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. 2022 Feb;11(4):1192-1201.
doi: 10.1002/cam4.4518. Epub 2022 Jan 5.

Disparities in telemedicine during COVID-19

Alexander S Qian  1 , Melody K Schiaffino  2   3 , Vinit Nalawade  1 , Lara Aziz  1 , Fernanda V Pacheco  1 , Bao Nguyen  1 , Peter Vu  4 , Sandip P Patel  4 , Maria Elena Martinez  3   5 , James D Murphy  1   3
Affiliations

Disparities in telemedicine during COVID-19

Alexander S Qian et al. Cancer Med. 2022 Feb.

Abstract

Background: Oncology rapidly shifted to telemedicine in response to the COVID-19 pandemic. Telemedicine can increase access to healthcare, but recent research has shown disparities exist with telemedicine use during the pandemic. This study evaluated health disparities associated with telemedicine uptake during the COVID-19 pandemic among cancer patients in a tertiary care academic medical center.

Methods: This retrospective cohort study evaluated telemedicine use among adult cancer patients who received outpatient medical oncology care within a tertiary care academic healthcare system between January and September 2020. We used multivariable mixed-effects logistic regression models to determine how telemedicine use varied by patient race/ethnicity, primary language, insurance status, and income level. We assessed geospatial links between zip-code level COVID-19 infection rates and telemedicine use.

Results: Among 29,421 patient encounters over the study period, 8,541 (29%) were delivered via telemedicine. Several groups of patients were less likely to use telemedicine, including Hispanic (adjusted odds ratio [aOR] 0.86, p = 0.03), Asian (aOR 0.79, p = 0.002), Spanish-speaking (aOR 0.71, p = 0.0006), low-income (aOR 0.67, p < 0.0001), and those with Medicaid (aOR 0.66, p < 0.0001). Lower rates of telemedicine use were found in zip codes with higher rates of COVID-19 infection. Each 10% increase in COVID-19 infection rates was associated with an 8.3% decrease in telemedicine use (p = 0.002).

Conclusions: This study demonstrates racial/ethnic, language, and income-level disparities with telemedicine use, which ultimately led patients with the highest risk of COVID-19 infection to use telemedicine the least. Additional research to better understand actionable barriers will help improve telemedicine access among our underserved populations.

Keywords: QOL; community outreach; ethical considerations; medical oncology.

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Conflict of interest statement

JM reports receiving compensation for consulting from Boston Consulting Group. Dr. Patel receives scientific advisory income from: Amgen, AstraZeneca, Beigene, Bristol‐Myers Squibb, Certis, Eli Lilly, Genentech, Illumina, Merck, Pfizer, Rakuten, Tempus. Dr. Patel's university receives research funding from: Bristol‐Myers Squibb, Eli Lilly, Incyte, AstraZeneca/MedImmune, Merck, Pfizer, Roche/Genentech, Xcovery, Fate Therapeutics, Genocea, Iovance.

Figures

FIGURE 1
FIGURE 1
Total number of oncology visit and telemedicine trends. The top panel (1A) demonstrates the number of patient encounters between January and September 2020. The bottom panel (1B) shows the percentage of visits conducted in person (light gray bars), over video (dark gray bars), or telephone (black bars) over the same study period
FIGURE 2
FIGURE 2
Trends in telemedicine use by patient characteristics. The plots in this figure demonstrate trends in telemedicine use between January and September 2020 stratified by patient race‐ethnicity (2A), preferred language (2B), patient insurance status (2C), and zip‐code level median household income (2D)
FIGURE 3
FIGURE 3
Multivariable analysis of telemedicine use. This figure represents the results of a multivariable mixed‐effects logistic regression to predict the use of telemedicine (defined as either video or telephone visits). The multivariable model included variables of race/ethnicity, preferred language, insurance status and household income level. Multivariable models also included potential confounders including patient sex, age at visit, and cancer type
FIGURE 4
FIGURE 4
Geospatial distribution of telemedicine visits and association with COVID‐19 infection rates. The top plot (3A) demonstrates the zip‐code level telemedicine rate in San Diego County by individual zip code. Telemedicine rate was defined as the number of telemedicine encounters divided by the total number of in person or telemedicine encounters. The white stars represent outpatient oncology clinics. The bottom plot (3B) demonstrates the relationship between the zip‐code level telemedicine rate and the zip code rates of COVID‐19. Each bubble represents an individual zip code and the area of the bubble correlates with the total number of patient encounters in that zip code. The black dashed line represents the trend between COVID‐19 infection rates and telemedicine rates
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