Diabetes Status and Susceptibility to the Effects of PM2.5 Exposure on Cardiovascular Mortality in a National Canadian Cohort
Bibliographic record
Abstract
BACKGROUND: Diabetes is infrequently coded as the primary cause of death but may contribute to cardiovascular disease (CVD) mortality in response to fine particulate matter (PM2.5) exposure. We analyzed all contributing causes of death to examine susceptibility of diabetics to CVD mortality from long-term exposure. METHODS: We linked a subset of the 2001 Canadian Census Health and Environment Cohort (CanCHEC) with 10 years of follow-up to all causes of death listed on death certificates. We used survival models to examine the association between CVD deaths (n = 123,500) and exposure to PM2.5 among deaths that co-occurred with diabetes (n = 20,600) on the death certificate. More detailed information on behavioral covariates and diabetes status at baseline available in the Canadian Community Health Survey (CCHS)-mortality cohort (n = 12,400 CVD deaths, with 2,800 diabetes deaths) complemented the CanCHEC analysis. RESULTS: Among CanCHEC subjects, comention of diabetes on the death certificate increased the magnitude of association between CVD mortality and PM2.5 (HR = 1.51 [1.39-1.65] per 10 μg/m) versus all CVD deaths (HR = 1.25 [1.21-1.29]) or CVD deaths without diabetes (HR = 1.20 [1.16-1.25]). Among CCHS subjects, diabetics who used insulin or medication (included as proxies for severity) had higher HR estimates for CVD deaths from PM2.5 (HR = 1.51 [1.08-2.12]) relative to the CVD death estimate for all respondents (HR = 1.31 [1.16-1.47]). CONCLUSIONS: Mention of diabetes on the death certificate resulted in higher magnitude associations between PM2.5 and CVD mortality, specifically among those who manage their diabetes with insulin or medication. Analyses restricted to the primary cause of death likely underestimate the role of diabetes in air pollution-related mortality. See video abstract at, http://links.lww.com/EDE/B408.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".