Disparities in Eye Care Utilization During the COVID-19 Pandemic

Am J Ophthalmol. 2022 Jan:233:163-170. doi: 10.1016/j.ajo.2021.07.024. Epub 2021 Jul 26.

Abstract

Purpose: To assess the relationship between telemedicine utilization and sociodemographic factors among patients seeking eye care.

Design: Comparative utilization analysis.

Methods: We reviewed the eye care utilization patterns of a stratified random sample of 1720 patients who were seen at the University of Michigan Kellogg Eye Center during the height of the COVID-19 pandemic (April 30 to May 25, 2020) and their odds of having a video, phone, or in-person visit compared with having a deferred visit. Associations between independent variables and visit type were determined using a multinomial logistic regression model.

Results: Older patients had lower odds of having a video visit (P = .007) and higher odds of having an in-person visit (P = .023) compared with being deferred, and in the nonretina clinic sample, older patients still had lower odds of a video visit (P = .02). Non-White patients had lower odds of having an in-person visit (P < .02) in the overall sample compared with being deferred, with a similar trend seen in the retina clinic. The mean neighborhood median household income was $76,200 (±$33,500) and varied significantly (P < .0001) by race with Blacks having the lowest estimated mean income.

Conclusion: Disparities exist in how patients accessed eye care during the COVID-19 pandemic with older patients-those for whom COVID-19 posed a higher risk of mortality-being more likely to be seen for in-person care. In our affluent participant sample, there was a trend toward non-White patients being less likely to access care. Reimbursing telemedicine solely through broadband internet connection may further exacerbate disparities in eye care.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Age Factors
  • COVID-19*
  • Delivery of Health Care*
  • Health Services / statistics & numerical data*
  • Health Services Accessibility*
  • Healthcare Disparities / ethnology*
  • Humans
  • Michigan
  • Pandemics
  • SARS-CoV-2
  • Sociodemographic Factors
  • Telemedicine / statistics & numerical data*
  • Telemedicine / trends