Economic Studies in Colorectal Cancer: Challenges in Measuring and Comparing Costs
Bibliographic record
Abstract
Estimates of the costs associated with cancer care are essential both for assessing burden of disease at the population level and for conducting economic evaluations of interventions to prevent, detect, or treat cancer. Comparisons of cancer costs between health systems and across countries can improve understanding of the economic consequences of different health-care policies and programs. We conducted a structured review of the published literature on colorectal cancer (CRC) costs, including direct medical, direct nonmedical (ie, patient and caregiver time, travel), and productivity losses. We used MEDLINE to identify English language articles published between 2000 and 2010 and found 55 studies. The majority were conducted in the United States (52.7%), followed by France (12.7%), Canada (10.9%), the United Kingdom (9.1%), and other countries (9.1%). Almost 90% of studies estimated direct medical costs, but few studies estimated patient or caregiver time costs or productivity losses associated with CRC. Within a country, we found significant heterogeneity across the studies in populations examined, health-care delivery settings, methods for identifying incident and prevalent patients, types of medical services included, and analyses. Consequently, findings from studies with seemingly the same objective (eg, costs of chemotherapy in year following CRC diagnosis) are difficult to compare. Across countries, aggregate and patient-level estimates vary in so many respects that they are almost impossible to compare. Our findings suggest that valid cost comparisons should be based on studies with explicit standardization of populations, services, measures of costs, and methods with the goal of comparability within or between health systems or countries. Expected increases in CRC prevalence and costs in the future highlight the importance of such studies for informing health-care policy and program planning.
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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 | 0.359 | 0.728 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.022 | 0.040 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".