Vitamin D deficiency in minority populations
Bibliographic record
Abstract
OBJECTIVE: Black and Hispanic individuals synthesize less vitamin D per unit of sun exposure than white individuals. The relationship between UV radiation and vitamin D insufficiency in minorities has not been well explored. DESIGN: Prospective cohort study. SETTING: Using the National Health and Nutrition Examination Survey, we obtained serum vitamin D levels for non-Hispanic Whites, Hispanics and non-Hispanic Blacks aged ≥18 years from 2000-2006. We linked these data with the average monthly solar UV index by census tract and data on sun exposure, vitamin D supplementation, health and demographics. We used multivariable regression analyses to assess vitamin D deficiency (<15 ng/ml) and insufficiency (<20 ng/ml) in January (when the UV index was lowest) by race/ethnicity and geography. SUBJECTS: Adults (n 14,319) aged ≥18 years. RESULTS: A 1-point increase in the UV index was associated with a 0·51 ng/ml increase in vitamin D (95% CI 0·35, 0·67 ng/ml; P<0·001). Non-Hispanic Black race and Hispanic ethnicity were associated with a 7·47 and 3·41 ng/ml decrease in vitamin D, respectively (both P<0·001). In January, an estimated 65·4% of non-Hispanic Blacks were deficient in vitamin D, compared with 28·9% of Hispanics and 14·0% of non-Hispanic Whites. An estimated 84·2% of non-Hispanic Blacks were insufficient in vitamin D v. 56·3% of Hispanics and 34·8% of non-Hispanic Whites. More non-Hispanic Blacks were estimated to be deficient in vitamin D in January in the highest UV index quartile than were non-Hispanic Whites in the lowest UV index quartile (60·2% v. 25·7%). CONCLUSIONS: Wintertime vitamin D insufficiency is pervasive among minority populations, and not uncommon among non-Hispanic Whites.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".