J Racial Ethn Health Disparities
August 2024
Objective: To examine the association of patient-provider racial and ethnic concordance on healthcare use within Hispanic ethnic subgroups.
Methods: We estimate multivariate probit models using data from the Medical Expenditure Panel Survey, the only national data source measuring how patients use and pay for medical care, health insurance, and out-of-pocket spending. We collect and utilize data on preventive care visits, visits for new health problems, and visits for ongoing health problems from survey years 2007-2017 to measure health outcomes.
Introduction: Given the increasing impact of the healthcare cost of hypertension on the economy, understanding the control of high blood pressure is warranted, particularly as it pertains to racial/ethnic disparities in hypertension control.
Objective: To understand the relationship between hypertension control and racial/ethnic concordance, we investigated whether the racial/ethnic concordance between a patient's race/ethnicity and that of the individual's provider is a predictor of high blood pressure control.
Methods: Data was collected for 612,524 patients from Kaiser Permanente Southern California who were at least 18 year old and received a diagnosis of hypertension between January 1, 2016 and December 31, 2019.
Int J Health Econ Manag
March 2022
The Affordable Care Act was implemented with the aim of increasing coverage and affordable access with hopes of improving health outcomes and reducing costs. Yet, disparities persist. Coverage and affordable access alone cannot explain the health care gap between racial/ethnic minorities and white patients.
View Article and Find Full Text PDFJ Racial Ethn Health Disparities
October 2019
Objective: To examine the association between race/ethnicity concordance and in-person provider visits following the implementation of the Affordable Care Act.
Design: Using 2014-2015 data from the Medical Expenditure Panel Survey, we examine whether having a provider of the same race or ethnicity ("race/ethnicity concordance") affects the probability that an individual will visit a provider. Multivariate probit models are estimated to adjust for demographic, socioeconomic, and health factors.