HEALTH POLICY CENTER RE S E AR C H RE P O R T Variation in Patients' Use of, Experiences with, and Access to Telehealth during the First Year of the COVID-19 Pandemic Laura Barrie Smith Fredric Blavin Claire O'Brien March 2022 AB O U T T HE U R BA N I NS T I T U TE The nonprofit Urban Institute is a leading research organization dedicated to developing evidence-based insights that improve people's lives and strengthen communities. For 50 years, Urban has been the trusted source for rigorous analysis of complex social and economic issues; strategic advice to policymakers, philanthropists, and practitioners; and new, promising ideas that expand opportunities for all. Our work inspires effective decisions that advance fairness and enhance the well-being of people and places. Copyright © March 2022. Urban Institute. Permission is granted for reproduction of this file, with attribution to the Urban Institute. Cover image by Tim Meko. Contents Acknowledgments iv Variation in Patients' Use of, Experiences with, and Access to Telehealth during the First Year of the COVID-19 Pandemic 1 Methods 2 Results 3 Telehealth Visits during the First Year of the Pandemic 3 Characteristics of and Patients' Experiences with Telehealth Visits by Health Insurance Type 6 Differences in Phone versus Video Telehealth Visits 8 Access to Telehealth Visits 12 Discussion 15 Appendix. Regression-Adjusted Results 18 Notes 22 References 23 About the Authors 24 Statement of Independence 25 Acknowledgments This report was funded by the National Institute for Health Care Reform. We are grateful to them and to all our funders, who make it possible for Urban to advance its mission. The views expressed are those of the authors and should not be attributed to the Urban Institute, its trustees, or its funders. Funders do not determine research findings or the insights and recommendations of Urban experts. Further information on the Urban Institute's funding principles is available at urban.org/fundingprinciples. The authors are grateful to Stephen Zuckerman and Michael Karpman for their input throughout the project. iv ACKNOWLEDGMENTS Variation in Patients' Use of, Experiences with, and Access to Telehealth during the First Year of the COVID-19 Pandemic During the initial months of the COVID-19 pandemic in the United States, the use of telehealth increased rapidly to reduce face-to-face contact between providers and patients. Though estimates of telehealth use during this time have emerged, additional information on the characteristics of telehealth visits and disparities in access to telehealth is needed to inform evolving telehealth policies (Mehrotra et al. 2021; Patel et al. 2020). Studies have found that the initial surge in telehealth during the pandemic varied by provider and patient characteristics, including providers' specialties and patients' income, insurance status, and rurality (Mehrotra et al. 2021; Patel, Mehrotra, et al. 2021; Patel, Rose, et al. 2021; Smith and Blavin 2021; Zhang et al. 2021).1 However, such research has assessed neither the characteristics of telehealth visits nor access to telehealth as experienced by patients and has been largely limited to data that do not represent all patients (e.g., claims data from a single payer type). This study uses nationally representative survey data to describe telehealth visits among nonelderly adults (ages 18 to 64) and elderly adults (ages 65 and older) in the US during the first year of the pandemic. We examine differences in the use of telehealth, in the characteristics of telehealth visits, in patients' experiences with telehealth visits, and between phone (i.e., audio-only) and video telehealth visits. This study expands upon prior evidence on telehealth during the pandemic in three ways: 1. We assess variation in telehealth visits using data with a sample size more than three times larger than that of other surveys and with a longer and more recent time frame (through April 2021) than that of other surveys (Kyle et al. 2021; Zhang et al. 2021). 2. We examine the characteristics of telehealth visits from patients' perspectives, including what health care issues their visits addressed and whether patients found visits to be convenient and easy to schedule. As noted, we also compare characteristics of telehealth visits by modality (phone or video) and patient health insurance type; we do so because though many health insurers made policy changes to facilitate telehealth uptake (e.g., removing geographic restrictions on patients and providers, expanding the covered modalities to include both phone and video for some services, and increasing reimbursement rates for telehealth visits), these changes varied across insurance plans and states (CCHP 2021). 3. We examine disparities in access to telehealth visits by characteristics including income, race and ethnicity, health insurance coverage type, health status, and rurality. Numerous payment and regulatory changes enacted during the public health emergency facilitated the surge in telehealth use. Policymakers now face decisions about which telehealth waivers should be extended or made permanent, and evidence from this study can inform such decisions to ensure they promote equity in access to and the use of telehealth. Methods This study uses data from the April 2021 round of the Urban Institute's Health Reform Monitoring Survey (HRMS), a nationally representative internet-based survey of adults. Overall, 9,067 adults ages 18 to 64 and 3,157 adults ages 65 and older completed the HRMS in April 2021. Survey weights adjust for unequal selection probabilities from the probability-based internet panel from which HRMS samples are drawn and are poststratified to represent the characteristics of national adult populations based on benchmarks from the Current Population Survey and the American Community Survey. Additional methodological information about the HRMS can be found at hrms.urban.org. We use the HRMS to assess the use of, the characteristics of, experiences with, and access to telehealth visits. The HRMS defines telehealth visits as "phone or video visits with a doctor or other health care provider to talk about your health." We create several variables reflecting patient characteristics: family income, race and ethnicity,2 age, health insurance coverage, residence in or outside a metropolitan statistical area, self-reported health status, presence of diagnosed physical health conditions, and presence of diagnosed mental health or substance use conditions. We conduct a descriptive analysis of telehealth visits, describe the characteristics of and patients' experiences with telehealth visits by modality and health insurance type, and assess access to telehealth visits separately for nonelderly adults and elderly adults. We tabulate each outcome variable by patient characteristics, testing for differences using two-tailed t-tests. Additionally, we use recycled prediction methods to estimate telehealth use for each patient subgroup, adjusting for other covariates in a regression model (appendix figures A.1 and A.2). 