Association of vitamin D status with socio-demographic factors in Calgary, Alberta: an ecological study using Census Canada data
Bibliographic record
Abstract
BACKGROUND: Low 25-hydroxyvitamin D levels are a global health problem with northern countries such as Canada at particular risk. A number of sociodemographic factors have been reported to be associated with low vitamin D levels but prior studies have been limited by the ability of the researchers to gather this data directly from clinical trial participants. The purpose of this study was to use a novel methodology of inferring sociodemographic variables to evaluate the correlates of vitamin D levels in individuals dwelling in the City of Calgary, Alberta, Canada. METHODS: We utilized data on vitamin D test results from Calgary Laboratory Services between January 1 2010 and August 31 2011. In addition to vitamin D level, we recorded age, sex, and vitamin D testing month as individual-level variables. We inferred sociodemographic variables by associating results with census dissemination areas and using Census Canada data to determine immigration status, education, median household income and first nations status as clustered variables. Associations between vitamin D status and the individual- and dissemination area-specific variables were examined using the population-averaged regression model by a generalized estimating equations approach to account for the clustering in the data. RESULTS: 158,327 individuals were included. Age, sex, month of vitamin D testing (at an individual level), and education, immigrant status, first nations status and income (at an aggregate level) were all statistically significant predictors of vitamin D status. CONCLUSIONS: Vitamin D status was associated with a number of sociodemographic variables. Knowledge of these variables may improve targeted education and public health initiatives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".