Abstract
Background:
Routine ambulatory care is essential for older adults with ADRD to manage their health conditions. The federal government expanded telemedicine coverage to mitigate the impact of the COVID-19 pandemic on ambulatory services, which may provide an opportunity to improve access to care. This study aims to examine differences in telemedicine use for ambulatory services by race, ethnicity, and community-level socioeconomic status among community-dwelling older adults with ADRD.
Methods:
This retrospective cohort study used Medicare claims data between 04/01/2020 and 12/31/2021. We included community-dwelling Medicare fee-for-service beneficiaries aged 65 years and older with ADRD. The outcome variable is individual’s use (yes/no) of telemedicine evaluation and management (tele-EM) visits in each quarter. The key independent variables are race, ethnicity, and community-level socioeconomic status.
Results:
The analytical sample size of the study was 2,068,937, including 9.9% Black, 82.7% White, and 7.4% Hispanic individuals. In general, we observed a decreasing trend of tele-EM use, and the average rate of quarterly tele-EM use was 23.0%. Tele-EM utilization varied by individual race, ethnicity, and community-level socio-economic status. On average, White and Black individuals in deprived communities were 3.5 and 2.4 percentage-points less likely to use tele-EM compared to their counterparts in less-deprived communities (P<0.001). However, Hispanic individuals in deprived communities were 2.4 percentage-points more likely to utilize tele-EM compared to those in less-deprived communities (P<0.001). Additionally, we observed various racial and ethnic differences in telemedicine use in deprived communities versus less-deprived communities.
Conclusions:
We observed various racial and ethnic differences in telemedicine use, both within and between communities by socioeconomic status. Telemedicine is a viable healthcare delivery option that may influence healthcare access for racial and ethnic minorities, and for individuals in socioeconomically deprived communities. Further policies or interventions may be needed to ensure all individuals have equal access to newly available care delivery models.
Keywords: Telemedicine, dementia, COVID-19, racial and ethnic disparity, socioeconomic status
INTRODUCTION
Older adults with Alzheimer’s disease and related dementias (ADRD) commonly experience numerous co-morbid conditions and are at an increased risk of developing psychological symptoms such as depression and anxiety.1-4 Routine ambulatory care services are essential in managing dementia and co-morbid conditions to reduce the risk of adverse health outcomes and improve the quality of life for these individuals.5-8 However, many older adults with ADRD may encounter barriers to timely and appropriate ambulatory care.9-11 Because many of these individuals have cognitive and physical function impairments and rely on caregivers for assistance with daily activities, traveling to in-person appointments may be difficult for them.12
Telemedicine, which allows for a remote provision of medical services through communication technology,13 has the potential to improve access to routine ambulatory services by reducing travel barriers and supporting patients to receive timely care in a comfortable and familiar environment.14,15 However, before the COVID-19 pandemic, Medicare coverage for telemedicine was only allowed in designated rural areas or for particular healthcare venues.16 During the COVID-19 pandemic, the Centers for Medicare and Medicaid Services (CMS) greatly expanded telemedicine coverage for Medicare beneficiaries, lifting the location restrictions and reimbursing healthcare providers for telemedicine at the same rate as in-person services.16,17 As a result, there was a significant increase in the use of telemedicine services after the implementation of this telemedicine policy.18-20
At the same time, concerns have emerged that the telemedicine policy may have different impacts on ambulatory care across racial and ethnic subgroups. Black and Hispanic individuals generally have higher care needs due to a higher disease burden,16,17 but they are more likely to reside in socioeconomically deprived communities and face barriers to health services.23-26 While telemedicine policy may have provided an opportunity to improve care access for these individuals, it may also have limitations due to challenges such as low health literacy, inadequate access to digital devices, or inefficient support systems among this population.27
The current literature has provided mixed findings on the relationship between race, ethnicity, socioeconomic status, and telemedicine use. While some studies found that Black individuals were less likely to use telemedicine compared to their White counterparts at the beginning of the pandemic,28-32 others showed that individuals residing in deprived communities were more likely to use telemedicine compared to those in less deprived communities.20,33 The variation in telemedicine use by socioeconomic statuses across communities may contribute to racial or ethnic disparities, as racial and ethnic minorities are more likely to reside in deprived communities. These mixed findings suggest that more evidence is needed to explore racial and ethnic differences in telemedicine use and the role of the community's socioeconomic status.
Most studies on telemedicine did not focus on older adults with ADRD, who have unique care needs and potentially face greater barriers to accessing care.34 Additionally, no study has examined whether and how racial and ethnic differences in telemedicine use vary by the community's socioeconomic status. Some researchers have suggested that racial or ethnic inequities in healthcare use might be exacerbated in socioeconomically deprived communities as providers who serve minority-concentrated communities may be less able to deal with their care needs,35 while others have argued that racial or ethnic differences may be less in communities with a high concentration of minorities if health providers can better target services for them in these communities.36 Assessing the variation in telemedicine use by communities will provide insight into reducing disparities in access to care. Lastly, most studies on telemedicine have primarily focused on the early stages of the pandemic, whereas the impact of the telemedicine policy on utilization and related racial or ethnic differences may evolved.
Therefore, the main objective of this study was to address these knowledge gaps by examining the trend of ambulatory telemedicine use among community-dwelling older adults with ADRD and evaluating the differences in telemedicine use by individual race and ethnicity and community-level socioeconomic status, for the time from 04/01/2020 to 12/31/2021. We focused on community-dwelling older adults because more than half of individuals with ADRD reside in communities, and they are more likely to experience barriers to ambulatory care services.
METHODS
Data and study cohort
We linked the following 2020-2021 national data: Medicare beneficiary summary files (MBSF), including the base segments, chronic condition (CC) and other chronic condition files (OCC); Medicare claims, including carrier, inpatient, outpatient, and skilled nursing facility files; and the Minimum Data set 3.0. The Neighborhood Atlas Data,37 the American Community Survey (ACS) data,38 CDC COVID-19 infection data,39 Health Resources and Service Administration (HRSA) data40, and Agency for Healthcare Research and Quality (AHRQ) data41 were also linked with those individual-level data by zip-codes or county FIPS codes.
