Improving data capture of race and ethnicity for the Food and Drug Administration Sentinel database: a narrative review.

PURPOSE: The U.S. Food and Drug Administration's Sentinel System is a national medical product safety surveillance system consisting of a large multisite distributed database of administrative claims supplemented by electronic health-care record 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 (five-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. CONCLUSIONS: 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.

Time-series analysis of recent antihyperglycemic medication prescribing trends for a diverse sample of Medicare enrollees with type 2 diabetes mellitus in an integrated health system.

PURPOSE: Despite high type 2 diabetes mellitus (T2DM) prevalence in Medicare enrollees, newer therapeutic options, and revised treatment guidelines, little is known about US antihyperglycemic prescribing trends after 2015. This research describes recent monthly antihyperglycemic prescribing trends in a large, diverse population of Medicare enrollees from the US Mid-Atlantic region. METHODS: Encounter data (July 2018-July 2020) for Medicare enrollees 65 years of age or older with T2DM were extracted from electronic health records of a large integrated health system. Descriptive time-series regression models were estimated to describe monthly prescribing rates (ie, prescription orders per 100 eligible plan members with T2DM) overall and by medication subgroups for all-eligible and continuously-eligible samples. Trends in monthly prescription orders per 100 eligible plan members with T2DM were reported. RESULTS: The monthly all-eligible member sample (n > 22,000) exhibited an overall positive baseline monthly prescribing rate of 23.88 T2DM medication orders per 100 members with T2DM and a significant positive monthly prescribing rate trend (ie, change) of 0.12 T2DM medication orders per 100 members with T2DM (P < 0.05). Subgroup T2DM medication order rates per 100 members with T2DM at baseline were 16.28 for first-generation medications, 3.87 for human insulins, 3.04 for insulin analogs, 0.58 for second-generation medications, and 0.11 for combination medications. Human insulins, insulin analogs, and second-generation medications had positive monthly trends (P < 0.05). Among second-generation medications, sodium-glucose cotransporter 2 (SGLT-2) inhibitors and glucagon-like peptide 1 (GLP-1) agonists had positive monthly trends (P < 0.05). Continuously eligible members with T2DM (n = 19,185) had no significant overall monthly prescribing trend; however, human insulins, insulin analogs, and second-generation medications and the SGLT-2 inhibitor class had positive monthly prescribing trends (P < 0.05). CONCLUSION: In a diverse Medicare sample, this study observed increasing monthly trends for second-generation medications, human insulins, and insulin analogs consistent with emerging evidence. Among second-generation medications, SGLT-2 inhibitors became the most commonly prescribed over time.