SOCIAL DETERMINANTS OF HEALTH IN AGE-RELATED MACULAR DEGENERATION
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Bibliographic record
Abstract
PURPOSE: To investigate the relationship between social determinants of health and the prevalence of age-related macular degeneration (AMD). METHODS: This analysis included adult respondents (≥50 years old) from the 2017 National Health Interview Survey. The primary outcomes were self-reported diagnosis of AMD and self-reported vision loss because of AMD. Univariable and multivariable logistic regression models were used for analysis. RESULTS: A total of 14,267 National Health Interview Survey participants were included, of whom 668 (4.7%) reported an AMD diagnosis. In the multivariable analysis, respondents aged over 81 years had higher odds of AMD (odds ratio [OR] = 7.54, 95% confidence interval [CI], 4.76-11.96, P < 0.001) compared with those aged 50 to 60. Divorced, separated, or widowed participants (OR = 1.27, 95% CI, 1.01-1.61, P = 0.042) also had a higher odds of AMD compared with married participants. Conversely, Black/African-American (OR = 0.23, 95% CI, 0.14-0.39, P < 0.001), Asian (OR = 0.38, 95% CI, 0.16-0.88, P = 0.023), and gay, lesbian, or bisexual respondents (OR = 0.45, 95% CI, 0.22-0.93, P = 0.032) had lower odds of AMD compared with White and heterosexual respondents, respectively. Employment was also associated with lower odds of AMD (OR = 0.71, 95% CI, 0.53-0.96, P = 0.026) compared with unemployment. CONCLUSION: Several social determinants of health were associated with self-reported AMD diagnosis. These factors should be considered by policymakers and clinicians to effectively orchestrate public health initiatives aimed at promoting equitable care.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it