Literature DB >> 35720763

The Role of Social Support in Telehealth Utilization Among Older Adults in the United States During the COVID-19 Pandemic.

Grace S Chung1, Chad S Ellimoottil2, Jeffrey S McCullough1.   

Abstract

Background: Older adults may experience a significant digital divide and need support with using technology to transition to telehealth. This study examines the role of social support for telehealth utilization among older adults during the COVID-19 pandemic. Materials and
Methods: We used data from the COVID-19 Sample Person Interview to the National Health and Aging Trends Study. Using logistic regression, we measured the association between telehealth utilization and social support.
Results: Nearly one in five respondents used telehealth during the COVID-19 pandemic (weighted %: 20.6 [585/3188]). Currently living with family or friends and receipt of technical support were associated with telehealth utilization. Among residents of an assisted living facility, those who received communications technology support from the facility were more likely to use telehealth.
Conclusion: Health care providers and policies should aim to reduce barriers to telehealth among older adults, with efforts such as digital literacy support and training. © Grace S. Chung et al., 2021; Published by Mary Ann Liebert, Inc.

Entities:  

Keywords:  COVID-19; aging; social support; telehealth

Year:  2021        PMID: 35720763      PMCID: PMC9049795          DOI: 10.1089/tmr.2021.0025

Source DB:  PubMed          Journal:  Telemed Rep        ISSN: 2692-4366


Introduction

Older adults face barriers to health care access during the COVID-19 pandemic. Since COVID-19, many providers have become more accessible through telehealth, yet older adults use less telemedicine.[1,2] They may experience a significant digital divide and need support with using technology to transition to telehealth.[3] This study examines the role of social support for telehealth utilization among older adults during the COVID-19 pandemic using the National Health and Aging Trends Study (NHATS) COVID-19 supplement.

Methods

We used data from the COVID-19 Sample Person Interview to NHATS and demographic data from Rounds 9 and 10.[4] These data were from 3,188 participants aged 70 years and older. COVID-19 questionnaires were mailed from the end of June 2020 through October 2020, and collection continued through January 2021.[5] The unweighted response rate was 83.5%. NHATS is sponsored by the National Institute on Aging (grant number NIA U01AG32947) and was conducted by Johns Hopkins University. We measured the association between telehealth utilization and social support, conditional upon respondent characteristics. Our first model only included respondent characteristics, specifically age, gender, race/ethnicity, income, and presence of any chronic conditions associated with high risk for severe COVID-19. The second model controls for technological skill, measured by frequent use of e-mails or texts. The third model incorporates social support metrics: currently living with family or friends and receipt of support with learning a new technology or program to go online. Finally, among residents in an assisted living facility, we looked at communications technology support from the facility as a predictor. All models were estimated by logistic regression using the survey's weights.[6,7] Marginal effects and standard errors (SEs) are reported.* This study was exempt from institutional review board review.

Results

Nearly one in five respondents used telehealth during the COVID-19 pandemic (weighted %: 20.6 [585/3188]) (Table 1). Model 1 shows that compared with 70–75-year olds, the probability of telehealth utilization is lower by 6 percentage points (SE: 3) for those aged 80–85 years and by 7 percentage points (SE: 2) for those aged 85 years and older. Compared with older adults with low income (≤$40,125), the likelihood of telehealth utilization is higher by 7 percentage points (SE: 2) for those with middle ($40,125–$85,525) and by 14 percentage points (SE: 2) for those with high (>$85,525) income. Upon adding technological skill in Model 2, the age effects are no longer statistically significant. However, technology skill increases the probability of telehealth use by 11 percentage points (SE: 2).
Table 1.

