Effect of Payment Incentives on Cancer Screening in Ontario Primary Care
Bibliographic record
Abstract
PURPOSE: There is limited evidence for the effectiveness of pay for performance despite its widespread use. We assessed whether the introduction of a pay-for-performance scheme for primary care physicians in Ontario, Canada, was associated with increased cancer screening rates and determined the amounts paid to physicians as part of the program. METHODS: We performed a longitudinal analysis using administrative data to determine cancer screening rates and incentive costs in each fiscal year from 1999/2000 to 2009/2010. We used a segmented linear regression analysis to assess whether there was a step change or change in screening rate trends after incentives were introduced in 2006/2007. We included all Ontarians eligible for cervical, breast, and colorectal cancer screening. RESULTS: We found no significant step change in the screening rate for any of the 3 cancers the year after incentives were introduced. Colon cancer screening was increasing at a rate of 3.0% (95% CI, 2.3% to 3.7%) per year before the incentives were introduced and 4.7% (95% CI, 3.7% to 5.7%) per year after. The cervical and breast cancer screening rates did not change significantly from year to year before or after the incentives were introduced. Between 2006/2007 and 2009/2010, $28.3 million, $31.3 million, and $50.0 million were spent on financial incentives for cervical, breast, and colorectal cancer screening, respectively. CONCLUSIONS: The pay-for-performance scheme was associated with little or no improvement in screening rates despite substantial expenditure. Policy makers should consider other strategies for improving rates of cancer screening.
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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 | 0.000 |
| 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".