Data quality methods through remote source data verification auditing: results from the Congenital Cardiac Research Collaborative.

BACKGROUND: Multicentre research databases can provide insights into healthcare processes to improve outcomes and make practice recommendations for novel approaches. Effective audits can establish a framework for reporting research efforts, ensuring accurate reporting, and spearheading quality improvement. Although a variety of data auditing models and standards exist, barriers to effective auditing including costs, regulatory requirements, travel, and design complexity must be considered. MATERIALS AND METHODS: The Congenital Cardiac Research Collaborative conducted a virtual data training initiative and remote source data verification audit on a retrospective multicentre dataset. CCRC investigators across nine institutions were trained to extract and enter data into a robust dataset on patients with tetralogy of Fallot who required neonatal intervention. Centres provided de-identified source files for a randomised 10% patient sample audit. Key auditing variables, discrepancy types, and severity levels were analysed across two study groups, primary repair and staged repair. RESULTS: Of the total 572 study patients, data from 58 patients (31 staged repairs and 27 primary repairs) were source data verified. Amongst the 1790 variables audited, 45 discrepancies were discovered, resulting in an overall accuracy rate of 97.5%. High accuracy rates were consistent across all CCRC institutions ranging from 94.6% to 99.4% and were reported for both minor (1.5%) and major discrepancies type classifications (1.1%). CONCLUSION: Findings indicate that implementing a virtual multicentre training initiative and remote source data verification audit can identify data quality concerns and produce a reliable, high-quality dataset. Remote auditing capacity is especially important during the current COVID-19 pandemic.

An altmetric analysis of online news on India's first indigenous COVID-19 vaccine.

BACKGROUND: Covaxin is the first indigenous vaccine developed in India against COVID-19. The purpose of this study was to analyze the news stories on Covaxin published in the online media between two statements issued by Indian Council for Medical Research on 2nd and 4th July for their content, quality of information, and reporting standards. MATERIALS AND METHODS: A systematic search was performed on Google to identify the news stories related to Covaxin in the English language published between these two statements. The selected news stories were subjected to content analysis and reviewed using the screening points developed through a consultation by two independent experts using ten prevalidated criteria for health news review. The data were analyzed in MS Excel and StataMP14. RESULTS: The final analysis included 24 news stories. The mean and median score of the news stories is 10.71 and 12 (out of 20), respectively, with a score ranging from 2 to 17. The stories did not promote disease or vaccine mongering (100%), adequately mentioned the true novelty of the vaccine (95.8%), and source of the information (83.3%). However, they mostly failed to mention the information on costs, research data related to benefits, and harms and quality of the available evidence. CONCLUSION: There is a lack of reporting of detailed analysis about the methodology of development of the vaccine and limitations in its research design by health journalists. It is important to train journalists on proper reporting of health news to improve its quality in Indian media.