Exploring community-level health impacts of extreme temperatures and air pollution in older adult and immigrant populations living in Edmonton, AB
Bibliographic record
Abstract
BACKGROUND AND AIM: Climate change and air pollution pose a significant challenge to public health as the health impacts of these exposures are felt at a local scale and depend on socio-environmental context. However, knowledge of what factors promote or reduce resilience to these exposures in Edmonton, AB (pop 972,223) is largely missing. Assessing the relationship between exposures to air pollution and extreme temperatures and community health, we will generate novel insights into the development of climate change and air pollution resilience in older adults and immigrants. Findings will inform community-level planning for effective, targeted adaptation measures and lay groundwork for developing a real-time vulnerability index based on climate change. METHODS: This exploratory ecological study assessed spatial differences in the association between climatic and air pollution variables (extreme temperatures, ambient air pollution) and health events (cardiovascular, respiratory, mental health, and musculoskeletal outcomes) among Edmonton’s Dissemination Areas using generalized linear models. Community-level factors (demographics, socioeconomic status, social isolation, active living environment, health facility availability) were explored in terms of confounding and effect modification. RESULTS:Preliminary results indicate certain air pollutants are associated with a higher risk for increased rates of health events per capita, as well as increasing age. We observed a healthy immigrant effect; areas with higher proportions of immigrants demonstrated lower rates of cardiovascular, mental health, and injury events. Material and social deprivation, access to green space, and active living environment were identified as critical community-level factors in specific relationships. CONCLUSIONS:The effects of air pollution and climate change exposures on a northern metropolitan’s community health depend on specific exposures, outcomes, and community characteristics. We need to further characterize how population composition and community characteristics (i.e. access to healthcare, social support) drive local health risk to target higher risk populations with meaningful approaches to reduce the health impact of climate change and air pollution. KEYWORDS: climate change, air pollution, community health, older adults, immigrants
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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 | 0.001 |
| 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".