Factors contributing to racial disparities in influenza vaccinations

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.

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.

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.

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.