Our study cohort included Medicare fee-for-service (FFS) community-dwelling older adults (i.e., aged 65 and older) with ADRD. The diagnosis of ADRD was determined based on the MBSF CC file.42 The community-dwelling population was defined as those without a nursing home stay for at least 90 days before the baseline date (i.e., 04/01/2020). We then followed this cohort from 04/01/2020 to 12/31/2021. As we were focused on the community-dwelling population, the following-up window ended if an individual died or became a nursing home long-stayer (i.e., those who stayed in nursing homes for more than 100 days), whichever came first. Lastly, we only included non-Hispanic White, non-Hispanic Black, and Hispanic individuals, who accounted for 94.7% of the identified population, to align with our study objective and ensure an adequate sample size for each racial and ethnic subgroup. Race and ethnicity were defined based on MBSF using the Research Triangle Institute (RTI) race code.43
Variables
Outcome
The outcome variable was defined as an individual’s use (yes/no) of telemedicine evaluation and management (tele-EM) visits in each quarter between 4/1/2020 and 12/31/2021. Tele-EM services were determined based on Healthcare Common Procedure Coding System (HCPCS) codes and modifiers in the carrier file. 44-46 As we were focused on ambulatory care services, the outcome variable was restricted to tele-EM services occurring in ambulatory care settings (i.e., not in the hospital, emergency department [ED], or nursing homes). COVID-19-related visits were excluded from the main analysis.
Key independent variables
The key independent variables included individual race and ethnicity (i.e., non-Hispanic White, non-Hispanic Black, and Hispanic), time trends (i.e., quarters), and socioeconomic status of communities (at the zip-code level). The socioeconomic status of the communities was measured by the Area Deprivation Index (ADI).37 ADI is a composite measure that includes factors for the domains of income, education, employment, and housing quality at the census block level. It is frequently used to measure the socioeconomic disadvantage status in healthcare studies.47 ADI was assigned to individuals based on the zip code of their residence.20,48 Based on the literature, we considered a community as socioeconomically deprived if the ADI score is equal to or above 85.46 The time trend (i.e., quarters) was used as a continuous variable.
Other Covariates
We incorporated a set of covariates at both individual and community (i.e., zip-code and county) levels in our analysis. At the individual level, we included age, gender, dual eligibility for Medicare and Medicaid, and chronic conditions (e.g., hypertension, depression, schizophrenia, etc.) based on the MBSF. To further capture individual health conditions, we also considered any ED visits and hospitalizations in the preceding quarter.
We obtained several zip-code and county level covariates as community level characteristics that may influence the use of telemedicine and outpatient services. Based on the ACS data, we included several zip-code-level characteristics, such as the percentage of the population with an education level less than high school, the percentage of Black and Hispanic individuals, the percentage of the households with broadband subscriptions, the percentage of the population whose primary language is not English.38,41,49 Based on the data obtained from AHRQ41 and HRSA40, we included the number of hospital beds and provider shortage level (mental health and primary care providers). Finally, we adjusted for the potential impact of the COVID-19 outbreak by including the county-level COVID-19 infection rate.39
Statistical Method
The analysis was conducted at the person-quarter level. We first described the study cohort's characteristics and their use of tele-EM services over time, by race, ethnicity, and the socioeconomic status of the community (at zip-code level). We then estimated a set of linear probability models with robust standard errors and individual random effects to account for correlation in repeated measures of each individual.
We first conducted pooled analyses using the entire cohort. We started with a model with only individual characteristics (Model 1), and then added community-level characteristics (Model 2), including ADI, other zip-code-level characteristics (e.g., percentages of Black and Hispanic individuals), and county-level characteristics (e.g., the number of hospital beds), to examine whether and how those community characteristics could contribute to individual-level racial and ethnic differences. We also added interactions between individual race and ethnicity, ADI, and time (quarter) to explore how the relationships between tele-EM use and race and ethnicity, and ADI varied by time. Furthermore, to examine whether racial and ethnic differences varied by community-level socioeconomic status, we stratified the analysis by ADI level (>=85 versus not) and repeated the analyses (Models 3 & 4). Lastly, to facilitate interpretation, we computed the average adjusted probabilities using margins and the average marginal effects (AMEs) of tele-EM use by race and ethnicity for the entire study period, for both the pooled and stratified analyses.
We conducted several sensitivity analyses to test the robustness of the findings. First, we included all telemedicine visits for EM, regardless of the location of services (e.g., those that occurred in hospitals or nursing homes) or the primary diagnosis of the visits (i.e., including those with a primary diagnosis of COVID-19 infection) to check whether the result was sensitive to the tele-EM services specification. Second, we added a spline knot in the 1st quarter of 2021, based on the slope change in the descriptive trend plot, to evaluate whether the trend of telemedicine visits was consistent during the study period.
The analyses were performed in SAS 9.4 and STATA16. We used the xtreg command to estimate the models, and the margins command to calculate average adjusted probabilities. This study has been approved by IRB at the investigators’ institution (the institution’s name is blanked out for blind review purposes). We followed the STROBE reporting guideline for this study.
Results
The analytical sample included 2,068,937 community-dwelling Medicare FFS beneficiaries with ADRD and 12,447,209 person-quarters. Among the cohort, 203,452 (9.9%) were Black individuals, 1,711,707 (82.7%) were White individuals and 153,778 (7.4%) were Hispanic individuals. About 6.2% of the identified individuals lived in socioeconomically deprived communities (i.e., ADI>=85). During the study period, the average quarterly prevalence of tele-EM visits was 23.0%, with 22.5%, 23.8%, and 26.9% for White, Black, and Hispanic individuals, respectively. Table 1 listed selected individual and community characteristics by race and ethnicity (full summary of variables in supplementary table 1). For instance, Black (44.9%) and Hispanic (62.9%) individuals were more likely to be Medicare-Medicaid dual eligible than White (18.6%) individuals. Black (16.3%) and Hispanic (7.8%) individuals were more likely to reside in socioeconomically deprived communities than White (4.9%) individuals. The distribution of chronic diseases also varied by race and ethnicity. For example, Black and Hispanic individuals were more likely to have schizophrenia diagnosis (11.2%, 7.9%, and 6.9% for Black, Hispanic, and White individuals, respectively). Around 3% of the sample was excluded because of the missing values in community-level (i.e., zip-code-level or county-level) characteristics (More details can be found in supplementary figure 1).
