Because of the coronavirus disease 2019 (COVID-19) pandemic, ophthalmologists are being encouraged to deliver eye care using the internet with technologies such as patient portals and online visits.1 However, many patients with eye diseases may not have experience using the internet for health care activities. Individuals with lower sociodemographic status may have less experience using technology for health care.2 It is important for ophthalmologists to understand whether these disparities exist for patients with eye diseases and to what extent. The purposes of this study were to assess the proportion of US adults with self-reported eye disease who have experience using the internet for health care and to evaluate differences in their experience based on age, race/ethnicity, and socioeconomic status.
For this cross-sectional study, we used 2016 to 2017 data from the National Health Interview Survey (NHIS), a cross-sectional survey annually administered to a nationally representative sample of noninstitutionalized US adults. We pooled the data from the 2016 to 2017 NHIS using methods described in the NHIS documentation.3 We included all participants who reported a diagnosis of macular degeneration, glaucoma, or diabetic retinopathy, common chronic eye diseases that require ongoing clinical care. Data were analyzed from March 1 to April 5, 2020. The University of Utah institutional review board determined this study to be nonhuman subjects research; therefore, informed consent was not required. All data were deidentified. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.
Experience using the internet for health care activities was defined as self-reported use of at least 1 of 5 online health care practices (“looking up health information,” “requesting a prescription medication,” “scheduling a medical appointment,” “communicating with a health care provider,” and “using online groups to learn about health topics”) in the 12 months before the interview. The demographic variables were sex, age, race, Hispanic ethnicity, educational attainment, and poverty ratio (a ratio of annual household income to the federal poverty threshold).
Using the methods described in the NHIS variance estimation technical report,3 we calculated the weighted percentages of participants for each sociodemographic group who did and did not report use of the internet for health care activities in the prior 12 months and calculated unadjusted 2-sided P values from Pearson χ2 tests. We performed multivariable logistic regression to evaluate disparities in prior use of the internet for health care activities, adjusting for demographics and self-reported macular degeneration, diabetic retinopathy, and glaucoma. Multiple imputation values and methods provided by the NHIS were used for missing income data.4 Adjusted predicted percentages were calculated. Analyses were conducted using Stata software, version 16 (StataCorp LLC).
Of the 3667 participants (mean [SD] age, 67.2 [16.2] years; 2209 [57.3 %] female) with a chronic eye disease in this nationally representative survey, 846 (47.8%; 95% CI, 45.3%-49.7%) used the internet for health care activities in the prior 12 months. Table 1 presents participant characteristics, several of which were associated with being less likely to use the internet, including age (83 [15.8%] of those >85 years of age vs 107 [79.3%] of those 18-39 years of age, P < .001), Black race (166 [39.1%] of Black participants vs 1308 [48.4%] of White participants, P = .01), Hispanic ethnicity (90 [36.6%] of Hispanic participants vs 1480 [48.9%] of non-Hispanic participants, P = .006), and lower educational attainment (90 [15.7%] of those with no high school diploma vs 624 [69.9%] of college graduates, P < .001). These differences remained in the multivariable regression model (Table 2).
In this nationally representative sample of respondents with self-reported eye disease, we found differences in experience using the internet for health care based on age, race/ethnicity, and socioeconomic status. Although our study is limited in that it relied on self-reported data, as ophthalmologists shift to using the internet more for eye care, it is important to be aware of these differences because they could exacerbate current disparities.5 Particularly during the COVID-19 pandemic and any future infectious outbreaks when only emergency eye care is delivered in person,1 ophthalmologists should give increased attention to older patients, patients in racial/ethnic minority groups, those with less educational attainment, and those with lower household incomes to ensure that they receive health care. Decreased vision in patients with eye disease may make use of the internet particularly difficult. The current Centers for Medicare & Medicaid Services policy to reimburse for telephone visits and internet-based video visits may help reduce these disparities.6 In the future, policies to increase access to broadband internet may improve equity in high-quality virtual care.
Accepted for Publication: June 28, 2020.
Corresponding Author: Brian C. Stagg, MD, MS, John Moran Eye Center, Department of Ophthalmology and Visual Sciences, University of Utah, 65 Mario Capecchi Dr, Salt Lake City, UT 84132 (brian.stagg@hsc.utah.edu).
Published Online: August 27, 2020. doi:10.1001/jamaophthalmol.2020.3016
Author Contributions: Dr Stagg had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Stagg, Gupta, Hess.
Acquisition, analysis, or interpretation of data: Stagg, Ehrlich, Newman-Casey, Stein, Kawamoto.
Drafting of the manuscript: Stagg.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Stagg.
Supervision: Ehrlich, Newman-Casey, Hess.
Conflict of Interest Disclosures: Dr Stagg reported receiving grants from Research to Prevent Blindness during the conduct of the study. Dr Ehrlich reported receiving grants from National Institutes of Health during the conduct of the study. Dr Newman-Casey reported receiving grants from the National Eye Institute, Research to Prevent Blindness, and Centers for Disease Control and Prevention during the conduct of the study. Dr Stein reported receiving grants from the National Institutes of Health and Research to Prevent Blindness during the conduct of the study. Dr Kawamoto reported receiving grants from the National Institutes of Health, Research to Prevent Blindness, and Centers for Disease Control and Prevention during the conduct of the study; receiving personal fees from McKesson InterQual, Premier, Klesis Healthcare, RTI International, Mayo Clinic, Vanderbilt University, University of Washington, University of California at San Francisco, and US Office of the National Coordinator for Health IT (via Employer Services Assurance Corporation, JBS International, A+ Government Solutions, Hausam Consulting, and Security Risk Solutions); receiving grants and personal fees from Hitachi and Pfizer; and serving on the Health Level Seven International Board outside the submitted work. Dr Hess reported serving on a data safety monitoring board for Astellas Pharmaceuticals. No other disclosures were reported.
Funding/Support: This work was supported grants R01EY026641 (Dr Stein), K23EY025320 (Dr Newman-Casey), and K23EY027848 (Dr Ehrlich) from the National Institutes of Health; an unrestricted grant from Research to Prevent Blindness to the Department of Ophthalmology & Visual Sciences, University of Utah, Salt Lake City (Dr Stagg); a Career Development Award from Research to Prevent Blindness (Dr Newman-Casey); and grant U01 DP006442 from the Centers for Disease Control.
Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
2.Kontos
E, Blake
KD, Chou
W-YS, Prestin
A. Predictors of eHealth usage: insights on the digital divide from the Health Information National Trends Survey 2012.
J Med internet Res. 2014;16(7):e172. doi:
10.2196/jmir.3117
PubMedGoogle Scholar 4.Division of Health Interview Statistics. Multiple imputation of family income and personal earnings in the National Health Interview Survey: methods and examples. August 2018. Accessed February 13, 2020.
https://www.cdc.gov/nchs/data/nhis/tecdoc17.pdf 5. National Academies of Sciences, Engineering, and Medicine. Making Eye Health a Population Health Imperative: Vision for Tomorrow. National Academies Press; 2016.