Disparities in Eye Care Utilization During the COVID-19 Pandemic





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.


T he widespread impact of the COVID-19 pandemic significantly altered the availability and delivery of health care. Mortality from COVID-19 disproportionately affects older and racial/ethnic minority Americans. The disparities that these groups, particularly Black Americans, face have been amplified by the pandemic, including limited access to health care services and housing or food insecurity. The COVID-19 pandemic may be exacerbating these problems with access to health care, in part, as fear of contracting coronavirus while seeking health care has increased.


Telemedicine utilization has slowly increased over the last decade, with a significant uptake in 2020 when the COVID-19 pandemic began. , Providing eye care via telemedicine is often perceived as challenging given the heavy reliance on physical examination and imaging for making ophthalmic diagnoses, though acceptance of telemedicine amongst ophthalmologists has increased during the pandemic. Telemedicine was critical in maintaining access to eye care, as ophthalmology was the specialty most negatively impacted by a decline in in-person patient visits as of May 2020. ,


As physical distancing has been a key strategy in mitigating the spread of COVID-19, telemedicine has been critical for providing necessary patient care during the pandemic. Yet substantial age, race, and socioeconomic digital divides exist in the use of telehealth technology, which may worsen already existing disparities in health and health care if health care delivery relies heavily on internet-based solutions for delivering telemedicine.


In this study, we compared the utilization of telemedicine services for eye care by different sociodemographic groups during the initial wave of the COVID-19 pandemic. We assessed the impact of age, race/ethnicity, income, proximity to clinic, and availability of high-speed broadband connection on the use and access to telemedicine services. Without a deep understanding of the factors that play into telemedicine utilization, health care providers risk further amplifying the disparities that many Americans face, particularly those from vulnerable populations.


METHODS


CLINICAL SETTING


The University of Michigan Kellogg Eye Center, located in Ann Arbor, Michigan, includes 108 faculty physicians who provided medical education and multispecialty care to approximately 206,000 outpatients in 2019. In the state of Michigan, the shelter-in-place order began on March 23, 2020. From March 23 to May 25, 2020, the University of Michigan instituted a policy to provide in-person care only for urgent patients and to defer care or to provide telemedicine-based eye care for all other patients. We conducted a telephone survey of Kellogg Eye Center patients during this period to understand patients’ experiences with telemedicine compared with in-person or deferred care during the COVID-19 pandemic. This study was approved by the University of Michigan Institutional Review Board as exempt, quality improvement research and adhered to the tenets of the Declaration of Helsinki. Verbal consent was obtained from study participants over the phone.


PARTICIPANTS AND SAMPLE SELECTION


A stratified random sample was selected from approximately 13,000 patients who had a scheduled visit at the Kellogg Eye Center between March 23 and May 8, 2020. Patients were called by the study team between April 30 and May 25, 2020. Recruitment was stratified by visit type to ensure sufficient responses for each visit type. Because the group sizes were not equal (eg, there were fewer video visits compared with deferred visits), 92% of patients who received video visits, 68% of patients who received phone visits, 38% of patients who received in-person visits, and 15% of patients whose visits were deferred were contacted to provide a reasonable sample size of patients who had experienced each visit type. Patients were stratified using the following algorithm: 1) anyone who received any in-person care at the eye center was classified as an in-person visit; 2) anyone who received a video visit, but no in-person visits, was classified as a video visit; 3) anyone who received a phone visit, but not a video or an in-person visit, was classified as a phone visit; 4) anyone whose care had all been deferred was classified as deferred. Duplicates entries were removed from call lists. A maximum of 3 attempts were made to call each patient.


SOCIODEMOGRAPHIC AND CLINICAL DATA


Age, race, gender, and address were extracted from the electronic health record research data warehouse (EPIC Clarity). Participant addresses were used to compute the straight-line distance to the Kellogg Eye Center. Patient address was also used to determine US Census tract, which was then used to extract median household income from the American Community Survey 2014 to 2018, 5-year estimates. Census tract data were used to assess whether high-speed broadband internet (downstream speed >500 Mbps from ≥1 residential provider) was available from the public FCC Fixed Broadband Deployment Data.


SURVEY /INTERVIEW DATA COLLECTION AND ANALYSIS


After obtaining verbal consent, the research team conducted telephone interviews that included 4 survey questions assessing perception of eye health and satisfaction with care and an open-ended interview question about how participants felt about their eye care or its deferral. Field notes were taken on the open-ended responses, the data were analyzed with a Grounded Theory approach, and then a mixed methods lens was used to assess whether differences in themes were present between racial groups (see Supplemental Methods).


