The Direct Healthcare Expenditures of Prostate Cancer by Disease Severity: Evidence from US National Survey Data
Bibliographic record
Abstract
Background: In the United States, approximately 2.8 million men have a history of prostate cancer (PC). Objective: This study quantified the effects of PC, overall and by disease severity on direct healthcare costs to insurers and patients. Methods: Using 1996–2010 data from the Medical Expenditure Panel Survey (MEPS), a large, nationally representative US database, multivariate analyses were used to assess the relationship between PC and direct annual healthcare costs to insurers and patients, at individual and US aggregate levels. Men aged 40 years and older with International Classification of Diseases, Ninth Revision (ICD-9) diagnosis code 185 were identified. Disease severity was determined with clinical assistance and based, in part, on the data in MEPS. The cohorts were: localized cancer not treated with chemotherapy, localized cancer treated with chemotherapy, and metastatic cancer. Results: The MEPS database included 1297 patients with PC: 811 patients with localized PC not treated with chemotherapy, 426 patients with PC treated with chemotherapy, and 60 patients with metastatic PC. PC had a larger effect on incremental costs for metastatic patients, $20 357, vs $16 709 for localized PC with chemotherapy, and $5238 for localized PC with no chemotherapy. When aggregated to the US population, PC accounted for an incremental annual cost of $15 billion. The largest aggregate annual costs were incurred by patients with localized PC treated with chemotherapy ($8.6 billion), compared to those not treated with chemotherapy ($4.8 billion) and metastatic patients ($1.6 billion). Conclusions: The aggregate annual costs of PC are substantial for all groups examined and greatest for patients with localized cancer treated with chemotherapy. This reflects the relatively high prevalence and high per capita healthcare expenditures associated with this group. With a growing and aging population, the prevalence of PC is expected to rise, increasing the burden on public health.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".