Spatial Association Between Ambient Fine Particulate Matter and Incident Hypertension
Bibliographic record
Abstract
BACKGROUND: Laboratory studies suggest that exposure to fine particulate matter (≤2.5 μm in diameter) (PM2.5) can trigger a combination of pathophysiological responses that may induce the development of hypertension. However, epidemiological evidence relating PM2.5 and hypertension is sparse. We thus conducted a population-based cohort study to determine whether exposure to ambient PM2.5 is associated with incident hypertension. METHODS AND RESULTS: We assembled a cohort of 35 303 nonhypertensive adults from Ontario, Canada, who responded to 1 of 4 population-based health surveys between 1996 and 2005 and were followed up until December 31, 2010. Incident diagnoses of hypertension were ascertained from the Ontario Hypertension Database, a validated registry of persons diagnosed with hypertension in Ontario (sensitivity=72%, specificity=95%). Estimates of long-term exposure to PM2.5 at participants' postal-code residences were derived from satellite observations. We used Cox proportional hazards models, adjusting for various individual and contextual risk factors including body mass index, smoking, physical activity, and neighbourhood-level unemployment rates. We conducted various sensitivity analyses to assess the robustness of the effect estimate, such as investigating several time windows of exposure and controlling for potential changes in the risk of hypertension over time. Between 1996 and 2010, we identified 8649 incident cases of hypertension and 2296 deaths. For every 10-µg/m(3) increase of PM2.5, the adjusted hazard ratio of incident hypertension was 1.13 (95% confidence interval, 1.05-1.22). Estimated associations were comparable among all sensitivity analyses. CONCLUSIONS: This study supports an association between PM2.5 and incident hypertension.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".