What Explains Inequalities in Telehealth Utilization Among North Carolina Medicaid Beneficiaries?

Background: Increased availability of telehealth can improve access to health care. However, there is evidence of persistent disparities in telehealth usage, as well as among people from minoritized racial and ethnic groups and rural residents. The objective of our work was to explore the degree to which disparities in telehealth use for behavioral health (BH) and musculoskeletal (MSK) related services during the COVID-19 pandemic are explained by observed beneficiary- and area-level characteristics. Methods: Using North Carolina Medicaid claims data of Medicaid beneficiaries with BH or MSK conditions, we apply nonlinear regression-based decomposition analysis-based models developed by Kitagawa, Oaxaca, and Blinder to determine which observed variables are associated with racial, ethnic, and rural inequalities in telehealth usage. Results: In the BH cohort, we found statistically significant differences in telehealth usage by race in the adult population, and by race, Hispanic ethnicity, and rurality in the pediatric population. In the MSK cohort, we found significant inequities by Hispanic ethnicity and rurality among adults, and by race and rurality among children. Inequalities in telehealth use between groups were small, ranging from 0.7 percentage points between urban and rural adults with MSK conditions to 3.8 percentage points between white adults and people of color among those with BH conditions. Overall, we found that racial and ethnic inequalities in telehealth use are not well explained by the observed variables in our data. Rural disparities in telehealth use are better explained by observed variables, particularly area-level broadband internet use. Conclusions: For inequalities between rural and urban residents, our analysis provides observational evidence that infrastructure such as broadband internet access is an important driver of differences in telehealth use. For racial and ethnic inequalities, the pathways may be more complex and difficult to measure, particularly when relying on administrative data sources in place of more detailed data on individual-level socioeconomic factors