Affiliations: [a] National Center for Health Statistics, Centers for Disease Control and Prevention, U.S. Department of Health and Human Services, Hyattsville, MD, USA | [b] National Center for Science and Engineering Statistics, | [c] Administration for Children and Families, U.S. Department of Health and Human Services, Washington, DC, USA | [d] Office of the Assistant Director for Planning and Evaluation, U.S. Department of Health and Human Services, Washington, DC, USA | [e] Centers for Medicare and Medicaid Services, U.S. Department of Health and Human Services, Baltimore, MD, USA | [f] National Center for Environmental Health, Centers for Disease Control and Prevention, U.S. Department of Health and Human Services, Atlanta, GA, USA
Correspondence:
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Corresponding author: Jennifer D. Parker, National Center for Health Statistics, 3311 Toledo Road, Room 4650, Hyattsville, MD 20782, USA. Tel.: +1 301 326 8555; E-mail: jdparker@cdc.gov.
Abstract: In 2020 the U.S. Federal Committee on Statistical Methodology (FCSM) released “A Framework for Data Quality”, organized by 11 dimensions of data quality grouped among three domains of quality (utility, objectivity, integrity). This paper addresses the use of the FCSM Framework for data quality assessments of blended data. The FCSM Framework applies to all types of data, however best practices for implementation have not been documented. We applied the FCSM Framework for three health-research related case studies. For each case study, assessments of data quality dimensions were performed to identify threats to quality, possible mitigations of those threats, and trade-offs among them. From these assessments the authors concluded: 1) data quality assessments are more complex in practice than anticipated and expert guidance and documentation are important; 2) each dimension may not be equally important for different data uses; 3) data quality assessments can be subjective and having a quantitative tool could help explain the results, however, quantitative assessments may be closely tied to the intended use of the dataset; 4) there are common trade-offs and mitigations for some threats to quality among dimensions. This paper is one of the first to apply the FCSM Framework to specific use-cases and illustrates a process for similar data uses.
Keywords: Data quality, blended data, data linkage, health surveys, administrative data
