Assessing the impact of attachment to primary care and unattachment duration on healthcare utilization and cost in Ontario, Canada: a population-based retrospective cohort study using health administrative data
Bibliographic record
Abstract
BACKGROUND: Insufficient access to primary care remains a major public health issue in Ontario, Canada, particularly for unattached residents (i.e., those who are not formally enrolled with a primary care provider, usually a family physician or occasionally a nurse practitioner). This study evaluates healthcare utilization and costs among unattached individuals, focusing on the impact of unattachment duration. METHODS: We conducted a population-based retrospective cohort study using health administrative data, comparing provincially insured residents who maintained a consistent attachment status over the 12-month period (April 1, 2021, to March 31, 2022) to those who were unattached. We employed multivariable regression analyses to examine the associations between attachment status, duration of unattachment, demographic and patient health characteristics, and healthcare utilization and costs. RESULTS: Prolonged periods of unattachment to primary care were significantly associated with increased healthcare costs, particularly in populations with a higher burden of comorbidities. In the context of healthcare costs, attached residents with low comorbidities had a median cost of $287, increasing to $3,711 (cost ratio: 12.93, CI: 12.86-13.01, p < 0.0001) for those with high comorbidities. Unattached individuals with low comorbidities had a median cost of $238 (cost ratio: 0.83, CI: 0.82-0.83, p < 0.0001), rising to $7,106 (cost ratio: 24.76, CI: 24.27-25.26, p < 0.0001) for high comorbidities, and up to $8,177 (cost ratio: 28.49, CI: 26.61-30.49, p < 0.0001) for long-term unattached with high comorbidities. CONCLUSIONS: Our findings underscore the substantial impact of long-term unattachment on both individual patients and the healthcare system, with higher levels of chronic disease further exacerbating these effects. These results are crucial for shaping programs and policies to maximize their impact on reducing emergency department visits, hospitalizations, and overall healthcare costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".