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 obtainina clinical inferences.
Keyphrases
- mental health
- end stage renal disease
- community dwelling
- physical activity
- middle aged
- healthcare
- public health
- chronic kidney disease
- newly diagnosed
- chronic obstructive pulmonary disease
- high resolution
- machine learning
- cross sectional
- risk assessment
- climate change
- patient reported outcomes
- health promotion
- deep learning
- patient reported