Sociodemographic disparities in eye examinations: A nationally representative survey analysis
Why this work is in the frame
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Bibliographic record
Abstract
To investigate associations between sociodemographic factors and eye examinations for adults in the United States. Cross-sectional study. Data were pooled from the 2022 National Health Interview Survey, a population-based nationwide survey of randomly sampled households. Data collection occurred from January 1st to December 31st, 2022. Participants aged ≥18 years from all 50 states and the District of Columbia for whom data were available on eye examinations were included. The main outcome was whether participants had an eye examination from a specialist within the past year of being interviewed. Logistic regression models were used for univariable and multivariable analyses. Across 27,246 adults, 14,812 (54.4 %) had an eye examination within the past year and 12,434 (45.6 %) did not. In our multivariable analysis, the following sociodemographic factors were associated with an increased odds of having undergone an eye examination within the past year: female sex (OR=1.48, 95 %CI=[1.39, 1.57, p < 0.01), Hispanic ethnicity (OR=1.22, 95 %CI=[1.09, 1.37], p < 0.01) or Asian race (OR=1.15, 95 %CI=[1.05, 1.33], p = 0.04). The following factors were associated with a reduced odds of having undergone an eye examination: being single compared to married (OR=0.87, 95 %CI=[0.81, 0.93], p < 0.01), residing in the West compared to the Northeast (OR=0.86, 95 %CI=[0.77, 0.96], p = 0.01), and those who lacked citizenship status (OR=0.73, 95 %CI=[0.63, 0.84], p < 0.01), or insurance (OR=0.58, 95 %CI=[0.51, 0.66], p < 0.01). Several sociodemographic factors were associated with the likelihood of undergoing an eye examination within the past year. Public health efforts dedicated to addressing inequities in access to eye examinations are imperative.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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