The Canadian Environmental Quality Index (Can-EQI): Development and calculation of an index to assess spatial variation of environmental quality in Canada’s 30 largest cities
Bibliographic record
Abstract
BACKGROUND: Multiple characteristics of the urban environment have been shown to influence population health and health-related behaviours, though the distribution and combined effects of these characteristics on health is less understood. A composite measure of multiple environmental conditions would allow for comparisons among different urban areas; however, this measure is not available in Canada. OBJECTIVES: To develop an index of environmental quality for Canada's largest urban areas and to assess the influence of population size on index values. METHODS: We conducted a systematic search of potential datasets and consulted with experts to refine and select datasets for inclusion. We identified and selected nine datasets across five domains (outdoor air pollution, natural environments, built environments, radiation, and climate/weather). Datasets were chosen based on known impacts on human health across the life course, complete geographic coverage of the cities of interest, and temporal alignment with the 2016 Canadian census. Each dataset was then summarized into dissemination areas (DAs). The Canadian Environmental Quality Index (Can-EQI) was created by summing decile ranks of each variable based on hypothesized relationships to health outcomes. RESULTS: We selected 30 cities with a population of more than 100,000 people which included 28,026 DAs and captured approximately 55% of the total Canadian population. Can-EQI scores ranged from 21.1 to 88.9 out of 100, and in Canada's largest cities were 10.2 (95% CI: -10.7, -9.7) points lower than the smallest cities. Mapping the Can-EQI revealed high geographic variability within and between cities. DISCUSSION: Our work demonstrates a valuable methodology for exploring variations in environmental conditions in Canada's largest urban areas and provides a means for exploring the role of environmental factors in explaining urban health inequalities and disparities. Additionally, the Can-EQI may be of value to municipal planners and decision makers considering the allocation of investments to improve urban conditions.
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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 | 0.002 | 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 | 0.001 | 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 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".