A spatial analysis of COPD prevalence, incidence, mortality and health service use in Ontario.
Bibliographic record
Abstract
BACKGROUND: Risk factors for chronic obstructive pulmonary disease (COPD) include smoking, occupational exposure and air pollution, which vary geographically, but relatively little is known about how COPD varies spatially. DATA AND METHODS: This population-based ecological analysis examines physician-diagnosed COPD prevalence, incidence, mortality, and health care services use in Ontario over a 10-year period. Data were mapped and analyzed at the sub-Local Health Integration Network level (n = 141). Comparative morbidity figures were calculated and analyzed for local clusters of high and low rates of COPD health and health service use outcomes. RESULTS: A total of 722,494 individuals were identified as having COPD over the study period. Clusters of high rates in health outcomes and in most indicators of health service use emerged in northern parts of Ontario and in industrial and more rural agricultural areas. Clusters of low rates were centered on major urban and suburban areas. An exception was COPD-specific physician visits, which were lower in northern areas suggesting greater reliance on acute care. INTERPRETATION: This study highlights the need for research focused on explaining the spatial patterns identified here.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".