Physician payment models and preventive cancer screening: a population-based retrospective cohort analysis from Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Ontario's primary care reforms have introduced three blended physician payment models: (i) blended fee-for-service (BFFS), (ii) blended capitation without interprofessional teams, and (iii) blended capitation with teams. Each model includes the same pay-for-performance incentives, yet their impact on cancer screening, including that during the COVID-19 pandemic, remains unclear. METHODS: We used linked administrative data (2018-23) to examine the associations between these models and colorectal, cervical, and breast cancer screening rates. Fractional probit regression models, adjusting for physician and patient characteristics, estimated the effects of each payment model relative to the BFFS. Stratified analyses explored heterogeneity by physician sex, age, practice size, rurality, and socioeconomic deprivation. RESULTS: Compared with the BFFS model, the blended capitation models were associated with higher screening rates, although initial differences were modest. By 2022, nonteam and team capitation models had colorectal screening rates 3.0% and 3.6% higher, respectively, than those of the BFFS. Similar but smaller increases were observed for cervical and breast cancer screening. These advantages persisted through COVID-19 disruptions and were most pronounced among physicians serving rural or socioeconomically disadvantaged populations. Stratified analyses indicated that female, younger, and higher-volume physicians performed better in capitation-based models. CONCLUSIONS: Blended capitation arrangements, especially those integrating interprofessional teams, appear more effective than the BFFS in delivering preventive cancer screening. Strengthening team-based primary care and targeted incentives could bolster preventable cancer screening rates in the population, even under pandemic-related challenges. These findings can inform policy decisions aimed at improving population health through optimized primary care provisions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".