Characteristics of telehealth users in NYC for COVID-related care during the coronavirus pandemic

Abstract

Objective: To explore whether racial/ethnic differences in telehealth use existed during the peak pandemic period among NYC patients seeking care for COVID-19 related symptoms.

Materials and methods: This study used data from a large health system in NYC - the epicenter of the US crisis - to describe characteristics of patients seeking COVID-related care via telehealth, ER, or office encounters during the peak pandemic period. Using multinomial logistic regression, we estimated the magnitude of the relationship between patient characteristics and the odds of having a first encounter via telehealth versus ER or office visit, and then used regression parameter estimates to predict patients' probabilities of using different encounter types given their characteristics.

Results: Demographic factors, including race/ethnicity and age, were significantly predictive of telehealth use. As compared to Whites, Blacks had higher adjusted odds of using both the ER versus telehealth (OR: 4.3, 95% CI: 4.0-4.6) and office visits versus telehealth (OR: 1.4, 95% CI: 1.3-1.5). For Hispanics versus Whites, the analogous ORs were 2.5 (95% CI: 2.3-2.7) and 1.2 (95% CI: 1.1-1.3). Compared to any age groups, patients 65+ had significantly higher odds of using either ER or office visits versus telehealth.

Conclusions: The response to COVID-19 has involved an unprecedented expansion in telehealth. While older Americans and minority populations among others are known to be disadvantaged by the digital divide, few studies have examined disparities in telehealth specifically, and none during COVID-19. Additional research into sociodemographic heterogeneity in telehealth use is needed to prevent potentially further exacerbating health disparities overall.

Keywords: Telehealth; healthcare delivery; healthcare disparities; telemedicine.

Figure 1.

Daily counts of COVID-19 cases, hospitalizations & deaths in NYC. Source: Figure based on authors’ analyses of the Github database. Note: This figure shows the number of confirmed cases by diagnosis date, hospitalizations by admission date, and deaths, by date of death, from COVID-19 on a daily basis since February 29, 2020. Given delays in reporting, which can take up to a week, recent data are incomplete.

Figure 2.

Predicted probabilities of encounter types for different patients. Source: Table based on authors’ analyses of the Mount Sinai deidentified COVID-19 database. Note: Figure based on parameter estimates from multinomial regression analyis of encounter type (ER vs telehealth vs office) on patient characteristics (race/ethnicity, age, preferred language, comorbidity sum).

Figure 3.

Predicted probabilities of encounter types for different patients across ages, or comorbidities. Source: Table based on authors’ analyses of the Mount Sinai deidentified COVID-19 database. Note: Figure based on parameter estimates from multinomial regression analyis of encounter type (ER vs telehealth vs office) on patient characteristics (race/ethnicity, age, preferred language, comorbidity sum), including interactions.