Do community demographics, environmental characteristics and access to care affect risks of developing ACOS and mortality in people with asthma?
Bibliographic record
Abstract
Individuals with asthma and chronic obstructive pulmonary disease (COPD) overlap syndrome (ACOS) have a more rapid decline in lung function, more frequent exacerbations and worse quality of life than those with asthma or COPD alone [1–3]. Various risk factors may be associated with the development of ACOS, such as smoking history and status, obesity, comorbidity and indoor and outdoor environmental exposures [1, 4–6]. The risk of developing ACOS may vary substantially by region, since demographic and environmental risk factors and community characteristics are not geographically homogeneous. Here, we use population-based data to estimate the incidence of ACOS in the asthma population and to measure the association between demographic factors, community-level characteristics and environmental factors and the risk of incident ACOS and all-cause mortality while accounting for spatial autocorrelation. Material deprivation increases ACOS and death risk in people with asthma; air pollution may also increase death risk Health administrative data were provided by the Institute for Clinical Evaluative Sciences (ICES) and air pollution data were provided by the Ministry of the Environment and Climate Change (MOECC). Neither ICES nor the MOECC had any role in study design, analysis, interpretation of data, or writing of the report. No endorsement by ICES or the MOECC is intended or should be inferred. Practice locations of currently certified respirologists were obtained from the website of the College of Physicians and Surgeons of Ontario ([www.cpso.on.ca/][1]). Practice location of currently certified asthma/COPD educators and certified respiratory therapists were provided by the College of Respiratory Therapists of Ontario. Finally, the geographic locations of pulmonary function testing laboratories or spirometry clinics were obtained from the Canadian Lung Association ( ). Kristian Larsen received a Postdoctoral Fellowship, in part, through the Hospital for Sick Children Research Training Centre and the CRRN. Andrea Gershon holds a New Investigator Career Award from CIHR and was also the recipient of 2015 Early Career Achievement Award of the Assembly on Behavioural Science and Health Services Research, American Thoracic Society. Teresa To was the recipient of the 2016 Meritorious Service Award of the Ontario Lung Association. Author contributions are as follows. T. To initiated and designed the study, conducted the spatial modelling and statistical analysis, interpreted findings and drafted the manuscript. J. Zhu compiled the data and conducted statistical analysis. K. Larsen compiled the geographical variables and conducted GIS mapping. L.Y. Feldman and K. Ryckman conducted a search of the literature, summarised relevant study findings and reviewed the manuscript. All authors interpreted findings, reviewed and commented on drafts, have seen and approved the final version. [1]: http://www.cpso.on.ca/
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".