Costs and Cost Effectiveness of a Health Care Provider–Directed Intervention to Promote Colorectal Cancer Screening
Bibliographic record
Abstract
PURPOSE: Colorectal cancer (CRC) screening remains underutilized in the United States. Prior studies reporting the cost effectiveness of randomized interventions to improve CRC screening have not been replicated in the setting of small physician practices. We recently conducted a randomized trial evaluating an academic detailing intervention in 264 small practices in geographically diverse New York City communities. The objective of this secondary analysis is to assess the cost effectiveness of this intervention. METHODS: A total of 264 physician offices were randomly assigned to usual care or to a series of visits from trained physician educators. CRC screening rates were measured at baseline and 12 months. The intervention costs were measured and the incremental cost-effectiveness ratio (ICER) was derived. Sensitivity analyses were based on varying cost and effectiveness estimates. RESULTS: Academic detailing was associated with a 7% increase in CRC screening with colonoscopy. The total intervention cost was $147,865, and the ICER was $21,124 per percentage point increase in CRC screening rate. Sensitivity analyses that varied the costs of the intervention and the average medical practice size were associated with ICERs ranging from $13,631 to $36,109 per percentage point increase in CRC screening rates. CONCLUSION: A comprehensive, multicomponent academic detailing intervention conducted in small practices in metropolitan New York was clinically effective in improving CRC screening rates, but was not cost effective.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".