Association Between Sociodemographic Factors and Self-Reported Diabetic Retinopathy: A Cross-sectional, Population-Based Analysis
Bibliographic record
Abstract
PURPOSE: This study aimed to investigate the relationship between sociodemographic and healthcare access factors with self-reported diabetic retinopathy (DR) prevalence in a nationally representative sample of the United States. DESIGN: This is a population based, cross-sectional analysis. METHODS: Data from those who answered the question, "Have you ever been told by a doctor or other health professional that you had diabetic retinopathy?" from the 2017 National Health Interview Survey (NHIS) was analyzed through logistic regression to examine the association between DR prevalence and social determinants of health (SDH). RESULTS: Of 26,966 eligible NHIS respondents (81.4%), 26,699 participants answered the DR question, of whom 266 (1.0%) self-reported a DR diagnosis. Multivariable analysis found a significant association between DR prevalence and the following social determinants of health:, poorer health status (OR = 5.9; 95% CI = 3.6-9.7; P < .001), disability (OR 2.1; 95% CI 1.3-3.2; P = .001), no employment status (OR = 1.8; 95% CI = 1.2-2.9; P = .009), and living in Southern regions of the US (OR = 1.9; 95% CI = 1.1-3.3; P = .020). Not having a usual place for healthcare (OR 0.3; 95% CI 0.1-0.7; P = .006) and female sex (OR = 0.6; 95% CI = 0.4-0.8; P = .002) were negatively associated with self-reported DR prevalence. CONCLUSION: Multiple sociodemographic factors are associated with self-reported DR prevalence. Health care providers and policymakers should tailor future interventions to address SDH in a holistic model of DR screening and care.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".