National Trends in Healthcare Expenditures for the Management of Skin Cancer in the United States
Bibliographic record
Abstract
BACKGROUND: Malignant melanoma and keratinocyte carcinomas account for a substantial proportion of healthcare expenditures in the United States. OBJECTIVE: To estimate trends in the economic burden of skin cancer in the United States between 1996 and 2016. METHODS: The Disease Expenditure Project and Global Burden of Disease databases were used to estimate annual total costs and population-standardized rates of change for skin cancer-related healthcare spending. RESULTS: Skin cancer expenditures totaled $23.4 billion in 2016, of which $1.4 billion (95% CI: $1.3-$1.6 billion) was melanoma-related and $22 billion (95% CI: $18-$28 billion) was keratinocyte carcinoma-related. Most spending on skin cancer management occurred in ambulatory care settings [60.7% (95% CI: 57.7%-64.3%) for melanoma and 87.8% (95% CI: 87.2%-88.2%) for keratinocyte carcinoma]. Pharmaceutical costs for melanoma have increased since 2010 to $365 million (95% CI: $327-$416 million), which represents 26.1% (95% CI: 22.6%-29.3%) of total melanoma expenditure. CONCLUSIONS: Skin cancer management in the United States is costly. Expenditures have increased substantially since 1996 without signs of plateauing in recent years.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".