Table 1.
Comparison of selected individual and community level characteristics by race and ethnicity.
| White individuals (1,711,707) N (%) |
Black individuals (203,452) N (%) |
Hispanic individuals (153,778) N (%) |
Total (2,068,937) N (%) |
|
|---|---|---|---|---|
| Average prevalence of tele-EM use per quarter a | 385,134 (22.5) |
48,422 (23.8) |
41,366 (26.9) |
474,892 (23.0) |
| Individual-level characteristics b | ||||
| Age (Mean [SD]) | 80.6 (10.9) |
76.9 (13.2) |
79.5 (12.0) |
80.1 (11.3) |
| Male | 673,962 (39.4) |
76,475 (37.6) |
56,020 (36.4) |
806,457 (39.0) |
| Female | 1,037,745 (60.6) |
126,977 (62.4) |
97,758 (63.6) |
1,262,480 (61.0) |
| Medicare-Medicaid Dual status | 318,932 (8.6) |
91,363 (44.9) |
96,671 (62.9) |
506,966 (24.5) |
| Hypertension | 1,305,473 (76.3) |
162,204 (79.7) |
112,508 (73.2) |
1,580,185 (76.4) |
| Depression | 773,801 (45.2) |
65,645 (32.3) |
60,628 (39.4) |
900,074 (43.5) |
| Schizophrenia | 118,202 (6.9) |
22,753 (1.2) |
12,125 (7.9) |
153,080 (7.4) |
| Community-level characteristics (all the following variables are constructed at the zip-code level) b, c | ||||
| Deprived communities (ADI>=85) | 83,356 (4.9) |
33,169 (6.3) |
12,010 (7.8) |
128,535 (6.2) |
| Percentage of Black individuals | ||||
| Low (<=2.26) | 630,433 (36.8) |
6,191 (3.1) |
50,044 (32.5) |
686,668 (33.2) |
| Middle (2.26 – 9.23] | 601,575 (35.1) |
25,521 (2.5) |
55,952 (36.4) |
683,048 (33.0) |
| High (>9.23) | 479,699 (28.1) |
171,740 (84.4) |
47,782 (31.1) |
699,221 (33.8) |
| Percentage of Hispanic individuals | ||||
| Low (<= 4.29) | 629,449 (36.8) |
73,114 (35.9) |
6,282 (4.1) |
708,845 (34.3) |
| Medium (4.29 - 12.18] | 614,991 (35.9) |
62,236 (30.6) |
18,983 (12.3) |
696,210 (33.7) |
| High (>12.18) | 467,267 (27.3) |
68,102 (33.5) |
128,513 (83.6) |
663,882 (32.1) |
| Percentage of individuals with education level less than high school | ||||
| Low (<= 6.29) | 644,899 (37.7) |
31,274 (15.4) |
22,135 (14.4) |
698,308 (33.8) |
| Medium (6.29 - 12.93] | 603,487 (35.3) |
59,519 (29.3) |
33,138 (21.5) |
696,144 (33.6) |
| High (>12.93) | 463,321 (27.1) |
112,659 (55.4) |
98,505 (64.1) |
674,485 (32.6) |
| Percentage of households with Broadband subscription | ||||
| Low (<= 79.50) | 519,761 (30.4) |
107,806 (53.0) |
65,462 (42.5) |
693,029 (33.5) |
| Medium (79.50 - 87.30] | 583,924 (34.1) |
54,230 (26.7) |
49,134 (32.0) |
687,288 (33.2) |
| High (>87.30) | 608,022 (35.5) |
41,416 (20.3) |
39,182 (25.5) |
688,620 (33.3) |
| Percentage of individuals with primary language not English | ||||
| Low (<=2.10) | 632,266 (36.9) |
64,029 (31.5) |
8,346 (5.4) |
704,641 (34.1) |
| Medium (2.19 - 5.90] | 624,579 (36.5) |
67,902 (33.4) |
24,694 (16.1) |
717,175 (34.7) |
| High (>5.90) | 454,862 (26.6) |
71,521 (35.2) |
120,738 (78.5) |
647,121 (31.3) |
All the numbers in the cells indicate Number (percentage), except for age, which is presented as mean (standard deviation [SD]).
Tele-EM represents outpatient evaluation and management via telemedicine. The average prevalence of tele-EM use per quarter were calculated by averaging the tele-EM use rates for the quarters during the entire study period.
We only included a selected set of variables in this Table. The distribution of the full set of variables are listed in supplementary table 1.
Among community-level characteristics with three levels, “(a, b]” for the medium level means “a < the variable value <= b”.
Figure 1 illustrates the unadjusted trend of tele-EM use rate by individual race and ethnicity, stratified by community socioeconomic status (i.e., zip-code-level ADI). There was a decreasing trend in tele-EM use during the study period. Black and Hispanic individuals appeared to have higher unadjusted rates of tele-EM use in both socioeconomically deprived and less-deprived communities.
Figure 1.
Unadjusted tele-EM utilization probabilities trends plot by race and ethnicity, stratified by ADI
The pooled analyses: the overall relationship between individual race and ethnicity, community socioeconomic status, and tele-EM use
Table 2 displays the regression results for key variables of interest (full results are presented in supplementary table 2). Model 1 is the pooled model (i.e., including individuals from both deprived and less-deprived communities) with only individual characteristics. At the beginning of the pandemic, there was no statistically significant difference in tele-EM use between Black and White individuals, while Hispanic individuals were 3.8 percentage-points more likely to use tele-EM compared to White individuals (p<0.001, Model 1). However, after adjusting for community-level (i.e., zip-code-level and county-level) characteristics (Model 2), neither racial nor ethnic differences were statistically significant at the beginning of the pandemic. Additionally, community-level (i.e., zip-code-level) socioeconomic status was related to the likelihood of tele-EM use. At the beginning of the pandemic, individuals living in deprived communities were less likely to have tele-EM visits compared to those in less-deprived communities (−2.5 percentage-points, P<0.001, Model 2).
