Spatial Analysis and Determinants of Asthma Health and Health Services Use Outcomes in Ontario
Bibliographic record
Abstract
This thesis explores the spatial patterns and determinants of asthma prevalence and health services use (ICD-10 codes J45, J46) for the total population (all ages and both sexes combined) of the province of Ontario, Canada, between 2003 and 2013. Asthma is characterized by high health services use and reduced quality of life for asthma sufferers, representing a considerable burden on individuals, society and the health care system. While recent evidence suggests increasing asthma prevalence in Ontario, little research has been done to understand the identified spatial variability of this disease. Using population-based, ecological-level data and refined spatial analysis techniques, this research aims to explore the spatial patterns of asthma prevalence and health services use in Ontario, and examine the contribution of potential risk factors including air pollution, pollen, deprivation, physician supply and rurality. Results indicated considerable spatial variability in asthma outcomes across Ontario. Similar patterns were found between asthma prevalence and physician visits; clusters of high rates were generally found in southern urban/suburban areas, and clusters of low rates were mainly identified in most northern and southern rural areas. Conversely, clusters of high rates of ED visits and hospitalizations were found in most northern and southern rural areas, whereas clusters of low rates were found in south urban/suburban areas near Toronto. Findings from the spatial regression analysis indicated that while rurality was negatively associated with asthma prevalence and physician visits, it was positively associated with ED visits. Moreover, positive associations were also found between material deprivation and asthma prevalence and ED visits, and between NO2 and asthma physician visits. This research contributes to a better understanding of area characteristics that influence asthma disparities, which can help develop better, locally relevant public health strategies aimed at reducing the burden of asthma in Ontario. Further, it demonstrates the importance of using a population-based framework and spatial analysis approaches, which take into account the spatial nature of asthma morbidity and their determinants.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".