2 VARIATION IN TELEHEALTH DURING THE FIST YEAR OF THE PANDEMIC Results Below we describe the characteristics of adults who had a telehealth visit during the first year of the pandemic and the characteristics of and patients' experiences with telehealth visits by patient health insurance type and telehealth modality. We also examine the characteristics of adults who had difficulties accessing telehealth. Telehealth Visits during the First Year of the Pandemic Overall, 37.4 percent of nonelderly adults had at least one telehealth visit during the first year of the pandemic, and this rate differed significantly across patient characteristics (figure 1). Nonelderly adults with incomes at or exceeding 400 percent of the federal poverty level (FPL) were significantly more likely to have had a telehealth visit than adults with incomes at or below 138 percent of FPL (39.8 versus 35.4 percent). Differences in the probability of telehealth use by race and ethnicity were small. Nonelderly adults who are not white, Black, or Hispanic/Latinx or who identified as multiple races were significantly less likely to have had a telehealth visit than white adults (32.6 versus 37.1 percent). VARIATION IN TELEHEALTH DURING THE FIST YEAR OF THE PANDEMIC 3 FIGURE 1 Share of Adults Ages 18 to 64 with a Telehealth Visit in the Past 12 months, Overall and by Selected Characteristics, April 2021 All nonelderly adults 37.4% Income At or below 138% of FPL^ 35.4% 139–399% of FPL 35.6% At or above 400% of FPL 39.8%** Race or ethnicity White^ 37.1% Black 39.5% Hispanic/Latinx 39.0% Other 32.6%** Age 18–34^ 32.1% 35–49 38.0%*** 50–64 42.9%*** Health insurance coverage Private^ 38.4% Public 46.4%*** Uninsured 17.7%*** Health status Excellent, very good, or good^ 35.3% Fair or poor 49.0%*** Physical condition(s) Does not have condition^ 26.2% Has condition 49.4%*** Mental or SU condition(s) Does not have condition^ 29.7% Has condition 56.9%*** Urban or rural residence Lives in an MSA^ 38.6% Does not live in an MSA 28.7%*** URBAN INSTITUTE Source: Authors' analysis of data from the Health Reform Monitoring Survey, April 2021. Notes: FPL is federal poverty level. MSA is metropolitan statistical area. SU is substance use. In the race or ethnicity category, "other" adults are non-Hispanic/Latinx adults who are not Black or white or are more than one race. Black and white adults are not Hispanic/Latinx. Estimates for nonelderly adults with unspecified health insurance coverage are not shown because of small sample size. The 0.3 percent of nonelderly adults who did not report if they had a telehealth visit are included in the denominator of these estimates. */**/*** Estimate differs significantly from that for the reference group (^) at the 0.10/0.05/0.01 level, using two-tailed t-tests. Telehealth use also varied across several other patient characteristics. First, nonelderly adults with private health insurance were significantly less likely to have had a telehealth visit than those with public health insurance (38.4 versus 46.4 percent), and uninsured nonelderly adults were even less likely to have had a telehealth visit (17.7 percent). Second, nonelderly adults with fair or poor health were significantly more likely to have had a visit than those with excellent, very good, or good health (49.0 versus 35.3 percent). Relatedly, nonelderly adults with a diagnosed physical or mental health condition were nearly twice as likely to have had a telehealth visit as those without such conditions. 4 VARIATION IN TELEHEALTH DURING THE FIST YEAR OF THE PANDEMIC Finally, nonelderly adults living outside metropolitan statistical areas were substantially less likely than those living in such areas to have had a telehealth visit (28.7 versus 38.6 percent). Among elderly adults, 46.2 percent had at least one telehealth visit during the first year of the pandemic (figure 2). These patients' characteristics and telehealth use were similar between the elderly and the nonelderly populations. However, the association between race and ethnicity and telehealth visits differed among elderly adults; both elderly Black adults and elderly Hispanic/Latinx adults were more likely than elderly white adults to have had a telehealth visit. FIGURE 2 Share of Adults Ages 65 and Older with a Telehealth Visit in the Past 12 months, Overall and by Selected Characteristics, April 2021 All elderly adults 46.2% Income At or below 138% of FPL^ 41.6% 139–399% of FPL 42.8% At or above 400% of FPL 52.1%*** Race or ethnicity White^ 43.5% Black 54.8%*** Hispanic/Latinx 57.6%** Age 65–79^ 47.4% 80+ 41.3%** Health status Excellent, very good, or good^ 43.2% Fair or poor 58.8%*** Physical condition(s) Does not have condition^ 23.2% Has condition 49.4%*** Mental or SU condition(s) Does not have condition^ 42.9% 63.4%*** Has condition Urban or rural residence Lives in an MSA^ 48.4% Does not live in an MSA 34.6%*** URBAN INSTITUTE Source: Authors' analysis of data from the Health Reform Monitoring Survey, April 2021. Notes: FPL is federal poverty level. MSA is metropolitan statistical area. SU is substance use. Estimates are not shown for the "other" racial group (adults who are not Hispanic/Latinx, Black, or white and adults identifying as more than one race) and for elderly adults with unspecified health insurance coverage because of small sample sizes. The 0.3 percent of elderly adults who did not report if they had a telehealth visit are included in the denominator of these estimates. */**/*** Estimate differs significantly from that for the reference group (^) at the 0.10/0.05/0.01 level, using two-tailed t-tests. VARIATION IN TELEHEALTH DURING THE FIST YEAR OF THE PANDEMIC 5 Most of these patterns among the elderly and nonelderly samples were consistent after adjusting for patient characteristics. The associations between telehealth use and income and race and ethnicity were even more pronounced within both samples after regression adjustment (appendix figure A.1). Characteristics