Prevalence-adjusted trends in U.S. healthcare spending on upper gastrointestinal cancers, 1996 to 2016.
Bibliographic record
Abstract
e16064 Background: Upper gastrointestinal cancers are rising in prevalence and associated with high healthcare costs. We estimated trends in the US healthcare spending in patients with esophageal and stomach cancer between 1996 and 2016. Methods: We used data on national healthcare spending developed by the Institute for Health Metrics and Evaluations Disease Expenditure Project. Corresponding prevalence of esophageal and stomach cancer was estimated from the Global Burden of Diseases Study. Prevalence-adjusted, temporal trends in the US healthcare spending in patients with upper gastrointestinal cancer, stratified by age and setting of care (ambulatory, inpatient, emergency department, pharmaceutical prescriptions, nursing care and government administration) were calculated using joinpoint regression, expressed as annual percent change (APC) with 95% confidence intervals. Results: Overall, annual US healthcare spending on esophageal cancer increased from $0.76 billion (95% CI 0.68-0.86) in 1996 to $1.06 billion (95% CI 0.88-1.29) in 2016, although after adjusting for increasing prevalence, there was a significant decrease in per capita spending of -0.4%/year (95% CI -0.7%, -0.1%). Annual US healthcare spending on stomach cancer increased from $1.23 billion (95% CI $1.14 billion - $1.34 billion) in 1996 to $1.49 billion (95% CI $1.20 billion - $2.03 billion) in 2016. Per capita spending increased by 1.8%/year (95% CI 1.4%, 2.1%) between 1996 and 2011, followed by a decrease in gastric cancer-related per capita spending after 2011 (APC -4.4%/year [95% CI -5.8%, -2.9%]). Inpatient care was the largest contributor to total cost of both cancers between 1996-2016: 61.9% for esophageal cancer and 73.1% in gastric cancer in 2016. The rising price and intensity of care (defined as the cost per encounter) was the largest driver of change from 1996-2016 for both cancers, accounting for $0.28 billion (95% CI 0.12-0.41) for esophageal cancer and $0.95 billion (95% CI 0.41-1.39) for stomach cancer. Conclusions: After adjusting for rising prevalence, US per capita healthcare spending on esophageal cancer has decreased significantly since 1996, while per capita spending on gastric cancer has remained stable. Inpatient care was the most significant contributor to costs for both cancers over the time period studied.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| 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) | 0.004 | 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".