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


  • 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



Age, Confounding variables, Stratified models, Telehealth


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.



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.

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.

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.

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.

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.

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.

E. R., & Topol, E. J. (2016). State of Telehealth. New England Journal of Medicine, 375(2), 154–161.

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.

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.

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.

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).

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).

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.

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.

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.

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.

National Center for Health Statistics. (2022a). National Health Interview Survey, 2021. Public-use data file and documentation. Available from

National Center for Health Statistics. (2022b). National Health Interview Survey, 2021 survey description. Available from:

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.

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).

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.

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).

Smith, S., & Raskin, S. (2021). Achieving health equity: Examining Telehealth in response to a pandemic. The Journal for Nurse Practitioners, 17(2), 214–217.

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.

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.

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).




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).



Telehealth Economics