Geographical disparities in access to hospital care in Ontario, Canada: a spatial coverage modelling approach
Bibliographic record
Abstract
OBJECTIVES: Previous studies on geographical disparities in healthcare access have been limited by not accounting for the healthcare provider's capacity, a key determinant of supply and demand relationships. DESIGN: This study proposed a spatial coverage modelling approach to evaluate disparities in hospital care access using Canadian Institute for Health Information data in 2007. SETTING: This study focusses on accessibility of inpatient and emergency cares at both levels of individual hospital and the administrative regions of Local Health Integration Network (LHIN) levels. MEASURES: We integrated a set of traffic and geographical data to precisely estimate travel time as a measure of the level of accessibility to the nearest hospital by three scenarios: walking, driving and a combination of the both. We estimated population coverage rates, using hospital capacities and population in the catchments, as a measure of the level of the healthcare availability. Hospital capacities were calculated based on numbers of medical staff and beds, occupation rates and annual working hours of healthcare providers. RESULTS: We observed significant disparities in hospital capacity, travel time and population coverage rate across the LHINs. This study included 25 teaching and 148 community hospitals. The teaching hospitals had stronger capacities with 489 209 inpatient and 130 773 emergency patients served in the year, while the population served in community hospitals were 2.64 times higher. Compared with north Ontario, more locations in the south could reach to hospitals within 30 min irrespective of the travel mode. Additionally, Northern Ontario has higher population coverage rates, for example, with 42.6~46.9% for inpatient and 15.7~44% for emergency cares, compared with 2.4~34.7% and 0.35~14.6% in Southern Ontario, within a 30 min catchment by driving. CONCLUSION: Creating a comprehensive, flexible and integrated healthcare system should be considered as an effective approach to improve equity in access to care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".