of and Patients' Experiences with Telehealth Visits by Health Insurance Type Table 1 summarizes the characteristics of and patients' experiences with telehealth visits overall and by health insurance type. Phone visits were significantly more common for nonelderly adults with public insurance (70.6 percent) and elderly adults with Medicare (65.6 percent) than for nonelderly adults with private insurance (49.1 percent). Video visits were less common; 74.1 percent of nonelderly adults with private coverage, 60.3 percent of nonelderly adults with public coverage, and 49.5 percent of elderly adults with Medicare had such visits. The shares of adults who reported having out-of-pocket costs for their last telehealth visit differed significantly by health insurance type: whereas just over half (52.3 percent) of nonelderly adults with private health insurance had out-of-pocket costs, only 18.5 percent of nonelderly adults with public health insurance and 29.0 percent of elderly adults with Medicare had out-of-pocket costs. TABLE 1 Characteristics of and Patients' Experiences with Telehealth among Adults Who Had a Telehealth Visit in the Past 12 Months, by Insurance Coverage Type and Age Group, April 2021 Nonelderly Nonelderly adults with adults with Elderly adults private public presumed to insurance insurance have Medicare Type of visit in the past 12 months (%) Phone 49.1 70.6*** 65.6*** Video 74.1 60.3*** 49.5*** OOP costs for last telehealth visit (%) No OOP costs 47.7 81.5*** 71.0*** Any OOP costs 52.3 18.5*** 29.0*** $1–20 11.9 7.1** 10.7 $21–50 20.3 6.7*** 11.6*** $51–100 8.9 2.3*** 3.4*** More than $100 11.2 2.4*** 3.3*** Types of health care issues addressed during telehealth visits (%) General preventive or routine care 53.1 63.0*** 72.0*** Mental health care or counseling 26.4 36.3*** 8.5*** Treatment or counseling for alcohol or drug use 2.3 5.6** 0.3*** COVID-19 screening 16.3 12.0*** 8.5*** New injury, illness, or health problem other than COVID-19 25.7 22.2** 20.4** 6 VARIATION IN TELEHEALTH DURING THE FIST YEAR OF THE PANDEMIC Nonelderly Nonelderly adults with adults with Elderly adults private public presumed to insurance insurance have Medicare Chronic or ongoing condition 32.0 46.9*** 44.5*** Other type of care 5.0 5.2 4.3% Share who strongly or somewhat agreed with the following (%) It was easy to schedule telehealth visits at a convenient time 89.0 83.6*** 82.8*** Wait times for telehealth visits were shorter than the wait times for similar in-person visits 67.7 67.7 64.4 Was able to receive needed prescription drug fills through telehealth visits 82.8 84.5 83.0 Sample size 2,454 938 1,586 Sample size for OOP measuresa 2,437 931 1,577 Source: Authors' analysis of data from the Health Reform Monitoring Survey, April 2021. Notes: OOP is out-of-pocket. Nonelderly adults are ages 18 to 64; elderly adults are ages 65 and older. Uninsured nonelderly adults are excluded because the sample size for those who had at least one telehealth visit was too small. Respondents who selected "not applicable" when asked about the ability to receive prescription drug fills (514 nonelderly adults with private insurance, 91 nonelderly adults with public insurance, and 304 elderly adults) are excluded from the corresponding measure. Among nonelderly respondents with public insurance, 66 percent had Medicaid only, 21 percent had Medicare and Medicaid, and 13 percent had only Medicare. a OOP measures exclude people who did not respond. */**/*** Estimate differs significantly from that for the reference group (^) at the 0.10/0.05/0.01 level, using two-tailed t-tests. Across patients with all health insurance types, the most common health care issue addressed during telehealth visits was general preventive or routine care. Compared with nonelderly adults with private insurance (32.0 percent), nonelderly adults with public insurance (46.9 percent) and elderly adults with Medicare (44.5 percent) were more likely to have had a chronic or ongoing health condition addressed. Nonelderly adults with public health insurance were more likely than those with private health insurance to have had visits that addressed mental health care or counseling (36.3 versus 26.4 percent); elderly adults with Medicare were much less likely to have had such a visit (8.5 percent). Most adults who had at least one telehealth visit during the first year of the pandemic reported having positive experiences, and this varied little by health insurance type. Most telehealth users agreed that it was easy to schedule telehealth visits at a convenient time (89.0 percent of nonelderly adults with private coverage, 83.6 percent of nonelderly adults with public coverage, and 82.8 percent of elderly adults with Medicare). Approximately two-thirds indicated their wait time for a telehealth visit was shorter than that for similar in-person visits (67.7 percent of nonelderly adults with private coverage, 67.7 percent of nonelderly adults with public coverage, and 64.4 percent of elderly adults with Medicare). More than 80 percent of adults agreed that they were able to receive needed prescription drug refills during their telehealth visits (82.8 percent of nonelderly adults with private VARIATION IN TELEHEALTH DURING THE FIST YEAR OF THE PANDEMIC 7 coverage, 84.5 percent of nonelderly adults with public coverage, and 83.0 percent of elderly adults with Medicare). Differences in Phone versus Video Telehealth Visits The characteristics of nonelderly adults who had at least one phone telehealth visit differed from those of nonelderly adults who had at least one video telehealth visit (table 2). Compared with phone telehealth users, video telehealth users were more likely to have incomes at or above 400 percent of FPL (56.3 versus 34.0 percent); to be white (65.7 versus 51.6 percent); to be 18 to 34 years old (36.5 versus 24.7 percent); to have private health insurance coverage (81.7 versus 62.3 percent); to be in excellent, very good, or good health (85.3 versus 79.0 percent); and to live in metropolitan areas (92.2 versus 87.5 percent). Similarly, nonelderly adults who had both phone and video visits tended to have higher incomes, were younger on average, and were less likely to be uninsured than those with phone visits only. However, nonelderly adults who had both phone and video visits were much more likely to have a mental health or substance use condition and to have had multiple visits than those who only had telehealth visits of one modality (table 2). The characteristics of phone