Geographic Equity in Hospital Utilization: Canadian Evidence Using a Concentration-Index Approach
Bibliographic record
Abstract
Distance-related geographic barriers challenge the ability of health systems to allocate health care resources equitably according to need. The paper adapts the concentration-index approach, commonly used for measuring income-related equity, to assess distance-related equity in hospital utilization in the province of Ontario, Canada. The analysis is based on individual-level data from the Canadian Community Health Survey, which provides information on respondents’ hospital utilization, health status, demographic, socio-economic status and location, merged with data on Ontario hospitals, and a geo-coded measure of each respondent’s distance to the nearest general acute-care hospital. We find no evidence of a relationship between distance to the nearest hospital and either the probability of hospitalization or the annual number of hospital nights. Supplementary analyses provide insight into hypothesized pathways between distance and hospitalization. Although having a regular medical doctor is positively associated with distance to the nearest hospital, controlling for this does not affect the estimated distance-hospitalization relationship. Both the size and occupancy rate of the nearest hospital are correlated with distance and are strongly related to the probability of hospitalization, but again controlling for these factors did not affect the estimated relationship between hospital use and distance to the nearest hospital. We do, however, find a strong positive gradient between the probability of hospitalization and distance to the nearest large hospital. This gradient is driven by the fact that, for most of those far from a large hospital, the nearest hospital is small with a low occupancy rate. Calculation of the distance-related horizontal inequity index confirms no distance-related inequity in hospital utilization when distance is measured to the nearest hospital of any size; however, when distance is instead measured to the nearest large hospital, we observe large, pro-distance inequity. These distance-use relationships are not captured by traditional geographic measures based on measures of urbanization/ruralness.
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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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".