Health management information system (HMIS) data quality and associated factors in Massaguet district, Chad

Abstract

Background Quality data from Health Management Information Systems (HMIS) are important for tracking the effectiveness of malaria control interventions. However, HMIS data in many resource-limited settings do not currently meet standards set by the World Health Organization (WHO). We aimed to assess HMIS data quality and associated factors in Chad. Methods A cross-sectional study was conducted in 14 health facilities in Massaguet district. Data on children under 15 years were obtained from the HMIS and from the external patient register covering the period January–December 2018. An additional questionnaire was administered to 16 health centre managers to collect data on contextual variables. Patient registry data were aggregated and compared with the HMIS database at district and health centre level. Completeness and accuracy indicators were calculated as per WHO guidelines. Multivariate logistic regressions were performed on the Verification Factor for attendance, suspected and confirmed malaria cases for three age groups (1 to < 12 months, 1 to < 5 years and 5 to < 15 years) to identify associations between health centre characteristics and data accuracy. Results Health centres achieved a high level of data completeness in HMIS. Malaria data were over-reported in HMIS for children aged under 15 years. There was an association between workload and higher odds of inaccuracy in reporting of attendance among children aged 1 to < 5 years (Odds ratio [OR]: 10.57, 95% CI 2.32–48.19) and 5– < 15 years (OR: 6.64, 95% CI 1.38–32.04). Similar association was found between workload and stock-outs in register books, and inaccuracy in reporting of malaria confirmed cases. Meanwhile, we found that presence of a health technician, and of dedicated staff for data management, were associated with lower inaccuracy in reporting of clinic attendance in children aged under five years. Conclusion Data completeness was high while the accuracy was low. Factors associated with data inaccuracy included high workload and the unavailability of required data collection tools. The results suggest that improvement in working conditions for clinic personnel may improve HMIS data quality. Upgrading from paper-based forms to a web-based HMIS may provide a solution for improving data accuracy and its utility for future evaluations of health interventions. Results from this study can inform the Ministry of Health and it partners on the precautions to be taken in the use of HMIS data and inform initiatives for improving its quality.

Gaps in research and capacity development for malaria surveillance and response in the Asia–Pacific: meeting report

“…Furthermore, while pilots of digital tools for improving timeliness of malaria case data collection have been explored [25][26][27][28], difficulty with scaling and challenges in reporting timeliness as discussed in the breakout group highlight its persistent impact on NMCP's ability to implement targeted and timely responses to outbreaks. While the GTS has emphasized the importance of highquality routine data by redefining surveillance as a core intervention in malaria control and elimination [4], there have been limited efforts to evaluate programmes' ability to capture quality routine malaria surveillance data when compared to Africa [29][30][31][32][33]. High quality of routine surveillance data and integration of various sources and types of data are needed to provide a complete picture of malaria incidence within a country in order to successfully plan for malaria control and elimination and inform targeted response strategies.…”

Gaps in research and capacity development for malaria surveillance and response in the Asia- Pacific: meeting report

“…Furthermore, while pilots of digital tools for improving timeliness of malaria case data collection have been explored(25-28), di culty with scaling and challenges in reporting timeliness as discussed in the breakout group highlight its persistent impact on NMCP's ability to implement targeted and timely responses to outbreaks. While the GTS has emphasized the importance of high-quality routine data by rede ning surveillance as a core intervention in malaria control and elimination(5), there have been limited efforts to evaluate programmes' ability to capture quality routine malaria surveillance data when compared to Africa (29)(30)(31)(32)(33). High quality of routine surveillance data and integration of various sources and types of data is needed to provide a complete picture of malaria incidence within a country in order to successfully plan for malaria control and elimination and inform targeted response strategies.…”

Assessing the impact of COVID-19 pandemic on maternal healthcare usage: evidence from routine health data in Kenya and Ethiopia

“…HMIS data are known for quality issues, including completeness, internal or external consistency and outliers. [26][27][28] We used a five-step process guided by WHO methodology for HMIS data cleaning. 29 The steps included running quality checks to determine reporting rates, completion rates, outliers and missing data, data aggregation, analysis and reporting (online supplemental material figure 1).…”