We have located links that may give you full text access.
Journal Article
Review
Improving data capture of race and ethnicity for the Food and Drug Administration Sentinel database: a narrative review.
Annals of Epidemiology 2023 July 20
PURPOSE: The U.S. Food and Drug Administration's Sentinel System is a national medical product safety surveillance system consisting of a large multi-site distributed database of administrative claims supplemented by electronic healthcare record (EHR) data. The program seeks to improve data capture of race and ethnicity for pharmacoepidemiology studies.
METHODS: We conducted a narrative literature review of published research on data augmentation and imputation methods to improve race and ethnicity capture in U.S. health care systems databases. We focused on methods with limited (5-digit ZIP codes only) or full patient identifiers available to link to external sources of self-reported data. We organized the literature by themes: 1) variation in data capture of self-reported data, 2) data augmentation from external sources of self-reported data, and 3) imputation methods, including Bayesian analysis and multiple regression.
RESULTS: Researchers reduced data missingness with high validity for Asian, Black, White, and Pacific Islander racial groups and Hispanic ethnicity. Native American and multiracial groups were difficult to validate due to relatively small sample sizes.
CONCLUSION: Limitations on accessible self-reported data for validation will dictate methods to improve race and ethnicity data capture. We recommend methods leveraging multiple sources that account for variations in geography, age, and sex.
METHODS: We conducted a narrative literature review of published research on data augmentation and imputation methods to improve race and ethnicity capture in U.S. health care systems databases. We focused on methods with limited (5-digit ZIP codes only) or full patient identifiers available to link to external sources of self-reported data. We organized the literature by themes: 1) variation in data capture of self-reported data, 2) data augmentation from external sources of self-reported data, and 3) imputation methods, including Bayesian analysis and multiple regression.
RESULTS: Researchers reduced data missingness with high validity for Asian, Black, White, and Pacific Islander racial groups and Hispanic ethnicity. Native American and multiracial groups were difficult to validate due to relatively small sample sizes.
CONCLUSION: Limitations on accessible self-reported data for validation will dictate methods to improve race and ethnicity data capture. We recommend methods leveraging multiple sources that account for variations in geography, age, and sex.
Full text links
Related Resources
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app