STATISTICAL ANALYSIS


Participant data were summarized overall, by visit type, and by race with counts and percentages or with means and standard deviations accounting for the survey design (weighted by inverse of probability of selection by visit type). Rao-Scott adjustments to the Pearson χ 2 test and to the likelihood ratio test were used to assess the presence of an association between 2 categorical variables and for a continuous and a categorical variable, respectively. The association between type of visit (4 levels) and each covariate was quantified by generalized odds ratios (gORs) from a multinomial logistic regression model with “deferred” as the reference class. Multinomial logistic regression is a generalization of binary logistic regression that is applicable when the response variable has >2 possible outcomes. Univariate models are presented because the purpose of this study was to describe associations and not assess for possible causation. Models were fit to the entire sample, to patients from the retina clinic, and to patients not from the retina clinic, as the retina clinic provided the largest proportion of in-person care during this time. The linearity of associations between the gORs and continuous covariates were assessed. Statistical analysis was conducted using R software (version 3.6.3; R Foundation for Statistical Computing, Vienna, Austria).


RESULTS


We identified a sample of 3274 participants of whom 1720 agreed to be interviewed (53% response rate). Participants were classified by visit type: 536 (31.2%) in-person visits, 320 (18.6%) phone visits, 95 (5.5%) video visits, and 769 (44.7%) deferred visits ( Table 1 ). The patient population had a mean ± SD age of 64.9 ± 16.4 years, a mean ± SD neighborhood median household income of $76,200 ± $33,500, and a mean ± SD distance from the Kellogg Eye Center of 37.5 ± 101 miles. The participant population was 80.2% White, 4.5% Asian, 10.7% Black, 4.6% other race, and 2.2% Hispanic. Most (89.8%) had neighborhood access to high-speed broadband.



TABLE 1

Associations Between Visit Type and Demographics













































































































































































































































Overall a (N = 1720) Cancelled (n = 769) In-Person (n = 536) Phone (n = 320) Video (n = 95) P value
Race, n (%)
White 1396 (80.2) 604 (78.5) 468 (87.3) 249 (77.8) 75 (78.9) .000
Asian 66 (4.5) 40 (5.2) 13 (2.4) 10 (3.1) 3 (3.2)
Black 180 (10.7) 87 (11.3) 42 (7.8) 39 (12.2) 12 (12.6)
Other 78 (4.6) 38 (4.9) 13 (2.4) 22 (6.9) 5 (5.3)
Ethnicity, n (%)
Hispanic 38 (2.2) 15 (2.1) 11 (2.1) 11 (3.7) 1 (1.1) .463
Non-Hispanic 1593 (97.8) 713 (97.9) 502 (97.9) 290 (96.3) 88 (98.9)
N-Miss 89 41 23 19 6
Census tract available broadband speed, n (%)
Low 179 (10.2) 67 (9.2) 68 (13.3) 37 (12.3) 7 (7.5) .025
High 1455 (89.8) 664 (90.8) 442 (86.7) 263 (87.7) 86 (92.5)
N-Miss 86 38 26 20 2
Section, n (%)
Adult strabismus 12 (1.1) 11 (1.4) 1 (0.2) 0 (0.0) 0 (0.0) .000
Comprehensive and cataract surgery 345 (22.7) 205 (26.7) 43 (8.0) 79 (24.7) 18 (18.9)
Cornea, external disease, and refractive surgery 327 (14.0) 87 (11.3) 65 (12.1) 137 (42.8) 38 (40.0)
Glaucoma 243 (18.6) 180 (23.4) 27 (5.0) 33 (10.3) 3 (3.2)
Neuro-ophthalmology 61 (2.5) 7 (0.9) 47 (8.8) 2 (0.6) 5 (5.3)
Ocular oncology 31 (1.6) 10 (1.3) 17 (3.2) 1 (0.3) 3 (3.2)
Optometry 144 (11.7) 115 (15.0) 22 (4.1) 5 (1.6) 2 (2.1)
Plastics 56 (3.4) 31 (4.0) 5 (0.9) 5 (1.6) 15 (15.8)
Retina and uveitis 485 (24.0) 123 (16.0) 308 (57.5) 50 (15.6) 4 (4.2)
Rheumatology 16 (0.3) 0 (0.0) 1 (0.2) 8 (2.5) 7 (7.4)
Age (y)
Mean ± SD 64.9 ± 16.4 64.7 ± 16.1 66.8 ± 17.3 62.6 ± 17.8 59.8 ± 15.0 .002
Census tract MHHI ($, in thousands)
Mean ± SD 76.2 ± 33.5 76.9 ± 34.4 74.7 ± 31.3 73.1 ± 29.6 74.5 ± 35.7 .195
N-Miss 79 34 25 18 2
Distance from Kellogg Eye Center (miles)
Mean ± SD 37.5 ± 101.0 36.7 ± 112.2 36.2 ± 35.9 50.1 ± 113.7 37.5 ± 37.6 .000
N-Miss 86 38 26 20 2

SD = standard deviation.

a Percentages, means, and standard deviations adjusted for stratified sampling.



