Abstract 9454: Risk of Incident Hypertension in Relation to Long-term Exposure to Ambient Air Pollution in Ontario, Canada
Bibliographic record
Abstract
Background - Laboratory studies suggest that exposure to fine particulate matter (2.5μm in diameter or less; PM2.5) can trigger a combination of changes in autonomic nervous system activity, systemic inflammation, and blood pressure which may in turn 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 long-term exposure to PM2.5 is associated with incident hypertension. Methods and Results - We assembled a cohort of 46,591 non-hypertensive adults from Ontario, Canada who responded to one of five 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=73% and specificity=95%). Six-year average concentrations of PM2.5 at the subjects’ residences were derived from satellite observations. We used Cox proportional hazards models, adjusting for various individual and contextual risk factors such as body mass index, smoking, physical activity, and family income. We examined the presence of effect modification for selected comorbidities and sociodemographic characteristics. During the follow-up period from 1996 to 2010, we identified a total of 12,061 incident cases of hypertension. For every 10μg/m3 increase of PM2.5, the adjusted hazard ratio (HR) was 1.16 (95% confidence interval (CI): 1.09-1.24). The strongest evidence for an interaction between PM2.5 and hypertension was for congestive heart failure (CHF): HRs of 2.75 (95% CI: 1.26-5.97) and 1.15 (95% CI: 1.08-1.23) among subjects with and without CHF, respectively (interaction p=0.02). Conclusions - This study suggests that exposure to PM2.5 may be a contributing risk factor to the development of 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".