The Confounding Influence of Older Age in Statistical Models of Telehealth Utilization

Authors

  • David Shilane Program in Applied Analytics, School of Professional Studies, Columbia University, New York, New York, USA
  • Heidi Ting’an Lu Program in Applied Analytics, School of Professional Studies, Columbia University, New York, New York, USA
  • Zhenyi Zheng Program in Applied Analytics, School of Professional Studies, Columbia University, New York, New York, USA

DOI:

https://doi.org/10.5195/ijt.2023.6565

Keywords:

Age, Confounding variables, Stratified models, Telehealth

Abstract

Older age is a potentially confounding variable in models of telehealth utilization. We compared unified and stratified logistic regression models using data from the 2021 National Health Interview Survey. A total of 27,626 patients were identified, of whom 38.9% had utilized telehealth. Unified and stratified modeling showed a number of important differences in their quantitative estimates, especially for gender, Hispanic ethnicity, heart disease, COPD, food allergies, high cholesterol, weak or failing kidneys, liver conditions, difficulty with self-care, the use of mobility equipment, health problems that limit the ability to work, problems paying bills, and filling a recent prescription. Telehealth utilization odds ratios differ meaningfully between younger and older patients in stratified modeling. Traditional statistical adjustments in logistic regression may not sufficiently account for the confounding influence of older age in models of telehealth utilization. Stratified modeling by age may be more effective in obtaining clinical inferences.

  

References

Adepoju, O. E., Chae, M., Ojinnaka, C. O., Shetty, S., & Angelocci, T. (2022). Utilization gaps during the COVID-19 pandemic: Racial and ethnic disparities in telemedicine uptake in federally qualified health center clinics. Journal of General Internal Medicine, 37(5), 1191–1197. https://doi.org/10.1007/s11606-021-07304-4

Barnett, M. L., Ray, K. N., Souza, J., & Mehrotra, A. (2018). Trends in telemedicine use in a large commercially insured population, 2005-2017. JAMA, 320(20), 2147. https://doi.org/10.1001/jama.2018.12354

Chang, J. E., Lai, A. Y., Gupta, A., Nguyen, A. M., Berry, C. A., & Shelley, D. R. (2021). Rapid transition to telehealth and the digital divide: Implications for Primary Care Access and equity in a post‐covid era. Milbank Quarterly, 99(2), 340–368. https://doi.org/10.1111/1468-0009.12509

Chunara, R., Zhao, Y., Chen, J., Lawrence, K., Testa, P. A., Nov, O., & Mann, D. M. (2020). Telemedicine and healthcare disparities: A cohort study in a large healthcare system in New York City during COVID-19. Journal of the American Medical Informatics Association, 28(1), 33–41. https://doi.org/10.1093/jamia/ocaa217

Darrat, I., Tam, S., Boulis, M., & Williams, A. M. (2021). Socioeconomic disparities in patient use of telehealth during the coronavirus disease 2019 surge. JAMA Otolaryngology–Head & Neck Surgery, 147(3), 287. https://doi.org/10.1001/jamaoto.2020.5161

Dixit, N., Van Sebille, Y., Crawford, G. B., Ginex, P. K., Ortega, P. F., & Chan, R. J. (2021). Disparities in telehealth use: How should the supportive care community respond? Supportive Care in Cancer, 30(2), 1007–1010. https://doi.org/10.1007/s00520-021-06629-4

E. R., & Topol, E. J. (2016). State of Telehealth. New England Journal of Medicine, 375(2), 154–161. https://doi.org/10.1056/nejmra1601705

Forducey, P. G., Glueckauf, R. L., Bergquist, T. F., Maheu, M. M., & Yutsis, M. (2012). Telehealth for persons with severe functional disabilities and their caregivers: Facilitating self-care management in the home setting. Psychological Services, 9(2), 144–162. https://doi.org/10.1037/a0028112

Franciosi, A. N., & Quon, B. S. (2021). Telehealth or TeleWealth? Equity challenges for the future of Cystic Fibrosis Care (commentary). Journal of Cystic Fibrosis, 20, 55–56. https://doi.org/10.1016/j.jcf.2021.08.025

Francke, J. A., Groden, P., Ferrer, C., Bienstock, D., Tepper, D. L., Chen, T. P., Sanky, C., Grogan, T. R., & Weissman, M. A. (2021). Remote enrollment into a telehealth-delivering patient portal: Barriers faced in an urban population during the COVID-19 pandemic. Health and Technology, 12(1), 227–238. https://doi.org/10.1007/s12553-021-00614-x

Haynes, N., Ezekwesili, A., Nunes, K., Gumbs, E., Haynes, M., & Swain, J. (2021). “Can you see my screen?” Addressing racial and ethnic disparities in telehealth. Current Cardiovascular Risk Reports, 15(12). https://doi.org/10.1007/s12170-021-00685-5