Table 2.
Regression results from linear probability models: pooled and stratified analysis
| Pooled analyses | Stratified analyses | |||
|---|---|---|---|---|
| Model 1a | Model 2 b | Model 3 c (ADI<85) |
Model 4 c (ADI>=85) |
|
| Time trend (quarters) | −3.6d | −3.6 d | −3.6 d | −2.6 d |
| Individual race and ethnicity (White individuals as the reference) | ||||
| Black | −0.1 | −0.2 | −0.6 d | 2.4 d |
| Hispanic | 3.8 d | −0.1 | −0.7 d | 7.8 d |
| Community socioeconomic status (less-deprived community, ADI<85, as the reference) | ||||
| ADI>=85 | - | −2.5 d | - | - |
| Variations in time trends by subgroups: interactions between race, ethnic, and community socioeconomic status with time trend | ||||
| Black×Time | 0.1 | 0.0 | 0.1 d | −0.6 d |
| Hispanic×Time | 0.1 d | 0.1 d | 0.1 d | −0.8 d |
| ADI>=85×Time | - | 0.6 d | - | - |
The estimations were based on linear probability models with individual random effects and robust standard errors. The models were estimated in STATA 16 using command xtreg.
Numbers in the cells represent percentage-points changes, which were calculated as probabilities x100. We only presented the variables of interest in Table-2. The full model results can be found in supplementary table 2.
Symbol “-” in the cells means the estimation is not applicable because the variable is not in the model.
Model 1 accounted for individual-level characteristics, without community-level (zip-code-level or county-level) variables. Individual covariates included socio-demographic characteristics (e.g., age, gender, Medicare-Medicaid dual status, etc.), chronic conditions (e.g., hypertension, depression, schizophrenia, etc.), and prior quarter health care utilization (i.e., any ED visits, hospitalizations).
Model 2 accounted for the variables in Model 1 and additional community-level characteristics (e.g., percentage of Black individuals, percentage of Hispanic individuals, percentage of households with broadband subscriptions, etc.).
Model 3 and model 4 were stratified analysis by zip-code-level ADI and accounted for individual-level characteristics and other community-level (i.e., zip-code-level and county-level) characteristics.
P<0.001.
There was a declining trend (−3.6 percentage-points per quarter, P<0.001, Model 2) in tele-EM use throughout the pandemic; however, this trend was less pronounced for Hispanic individuals than for White individuals (0.1 percentage-points less per quarter, P<0.001, Model 2) and for those in socioeconomically deprived communities compared to those in less-deprived communities (0.6 percentage-points less per quarter, P<0.001, Model 2).
Stratified analyses: variations in racial and ethnic differences in tele-EM use by community socioeconomic status
The stratified analyses (Model 3 and 4) in Table 2 further revealed that racial and ethnic differences in tele-EM use varied by community socioeconomic status. Specifically, at the beginning of the pandemic, Black (2.4 percentage-points, p<0.001, Model 4) and Hispanic individuals (7.8 percentage-points, p<0.001, Model 4) had higher probabilities of using tele-EM compared to White individuals in socioeconomically deprived communities, but lower probabilities of using tele-EM (Black: −0.6 percentage-points, p<0.001; Hispanic: −0.7 percentage-points, p<0.001, Model 3) compared to their White counterparts within less-deprived communities.
Tele-EM use declined in both socioeconomically deprived and less-deprived communities during the later stages of the pandemic. For White individuals, the decline was steeper for those residing in less-deprived communities than those in deprived communities (3.6 [Model 3] versus 2.6 [Model 4] percentage-points per quarter, P<0.001). However, the rate of decline for Black and Hispanic individuals was comparable in both deprived and less-deprived communities. For instance, for Black individuals, the reduction was 3.2 percentage-points per quarter [−2.6-0.6] for those in deprived communities, and 3.5 percentage-points per quarter [−3.6+0.1]) for those in less-deprived communities. Similarly, for Hispanics, the decrease was 3.4 and 3.5 percentage-points per quarter for deprived and less-deprived communities, respectively.
We plot the adjusted probabilities of tele-EM use in each quarter, by each racial and ethnic subgroup in deprived and less-deprived communities in Figure 2. Consistent with the previous findings, the trends of tele-EM use were similar across the three racial and ethnic subgroups in less-deprived communities; while in deprived communities, Hispanic individuals were more likely to use tele-EM than White and Black individuals.
Figure 2.
Adjusted tele-EM utilization probabilities trends plot by race and ethnicity, stratified by ADI
Average adjusted probabilities of tele-EM use throughout the pandemic, by individual race and ethnicity and community socioeconomic status
To summarize the findings, we calculated the average adjusted probabilities (i.e., margins) for each race and ethnic subgroup and average racial and ethnic differences (i.e., AMEs) in tele-EM use throughout the entire study period (i.e., across the entire 7 quarters), for the pooled analysis as well as the stratified analysis (Table 3). In the pooled analysis, before adjusting for community-level (i.e., zip-code level and county level) characteristics (Model 1), there was no statistically significant difference in the probability of telemedicine use between Black and White individuals, while Hispanic individuals were slightly more likely to use tele-EM on average (0.4 percentage-points, P <0.001). After accounting for community characteristics (Model 2), Black individuals were slightly less likely to use tele-EM by an average of 0.2 percentage-points (P <0.01) than White individuals. However, the ethnic difference was not statistically significant. This suggests that community-level characteristics may influence the observed racial and ethnic differences in tele-EM use.
Table 3.
