Eye Care Utilization Among Insured People With Diabetes in the U.S., 2010–2014
Abstract
OBJECTIVE: Diabetic retinopathy (DR) is the leading cause of blindness among working-age adults, and although screening with eye exams is effective, screening rates are low. We evaluated eye exam visits over a 5-year period in a large population of insured patients 10–64 years of age with diabetes. RESEARCH DESIGN AND METHODS: We used claims data from IBM Watson Health to identify patients with diabetes and continuous insurance coverage from 2010 to 2014. Diabetes and DR were defined using ICD-9 Clinical Modification codes. We calculated eye exam visit frequency by diabetes type over a 5-year period and estimated period prevalence and cumulative incidence of DR among those receiving an eye exam. RESULTS: Among the 298,383 insured patients with type 2 diabetes and no diagnosed DR, almost half had no eye exam visits over the 5-year period and only 15.3% met the American Diabetes Association (ADA) recommendations for annual or biennial eye exams. For the 2,949 patients with type 1 diabetes, one-third had no eye exam visits and 26.3% met ADA recommendations. The 5-year period prevalence and cumulative incidence of DR were 24.4% and 15.8%, respectively, for patients with type 2 diabetes and 54.0% and 33.4% for patients with type 1 diabetes. CONCLUSIONS: The frequency of eye exams was alarmingly low, adding to the abundant literature that systemic changes in health care may be needed to detect and prevent vision-threatening eye disease among people with diabetes.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
This care gap is a major source of health disparity, with racial and ethnic minorities, and under-resourced communities, having worse outcomes, and a disproportionately higher prevalence of DED.
“This care gap is a major source of health disparity, with racial and ethnic minorities, and under-resourced communities, having worse outcomes, and a disproportionately higher prevalence of DED2,5–9.”
From Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial (2024) · cites this paper as [7]
The adherence rate to diabetic eye exam recommendations in the U.S. is as low as 20%.
“However, studies have found the adherence rate to diabetic eye exam recommendations in the U.S. to be as low as 20%3.”
From Autonomous AI-assisted diabetic retinopathy screening at primary care is associated with increased presentation to eye care by at risk patients (2026) · cites this paper as [3]
Cites
Prevalence of Diabetic Retinopathy in the United States, 2005-2008
Zhang, X., Saaddine, J. B., Chou, C.-F., Cotch, M. F., Cheng, Y. J., Geiss, L. S., Gregg, E. W., Albright, A. L., Klein, B. E. K., & Klein, R. (2010) · JAMA · doi:10.1001/jama.2010.1111 · Cited by 1,196 · see citing works
Self-citationBursell, S.-E., Fonda, S. J., Lewis, D. G., & Horton, M. B. (2018) · PLOS ONE · doi:10.1371/journal.pone.0198551 · Cited by 68 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Wolf, R. M., Channa, R., Liu, T. Y. A., Zehra, A., Bromberger, L., Patel, D., Ananthakrishnan, A., Brown, E. A., Prichett, L., Lehmann, H. P., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-44676-z · Cited by 116 · see citing works
A reimbursement framework for artificial intelligence in healthcare
Abràmoff, M. D., Roehrenbeck, C., Trujillo, S., Goldstein, J., Graves, A. S., Repka, M. X., & Silva III, E. “. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00621-w · Cited by 117 · see citing works
Foundational Considerations for Artificial Intelligence Using Ophthalmic Images
Abràmoff, M. D., Cunningham, B., Patel, B., Eydelman, M. B., Leng, T., Sakamoto, T., Blodi, B., Grenon, S. M., Wolf, R. M., Manrai, A. K., et al. (2022) · Ophthalmology · doi:10.1016/j.ophtha.2021.08.023 · Cited by 96 · see citing works
Wolf, R. M., Channa, R., Abramoff, M. D., & Lehmann, H. P. (2020) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2020.3190 · Cited by 87 · see citing works
Patel, D., Ananthakrishnan, A., Lin, T., Channa, R., Liu, T. Y. A., & Wolf, R. M. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11237120 · Cited by 43 · see citing works
Dow, E. R., Khan, N. C., Chen, K. M., Mishra, K., Perera, C., Narala, R., Basina, M., Dang, J., Kim, M., Levine, M., et al. (2023) · Ophthalmology Science · doi:10.1016/j.xops.2023.100330 · Cited by 40 · see citing works
Autonomous artificial intelligence in diabetic retinopathy: From algorithm to clinical application
Channa, R., Wolf, R., & Abramoff, M. D. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/1932296820909900 · Cited by 33 · see citing works
Incorporating Artificial Intelligence into Healthcare Workflows: Models and Insights
Dai, T., & Abràmoff, M. D. (2023) · Tutorials in Operations Research: Advancing the Frontiers of OR/MS: From Methodologies to Applications · doi:10.1287/educ.2023.0257 · Cited by 29 · see citing works
Artificial Intelligence Improves Patient Follow-Up in a Diabetic Retinopathy Screening Program
Dow, E. R., Chen, K. M., Zhao, C., Knapp, A., Phadke, A., Weng, K., Do, D. V., Mahajan, V. B., Mruthyunjaya, P., Leng, T., et al. (2023) · Clinical Ophthalmology · doi:10.2147/opth.s422513 · Cited by 29 · see citing works
Economic challenges of artificial intelligence adoption for diabetic retinopathy
Chen, E. M., Chen, D., Chilakamarri, P., Lopez, R., & Parikh, R. (2021) · Ophthalmology · doi:10.1016/j.ophtha.2020.07.043 · Cited by 21 · see citing works
Wolf, R. M., Channa, R., Lehmann, H. P., Abramoff, M. D., & Liu, T. Y. A. (2024) · Clinical Diabetes · doi:10.2337/cd23-0019 · Cited by 12 · see citing works
Leong, A., Wolf, R. M., Channa, R., Wang, J., Lehmann, H., Abramoff, M. D., & Liu, T. Y. A. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02460-5 · Cited by 3 · see citing works
Diabetic retinopathy treatment cascade and care continuum in the USA: a systematic review
Fu, J. J., Applebaum, S. S., Granados, A., & Nwanyanwu, K. (2026) · BMJ Open · doi:10.1136/bmjopen-2026-117683 · Cited by 1 · see citing works
- Authors
- Benoit, S. R., Swenor, B., Geiss, L. S., Gregg, E. W., & Saaddine, J. B.
- Affiliations
- Centers for Disease Control and Prevention (US)
- Johns Hopkins University (US)
- Corresponding author
- Benoit, Stephen R.
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2019-01-24
- Volume
- 42
- Issue
- 3
- Pages
- 427-433
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- OpenAlex
- W2914931135
- Cited by
- 97
- References
- 2
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 12 · 2023: 19 · 2022: 14 · 2021: 21 · 2020: 16 · 2019: 5
- FWCI
- 13.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Diabetes Care (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Keywords
- diabetic retinopathy, diabetes screening, type 1 diabetes, type 2 diabetes, insured patients, period prevalence, cumulative incidence, vision-threatening eye disease, health care utilization
- MeSH terms
- Adolescent, Adult, Child, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Diabetic Retinopathy, Female, Health Resources, Humans, Male, Mass Screening, Middle Aged, Ophthalmology, Physical Examination, United States, Incidence, Prevalence, Insurance Coverage, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review