An estimate of the economic burden and premature deaths due to vitamin D deficiency in Canada
Bibliographic record
Abstract
The objective of this work is to estimate the economic burden and premature death rate in Canada attributable to low serum 25-hydroxyvitamin D (25(OH)D) levels. Vitamin D deficiency has been linked to many diseases and conditions in addition to bone diseases, including many types of cancer, several bacterial and viral infections, autoimmune diseases, cardiovascular diseases, and adverse pregnancy outcomes. Canadians have mean serum 25(OH)D levels averaging 67 nmol/L. The journal literature was searched for papers reporting dose-response relationships for vitamin D indices and disease outcomes. The types of studies useful in this regard include randomized controlled trials, observational, cross-sectional, and ecological studies, and meta-analyses. The mortality rates for 2005 were obtained from Statistics Canada. The economic burden data were obtained from Health Canada. The estimated benefits in disease reduction were based on increasing the mean serum 25(OH)D level to 105 nmol/L. It is estimated that the death rate could fall by 37,000 deaths (22,300-52,300 deaths), representing 16.1% (9.7-22.7%) of annuals deaths and the economic burden by 6.9% (3.8-10.0%) or $14.4 billion ($8.0 billion-$20.1 billion) less the cost of the program. It is recommended that Canadian health policy leaders consider measures to increase serum 25(OH)D levels for all Canadians.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".