Average adjusted probabilities in tele-EM use by race, ethnicity, and socioeconomic status for the entire study period.
| All individuals (Model 1) |
All individuals (Model 2) |
Individuals in communities with ADI<85 (Model 3) |
Individuals in communities with ADI>=85 (Model 4) |
Differences in probabilities between individuals in communities with ADI<85 versus >=85a |
|
|---|---|---|---|---|---|
| White individuals | 23.5c | 23.6 c | 23.7 c | 20.2 c | 3.5 c |
| Black individuals | 23.5 c | 23.4 c | 23.3 c | 20.9 c | 2.4 c |
| Hispanic individuals | 23.9 c | 23.7 c | 23.4 c | 25.8 c | −2.4 c |
| Differences in probabilities between Black and White individuals b | 0.0 | −0.2 d | −0.4 c | 0.7 c | |
| Differences in probabilities between Hispanic and White individuals b | 0.4 c | 0.1 | −0.3 c | 5.6 c |
Numbers in the cell indicate percentage-points, which were calculated as probabilities x100. The average adjusted probabilities for each group of population were calculated using margins for the entire study period based on the models listed in Table 2. The average adjusted probabilities were estimated in STATA 16 using command margins.
These differences were calculated as the differences in the average adjusted probabilities of tele-EM use between those in less deprived communities (i.e., zip-code-level areas) (based on Model 3) and those in deprived communities (based on Model 4), for each race and ethnicity.
These differences are average marginal effects (AMEs) calculated for each model (Models1-4), which were the differences in the average adjusted probabilities of tele-EM use across race and ethnicity.
P<0.001.
P<0.01.
The stratified analyses illustrated that the average probabilities of tele-EM use and racial and ethnic differences differed by community socioeconomic status (Table 3). For instance, in less deprived communities (Model 3), Black and Hispanic individuals were found to be 0.3-0.4 percentage-points less likely to utilize tele-EM compared to their White counterparts (P<0.001). However, in deprived communities (M4), the probabilities of tele-EM use for Black and Hispanic individuals were 0.7 and 5.6 percentage-points higher, respectively, compared to their White counterparts (P<0.001). (Table 3).
Furthermore, notable differences in tele-EM utilizations were observed when comparing deprived communities to non-deprived communities. In deprived communities, both White and Black individuals exhibited lower likelihoods of tele-EM use compared to their counterparts in less-deprived communities (−3.5 and −2.4 percentage-points differences for White and Black individuals, respectively, P<0.001). However, Hispanic individuals in deprived communities were more likely to utilize tele-EM compared to those in less-deprived communities (with a difference of 2.4 percentage-points, P<0.001).
The results of the first sensitivity analyses, which included all tele-EM use visits for evaluation and management, regardless of the location of service, were consistent with the main analyses. The sensitivity analysis with an additional knot at the 2021 Q1 showed consistent results of the trend in telemedicine use.
Discussion
In this study, we examined ambulatory care telemedicine use among community-dwelling older adults with ADRD after the telemedicine expansion policy and evaluated variations by individual race and ethnicity and community socioeconomic status. On average, more than 20% of community-dwelling individuals with ADRD used tele-EM during the study period. We observed that tele-EM use differed by race, ethnicity, and community of residence socioeconomic status. Additionally, we found that community socioeconomic status could contribute to racial and ethnic differences in tele-EM use.
While several studies have raised concerns about the potential exacerbation of racial and ethnic disparities after the expansion of telemedicine services,27,32,50 we did not observe a consistent pattern in racial or ethnic differences in telemedicine use among older adults with ADRD. More specifically, within less-deprived communities, on average, Black and Hispanic individuals were slightly less likely to use telemedicine compared to their White counterparts throughout the study period. Several factors may have contributed to these racial and ethnic differences. For instance, Black and Hispanic individuals were less likely to have broadband subscriptions at home,51,52 and they may also have cultural barriers. For instance, they were more likely to have privacy concerns related to the presence of personal information using the internet and have mistrust towards healthcare innovations compared to White individuals.34,53,54 On the other hand, we found that both Black and Hispanic individuals were more likely to use telemedicine than their White counterparts within socioeconomically deprived communities. Black and Hispanic residents in deprived communities may have greater barriers to accessing care or providers who can accommodate their needs and thus have a higher need for telemedicine use.25,55
Furthermore, our findings from both the pooled analyses and stratified analyses indicate a sizable variation in telemedicine use between deprived and less-deprived communities and highlight the role of community in modifying racial and ethnic differences. Specifically, on average, both White and Black individuals in deprived communities were less likely to use telemedicine than those in less-deprived communities. These findings align with prior studies 31,56 and suggest that limited access to digital devices, inadequate support systems, lower education levels, or digital literacy in deprived communities may have contributed to a lower rate of telemedicine adoption. In contrast, an interesting finding emerged regarding the higher probability of telemedicine use among Hispanic individuals in deprived communities than those in less-deprived communities. Hispanic individuals in deprived communities may have less proficiency in English and less access to Hispanic physicians who understand Hispanic culture. Telemedicine may be able to better facilitate their access to linguistically appropriate services, by connecting individuals to bilingual providers who may better address their needs but are not available in their communities. This observation further supports the notion that telemedicine has the potential to improve access to healthcare providers who can cater to the specific needs of certain populations.
Lastly, we observed fluctuation in telemedicine use during the pandemic. There is an overall decreasing trend in telemedicine use during the later stages of the pandemic. Individuals may have chosen to return to in-person care following the wide availability of the COVID-19 vaccine and the significant decrease in COVID-19-related hospitalizations and mortality rates.57 However, such trends varied by individual race, ethnicity, and community. For instance, at the beginning of the pandemic, individuals in deprived communities were less likely to use telemedicine than those in less-deprived communities, but they had a smaller decreasing trend of telemedicine use afterward. The findings for Black and Hispanic individuals in less-deprived communities showed a similar pattern. The findings suggest that these individuals may have delayed telemedicine adoption, despite the significant need for remote healthcare services at the beginning of the pandemic. Delayed access to healthcare services, including telemedicine, may result in worse health outcomes for older adults with ADRD. Policymakers and healthcare providers may need to offer additional support and assistance to socioeconomically disadvantaged communities and racial or ethnic minority populations to ensure equitable access to health services. In addition, the lower decline in telemedicine use in deprived communities compared to less-deprived communities may suggest that telemedicine can potentially improve healthcare accessibility in underprivileged communities, even as the overall demand for telemedicine services diminishes.