and video telehealth visits also varied. Video visits were more likely than phone visits to have associated out-of-pocket costs (52.1 versus 34.2 percent). Video visits were also more likely to be used for mental health care (27.5 versus 18.7 percent) or a new injury or illness (24.2 versus 18.4 percent). Though most adults who had phone visits agreed that their visits were convenient and easy to schedule, the wait times were shorter than similar in-person visits, and they were able to receive needed prescription refills, adults who had video visits were more likely to report these positive experiences (table 3). Compared with nonelderly adults with phone visits only, those who had both phone and video visits were much more likely to have addressed mental health care or counseling or chronic health issues during their telehealth visits and were more likely to agree that visits were convenient and easy to schedule and that wait times for telehealth visits were shorter than those for similar in-person visits (table 3). 8 VARIATION IN TELEHEALTH DURING THE FIST YEAR OF THE PANDEMIC TABLE 2 Characteristics of Adults Ages 18 to 64 Who Had a Telehealth Visit in the Past 12 Months, by Visit Modality, April 2021 Used Used phone Used phone only^ video only and video Income (%) At or below 138% of FPL 26.9 13.8*** 22.2** 139–399% of FPL 39.1 29.9*** 35.0* At or above 400% of FPL 34.0 56.3*** 42.7*** Race or ethnicity (%) White 51.6 65.7*** 56.6 Black 15.4 10.6* 15.1 Hispanic/Latinx 26.5 14.9** 19.7* Other 6.5 8.7 8.6 Age (%) 18–34 24.7 36.5*** 33.5*** 35–49 28.8 31.6 31.6 50–64 46.5 32.0*** 34.9*** Health insurance coverage (%) Private 62.3 81.7*** 68.4 Public 28.7 13.5*** 26.7 Uninsured 8.3 3.6*** 3.8*** Health status (%) Excellent, very good, or good 79.0 85.3*** 74.9* Fair or poor 20.7 14.5*** 24.4* Presence of physical condition(s) (%) Does not have condition 33.7 40.7** 31.6 Has condition 66.3 59.3** 68.4 Presence of mental or SU condition(s) (%) Does not have condition 63.3 58.8 45.6*** Has condition 36.7 41.2 54.4*** Urban or rural residence (%) Lives in an MSA 87.5 92.2** 90.5 Does not live in an MSA 12.5 7.8** 9.5 Number of telehealth visits 1 45.7 43.5 13.2*** 2–3 36.6 36.6 45.8*** 4–5 11.8 9.7 18.1*** 6 or more 5.8 10.1*** 22.5*** Sample size 1,092 1,492 959 Source: Authors' analysis of data from the Health Reform Monitoring Survey, April 2021. Notes: FPL is federal poverty level. SU is substance use. MSA is metropolitan statistical area. In the race or ethnicity category, "other" adults are non-Hispanic/Latinx adults who are not Black or white or are more than one race. Visit modality and number of visits were captured by two survey questions, and a small number of respondents reported they used both phone and video modalities despite having had only one visit. */**/*** Estimate differs significantly from that for the reference group (^) at the 0.10/0.05/0.01 level, using two-tailed t-tests. VARIATION IN TELEHEALTH DURING THE FIST YEAR OF THE PANDEMIC 9 TABLE 3 Characteristics of and Experiences with Telehealth Visits among Adults Ages 18 to 64 Who Had a Telehealth Visit in the Past 12 Months, by Visit Modality, April 2021 Used phone Used video Used phone only^ only and video OOP costs for last telehealth visit (%) No OOP costs 65.8 47.9*** 55.0*** Any OOP costs 34.2 52.1*** 45.0*** $1–20 8.8 11.3** 10.8 $21–50 12.8 20.3*** 17.5** $51–100 5.8 9.0*** 7.4 More than $100 6.8 11.5*** 9.2* Types of health care issues addressed during telehealth visits (%) General preventive or routine care 59.2 48.3*** 63.4* Mental health care or counseling 18.7 27.5*** 40.5*** Treatment or counseling for alcohol or drug use 3.0 1.3** 6.0** COVID-19 screening 15.1 13.2 19.5** New injury, illness, or health problem other than COVID-19 18.4 24.2*** 31.0*** Chronic or ongoing condition 31.5 29.9 47.4*** Other type of care 5.5 4.4 5.5 Share who strongly or somewhat agreed with the following (%) It was easy to schedule telehealth visits at a convenient time 84.2 90.0*** 87.8* Wait times for telehealth visits were shorter than the wait times for similar in-person visits 64.4 68.7** 70.5** Was able to receive needed prescription drug fills through telehealth visits 80.4 83.5* 84.1 Sample size 1,092 1,492 959 Sample size for OOP cost measuresa 1,089 1,481 952 Source: Authors' analysis of data from the Health Reform Monitoring Survey, April 2021. Notes: OOP is out-of-pocket. Respondents who selected "not applicable" when asked about the ability to receive prescription drug fills (181 respondents with only a phone visit, 331 respondents with only a video visit, and 109 respondents with both a phone and a video visit) are excluded from the corresponding measure. a OOP measures exclude people who did not respond. */**/*** Estimate differs significantly from that for the reference group (^) at the 0.10/0.05/0.01 level, using two-tailed t-tests. These patterns were largely similar among elderly adults. However, the differences by race and ethnicity, health status, and the presence of physical conditions were not statistically significant. Additionally, elderly adults with video visits were less likely to have a mental health or substance use condition than those with phone visits (14.9 versus 24.6 percent; table 4). Further, the share of elderly telehealth users reporting out-of-pocket costs and the share using telehealth for mental health care or counseling did not differ significantly by modality (table 5). 10 VARIATION IN TELEHEALTH DURING THE FIST YEAR OF THE PANDEMIC TABLE 4 Characteristics of Adults Ages 65 and Older Who Had a Telehealth Visit in the Past 12 Months, by Visit Modality, April 2021 Used phone Used video Used phone only^ only and video Income (%) At or below 138% of FPL 22.9 9.1*** 16.0 139–399% of FPL 42.3 34.3** 36.9 At or above 400% of FPL 34.8 56.5*** 47.2*** Race or ethnicity (%) White 71.6 72.4 70.7 Black 11.8 9.7 12.2 Hispanic/Latinx 11.5 8.2* 14.4 Other 5.2 9.7 2.7 Age (%) 65–79 79.3 82.9 85.6 80+ 20.7 17.1 14.4 Health status (%) Excellent, very good, or good 74.5 79.8 72.4 Fair or poor 25.4 19.9 26.9 Presence of physical condition(s) (%) Does not have condition 7.7 5.1 3.2** Has condition 92.3 94.9 96.8** Presence of mental or SU condition(s) (%) Does not have condition 75.4 85.1*** 70.8 Has condition 24.6 14.9*** 29.2 Urban or rural residence (%) Lives in an MSA 84.5 91.5*** 89.7 Does not live in an MSA 15.5 8.5*** 10.3 Number of telehealth visits 1 44.1 53.8** 9.8*** 2–3 45.5 34.4*** 55.1 4–5 6.4 7.7 22.6*** 6 or more 3.9 4.1 12.5*** Sample