Telehealth Usage Among Low-Income Racial and Ethnic Minority Populations During the COVID-19 Pandemic: Retrospective Observational Study

BACKGROUND: Despite considerable efforts to encourage telehealth use during the COVID-19 pandemic, we witnessed a potential widening of health inequities that may continue to plague the US health care system unless we mitigate modifiable risk factors.

OBJECTIVE: This study aimed to examine the hypothesis that there are systemic differences in telehealth usage among people who live at or below 200% of the federal poverty level. Factors that we consider are age, gender, race, ethnicity, education, employment status, household size, and income.

METHODS: A retrospective observational study was performed using the COVID-19 Research Database to analyze factors contributing to telehealth inequities. The study period ranged from March 2020 to April 2021. The Office Ally database provided US claims data from 100 million unique patients and 3.4 billion claims. The Analytics IQ PeopleCore Consumer database is nationally representative of 242.5 million US adults aged 19 years and older. We analyzed medical claims to investigate the influence of demographic and socioeconomic factors on telehealth usage among the low-income racial and ethnic minority populations. We conducted a multiple logistic regression analysis to determine the odds of patients in diverse groups using telehealth during the study period.

RESULTS: Among 2,850,831 unique patients, nearly 60% of them were female, 75% of them had a high school education or less, 49% of them were unemployed, and 62% of them identified as non-Hispanic White. Our results suggest that 9.84% of the patients had ≥1 telehealth claims during the study period. Asian (odds ratio [OR] 1.569, 95% CI 1.528-1.611, P<.001) and Hispanic (OR 1.612, 95% CI 1.596-1.628, P<.001) patients were more likely to use telehealth than non-Hispanic White and -Black patients. Patients who were employed full-time were 15% (OR 1.148, 95% CI 1.133-1.164, P<.001) more likely to use telehealth than unemployed patients. Patients who identified as male were 12% (OR 0.875, 95% CI 0.867-0.883, P<.001) less likely to use telehealth than those who identified as female. Patients with high school education or less were 5% (OR 0.953, 95% CI 0.944-0.962, P<.001) less likely to use telehealth than those with a bachelor's degree or higher. Patients in the 18-44-year age group were 32% (OR 1.324, 95% CI 1.304-1.345, P<.001) more likely to use telehealth than those in the ≥65-year age group.

CONCLUSIONS: Factors that impact telehealth usage include age, gender, race, education, employment status, and income. While low-income racial and ethnic minority communities are at greater risk for health inequities among this group, Hispanic communities are more likely to use telehealth, and non-Hispanic Black patients continue to demonstrate telehealth inequity. Gender, age, and household income contribute to health inequities across gradients of poverty. Strategies to improve health use should consider characteristics of subgroups, as people do not experience poverty equally.