Limitations
Our study has several limitations. Firstly, this analysis was based on claims data which, while covering a broad population, might not adequately capture the full spectrum of individuals' health conditions or healthcare needs. To mitigate this, we included prior healthcare utilizations in our model to further adjust for individual health conditions. It's also important to note that the claims data might not accurately represent the actual prevalence of certain conditions due to potential disparities in disease diagnosis, particularly in psychiatric conditions.58 Nonetheless, our study's findings still offer meaningful insights into disparities in telemedicine utilization considering the presence or absence of these health conditions. Secondly, the population is restricted to Medicare fee-for-service enrollees with a diagnosis of ADRD. Thus, the findings may not be generalized to those not enrolled in Medicare fee-for-service plans or populations without ADRD. Thirdly, the included zip-code-level and county-level variables may not sufficiently represent the characteristics of individuals. Lastly, the study period ended in December 2021. There may be further changes in telemedicine use in the later phases. Nevertheless, it is still important to evaluate its impact on this vulnerable population during this study period, as it would shed light on further policy modifications and interventions to improve health equity, especially during a public health crisis.
Conclusion
The COVID-19 pandemic has made telemedicine a viable healthcare delivery option that may enhance healthcare access for racial and ethnic minorities. The uptake of telemedicine has been different across population subgroups. The findings highlight the need for further research to better understand factors that influence telemedicine utilization among older adults with ADRD by racial and ethnic groups and socioeconomic status of the community. Furthermore, policies or interventions may be needed to ensure that all individuals have equal access to newly available care delivery models.
Supplementary Material
Key points
We observed variations in telemedicine use between socioeconomically deprived versus less-deprived communities among community-dwelling older adults with ADRD between April 2020 and December 2021.
Racial and ethnic differences in telemedicine use varied by community socioeconomic status.
Telemedicine may have different impacts on diverse populations and may improve healthcare access for some of the underserved populations.
Why does this matter
Understanding the disparities in the utilization of telemedicine may help policymakers address the unique needs and challenges experienced by diverse Alzheimer’s disease and related dementias (ADRD) populations.
Acknowledgements
Funding source: The National Institute on Aging (NIA). RF1AG063811 & RF1AG073052
Footnotes
The authors have no conflicts of interest.
The paper has been presented at Academy Health Conference 2023.
References
- 1.Chronic Diseases and Dementia. Alzheimer’s Disease and Dementia. Accessed March 10, 2023. https://alz.org/professionals/public-health/public-health-topics/chronic-diseases
- 2.Snowden MB, Steinman LE, Bryant LL, et al. Dementia and co-occurring chronic conditions: a systematic literature review to identify what is known and where are the gaps in the evidence? Int J Geriatr Psychiatry. 2017;32(4):357–371. doi: 10.1002/gps.4652 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Lyketsos CG, Carrillo MC, Ryan JM, et al. Neuropsychiatric symptoms in Alzheimer’s disease. Alzheimers Dement J Alzheimers Assoc. 2011;7(5):532–539. doi: 10.1016/j.jalz.2011.05.2410 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Onyike CU. Psychiatric Aspects of Dementia. Contin Lifelong Learn Neurol. 2016;22(2 Dementia):600–614. doi: 10.1212/CON.0000000000000302 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Thyrian JR, Hertel J, Wucherer D, et al. Effectiveness and Safety of Dementia Care Management in Primary Care: A Randomized Clinical Trial. JAMA Psychiatry. 2017;74(10):996–1004. doi: 10.1001/jamapsychiatry.2017.2124 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Chao YH, Huang WY, Tang CH, et al. Effects of continuity of care on hospitalizations and healthcare costs in older adults with dementia. BMC Geriatr. 2022;22(1):724. doi: 10.1186/s12877-022-03407-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Klein OA, Boekholt M, Afrin D, et al. Effectiveness of a digitally supported care management programme to reduce unmet needs of family caregivers of people with dementia: study protocol for a cluster randomised controlled trial (GAIN). Trials. 2021;22(1):401. doi: 10.1186/s13063-021-05290-w [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Cerejeira J, Lagarto L, Mukaetova-Ladinska EB. Behavioral and Psychological Symptoms of Dementia. Front Neurol. 2012;3:73. doi: 10.3389/fneur.2012.00073 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Engelsma T, Jaspers MWM, Peute LW. Considerate mHealth design for older adults with Alzheimer’s disease and related dementias (ADRD): A scoping review on usability barriers and design suggestions. Int J Med Inf. 2021;152:104494. doi: 10.1016/j.ijmedinf.2021.104494 [DOI] [PubMed] [Google Scholar]
- 10.Lavingia R, Jones K, Asghar-Ali AA. A Systematic Review of Barriers Faced by Older Adults in Seeking and Accessing Mental Health Care. J Psychiatr Pract. 2020;26(5):367–382. doi: 10.1097/PRA.0000000000000491 [DOI] [PubMed] [Google Scholar]
