National Demographic and Economic Trends of Acute Diverticulitis in the United States From 2000-2015
Bibliographic record
Abstract
Introduction: Acute diverticulitis (AD) remains a common health problem often requiring inpatient admission. The purpose of this study is to analyze trends in demographics, in-hospital morbidity and mortality, and costs of hospitalizations for AD. Methods: The Nationwide Inpatient Sample (NIS) database generated from the Healthcare Cost and Utilization Project was used to obtain a subset of discharge level data obtained from every 3 years from 2000 to 2015 (until Q3). All hospital discharges with ICD-9 CM primary diagnosis codes of diverticulitis (with and without hemorrhage) were identified. SPSS version 23 was used to conduct analysis on demographics, charges, morbidity and mortality. Results: 1,193,648 total discharges for AD were recorded between 2000-2015 with an overall increase in incidence from 167,249 in 2000 to 227,935 in 2012. There was a consistent predominance for female over males in all years (approximate ratio of 1.4:1). The majority of patients identified were Caucasians over the age of 45, with a regional predominance in the South. Median length of stay (LOS) remained stable at 4 days until 2015 (median 3 days). The number of AD admissions with associated surgical procedure decreased from 24.4% in 2000 to 21.1% in 2015. In-hospital mortality reduced from 1.2% to 0.5% between 2000-2012. Despite stable LOS, reduction in mortality and surgical intervention, the median charges per hospitalization increased by approximately 2.5 fold from 2000 ($9444) to 2015 ($24,472). Conclusion: This study highlights that the number of hospitalizations for acute diverticulitis continues to increase from 2000 to 2015. Despite improvements of markers for morbidity and mortality, there has been an increase in inpatient hospital costs for AD. This is consistent with prior studies showing increasing emergency department burden of AD (1). Further studies are needed to elucidate factors contributing to the increasing costs, increasing incidence, and regional variation such as the specific dietary and environmental factors that could help explain the higher incidence in the Southern U.S (2). Future strategies should focus on prevention of AD and reduction of costs per hospital stay 1. Bollom, A et al. Emergency Department Burden of Diverticulitis in the USA, 2006-2013. Digestive Diseases and Sciences. 2017. 62:2694-2703. 2. Nguyen, G et al. Epidemiological trends and geographic variation in hospital admissions for diverticulitis in the United States. 2011. World Journal of Gastroenterology, 1600-1605.183 Figure 1. Nationwide Inpatient Sample 2000-2015, every 3 years, Epidemiological Data for Acute Diverticulitis
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".