Does attachment to a family physician reduce emergency department visits? A difference-in-differences analysis of Quebec’s centralized waiting lists for unattached patients
Bibliographic record
Abstract
Abstract Background Patients without a regular primary care provider – unattached patients – are more likely to visit hospital emergency departments (ED), leading to poor patient and health system outcomes. In many Canadian provinces, policy responses to improve primary care access and reduce ED utilization of unattached patients have included centralized waiting lists to help find a primary care provider and formal attachment (rostering, empanelment, enrollment, registration) to a family physician. While previous work suggests attachment improves access and continuity of primary care (1), it is unknown whether this translates into fewer ED visits. The aim of this study was to determine whether the rate of emergency department visits significantly decreases in patients attached to a family physician through Quebec’s centralized waiting lists for unattached patients. Methods We used a quasi-experimental difference-in-differences approach, studying patients attached through Quebec’s centralized waiting lists in 2012–2014. We used administrative medical services physicians’ billing data from the Régie de l’Assurance Maladie du Québec (RAMQ). Attachment was determined based on fee codes used to formalize attachment. We compared the change in the rate of emergency department visits over two 12-month periods, for ‘exposed’ patients who became attached (n = 207,669) and ‘control’ patients who remained unattached during the study period (n = 90,637). To balance baseline patient characteristics in the exposed and control cohorts, we calculated a propensity score including age, sex, Charlson-co-morbidity index, medical vulnerability, and region remoteness and performed inverse probability of treatment weighting. We used descriptive statistics and estimated negative binomial regression models, fitted with generalized estimating equations. Results After weighting, cohorts had similar characteristics (standardized differences < 10%). Attached (exposed) patients’ mean annual ED visits decreased from 0.60 to 0.49 (18.3%) following attachment, while unattached (control) patients’ increased from 0.54 to 0.69 (27.8%). The difference-in-differences estimate (Time period*exposure) showed a significant 36% relative reduction (IRR = 0.64, p < 0.001) in the rate of ED visits for patients who were attached, compared to patients who remained unattached on the centralized waiting lists during the study period. Conclusion Our findings suggest that attachment to a family physician through centralized waiting lists for unattached patients significantly reduces the rate of ED utilization.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".