Increased Mortality in a Nationwide Study of Gastrointestinal Hospitalizations in the United States During the 2020 Coronavirus Pandemic
Bibliographic record
Abstract
Background The impact of the coronavirus disease-2019 (COVID-19) pandemic on patients with acute gastrointestinal (GI) presentations including acute pancreatitis, diverticulitis, and GI bleeding, requiring hospitalization, has not been fully characterized at the population level in the United States. Aims We used the National Inpatient Sample to describe inpatient gastroenterology outcomes in the United States during the first year of the pandemic (2020), using 2018 and 2019 as comparator years. Methods Using the National Inpatient Sample, we explored year-to-year and month-to-month trends in hospitalizations, length of stay, and inpatient mortality for GI presentations, including luminal, biliary, infectious, inflammatory, and pancreatic diseases, with regression modeling. Relative change was used to compare time periods. Results We observed significantly lower rates of hospitalization for most acute GI conditions in 2020 relative to 2019. Despite this, we noted an increase in all-cause mortality (0.9% in 2019 and 1.1% in 2020, p<0.001) and hospital costs for patients hospitalized with acute presentations of GI-related conditions in 2020 relative to 2019. Importantly, we also observed increased mortality among COVID-19-positive patients who were hospitalized for acute pancreatitis (OR 2.56; 95% CI 1.37-6.53), variceal upper GI bleeding (OR 2.88; 95% CI 1.29-3.84), ulcerative colitis (OR 4.50; 95% CI 1.14-7.74), and acute cholangitis (OR 2.43; 95% CI 1.14-4.93). In 2020, the lowest number of admissions for all conditions occurred in April, coinciding with lockdowns ordered by most state governments. Conclusions Acute GI-related hospitalizations, in general, decreased in 2020 but this was associated with higher hospital costs and all-cause mortality increased compared with the pre-pandemic period.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".