Global burden of paralytic ileus and intestinal obstruction, 1990–2021: a GBD 2021 analysis
Bibliographic record
Abstract
PURPOSE: Paralytic ileus and intestinal obstruction (PI&IO) are significant global surgical emergencies associated with high morbidity and mortality. This study aimed to comprehensively assess the global, regional, and national burden of PI&IO from 1990 to 2021. METHODS: We used data from the Global Burden of Diseases Study (GBD) 2021, covering 204 countries and territories. We estimated the number of incident cases and years of life lost (YLLs), along with age-standardized incidence rates (ASIR) and age-standardized YLLs rates (ASYR), each with corresponding 95% uncertainty intervals (UIs). Analyses were stratified by age, sex, region, and Socio-demographic Index (SDI). RESULTS: In 2021, there were approximately 15.8 million PI&IO cases (95% UI: 15.2-16.3 million) and 6.5 million YLLs (95% UI: 5.6-7.2 million) globally. The ASIR was 191.9 per 100,000 (95% UI: 185.4-198.8), showing no significant change since 1990. In contrast, the ASYR decreased slightly reaching 82.3 per 100,000 in 2021. Regionally, ASIRs were highest in high-income Asia Pacific, North America, and Australasia, while ASYRs peaked in Eastern and Western Sub-Saharan Africa. Nationally, Canada, Japan, and Cabo Verde had the highest ASIRs, whereas Mozambique, Eritrea, and Somalia reported the highest ASYRs. Age-specific trends revealed a J-shaped incidence curve and a U-shaped YLLs pattern, with the greatest burden in infants under 1 year and adults aged 80 years or older. YLLs in infants have declined steadily over the past three decades. A positive correlation was observed between ASIR and SDI, while ASYR was negatively associated with SDI. CONCLUSION: PI&IO continues to impose a considerable global health burden, with pronounced disparities across regions and socioeconomic contexts. While high-SDI regions experience higher incidence due to enhanced detection, low-SDI countries face persistently high YLLs owing to limited access to timely diagnosis and surgical care. Targeted public health strategies, including early intervention, healthcare infrastructure investment, and policies tailored to vulnerable populations such as infants, the elderly, and residents of low-resource settings, are essential to reduce preventable mortality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 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) | 0.001 | 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".