the previous 12 months were significantly more at risk of rejection of their batch compared to health workers who received training in RHIS in the previous 12 months (HR: 1.49; p = 0.026) (Table 2). This difference was also noted according to the level of the health worker’s engagement in RHIS activities (HR: 1.56; p < 0.01) (Figure 1) and the sector of employment (HR: 1.87; p < 0.001) (Figure 2). Therefore, a batch from the private sector is more at risk of rejection than a batch from the public sector, and a batch from people with a low level of work engagement is more at risk of rejection than a batch from a health worker with a high level of work engagement. There were no differences if the health workers received a financial incentive (p = 0.690), had the material resources (p = 0.078) or were supervised over the last six months (0.349). Likewise, the perception of the complexity of the technical factors was not significantly associated with the probability of the batch’s rejection (p = 0.466). This comparison, according to the two categories of perceived self-efficacy, was not statistically significant (p = 0.052).

Bottom Line: A 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).This 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Epidemiology and Biostatistics Department, Public Health Regional Institute, University of Abomey-Calavi, Abomey-Calavi, Benin ; Center of research in Epidemiology, Biostatistics and Clinical Research, School of Public Health, Université Libre de Bruxelles, Bruxelles, Belgium.

ABSTRACT

Background: Routine 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.

Methods: This 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.

Results: A 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.

Conclusion: This 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.

No MeSH data available.