The mean neighborhood median household income varied significantly ( P < .0001) by race with estimated means of $89,600, $78,400, and $54,500 for Asians, Whites, and Blacks, respectively ( Table 2 ). The mean distance in miles from the Kellogg Eye Center was lower ( P = .007) for Whites (34.6 miles) and Blacks (37.8 miles) and higher for Asians (72.1 miles) and those of other races (52.2 miles). Neighborhood access to high-speed broadband was nearly universal for all races ranging from 88.2% for Whites to 100% for Asians. The population mean age differed significantly ( P < .0001) between races, with Whites having a higher mean age of 66.3 (95% confidence interval [CI] 65.4-67.2) years and Blacks with a lower mean age of 59.4 (95% CI 56.4-62.5) years. There was no significant difference between satisfaction with eye care ( P = .7) or perception of eye care (see Supplemental Results and Supplemental Table 1) between different racial groups.



TABLE 2

Associations Between Race and Other Demographics










































































































































































































Total a
(N = 1720)
Asian a
(n = 66)
Black a
(n = 180)
Other a
(n = 78)
White a
(n = 1396)
P value
Ethnicity, n (%)
Hispanic 38 (2.2) 0 (0.0) 1 (0.4) 21 (28.7) 16 (1.2) .000
Non-Hispanic 1593 (97.8) 61 (100.0) 176 (99.6) 40 (71.3) 1316 (98.8)
N-Miss 89 5 3 17 64
Census tract available broadband speed, n (%)
Low 179 (10.2) 0 (0.0) 8 (4.8) 6 (4.4) 165 (11.8) .000
High 1455 (89.8) 65 (100.0) 163 (95.2) 68 (95.6) 1159 (88.2)
N-Miss 86 1 9 4 72
Section, n (%)
Adult strabismus 12 (1.1) 0 (0.0) 2 (1.2) 1 (2.0) 9 (1.1) .000
Comprehensive and cataract surgery 345 (22.7) 15 (18.9) 44 (28.6) 24 (34.8) 262 (21.5)
Cornea, external disease, and refractive surgery 327 (14.0) 7 (6.7) 30 (10.7) 12 (7.5) 278 (15.2)
Glaucoma 243 (18.6) 22 (43.1) 32 (20.2) 14 (22.5) 175 (16.8)
Neuro-ophthalmology 61 (2.5) 0 (0.0) 4 (1.4) 2 (1.1) 55 (2.9)
Ocular oncology 31 (1.6) 0 (0.0) 3 (0.6) 2 (4.1) 26 (1.7)
Optometry 144 (11.7) 7 (12.1) 21 (17.2) 3 (4.9) 113 (11.4)
Plastics 56 (3.4) 1 (0.3) 6 (2.4) 3 (4.4) 46 (3.7)
Retina and uveitis 485 (24.0) 14 (18.8) 35 (17.2) 17 (18.5) 419 (25.5)
Rheumatology 16 (0.3) 0 (0.0) 3 (0.5) 0 (0.0) 13 (0.3)
Age (y)
Mean ± SD 64.9 ± 16.4 57.9 ± 20.3 59.4 ± 18.1 58.9 ± 16.6 66.3 ± 15.6 .000
Census tract MHHI ($, in thousands)
Mean ± SD 76.2 ± 33.5 89.6 ± 41.0 54.5 ± 27.3 74.9 ± 32.1 78.4 ± 32.7 .000
N-Miss 79 1 9 3 66
Distance from Kellogg Eye Center (miles)
Mean ± SD 37.5 ± 101.0 72.1 ± 304.2 37.8 ± 80.8 52.2 ± 82.8 34.6 ± 78.3 .007
N-Miss 86 1 9 4 72

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Jan 3, 2022 | Posted by in OPHTHALMOLOGY | Comments Off on Disparities in Eye Care Utilization During the COVID-19 Pandemic

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