Validation of US CDC National Death Index mortality data, focusing on differences in race and ethnicity.

OBJECTIVES: The US Center for Disease Control and Prevention's National Death Index (NDI) is a gold standard for mortality data, yet matching patients to the database depends on accurate and available key identifiers. Our objective was to evaluate NDI data for future healthcare research studies with mortality outcomes. METHODS: We used a Kaiser Permanente Mid-Atlantic States' Virtual Data Warehouse (KPMAS-VDW) sourced from the Social Security Administration and electronic health records on members enrolled between 1 January 2005 to 31 December 2017. We submitted data to NDI on 1 036 449 members. We compared results from the NDI best match algorithm to the KPMAS-VDW for vital status and death date. We compared probabilistic scores by sex and race and ethnicity. RESULTS: NDI returned 372 865 (36%) unique possible matches, 663 061 (64%) records not matched to the NDI database and 522 (<1%) rejected records. The NDI algorithm resulted in 38 862 records, presumed dead, with a lower percentage of women, and Asian/Pacific Islander and Hispanic people than presumed alive. There were 27 306 presumed dead members whose death dates matched exactly between the NDI results and VDW, but 1539 did not have an exact match. There were 10 017 additional deaths from NDI results that were not present in the VDW death data. CONCLUSIONS: NDI data can substantially improve the overall capture of deaths. However, further quality control measures were needed to ensure the accuracy of the NDI best match algorithm.

A Mixed Methods Study Describing the Quality of Healthcare Received by Transgender and Gender Nonconforming Patients at a Large Integrated Health System.

Transgender and gender nonconforming (TGNC) patients have been seeking medical care in higher numbers and have faced unique social, personal, and health issues that affect the quality of care they receive. The purpose of this study was to conduct a mixed-methods study to describe TGNC care at Kaiser Permanente Mid-Atlantic States, a large integrated health system. We used a transgender registry to describe a TGNC patient population and compared healthcare utilization between TGNC patients and non-TGNC patients. Four focus groups were also conducted among 28 patients. Atlas.ti software was used to code and analyze themes for the qualitative analysis. Among the 282 adults TGNC patients, the mean age was 32.6 years. Of the study sample, 59% were White, and 27% were Black. TGNC patients demonstrated an increased use of email/telephone visits and the online patient portal and more cancellations and no-shows compared to non-TGNC controls. Of the 28 TGNC patients who participated in the focus groups, 39% identified as female, 21% as a transman, and 18% as non-binary/genderqueer. Participants were predominantly White (68%), highly educated (74%), and reported use of hormones (89%). Themes that emerged from our qualitative analysis included: limited availability of TGNC information; positive and negative sentiments regarding patient-provider interactions; issues with case management; limited access to care; lack of coordination of care; negative staff experiences. We identified specific areas in a health system to improve the quality of care of TGNC patients, including specific TGNC training for providers and staff, a source of TGNC information/resources, and hiring and training TGNC-specific case managers.

Factors contributing to racial disparities in influenza vaccinations.

BACKGROUND: Racial/ethnic disparities in rates of influenza vaccinations in the US remain an issue even among those with access, no out-of-pocket costs, and after adjusting for confounders. We used an approach called the Oaxaca-Blinder (OB) decomposition method to ascertain the contribution of covariates individually and in aggregate to the racial disparity in influenza vaccination. METHODS: We included members > = 18 years of age as of 05/01/2014 with continuous enrollment through 04/30/2015. Influenza vaccination was defined by diagnosis, procedure, or medication codes, or documentation in the immunization table. Characteristics were reported by race. Logistic regression models estimated the odds of vaccination associated with: (1) race; and (2) covariates stratified by race. The Oaxaca-Blinder (OB) method calculated the contribution of covariates to the difference or disparity in vaccination between Blacks and Whites. RESULTS: We found that among adults, 44% were vaccinated; 55% were Black; and 45% were White. Black members have 42% lower odds of vaccination than White members. The contribution of the differences in the average value of the study covariates between Black and White members (the OB covariate effect) accounted for 29% of the racial disparity. The contributions to the total White-Black disparity in vaccination included: age (16%), neighborhood median income (11%), and registration on the online patient portal (13%). The contribution of the differences in how the covariates impact vaccination (OB coefficient effect) accounted for 71% of the disparity in vaccination between Blacks and Whites. CONCLUSION: In conclusion, equalizing average covariate values in Blacks and Whites could reduce the racial disparity in influenza vaccination by 29%. For health system vaccine campaigns, improving registration on the patient portal may be a target component of an effective system-level strategy to reduce racial disparities in vaccination. Additional information on patient-centered factors could further improve the value of the OB approach.