Trends in health care spending on kidney cancer in the United States, 1996–2016
Bibliographic record
Abstract
BACKGROUND: Paradigm shifts in kidney cancer management have led to higher health care spending. Here, total and per capita health care spending and primary drivers of change in health expenditures for kidney cancer in the United States between 1996 and 2016 are estimated. METHODS: Public databases developed by the Institute for Health Metrics and Evaluation for the Disease Expenditure Project were used. The prevalence of kidney cancer was estimated from the Global Burden of Disease Study. Changes in health care spending on kidney cancer were assessed by joinpoint regression and expressed as annual percent changes (APCs). RESULTS: In 2016, total health care spending on kidney cancer was $3.42 billion (95% CI, $2.91 billion to $3.89 billion) compared with $1.18 billion (95% CI, $1.07 billion to $1.31 billion) in 1996. Per capita spending had two inflection points in 2005 and 2008, close to the approval years of targeted therapies, which corresponded to APCs of +2.9% (95% CI, +2.3% to +3.6%; p < .001) per year, 1996-2005; +9.2% (95% CI, +3.4% to +15.2%; p = .004) per year, 2005-2008; and +3.1% (95% CI, +2.2% to +3.9%; p < .001) per year, 2008-2016. Inpatient care was the largest contributor to health expenditures, which accounted for $1.56 billion (95% CI, $1.19 billion to $1.95 billion) in 2016. Price and intensity of care was the primary driver of increased health expenditures, whereas service utilization was the primary driver of reduced health expenditures. CONCLUSIONS: Prevalence-adjusted health care spending on kidney cancer continues to rise in the United States, which is primarily attributable to inpatient care and driven by the price and intensity of care over time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".