Jain, V., Al Rifai, M., Lee, M. T., Kalra, A., Petersen, L. A., Vaughan, E. M., Wong, N. D., Ballantyne, C. M., & Virani, S. S. (2020). Racial and geographic disparities in internet use in the U.S. among patients with hypertension or diabetes: Implications for telehealth in the era of COVID-19. Diabetes Care, 44(1). https://doi.org/10.2337/dc20-2016

Kim, S., Gajos, K. Z., Muller, M., & Grosz, B. J. (2016). Acceptance of mobile technology by older adults. Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services. https://doi.org/10.1145/2935334.2935380

Koonin, L. M., Hoots, B., Tsang, C. A., Leroy, Z., Farris, K., Jolly, B., Antall, P., McCabe, B., Zelis, C. B. R., Tong, I., & Harris, A. M. (2020). Trends in the use of telehealth during the emergence of the COVID-19 pandemic — United States, January–March 2020. MMWR. Morbidity and Mortality Weekly Report, 69(43), 1595–1599. https://doi.org/10.15585/mmwr.mm6943a3

Lau, K. H., Anand, P., Ramirez, A., & Phicil, S. (2022). Disparities in telehealth use during the COVID-19 pandemic. Journal of Immigrant and Minority Health, 24(6), 1590–1593. https://doi.org/10.1007/s10903-022-01381-1

Mehrotra, A., Huskamp, H. A., Souza, J., Uscher-Pines, L., Rose, S., Landon, B. E., Jena, A. B., & Busch, A. B. (2017). Rapid growth in mental health telemedicine use among rural Medicare beneficiaries, wide variation across states. Health Affairs, 36(5), 909–917. https://doi.org/10.1377/hlthaff.2016.1461

National Center for Health Statistics. (2022a). National Health Interview Survey, 2021. Public-use data file and documentation. Available from https://www.cdc.gov/nchs/nhis/data-questionnaires-documentation.htm.

National Center for Health Statistics. (2022b). National Health Interview Survey, 2021 survey description. Available from: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2021/srvydesc-508.pdf.

Odeh, B., Kayyali, R., Nabhani-Gebara, S., Philip, N., Robinson, P., & Wallace, C. R. (2015). Evaluation of a telehealth service for COPD and HF patients: Clinical outcome and patients’ perceptions. Journal of Telemedicine and Telecare, 21(5), 292–297. https://doi.org/10.1177/1357633x15574807

Peek, S. T., Wouters, E. J., Luijkx, K. G., & Vrijhoef, H. J. (2016). What it takes to successfully implement technology for aging in place: Focus groups with stakeholders. Journal of Medical Internet Research, 18(5). https://doi.org/10.2196/jmir.5253

Perzynski, A. T., Roach, M. J., Shick, S., Callahan, B., Gunzler, D., Cebul, R., Kaelber, D. C., Huml, A., Thornton, J. D., & Einstadter, D. (2017). Patient portals and broadband internet inequality. Journal of the American Medical Informatics Association, 24(5), 927–932. https://doi.org/10.1093/jamia/ocx020

R Core Team. (2022). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

Sieck, C. J., Rastetter, M., & McAlearney, A. S. (2021). Could telehealth improve equity during the COVID-19 pandemic? The Journal of the American Board of Family Medicine, 34(Supplement). https://doi.org/10.3122/jabfm.2021.s1.200229

Smith, S., & Raskin, S. (2021). Achieving health equity: Examining Telehealth in response to a pandemic. The Journal for Nurse Practitioners, 17(2), 214–217. https://doi.org/10.1016/j.nurpra.2020.10.001

van der Burg, J. M. M., Aziz, N. A., Kaptein, M. C., Breteler, M. J. M., Janssen, J. H., van Vliet, L., Winkeler, D., van Anken, A., Kasteleyn, M. J., & Chavannes, N. H. (2020). Long-term effects of telemonitoring on healthcare usage in patients with heart failure or COPD. Clinical eHealth, 3, 40–48. https://doi.org/10.1016/j.ceh.2020.05.001

Weber, E., Miller, S. J., Astha, V., Janevic, T., & Benn, E. (2020). Characteristics of telehealth users in NYC for covid-related care during the coronavirus pandemic. Journal of the American Medical Informatics Association, 27(12), 1949–1954. https://doi.org/10.1093/jamia/ocaa216

Westby, A., Nissly, T., Gieseker, R., Timmins, K., & Justesen, K. (2021). Achieving equity in telehealth: “centering at the margins” in access, provision, and reimbursement. The Journal of the American Board of Family Medicine, 34(Supplement). https://doi.org/10.3122/jabfm.2021.s1.200280

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Published

2023-12-12

How to Cite

Shilane, D., Lu, H. T., & Zheng, Z. (2023). The Confounding Influence of Older Age in Statistical Models of Telehealth Utilization. International Journal of Telerehabilitation, 15(2). https://doi.org/10.5195/ijt.2023.6565

Issue

Section

Telehealth Economics