Trends in the Economic Burden of Chronic Liver Diseases and Cirrhosis in the United States: 1996–2016
Bibliographic record
Abstract
INTRODUCTION: The management of chronic liver diseases (CLDs) and cirrhosis is associated with substantial healthcare costs. We aimed to estimate trends in national healthcare spending for patients with CLDs or cirrhosis between 1996 and 2016 in the United States. METHODS: National-level healthcare expenditure data developed by the Institute for Health Metrics and Evaluations for the Disease Expenditure Project and prevalence of CLDs and cirrhosis derived from the Global Burden of Diseases Study were used to estimate temporal trends in inflation-adjusted US healthcare spending, stratified by setting of care (ambulatory, inpatient, emergency department, and nursing care). Joinpoint regression was used to evaluate temporal trends, expressed as annual percent change (APC) with 95% confidence intervals (CIs). Drivers of change in spending for ambulatory and inpatient services were also evaluated. RESULTS: Total expenditures in 2016 were $32.5 billion (95% CI, $27.0-$40.4 billion). Over 65% of spending was for inpatient or emergency department care. From 1996 to 2016, there was a 4.3%/year (95% CI, 2.8%-5.8%) increase in overall healthcare spending for patients with CLDs or cirrhosis, driven by a 17.8%/year (95% CI, 14.5%-21.6%) increase in price and intensity of hospital-based services. Total healthcare spending per patient with CLDs or cirrhosis began decreasing after 2008 (APC -1.7% [95% CI, -2.1% to -1.2%]), primarily because of reductions in ambulatory care spending (APC -9.1% [95% CI, -10.7% to -7.5%] after 2011). DISCUSSION: Healthcare expenditures for CLDs or cirrhosis are substantial in the United States, driven disproportionately by acute care in-hospital spending.
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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 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".