- 11.Jacobson M, Joe E, Zissimopoulos J. Barriers to seeking care for memory problems: A vignette study. Alzheimers Dement Transl Res Clin Interv. 2022;8(1):e12238. doi: 10.1002/trc2.12238 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Goldberg EM, Jiménez FN, Chen K, et al. Telehealth was beneficial during COVID-19 for older Americans: A qualitative study with physicians. J Am Geriatr Soc. 2021;69(11):3034–3043. doi: 10.1111/jgs.17370 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.van Dyk L. A Review of Telehealth Service Implementation Frameworks. Int J Environ Res Public Health. 2014;11(2):1279–1298. doi: 10.3390/ijerph110201279 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Patel SY, Mehrotra A, Huskamp HA, Uscher-Pines L, Ganguli I, Barnett ML. Variation In Telemedicine Use And Outpatient Care During The COVID-19 Pandemic In The United States. Health Aff (Millwood). 2021;40(2):349–358. doi: 10.1377/hlthaff.2020.01786 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Brotman JJ, Kotloff RM. Providing Outpatient Telehealth in the United States: Before and During COVID-19. Chest. Published online November 25, 2020. doi: 10.1016/j.chest.2020.11.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.MEDICARE TELEMEDICINE HEALTH CARE PROVIDER FACT SHEET ∣ CMS. Accessed October 15, 2020. https://www.cms.gov/newsroom/fact-sheets/medicare-telemedicine-health-care-provider-fact-sheet [Google Scholar]
- 17.Weigel G, May 11 MFP, 2020. Opportunities and Barriers for Telemedicine in the U.S. During the COVID-19 Emergency and Beyond. KFF. Published May 11, 2020. Accessed December 16, 2020. https://www.kff.org/womens-health-policy/issue-brief/opportunities-and-barriers-for-telemedicine-in-the-u-s-during-the-covid-19-emergency-and-beyond/ [Google Scholar]
- 18.Telemedicine: What Should the Post-Pandemic Regulatory and Payment Landscape Look Like? ∣ Commonwealth Fund. Accessed March 10, 2023. https://www.commonwealthfund.org/publications/issue-briefs/2020/aug/telemedicine-post-pandemic-regulation [Google Scholar]
- 19.Chaves L. Telehealth Expansion in Medicare. CareJourney. Published October 21, 2020. Accessed March 10, 2023. https://carejourney.com/telehealth-expansion-in-medicare-policy-changes-recent-trends-in-adoption-and-future-impact/ [Google Scholar]
- 20.Bose S, Dun C, Zhang GQ, Walsh C, Makary MA, Hicks CW. Medicare Beneficiaries In Disadvantaged Neighborhoods Increased Telemedicine Use During The COVID-19 Pandemic. Health Aff (Millwood). 2022;41(5):635–642. doi: 10.1377/hlthaff.2021.01706 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Novak P, Chu J, Ali MM, Chen J. Racial and Ethnic Disparities in Serious Psychological Distress among Those with Alzheimer’s Disease and Related Dementias. Am J Geriatr Psychiatry Off J Am Assoc Geriatr Psychiatry. 2020;28(4):478–490. doi: 10.1016/j.jagp.2019.08.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Gupta S. Racial and ethnic disparities in subjective cognitive decline: a closer look, United States, 2015–2018. BMC Public Health. 2021;21(1):1173. doi: 10.1186/s12889-021-11068-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Brown EJ, Polsky D, Barbu CM, Seymour JW, Grande D. Racial Disparities In Geographic Access To Primary Care In Philadelphia. Health Aff Proj Hope. 2016;35(8):1374–1381. doi: 10.1377/hlthaff.2015.1612 [DOI] [PubMed] [Google Scholar]
- 24.Singh GK, Daus GP, Allender M, et al. Social Determinants of Health in the United States: Addressing Major Health Inequality Trends for the Nation, 1935-2016. Int J MCH AIDS. 2017;6(2):139–164. doi: 10.21106/ijma.236 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Kirby JB, Kaneda T. Neighborhood socioeconomic disadvantage and access to health care. J Health Soc Behav. 2005;46(1):15–31. doi: 10.1177/002214650504600103 [DOI] [PubMed] [Google Scholar]
- 26.Hood CM, Gennuso KP, Swain GR, Catlin BB. County Health Rankings: Relationships Between Determinant Factors and Health Outcomes. Am J Prev Med. 2016;50(2):129–135. doi: 10.1016/j.amepre.2015.08.024 [DOI] [PubMed] [Google Scholar]
- 27.Roberts ET, Mehrotra A. Assessment of Disparities in Digital Access Among Medicare Beneficiaries and Implications for Telemedicine. JAMA Intern Med. 2020;180(10):1386–1389. doi: 10.1001/jamainternmed.2020.2666 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Pierce RP, Stevermer JJ. Disparities in the use of telehealth at the onset of the COVID-19 public health emergency. J Telemed Telecare. 2023;29(1):3–9. doi: 10.1177/1357633X20963893 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Samson LW, Tarazi W, Turrini G, Sheingold S. Medicare Beneficiaries’ Use of Telehealth in 2020: Trends by Beneficiary Characteristics and Location. Published online 2021. [Google Scholar]
- 30.Adepoju OE, Chae M, Ojinnaka CO, Shetty S, Angelocci T. Utilization Gaps During the COVID-19 Pandemic: Racial and Ethnic Disparities in Telemedicine Uptake in Federally Qualified Health Center Clinics. J Gen Intern Med. 2022;37(5):1191. doi: 10.1007/s11606-021-07304-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Eberly LA, Kallan MJ, Julien HM, et al. Patient Characteristics Associated With Telemedicine Access for Primary and Specialty Ambulatory Care During the COVID-19 Pandemic. JAMA Netw Open. 2020;3(12):e2031640. doi: 10.1001/jamanetworkopen.2020.31640 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Hsiao V, Chandereng T, Lankton RL, et al. Disparities in Telemedicine Access: A Cross-Sectional Study of a Newly Established Infrastructure during the COVID-19 Pandemic. Appl Clin Inform. 2021;12(3):445–458. doi: 10.1055/s-0041-1730026 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Katz AJ, Haynes K, Du S, Barron J, Kubik R, Chen RC. Evaluation of Telemedicine Use Among US Patients With Newly Diagnosed Cancer by Socioeconomic Status. JAMA Oncol. 2022;8(1):161–163. doi: 10.1001/jamaoncol.2021.5784 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Yee V, Bajaj SS, Stanford FC. Paradox of telemedicine: building or neglecting trust and equity. Lancet Digit Health. 2022;4(7):e480–e481. doi: 10.1016/S2589-7500(22)00100-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Auchincloss AH, Van Nostrand JF, Ronsaville D. Access to health care for older persons in the United States: personal, structural, and neighborhood characteristics. J Aging Health. 2001;13(3):329–354. doi: 10.1177/089826430101300302 [DOI] [PubMed] [Google Scholar]
