The Impact of Rurality and Geography on Healthcare Service Access for Children with Asthma
Bibliographic record
Abstract
Background: Asthma is a highly prevalent chronic disease amongst the Ontario pediatric population; however, the extent to which rural status and distance are associated with unplanned emergency department (ED) use by this population is largely unknown. Objective: To explore the impact of rurality and geographical distance to services on healthcare utilization by analyzing the use of general practitioner/family physician (GP/FP), specialist and unplanned ED services. Methods: A population level retrospective cohort study of Ontario children ages 5-14 with newly diagnosed asthma was conducted using health administrative data from the Institute for Clinical Evaluative Sciences (ICES). Bivariate descriptive statistics were used to assess healthcare use depending on rurality and distance to services. Adjusted logistic regression models were used to analyze the association between unplanned ED use with rural status and distance to GP/FP and specialist healthcare services, while controlling for confounders. Results: In total, 19,732 individuals met the inclusion criteria. Rurality and geographical distance to services were significantly associated with GP/FP, specialist and unplanned ED use. Rural participants were more likely to visit a GP/FP (OR 1.74, 95% CI 1.50-2.01) and less likely to visit a specialist (OR 0.56, 95% CI 0.49-0.64) in comparison to urban participants. Distance to a GP/FP or specialist was weakly associated with the number of healthcare provider visits; rs=-0.04 and rs=-0.09 respectively. Individuals who lived in rural locations (OR 2.00, 95% CI 1.64-2.44) and travelled >50km to a GP/FP (OR 1.25, 95% CI 1.06-1.48) or specialist (OR 1.20, 95% CI 1.05-1.38) were more likely to utilize an ED. Conclusion: Children with asthma utilize healthcare services differently based on rural status and distance to services. Rural children were more likely to utilize GP/FP services, less likely to utilize specialist services and more likely to have an unplanned ED visit when compared to urban children. Children who resided further (>50km) from a healthcare provider were less likely to have a GP/FP or specialist visit when compared to those who resided closer (≤50km). To ensure equitable access to care, there is a need to accommodate for these factors in the planning and provision of asthma healthcare services.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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".