Willingness to Pay for Colorectal Cancer Screening and Effect of Copayment in Southern Thailand
Bibliographic record
Abstract
Background: The incidence rate of colorectal cancer in Thailand is increasing. Hence, the nationwide screening programme with copayment is being considered. There are two proposed screening alternatives: annual fecal immunochemical test (FIT) and once-in-10-year colonoscopy. A copayment for FIT is 60 Thai baht (THB) per test (≈ 1.7 USD); a copayment for colonoscopy is 2,300 THB per test (≈ 65.5 USD). Methods: The willingness to pay (WTP) technique, which is theoretically founded on a cost-benefit analysis, was used to assess an effect of copayment on the uptake. Subjects were patients aged 50-69 years without cancer or screening experience. WTP for the proposed tests was elicited. Results: Nearly two thirds of subjects were willing to pay for FIT. Less than half of subjects were willing to pay for colonoscopy. Among them, median WTP for both tests was greater than the proposed copayments. In a probit model, knowing CRC patient and presence of companion were associated with non-zero WTP for FIT. Presence of companion, female, and family history of cancer were associated with non-zero WTP for colonoscopy. After adjustment for starting price in the linear model, marital status, drinking behavior, and risk attitude were associated with WTP. None of factors was significant for colonoscopy. Uptake decreased as levels of copayment increased. At proposed copayments, the uptake rates of 59.8% and 21.6% were estimated for colonoscopy and FIT respectively. The demand for FIT was price inelastic; the demand for colonoscopy was price elastic. Estimates of optimal copayment were 62.1 THB for FIT and 460.2 THB for colonoscopy. At the optimal copayment, uptake rates would be 59.8% for FIT and 42.3% for colonoscopy.Conclusion(s): More subjects were willing to pay for FIT than for colonoscopy (59.0% versus 46.5%). The estimated uptake rates were 59.8% and 21.6% for colonoscopy and FIT at the proposed copayments.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".