Association of type 1 diabetes and concentrations of drinking water components in Newfoundland and Labrador, Canada
Bibliographic record
Abstract
OBJECTIVE: To determine the association between drinking water quality and rates of type 1 diabetes in the Newfoundland and Labrador (NL) population, which has one of the highest incidences of type 1 diabetes reported globally. RESEARCH DESIGN AND METHODS: The study used a community-based, case-control design. We first calculated incidence rates of type 1 diabetes at the provincial, regional and community levels. The connection between incidence rates and components in public water supplies were then analyzed in three ways: to evaluate differences in water quality between communities with and without incident cases of type 1 diabetes, and to analyze the relationship between water quality and incidence rates of type 1 diabetes at both the community and regional levels. RESULTS: The provincial incidence of type 1 diabetes was 51.7/100 000 (0-14 year age group) for the period studied. In the community-based analysis, there were significant associations found between higher concentrations of arsenic (β=0.268, P=0.013) and fluoride (β=0.202, P=0.005) in drinking water and higher incidence of type 1 diabetes. In the regional analysis, barium (β=-0.478, P=0.009) and nickel (β=-0.354, P=0.050) concentrations were negatively associated with incidence of type 1 diabetes. CONCLUSIONS: We confirmed the high incidence of type 1 diabetes in NL. We also found that concentrations of some components in drinking water were associated with higher incidence of type 1 diabetes, but no component was found to have a significant association across the three different levels of analysis performed.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".