Determinants of COVID-19 data quality in the District Health Information Management System version 2 in Ahafo Region, Ghana

Abstract

AbstractBackgroundIn ensuring public health efforts in combating pandemics such as coronavirus disease 2019 (COVID-19), transparent data reporting that is of high quality and easily accessible is crucial for tracking epidemic progress and making informed decisions. In Ghana, no published studies have been conducted to evaluate the quality of COVID-19 data submitted onto the national web-based platform, District Health Information Management System version 2 (DHIMS-2) during the COVID-19 period. In this regard, this study seeks to assess the estimates and determinants of COVID-19 data quality in the DHIMS-2 in the Ahafo region of Ghana.MethodsA facility-based cross-sectional study design was employed, with a desk review of COVID-19 records in DHIMS-2 and primary data sources (registers and monthly reporting forms). This study involved all 23 different levels of healthcare facilities that reported on COVID-19 in the Ahafo region from March 2020 to December 2022. We assessed COVID-19 data quality using three dimensions of completeness, accuracy, and timeliness according to the World Health Organization data quality guide. Mixed-effect logistic regression was then employed to identify the determinants of COVID-19 data quality at a 95% confidence interval.ResultsThe overall COVID-19 data quality was estimated at 35.9% (95%CI=32.6%, 39.4%) while the rate of the data dimensions of timeliness, completeness, and accuracy were 50.2% (95%CI=46.7%, 53.8%), 50.6% (95%CI=47.1%, 54.2%), and 72.4% (95%CI=69.1%, 75.5%) respectively. It was found that the availability of a functional data validation team at the health facilities (AOR=18.3; 95%CI=1.62, 20.7; p= 0.019), training of data managers in COVID-19 data management (AOR=9.37; 95%CI=2.56, 34.3; p=0.001), and data managers with two-year professional training (certificate background) (AOR=3.42; 95%CI=1.95, 12.2; p=0.025) were independently associated with COVID-19 data quality.ConclusionThe overall COVID-19 data quality in the Ahafo region was quite poor. Dimensionally, while the rate of data timeliness was high, that of data completeness, and accuracy were relatively low. The interaction of the independent correlates of COVID-19 data quality requires the healthcare system to identify stringent measures to strengthen the health information system to enhance planning and decision-making, especially during disease outbreaks.