Urban-Rural Disparities and Temporal Trends in Peptic Ulcer Disease Epidemiology, Treatment, and Outcomes in the United States
Bibliographic record
Abstract
INTRODUCTION: The incidence of peptic ulcer disease (PUD) has been decreasing over time with Helicobacter pylori eradication and use of acid-suppressing therapies. However, PUD remains a common cause of hospitalization in the United States. We aimed to evaluate contemporary national trends in the incidence, treatment patterns, and outcomes for PUD-related hospitalizations and compare care delivery by hospital rurality. METHODS: Data from the National Inpatient Sample were used to estimate weighted annual rates of PUD-related hospitalizations. Temporal trends were evaluated by joinpoint regression and expressed as annual percent change with 95% confidence intervals (CIs). We determined the proportion of hospitalizations requiring endoscopic and surgical interventions, stratified by clinical presentation and rurality. Multivariable logistic regression was used to assess independent predictors of in-hospital mortality and postoperative morbidity. RESULTS: There was a 25.8% reduction (P < 0.001) in PUD-related hospitalizations from 2005 to 2014, although the rate of decline decreased from -7.2% per year (95% CI: 13.2% to -0.7%) before 2008 to -2.1% per year (95% CI: 3.0% to -1.1%) after 2008. In-hospital mortality was 2.4% (95% CI: 2.4%-2.5%). Upper endoscopy (84.3% vs 78.4%, P < 0.001) and endoscopic hemostasis (26.1% vs 16.8%, P < 0.001) were more likely to be performed in urban hospitals, whereas surgery was performed less frequently (9.7% vs 10.5%, P < 0.001). In multivariable logistic regression, patients managed in urban hospitals were at higher risk for postoperative morbidity (odds ratio 1.16 [95% CI: 1.04-1.29]), but not death (odds ratio 1.11 [95% CI: 1.00-1.23]). DISCUSSION: The rate of decline in hospitalization rates for PUD has stabilized over time, although there remains significant heterogeneity in treatment patterns by hospital rurality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".