size 682 536 326 Source: Authors' analysis of data from the Health Reform Monitoring Survey, April 2021. Notes: FPL is federal poverty level. MSA is metropolitan statistical area. SU is substance use. In the race or ethnicity category, "other" adults are non-Hispanic/Latinx adults who are not Black or White or are more than one race. Black and white adults are not Hispanic/Latinx. Visit modality and number of visits were captured by two survey questions, and a small number of respondents reported they used both phone and video modalities despite having had only one visit. */**/*** Estimate differs significantly from that for the reference group (^) at the 0.10/0.05/0.01 level, using two-tailed t-tests. VARIATION IN TELEHEALTH DURING THE FIST YEAR OF THE PANDEMIC 11 TABLE 5 Characteristics of and Experiences with Telehealth Visits among Adults Ages 65 and Older Who Had a Telehealth Visit in the Past 12 Months, by Visit Modality, April 2021 Used phone Used video Used both only^ only phone and video OOP costs for last telehealth visit (%) No OOP costs 72.2 67.6 73.2 Any OOP costs 27.8 32.4 26.8 $1–20 10.0 12.3 10.0 $21–50 11.0 13.0 11.3 $51–100 3.2 3.6 3.4 More than $100 3.6 3.5 2.0 Types of health care issues addressed during telehealth visits (%) General preventive or routine care 74.1 65.5*** 78.5 Mental health care or counseling 7.9 6.3 15.1*** Treatment or counseling for alcohol or drug use 0.2 0.4 0.5 COVID-19 screening 9.4 3.7*** 14.5* New injury, illness, or health problem other than COVID-19 18.5 19.4 28.7** Chronic or ongoing condition 42.8 41.7 55.4*** Other type of care 3.3 5.9** 4.7 Share who strongly or somewhat agreed with the following (%) It was easy to schedule telehealth visits at a convenient time 76.6 88.6*** 89.6*** Wait times for telehealth visits were shorter than the wait times for similar in-person visits 60.6 67.8* 69.8** Was able to receive needed prescription drug fills through telehealth visits 78.6 89.5*** 85.4* Sample size 682 536 326 Sample size for OOP cost measuresa 679 533 325 Source: Authors' analysis of data from the Health Reform Monitoring Survey, April 2021. Notes: OOP is out-of-pocket. Respondents who selected "not applicable" when asked about the ability to receive prescription drug fills (110 respondents with only a phone visit, 135 respondents with only a video visit, and 53 with both a phone and a video visit) are excluded from the corresponding measure. a OOP measures exclude people who did not respond. */**/*** Estimate differs significantly from that for the reference group (^) at the 0.10/0.05/0.01 level, using two-tailed t-tests. Access to Telehealth Visits Nearly 6 percent of nonelderly adults reported wanting but not getting a telehealth visit during the first year of the pandemic, and the characteristics of these patients differed significantly (figure 3). Adults with incomes at or exceeding 400 percent of FPL were less likely to report this access problem than those with incomes at or below 138 percent of FPL (3.1 versus 10.4 percent). Additionally, this experience was more common among nonelderly adults who are Black (8.8 percent) and Hispanic/Latinx (8.2 percent) than among those who are white (4.7 percent). 12 VARIATION IN TELEHEALTH DURING THE FIST YEAR OF THE PANDEMIC FIGURE 3 Share of Adults Ages 18 to 64 Who Wanted a Telehealth Visit But Had Not Had One in the Past 12 Months, Overall and by Selected Characteristics, April 2021 All nonelderly adults 5.9% Income At or below 138% of FPL^ 10.4% 139–399% of FPL 6.8%*** At or above 400% of FPL 3.1%*** Race or ethnicity White^ 4.7% Black 8.8%*** Hispanic/Latinx 8.2%*** Other 5.4% Age 18–34^ 6.6% 35–49 5.9% 50–64 5.2%** Health insurance coverage Private^ 4.6% Public 10.6%*** Uninsured 7.3%** Health status Excellent, very good, or good^ 5.0% Fair or poor 11.2%*** Physical condition(s) Does not have condition^ 3.9% Has condition 8.1%*** Mental or SU condition(s) Does not have condition^ 3.6% Has condition 11.9%*** Urban or rural residence Lives in an MSA^ 6.1% Does not live in an MSA 4.7%* URBAN INSTITUTE Source: Authors' analysis of data from the Health Reform Monitoring Survey, April 2021. Notes: FPL is federal poverty level. MSA is metropolitan statistical area. SU is substance use. In the race and ethnicity category, "other" adults are non-Hispanic/Latinx adults who are not Black or white or are more than one race. Black and white adults are not Hispanic/Latinx. Estimates for nonelderly adults with unspecified health insurance coverage are not shown because of small sample size. The 0.4 percent of nonelderly adults who did not report wanting a telehealth visit but not getting one are included in the denominator of these estimates. Access to telehealth also differed by patient health insurance coverage type and health status. Nonelderly adults with public health insurance (10.6 percent) and those without insurance (7.3 percent) were significantly more likely to have wanted but not had a telehealth visit than those with private health insurance coverage (4.6 percent). Nonelderly adults with fair or poor health were also significantly more likely to have had this experience than those in excellent, very good, or good health (11.2 versus 5.0 percent). The data show a similar pattern according to the presence of a physical or mental health condition; adults with either type of condition were more than twice as likely as those VARIATION IN TELEHEALTH DURING THE FIST YEAR OF THE PANDEMIC 13 without such conditions to have wanted but not had a telehealth visit. These patterns are similar among the elderly population (figure 4). FIGURE 4 Share of Adults Ages 65 and Older Who Wanted a Telehealth Visit but Had Not Had One in the Past 12 Months, Overall and by Selected Characteristics, April 2021 All elderly adults 3.6% Income At or below 138% of FPL^ 7.5% 139–399% of FPL 2.5%** At or above 400% of FPL 3.0%** Race or ethnicity White^ 3.3% Black 6.2%* Hispanic/Latinx 5.7% Age 65–79^ 3.3% 80+ 4.8% Health status Excellent, very good, or good^ 2.7% 7.4%*** Fair or poor Physical condition(s) Does not have condition^ 1.4% Has condition 4.0%** Mental or SU condition(s) Does not have condition^ 3.0% Has condition 7.2%*** Urban or rural residence Lives in an MSA^ 4.0% Does not live in an MSA 1.9%** URBAN INSTITUTE Source: Authors' analysis of data from the Health Reform Monitoring Survey, April 2021. Notes: FPL is federal poverty level. MSA is metropolitan statistical area. SU is substance use. Black and white adults are not Hispanic/Latinx. Estimates are not