Participant Characteristics by Telehealth Use

CharacteristicNo. (%)
TotalTelehealth useNo telehealth use
Total3,188 (100.0)585 (20.6)2,603 (79.4)
Age, years
 ≥70 and <75543 (100.0)128 (23.9)415 (76.1)
 ≥75 and <80932 (100.0)200 (22.6)732 (77.4)
 ≥80 and <85755 (100.0)118 (16.4)637 (83.6)
 ≥85958 (100.0)139 (15.1)819 (84.9)
Gender
 Male1,343 (100.0)252 (20.9)1,091 (79.1)
 Female1,845 (100.0)333 (20.4)1,512 (79.6)
Race/ethnicity
 White, non-Hispanic2,426 (100.0)438 (20.3)1,988 (79.7)
 Black, non-Hispanic527 (100.0)92 (20.1)435 (80.0)
 Other235 (100.0)55 (23.8)180 (76.2)
Income ($)
 ≤40,1251,590 (100.0)227 (15.5)1,363 (84.5)
 >40,125 and ≤85,525944 (100.0)191 (21.8)753 (78.2)
 >85,525654 (100.0)167 (28.4)487 (71.6)
Has a high-risk chronic condition[a]
 No472 (100.0)66 (17.3)406 (82.7)
 Yes2,716 (100.0)519 (21.3)2,197 (78.7)
E-mails or texts often
 No2,026 (100.0)274 (14.6)1,752 (85.4)
 Yes1,162 (100.0)311 (28.1)851 (71.9)
Currently living with family or friends
 No1,047 (100.0)140 (15.3)907 (84.7)
 Yes2,141 (100.0)445 (22.8)1,696 (77.2)
Learned a new technology or program to go online[b] with someone's help
 No2,409 (100.0)387 (18.6)2,022 (81.4)
 Yes470 (100.0)149 (32.2)321 (67.8)
Assisted living facility helped residents keep in touch with family or friends online
 No76 (100.0)11 (13.8)65 (86.2)
 Yes139 (100.0)34 (28.4)105 (71.6)

High-risk chronic conditions include heart disease, hypertension, history of stroke, cancer, diabetes, or lung disease.

This includes learning to use a smartphone, computer, or iPad or a program such as Zoom or FaceTime.

Participant Characteristics by Telehealth Use High-risk chronic conditions include heart disease, hypertension, history of stroke, cancer, diabetes, or lung disease. This includes learning to use a smartphone, computer, or iPad or a program such as Zoom or FaceTime. Model 3 incorporates social determinants. The likelihood of telehealth utilization is 5 percentage points (SE: 2) higher for those currently living with family or friends and 8 percentage points (SE: 2) higher for those receiving technical support. The receipt of technical support is a significant predictor conditional on living with family or friends (0.09, SE: 0.03, p = 0.003), but is no longer significant conditional on neither living with family or friends nor residing in an assisted living facility (0.08, SE: 0.04, p = 0.06) (not reported in Tables). Among residents of an assisted living facility, the receipt of communications technology support from the facility increases the probability of telehealth use by 14 percentage points (SE: 7) (Table 2).
Table 2.

Results of Multivariable Logistic Regression Analyses Examining Telehealth Use as the Outcome

CharacteristicModel 1 Marginal probability (SE)Model 2 Marginal probability (SE)Model 3[a] Marginal probability (SE)Model 4[b] Marginal probability (SE)
Currently living with family or friends  0.05 (0.02)[*] 
Learned a new technology or program to go online with someone's help  0.08 (0.02)*** 
Assisted living facility helped residents keep in touch with family or friends online   0.14 (0.07)[*]
E-mails or texts often 0.11 (0.02)***0.10 (0.02)***0.10 (0.07)
Age (years)
 <75ReferentReferentReferentReferent
 ≥75 and <80−0.01 (0.02)−0.004 (0.02)−0.005 (0.02)0.24 (0.17)
 ≥80 and <85−0.06 (0.03)[*]−0.04 (0.03)−0.04 (0.03)−0.18 (0.15)
 ≥85−0.07 (0.02)**−0.04 (0.02)−0.03 (0.02)−0.07 (0.15)
Female gender0.02 (0.02)0.007 (0.02)0.008 (0.02)−0.17 (0.06)**
Race/ethnicity
 White, non-HispanicReferentReferentReferentReferent
 Black, non-Hispanic0.03 (0.03)0.04 (0.03)0.04 (0.03)−0.06 (0.11)
 Other0.07 (0.04)0.07 (0.04)0.06 (0.04)−0.15 (0.07)[*]
Income ($)
 ≤40,125ReferentReferentReferentReferent
 >40,125 and ≤85,5250.07 (0.02)**0.05 (0.02)[*]0.04 (0.02)−0.06 (0.07)
 >85,5250.14 (0.02)***0.10 (0.03)***0.07 (0.02)**0.08 (0.07)
Has a high-risk chronic condition0.06 (0.03)[*]0.06 (0.03)[*]0.07 (0.03)[*]0.05 (0.10)
No. of observations3,1883,1883,188215

