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.

  

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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

Vol. 15 No. 2 (2023)

Section

Telehealth Economics

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Copyright (c) 2023 David Shilane, Heidi Ting’an Lu, Zhenyi Zheng

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This work is licensed under a Creative Commons Attribution 4.0 International License.