shown for the "other" racial group (adults who are not Hispanic/Latinx, Black, or white and adults identifying as more than one race) and elderly adults with unspecified health insurance coverage because of small sample sizes. The 0.1 percent of elderly adults who did not report that they wanted a telehealth visit but did not get one in the last year are included in the denominator of these estimates. */**/*** Estimate differs significantly from that for the reference group (^) at the 0.10/0.05/0.01 level, using two-tailed t-tests. After adjusting for patient characteristics, these patterns were largely consistent, with a couple exceptions. Nonelderly adults who are not white, Black, or Hispanic/Latinx or who identify as multiple races were significantly more likely than nonelderly white adults to have wanted but not had a telehealth visit (7.1 versus 4.7 percent; appendix figure A.2). Additionally, differences in access to 14 VARIATION IN TELEHEALTH DURING THE FIST YEAR OF THE PANDEMIC telehealth by health insurance coverage type were not significant after adjusting for other characteristics. The most common reported reasons for wanting but not getting a telehealth visit among nonelderly adults were taking too long to get an appointment (30.4 percent); needing a test, treatment, or medication that could only be provided in person (30.3 percent); providers not taking visits by phone or video (28.1 percent); and the unaffordability of the out-of-pocket costs (27.6 percent; data not shown). Discussion When the COVID-19 public health emergency was first declared in March 2020, policymakers and payers implemented many changes to facilitate an abrupt increase in telehealth. For example, Medicare allowed telehealth visits to be initiated in patients' homes, states removed geographic restrictions on providers' licensing for providing telehealth, and many insurers waived out-of-pocket costs for telehealth visits (FSMB 2022; Koma, Cubanski, and Neuman 2021; Mehrotra, Wang, and Snyder 2020). Nearly two years later, many of the temporary policy changes remain in place. Some of these changes have been extended through the end of 2023; however, many other policies will expire when the public health emergency ends, unless lawmakers act to make them permanent.3 Results from our nationally representative survey of US adults indicate that more than one-third of nonelderly adults and nearly half of elderly adults had at least one telehealth visit during the first year of the pandemic. Our estimates of telehealth use are comparable with other recently published estimates, and slight discrepancies between them likely owe to methodological differences (e.g., survey and sample designs and definitions of telehealth; Zhang et al. 2021). Being in fair or poor health, having a physical or mental health condition, having a high income, and having health insurance coverage were associated with having had a telehealth visit. Adults living in metropolitan areas were also much more likely to have had a telehealth visit than those living in nonmetropolitan areas, a finding consistent with other studies documenting urban-rural disparities in telehealth use during the pandemic (Kyle et al. 2021; Patel, Rose, et al. 2021).4 Most adults who used telehealth reported positive experiences with their visits, regardless of health insurance type and telehealth modality, a finding consistent with other research documenting high satisfaction with telehealth (Kyle et al. 2021; Smith and Blavin 2021). However, telehealth visit characteristics differed somewhat by health insurance type and visit modality: Publicly insured nonelderly adults were more likely than those with private insurance to not have faced out-of-pocket VARIATION IN TELEHEALTH DURING THE FIST YEAR OF THE PANDEMIC 15 costs for their visits, suggesting more generous reimbursement for telehealth visits by Medicaid and Medicare plans than by private insurers. Publicly insured nonelderly adults were also more likely to have had a phone visit and were less likely to have had a video visit than those with private insurance, which may reflect differences in the telehealth modalities insurers cover or differences in patient access to video technology or broadband. For nonelderly adults, video visits were also more common among younger adults, white adults, and adults with higher incomes. To the extent that video technology is a more effective tool for providing telehealth, these differences suggest efforts are needed to improve equity in access to video visits. Finally, though most adults did not report issues accessing telehealth, our results highlight disparities in access to telehealth by income and health status. Adults with lower incomes were more likely to have wanted but not had a telehealth visit, a finding consistent with evidence of less telehealth use in low-income communities (Patel, Mehrotra, et al. 2021; Patel, Rose, et al. 2021). Adults in fair or poor health and adults with at least one physical or mental health condition were also much more likely to report trouble accessing telehealth than their counterparts. This group of adults was also the most likely to have used telehealth, suggesting high demand. As one of the first studies to assess telehealth use throughout the first year of the pandemic, this analysis provides important new insights into adults' experiences with telehealth. However, our study has limitations. First, the patterns we observe between adults' characteristics and their telehealth experiences are associations, not causal relationships, and they may be confounded by other unobserved factors. Second, the data reflect self-reported information and may be imperfect because of recall bias. Third, some survey questions required respondents to compare telehealth visits with "similar" in-person visits, and it may be difficult for respondents to make such comparisons because, for example, most tests and procedures can only be performed in person. As policymakers face consequential decisions about the permanence of pandemic-era changes to telehealth regulation, having evidence on the use of and access to telehealth, the characteristics of and patients' experiences with telehealth visits, health outcomes and spending associated with telehealth, and the role of telehealth in improving or perpetuating health inequities is critical. Our findings suggest the overall demand for telehealth visits will likely remain high even after the pandemic subsides, especially if telehealth visits continue to be