July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Publicly available datasets identify priority regions for ocular telehealth intervention
Author Affiliations & Notes
  • Samantha D'Amico
    University of Vermont Medical Center , Burlington, Vermont, United States
    Larner College of Medicine at the University of Vermont, Vermont, United States
  • Nathan Benner
    Larner College of Medicine at the University of Vermont, Vermont, United States
  • Brian Y Kim
    University of Vermont Medical Center , Burlington, Vermont, United States
    Larner College of Medicine at the University of Vermont, Vermont, United States
  • Christopher J. Brady
    University of Vermont Medical Center , Burlington, Vermont, United States
    Larner College of Medicine at the University of Vermont, Vermont, United States
  • Footnotes
    Commercial Relationships   Samantha D'Amico, None; Nathan Benner, None; Brian Kim, None; Christopher Brady, None
  • Footnotes
    Support  Samantha D'Amico and Nathan Benner were supported in part by the Elliot W. Shipman Research Fund, Christopher Brady was supported in part by NIGMS Grant P20 GM103644
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 5448. doi:
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      Samantha D'Amico, Nathan Benner, Brian Y Kim, Christopher J. Brady; Publicly available datasets identify priority regions for ocular telehealth intervention. Invest. Ophthalmol. Vis. Sci. 2019;60(9):5448.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Remote telehealth systems can expand the reach of ocular services, but technical and policy challenges exist. We used publicly available datasets to identify regions with the highest potential benefit and likelihood of success from expansion of ocular telehealth given the current local policy environment.

Methods : We merged 2016 data from several datasets in order to estimate visual disability, density of eye-care providers (ECP), and vitreoretinal capacity on a county level. Quartile population reporting visual disability was calculated from Census (American Community Survey) data and quartile of ECP/100,000 was calculated using Health and Human Services data (Area Health Resource File). The relationship between these data were analyzed in a linear regression model. We used Medicare Provider Utilization and Payment data to estimate physician capacity to treat vitreoretinal disorders. Estimates for visual disability and density of ECP were then compared to the favorability ratings from the American Telemedicine Association (ATA) Gaps Analysis: Coverage and Reimbursement report to assess the likelihood of successful ocular telehealth implementation.

Results : Areas with a higher ECP density had lower overall visual disability in a univariate regression and this effect persisted when adjusted for rural status, total population, and per capita income (p<0.001). For each additional ECP/100,000, there was a 0.026% decrease in visual disability, which translates to 669 fewer people reporting visual disability considering a median county population of 27,500. States including Mississippi, New Mexico, Oklahoma, Tennessee, and Virginia with an “A” grade from the ATA, within the highest quartile for visual disability, and >10 counties in the lowest quartile for ECP density were determined to have the greatest potential impact from the implementation of ocular telehealth systems.

Conclusions : Areas with low densities of ECPs were associated with higher levels of visual disability and would likely benefit from implementation of ocular telehealth systems. States with an “A” grade from the ATA should be prioritized, as current policy is favorable. States with lower grades would also benefit from expansion of telehealth, but will likely need policy interventions in addition to technical telemedicine implementation in order to be successful.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

 

Map of degree of disability subdivided by population-weighted quartiles.

Map of degree of disability subdivided by population-weighted quartiles.

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