The impact of data quality and source data verification on epidemiologic inference: a practical application using HIV observational data

Abstract

BackgroundData audits are often evaluated soon after completion, even though the identification of systematic issues may lead to additional data quality improvements in the future. In this study, we assess the impact of the entire data audit process on subsequent statistical analyses.MethodsWe conducted on-site audits of datasets from nine international HIV care sites. Error rates were quantified for key demographic and clinical variables among a subset of records randomly selected for auditing. Based on audit results, some sites were tasked with targeted validation of high-error-rate variables resulting in a post-audit dataset. We estimated the times from antiretroviral therapy initiation until death and first AIDS-defining event using the pre-audit data, the audit data, and the post-audit data.ResultsThe overall discrepancy rate between pre-audit and audit data (n = 250) across all audited variables was 17.1%. The estimated probability of mortality and an AIDS-defining event over time was higher in the audited data relative to the pre-audit data. Among patients represented in both the post-audit and pre-audit cohorts (n = 18,999), AIDS and mortality estimates also were higher in the post-audit data.ConclusionThough some changes may have occurred independently, our findings suggest that improved data quality following the audit may impact epidemiological inferences.

Analysis of Error-prone Electronic Health Records with Multi-wave Validation Sampling: Association of Maternal Weight Gain during Pregnancy with Childhood Outcomes

“…Though generally we can hope that errors in EHR data yield estimates with minimal bias, we cannot know this will be the case until we actually validate the study data and examine the quality of the error-prone EHR and directly calculate its impact on estimates. The impact that poor data quality can have on study estimates has been observed time and again to be potentially substantial [18,21,20]. Hence, the size and choice of the validation sample and the analysis methods are critical and can greatly impact precision.…”

Looking beyond the numbers: quality assurance procedures in the Global Network for Women’s and Children’s Health Research Maternal Newborn Health Registry

“…In addition to the standardized definitions and procedures, other studies have emphasized the importance of leveraging local capacity with central technical support, similar to the model of the GN [ 14 , 15 ]. Additionally, as with the GN, the use of ongoing data metrics in routine monitoring reports have been shown to improve data quality [ 5 , 16 ].…”

Improving data quality in observational research studies: Report of the Cure Glomerulonephropathy (CureGN) network

“…Verifying key data elements against source documents through audits improved the quality of the research databases [ 14 ]. Another HIV study [ 15 ] found a discrepancy rate of 17% between pre- and post-audit data of 250 participants, and then investigated the effect of the poor data quality on epidemiologic inference. In 2020, Guidelines for Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD) were published [ 16 ].…”