convenient, easy to schedule, and free at the point of service for most insured people. However, the uneven use of telehealth across patients that we identify should serve as caution. Careful policymaking will be necessary to ensure that increased reliance on telehealth after the public health emergency ends does not further exacerbate existing health disparities.5 Telehealth policies should promote access to telehealth, especially video technology, for people with 16 VARIATION IN TELEHEALTH DURING THE FIST YEAR OF THE PANDEMIC low incomes, people with public health insurance coverage and people who are uninsured, people in poor health, and rural populations. VARIATION IN TELEHEALTH DURING THE FIST YEAR OF THE PANDEMIC 17 Appendix. Regression-Adjusted Results FIGURE A.1 Regression-Adjusted Shares of Nonelderly and Elderly Adults with a Telehealth Visit in the Past 12 months, Overall and by Selected Characteristics, April 2021 Nonelderly adults Income At or below 138% of FPL^ 31.3% 139–399% of FPL 36.6%*** At or above 400% of FPL 41.0%*** Race or ethnicity White^ 35.4% Black 40.3%*** Hispanic/Latinx 43.0%*** Other 34.5% Age 18–34^ 35.4% 35–49 37.7% 50–64 39.3%** Health insurance coverage Private^ 38.3% Public 42.9%*** Uninsured 23.3%*** Health status Excellent, very good, or good^ 36.5% Fair or poor 41.9%*** Physical condition(s) Does not have condition^ 29.5% Has condition 45.8%*** Mental or SU condition(s) Does not have condition^ 30.4% 55.0%*** Has condition Urban or rural residence Lives in an MSA^ 38.6% Does not live in an MSA 28.5%*** 18 APPENDIX Elderly adults Income At or below 138% of FPL^ 37.1% 139–399% of FPL 43.4%* At or above 400% of FPL 53.7%*** Race or ethnicity White^ 43.4% Black 53.5%*** Hispanic/Latinx 60.8%*** Age 65–79^ 47.0% 80+ 42.9%* Health status Excellent, very good, or good^ 43.7% Fair or poor 56.8%*** Physical condition(s) Does not have condition^ 26.8% Has condition 48.9%*** Mental or SU condition(s) Does not have condition^ 43.5% Has condition 60.4%*** Urban or rural residence Lives in an MSA^ 47.8% Does not live in an MSA 37.8%*** URBAN INSTITUTE Source: Authors' analysis of data from the Health Reform Monitoring Survey, April 2021. Notes: FPL is federal poverty level. MSA is metropolitan statistical area. SU is substance use. Nonelderly adults are ages 18 to 64; elderly adults are ages 65 and older. In the race or ethnicity category, "other" adults are non-Hispanic/Latinx adults who are not Black or white or are more than one race. Black and white adults are not Hispanic/Latinx. Estimates are not shown for elderly adults in the "other" racial group and elderly and nonelderly adults with unspecified health insurance coverage because of small sample sizes. The 0.3 percent of nonelderly adults and 0.3 percent of elderly adults who did not report if they had a telehealth visit are included in the denominator of these estimates. Estimates are regression adjusted on the basis of models that control for family income, race and ethnicity, age group, living in or outside a metropolitan statistical area, health status, the presence of physical conditions, and the presence of mental conditions. Additionally, estimates for nonelderly adults control for health insurance coverage. */**/*** Estimate differs significantly from that for the reference group (^) at the 0.10/0.05/0.01 level, using two-tailed t-tests. APPENDIX 19 FIGURE A.2 Regression-Adjusted Shares of Nonelderly and Elderly Adults Reporting They Wanted a Telehealth Visit But Did Not Get One in the Past 12 Months, Overall and by Selected Characteristics, April 2021 Nonelderly adults Income At or below 138% of FPL^ 8.3% 139–399% of FPL 6.8% At or above 400% of FPL 4.1%*** Race or ethnicity White^ 4.7% Black 8.3%*** Hispanic/Latinx 7.6%*** Other 7.1%** Age 18–34^ 6.6% 35–49 6.0% 50–64 5.2%* Health insurance coverage Private^ 5.7% Public 6.6% Uninsured 6.5% Health status Excellent, very good, or good^ 5.6% Fair or poor 7.9%* Physical condition(s) Does not have condition^ 4.3% Has condition 7.6%*** Mental or SU condition(s) Does not have condition^ 3.9% 11.0%*** Has condition Urban or rural residence Lives in an MSA^ 6.2% Does not live in an MSA 4.0%*** 20 APPENDIX Elderly adults Income At or below 138% of FPL^ 6.4% 139–399% of FPL 2.6%** At or above 400% of FPL 3.5% Race or ethnicity White^ 3.4% Black 5.6% Hispanic/Latinx 5.2% Age 65–79^ 3.3% 80+ 5.0% Health status Excellent, very good, or good^ 3.0% Fair or poor 6.3%** Physical condition(s) Does not have condition^ 2.3% Has condition 3.8% Mental or SU condition(s) Does not have condition^ 3.1% 6.4%** Has condition Urban or rural residence Lives in an MSA^ 3.9% Does not live in an MSA 2.4% URBAN INSTITUTE Source: Authors' analysis of data from the Health Reform Monitoring Survey, April 2021. Note: FPL is federal poverty level. MSA is metropolitan statistical area. SU is substance use. Nonelderly adults are ages 18 to 64; elderly adults are ages 65 and older. In the race or ethnicity category, "other" adults are non-Hispanic/Latinx adults who are not Black or white or are more than one race. Black and white adults are not Hispanic/Latinx. Estimates are not shown for elderly adults in the "other" racial group and elderly and nonelderly adults with unspecified health insurance coverage because of small sample sizes. The shares of nonelderly adults (0.4 percent) and elderly adults (0.1 percent) who did not report if they wanted a telehealth visit but did not get one in the last year are included in the denominator of these estimates. Estimates are regression adjusted on the basis of models that control for family income, race and ethnicity, age group, living in or outside a metropolitan statistical area, health status, the presence of physical conditions, and the presence of mental conditions. Additionally, the estimates for nonelderly adults control for health insurance coverage. */**/*** Estimate differs significantly from that for the reference group (^) at the 0.10/0.05/0.01 level, using two-tailed t-tests. APPENDIX 21 Notes 1 Sadiq Y. Patel and Ateev Mehrotra, "The Surge of Telehealth during the Pandemic Is Exacerbating Urban-Rural Disparities in Access to Mental Health Care," Health Affairs Blog, October 7, 2021, https://www.healthaffairs.org/do/10.1377/hblog20211004.155145/full/. 2 We use "Hispanic/Latinx" throughout this brief to reflect the different ways people self-identify. Also, white adults and Black adults did not identify as Hispanic/Latinx. 