Estimated economic benefit of increasing 25-hydroxyvitamin D concentrations of Canadians to or above 100 nmol/L
Bibliographic record
Abstract
Mounting evidence from observational and clinical trials indicates that optimal vitamin D reduces the risk of many diseases. We used observational studies and recent data on 25-hydroxyvitamin D [25(OH)D] concentrations of Canadians from Cycle 3 of the Canadian Health Measures Survey to estimate the reduction in disease incidence, mortality rates, and the total economic burden (direct plus indirect) of disease if 25(OH)D concentrations of all Canadians were raised to or above 100 nmol/L. Recently, the mean 25(OH)D concentration of Canadians varied depending on age and season (51-69 nmol/L), with an overall mean of 61 nmol/L. The diseases affected by 25(OH)D concentration included cancer, cardiovascular disease, dementia, diabetes mellitus, multiple sclerosis, respiratory infections, and musculoskeletal disorders. We used 25(OH)D concentration-health outcome relations for breast cancer and cardiovascular disease and results of clinical trials with vitamin D for respiratory infections and musculoskeletal disorders to estimate the reductions in disease burden for increased 25(OH)D concentrations. If all Canadians attained 25(OH)D concentrations>100 nmol/L, the calculated reduction in annual economic burden of disease was $12.5 ± 6 billion on the basis of economic burdens for 2016 and a reduction in annual premature deaths by 23,000 (11,000-34,000) on the basis of rates for 2011. However, the effects on disease incidence, economic burden, and mortality rate would be phased in gradually over several years primarily because once a chronic disease is established, vitamin D affects its progression only modestly. Nevertheless, national policy changes are justified to improve vitamin D status of Canadians through promotion of safe sun exposure messages, vitamin D supplement use, and/or facilitation of food fortification.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".