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 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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".