3 Eric Wicklund, "Telehealth Groups Pressure CMS to Expand Coverage in 2022 Physician Fee Schedule," mHealthIntelligence, September 20, 2021, https://mhealthintelligence.com/news/telehealth-groups-pressure- cms-to-expand-coverage-in-2022-physician-fee-schedule. 4 Patel and Mehrotra, "Surge of Telehealth during the Pandemic Is Exacerbating Urban-Rural Disparities in Access to Mental Health Care." 5 Patel and Mehrotra, "Surge of Telehealth during the Pandemic Is Exacerbating Urban-Rural Disparities in Access to Mental Health Care." 22 NOTES References CCHP (Center for Connected Health Policy). 2021. "COVID-19 Telehealth Coverage Policies." West Sacramento, CA: CCHP. FSMB (Federation of State Medical Boards). 2022. "U.S. States and Territories Modifying Requirements for Telehealth in Response to COVID-19." Euless, TX: FSMB. Koma, Wyatt, Juliette Cubanski, and Tricia Neuman. 2021. "Medicare and Telehealth: Coverage and Use during the COVID-19 Pandemic and Options for the Future." San Francisco: Kaiser Family Foundation. Kyle, Michael A., Robert J. Blendon, Mary G. Findling, and John M. Benson. 2021. "Telehealth Use and Satisfaction among U.S. Households: Results of a National Survey." Journal of Patient Experience 8:1–7. https://doi.org/10.1177/23743735211052737. Mehrotra, Ateev, Michael E. Chernew, David Linetsky, Hilary Hatch, David A. Cutler, and Eric C. Schneider. 2021. "The Impact of COVID-19 on Outpatient Visits in 2020: Visits Remained Stable, Despite a Late Surge in Cases." New York: Commonwealth Fund. Mehrotra, Ateev, Bill Wang, and Gregory Snyder. 2020. "Telemedicine: What Should the Post-pandemic Regulatory and Payment Landscape Look Like?" New York: Commonwealth Fund. Patel, Sadiq Y., Ateev Mehrotra, Haiden A. Huskamp, Lori Uscher-Pines, Ishani Ganguli, and Michael L. Barnett. 2020. "Trends in Outpatient Care Delivery and Telemedicine during the COVID-19 Pandemic in the US." JAMA Internal Medicine 181 (3): 388–91. https://doi.org/10.1001/jamainternmed.2020.5928. ---. 2021. "Variation in Telemedicine Use and Outpatient Care during the COVID-19 Pandemic in the United States." Health Affairs 40 (2): 349–58. https://www.healthaffairs.org/doi/10.1377/hlthaff.2020.01786. Patel, Sadiq Y., Sherri Rose, Michael L. Barnett, Haiden A. Huskamp, Lori Uscher-Pines, and Ateev Mehrotra. 2021. "Community Factors Associated with Telemedicine Use during the COVID-19 Pandemic." JAMA Network Open 4 (5): e2110330. https://doi.org/10.1001/jamanetworkopen.2021.10330. Smith, Laura Barrie, and Fredric Blavin. 2021. "One in Three Adults Used Telehealth during the First Six Months of the Pandemic, but Unmet Needs for Care Persisted." Washington, DC: Urban Institute. Zhang, Donglan, Lu Shi, Xuesong Han, Yan Li, Nahyo A. Jalajel, Sejal Patel, Zhuo Chen, et al. 2021. "Disparities in Telehealth Utilization during the COVID-19 Pandemic: Findings from a Nationally Representative Survey in the United States." Journal of Telemedicine and Telecare (October). https://doi.org/10.1177/1357633X211051677. REFERENCES 23 About the Authors Laura Barrie Smith is a research associate in the Health Policy Center at the Urban Institute. She studies health policy topics related to primary care services delivery, access to care, Medicare and Medicaid, health IT, and the health care workforce. She has extensive experience using large claims and electronic health record datasets in her research. Before joining Urban, Smith worked at the Lewin Group, where she focused on Medicare and Medicaid policy research. Smith holds a BA in mathematics from St. Olaf College and a PhD in health services research, with an emphasis in health economics, from the University of Minnesota. Fredric Blavin is a principal research associate in the Health Policy Center with expertise on a wide range of topics, including private health insurance markets, health care reform, health information technology, provider supply, health care spending, child and maternity health, Medicaid/Children's Health Insurance Program (CHIP) policy, and the Health Insurance Policy Simulation model. In addition, Blavin has extensive survey development experience, including Urban's Health Reform Monitoring Survey and the National Ambulatory Medical Care Survey Physician Workflow Supplements. Before joining Urban, Blavin worked as an economist at the Office of the National Coordinator for Health Information Technology within the US Department of Health and Human Services. Blavin has published widely in peer-reviewed journals on various topics, including the impact of express lane eligibility programs on Medicaid/CHIP enrollment, trends in health care financial burdens and prescription drug spending, measuring and forecasting electronic health record adoption, value-based insurance design, the role of private health insurance in developing countries, and the cost and coverage implications of various state and national health reform policies. Blavin received his PhD in managerial science and applied economics from the University of Pennsylvania in 2011. Claire O'Brien is a research assistant in the Health Policy Center, where she uses quantitative analysis to study the impact of health reforms. This includes using Medicaid claims to evaluate integrated care plans for dually eligible Medicare and Medicaid beneficiaries and to study racial disparities within the Medicaid program. Additionally, O'Brien takes part in the implementation and analysis of the Urban Institute's Health Reform Monitoring Survey. Finally, O'Brien monitors changes in the Affordable Care Act's Marketplaces and models what the effects of a public option would be. O'Brien has a BA in economics and applied math with a minor in poverty studies from the University of Notre Dame. 24 ABOUT THE AUTHORS STATEMENT OF INDEPENDENCE The Urban Institute strives to meet the highest standards of integrity and quality in its research and analyses and in the evidence-based policy recommendations offered by its researchers and experts. We believe that operating consistent with the values of independence, rigor, and transparency is essential to maintaining those standards. As an organization, the Urban Institute does not take positions on issues, but it does empower and support its experts in sharing their own evidence-based views and policy recommendations that have been shaped by scholarship. Funders do not determine our research findings or the insights and recommendations of our experts. Urban scholars and experts are expected to be objective and follow the evidence wherever it may lead. 500 L'Enfant Plaza SW Washington, DC 20024 www.urban.org