What can population-based physician billing data tell us about the prevalence, costs and disorders associated with different types of cancers based on the 16 years prevalence of cancer diagnosis?
Bibliographic record
Abstract
Background: Annual rates of cancer diagnosis and costs are reported for specific cancers and age groups over 16 years using health utilization data in addition to the odds ratios for broad International Classification of Disease (ICD) categories of associated disorders. Methods: Using physician assigned ICD diagnosis, annual cancer diagnosis rates of six cancers (colorectal, breast, prostate, lung, mesothelioma, and pancreatic) were measured for the period of 1994-2009 in the Calgary, Alberta catchment area. As well, the patient cohort diagnosed with any neoplasm (n = 261,896) was analyzed by year for three age groups: youth (< 25 years), adult (26 years – 69 years), and geriatric (≥ 70 years). Total direct cancer diagnosis costs and associated disorders costs were calculated by year and mean total costs compared by type of cancer. Odds ratio were calculated for each broad category of ICD diagnosis given the presence or absence of specified cancer types. Results: Annual rates of diagnosis increased for all six cancers and all three age groups. All six cancers showed their annual rates of diagnosis to be at least 2.1 times greater in 2009 compared to 1994. Colorectal cancer maintained the highest annual cancer rate of diagnosis, the geriatric group had the highest annual rates of cancer diagnosis out of the three age groups, and the youth group annual rates of cancer diagnosis increased by a factor of 2.6. Breast cancer had the highest associated per patient costs whereas prostate cancer had the lowest. In addition to other neoplasms, odds ratios indicated that most cancer types were associated with disorders of the blood and blood producing organs. Conclusion: Prevalence has been steadily increasing in the Calgary, AB catchment over the study period. Trends in annual rates of diagnosis have implications for future burden on healthcare systems and provide a basis for comparison of local rates and expenditures with other healthcare principalities.
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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 | 0.010 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".