Measuring equity in per capita primary care investment in Ontario: Challenges for data linkage and analysis
Bibliographic record
Abstract
IntroductionFifteen years ago almost all primary care physicians (PCPs) were paid fee-for-service. Now, many physicians receive other payments as well, including capitation payments, incentives and bonuses and funding for other health professionals. It is challenging to track these changes in primary care payment and understand how they relate to individual patients.
 Objectives and ApproachThe objectives of this study were to assess changes in PCP payments from 2002/03 to 2011/12 and examine differences in per capita investment by urban-rural status, recent arrival (proxy for immigrant status) and income quintile. This required a three-step approach: assigning payments to physicians, assigning patients to physicians and then apportioning the payments by patient. Payments were apportioned based on the type of payment and how the data were captured. For example, capitation payments were paid monthly, but without any detail as to which patients they were for, so all capitation payments were summed and apportioned among all rostered patients.
 ResultsAll PCPs for whom we had payment data and to whom patients could be assigned were included. Three types of physician-patient 'relationships' were identified: the patient was on the physician's formal roster; the patient was 'virtually' rostered to the physician who provided the plurality of their care; or the patient was part of the physician's overall panel, which includes all patients seen during the year, rostered and not. The type of relationship determined which payment were allocated to each patient. When the $3.5B in payments were apportioned and different populations compared, we found inequities in new primary care investment by income, immigrant status and rurality. For example, we found a disproportionate investment in interdisciplinary teams for non-immigrant Ontarians living in more well-off suburban areas.
 Conclusion/ImplicationsEstimating per capita primary care investment is a challenging but worthwhile undertaking. The results of this study suggest that the Government of Ontario should facilitate increased participation in new primary care models by immigrants and people living in major urban centres.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".