p < 0.05; **p < 0.01; ***p < 0.001.

Missing values of the “learned a new technology or program to go online with someone's help” imputed using multiple random imputation.

Limited to study participants in an assisted living facility.

SE, standard error.

Results of Multivariable Logistic Regression Analyses Examining Telehealth Use as the Outcome p < 0.05; **p < 0.01; ***p < 0.001. Missing values of the “learned a new technology or program to go online with someone's help” imputed using multiple random imputation. Limited to study participants in an assisted living facility. SE, standard error.

Discussion

We find that nearly one in five older U.S. adults used telehealth during the study period. Affluent and tech-savvy seniors are much more likely to use telemedicine. Respondents' living arrangements (living with family or friends) and receipt of technical support from family or friends are also important predictors, together accounting for more than half of telehealth utilization. Facility staff appear to play a similar role for respondents in assisted living facilities. Previous studies have found limited telemedicine adoption among the elderly. Data from the National Poll on Healthy Aging showed that only 4% of adults aged 50–80 years had ever participated in a telehealth visit in May 2019.[8] A study by Lam et al. of 4,525 older adults in the United States using 2018 NHATS data estimated that 38% were not ready for video visits, primarily due to inexperience with technology.[9] Our study highlights the important role of social support for telehealth utilization among older adults who have limited experience and comfort with technology. A major limitation of the study is its cross-sectional design with most of the questionnaires completed in July and August 2020. Thus, we cannot address how patterns changed as the pandemic, and experience with telemedicine evolved over time. Furthermore, our ability to assess causal relationships is limited. Last but not least, as the data do not document Medicare FFS versus Medicare Advantage plans, we are unable to address the roles of insurance coverage and benefit design, which has serious implications for the future of Medicare telehealth policy.

Conclusion

Health care providers and policies should aim to reduce barriers to telehealth among older adults, with efforts such as digital literacy support and training. Future research evaluating interventions to improve technology skills among the elderly is warranted.
  4 in total

1.  Assessing Telemedicine Unreadiness Among Older Adults in the United States During the COVID-19 Pandemic.

Authors:  Kenneth Lam; Amy D Lu; Ying Shi; Kenneth E Covinsky
Journal:  JAMA Intern Med       Date:  2020-10-01       Impact factor: 21.873

2.  The association between Internet use and health-related outcomes in older adults and the elderly: a cross-sectional study.

Authors:  Mariusz Duplaga
Journal:  BMC Med Inform Decis Mak       Date:  2021-05-06       Impact factor: 2.796

3.  Disparities in Telemedicine Access: A Cross-Sectional Study of a Newly Established Infrastructure during the COVID-19 Pandemic.

Authors:  Vivian Hsiao; Thevaa Chandereng; Robin L Lankton; Jeffrey A Huebner; Jeffrey J Baltus; Grace E Flood; Shannon M Dean; Amye J Tevaarwerk; David F Schneider
Journal:  Appl Clin Inform       Date:  2021-06-09       Impact factor: 2.762

4.  Who Is (and Is Not) Receiving Telemedicine Care During the COVID-19 Pandemic.

Authors:  Jonathan H Cantor; Ryan K McBain; Megan F Pera; Dena M Bravata; Christopher M Whaley
Journal:  Am J Prev Med       Date:  2021-03-06       Impact factor: 5.043

  4 in total

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