The Prevalence of Diabetic Retinopathy in American Indians or Alaska Natives and Non-Indigenous Americans
Bibliographic record
Abstract
TOPIC: To estimate the prevalence of diabetic retinopathy (DR) in adult American Indian or Alaska Native (AIAN) and non-AIAN patients with diabetes. CLINICAL RELEVANCE: Although diabetes mellitus is more prevalent among AIAN patients compared with non-AIAN patients, the evidence is inconsistent regarding whether AIAN patients have a higher prevalence or severity of DR. METHODS: We searched Ovid MEDLINE, EMBASE, and Web of Science databases from inception through February 23, 2025. We included primary studies evaluating the prevalence of DR in Americans with diabetes. The prevalence of (1) DR, (2) diabetic macular edema (DME), (3) proliferative diabetic retinopathy (PDR), (4) vision-threatening DR (VTDR; including DME, PDR, and severe nonproliferative DR), and (5) PDR complications were estimated. Meta-analyses were performed using Freeman-Tukey double arcsine transformations and random-effects modelling. The Joanna Briggs Institute Appraisal Checklist for Prevalence Studies was used to assess risk of bias. Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) guidelines were used to assess certainty of evidence. RESULTS: Overall, 53 studies of 10 070 617 individuals were included. In the AIAN and non-AIAN groups, the pooled prevalence of DR was estimated to be 21% (95% confidence interval [CI], 13%-30%; GRADE, low) and 20% (95% CI, 16%-25%; GRADE, low), respectively. The prevalence of PDR was estimated to be 3% (95% CI, 1%-6%; GRADE, low) and 2% (95% CI, 1%-4%; GRADE, low), respectively. The prevalence of DME was estimated to be 3% (95% CI, 2%-4%; GRADE, low) and 3% (95% CI, 2%-4%; GRADE, low), respectively. The prevalence of VTDR was estimated to be 3% (95% CI, 1%-7%; GRADE, low) and 5% (95% CI, 4%-7%; GRADE, low), respectively. High-quality evidence was lacking. Comparative analysis demonstrated that no difference may exist in the rate of DR between AIAN and non-AIAN patients (odds ratio, 0.67; 95% CI, 0.31-1.48; GRADE, low). DISCUSSION: No appreciable difference seems to exist in the prevalence of DR between AIAN and non-AIAN patients, although evidence is limited by the heterogeneity of studies. The high disease burden highlights that public health strategies are needed equally for AIAN as well as non-AIAN patients. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".