- 36.Beckett MK, Martino SC, Agniel D, et al. Distinguishing neighborhood and individual social risk factors in health care. Health Serv Res. 2022;57(3):458–471. doi: 10.1111/1475-6773.13884 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Kind AJH, Buckingham WR. Making Neighborhood-Disadvantage Metrics Accessible — The Neighborhood Atlas. N Engl J Med. 2018;378(26):2456–2458. doi: 10.1056/NEJMp1802313 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Bureau UC. American Community Survey (ACS). Census.gov. Accessed March 10, 2023. https://www.census.gov/programs-surveys/acs [Google Scholar]
- 39.CDC. COVID Data Tracker. Centers for Disease Control and Prevention. Published March 28, 2020. Accessed March 7, 2022. https://covid.cdc.gov/covid-data-tracker [Google Scholar]
- 40.Scoring Shortage Designations ∣ Bureau of Health Workforce. Accessed March 10, 2023. https://bhw.hrsa.gov/workforce-shortage-areas/shortage-designation/scoring
- 41.Agency for Healthcare Research and Quality (AHRQ). Accessed October 29, 2021. https://www.ahrq.gov/ [Google Scholar]
- 42.Alzheimer’s Disease and Related Disorders or Senile Dementia End-of-Year Indicator ∣ ResDAC. Accessed July 3, 2023. https://resdac.org/cms-data/variables/mbsf-27-cc/alzheimers-disease-and-related-disorders-or-senile-dementia-end-year-indicator [Google Scholar]
- 43.Research Triangle Institute (RTI) Race Code ∣ ResDAC. Accessed January 6, 2024. https://resdac.org/cms-data/variables/research-triangle-institute-rti-race-code [Google Scholar]
- 44.List of Telehealth Services ∣ CMS. Accessed December 3, 2020. https://www.cms.gov/Medicare/Medicare-General-Information/Telehealth/Telehealth-Codes [Google Scholar]
- 45.Telemedicine CPT & HCPCS Level II Codes & Modifiers ∣ AASM. Accessed August 15, 2022. https://aasm.org/clinical-resources/coding-reimbursement/telemedicine-codes/ [Google Scholar]
- 46.CPT® Code - Evaluation and Management Services 99091-99499 - Codify by AAPC. Accessed August 13, 2022. https://www.aapc.com/codes/cpt-codes-range/99091-99499
- 47.Powell WR, Sheehy AM, Kind AJH. The Area Deprivation Index Is The Most Scientifically Validated Social Exposome Tool Available For Policies Advancing Health Equity. Health Aff Forefr. doi: 10.1377/forefront.20230714.676093 [DOI] [Google Scholar]
- 48.Chang HY, Hatef E, Ma X, Weiner JP, Kharrazi H. Impact of Area Deprivation Index on the Performance of Claims-Based Risk-Adjustment Models in Predicting Health Care Costs and Utilization. Popul Health Manag. 2021;24(3):403–411. doi: 10.1089/pop.2020.0135 [DOI] [PubMed] [Google Scholar]
- 49.Bureau UC. Computer and Internet Use. Census.gov. Accessed March 10, 2023. https://www.census.gov/programs-surveys/acs/ [Google Scholar]
- 50.Truong M, Yeganeh L, Cook O, Crawford K, Wong P, Allen J. Using telehealth consultations for healthcare provision to patients from non-Indigenous racial/ethnic minorities: a systematic review. J Am Med Inform Assoc. 2022;29(5):970–982. doi: 10.1093/jamia/ocac015 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Haynes N, Ezekwesili A, Nunes K, Gumbs E, Haynes M, Swain J. “Can you see my screen?” Addressing Racial and Ethnic Disparities in Telehealth. Curr Cardiovasc Risk Rep. 2021;15(12):23. doi: 10.1007/s12170-021-00685-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Atske S, Perrin A. Home broadband adoption, computer ownership vary by race, ethnicity in the U.S. Pew Research Center. Accessed December 15, 2023. https://www.pewresearch.org/short-reads/2021/07/16/home-broadband-adoption-computer-ownership-vary-by-race-ethnicity-in-the-u-s/ [Google Scholar]
- 53.White-Williams C, Liu X, Shang D, Santiago J. Use of Telehealth Among Racial and Ethnic Minority Groups in the United States Before and During the COVID-19 Pandemic. Public Health Rep. 2022;138(1):149–156. doi: 10.1177/00333549221123575 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.George S, Hamilton A, Baker RS. How Do Low-Income Urban African Americans and Latinos Feel about Telemedicine? A Diffusion of Innovation Analysis. Int J Telemed Appl. 2012;2012:715194. doi: 10.1155/2012/715194 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Khanijahani A, Iezadi S, Gholipour K, Azami-Aghdash S, Naghibi D. A systematic review of racial/ethnic and socioeconomic disparities in COVID-19. Int J Equity Health. 2021;20(1):248. doi: 10.1186/s12939-021-01582-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Zhai Y. A Call for Addressing Barriers to Telemedicine: Health Disparities during the COVID-19 Pandemic. Psychother Psychosom. Published online June 4, 2020:1–3. doi: 10.1159/000509000 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Watson OJ, Barnsley G, Toor J, Hogan AB, Winskill P, Ghani AC. Global impact of the first year of COVID-19 vaccination: a mathematical modelling study. Lancet Infect Dis. 2022;22(9):1293–1302. doi: 10.1016/S1473-3099(22)00320-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Office of the Surgeon General (US), Center for Mental Health Services (US), National Institute of Mental Health (US). Mental Health: Culture, Race, and Ethnicity: A Supplement to Mental Health: A Report of the Surgeon General. Substance Abuse and Mental Health Services Administration (US); 2001. Accessed January 6, 2024. http://www.ncbi.nlm.nih.gov/books/NBK44243/ [PubMed] [Google Scholar]
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