The global, regional, and national burden of foreign bodies from 1990 to 2019: a systematic analysis of the global burden of disease study 2019
Bibliographic record
Abstract
BACKGROUND: Foreign bodies (FBs) are a common emergency in medical institutions, that can occur in any area and among people of any age, which are common public health problems. Understanding the epidemiological characteristics of FBs is crucial for their prevention and control. The purpose of this study was to analyze the epidemiological characteristics of FBs worldwide through the data from the Global Burden of Disease Study 2019 (GBD 2019). METHODS: We obtained data from the GBD 2019, which is an important public database to understand the disease burden of FBs. Joinpoint was used to analyze temporal trends in the incidence and death trends of FBs, which is widely used to study the long-term temporal trend of the burden of diseases. SaTScan was used to detect spatial-temporal clusters of pulmonary aspiration and foreign body in the airway (PAFBA), which is based on a Poisson model, scanning the number of people and diseases in the study area to obtain the spatial-temporal clusters of diseases. RESULTS: Globally, the age-standardized incidence rate (ASIR) and the age-standardized death rate (ASDR) of FBs in 2019 were 869.23/100,000 (679.92/100,000-1120.69/100,000) and 1.55/100,000 (1.41/100,000-1.67/100,000), respectively. The ASIR and ASDR showed downtrends with average annual percent changes (AAPCs) of -0.31% and - 1.47% from 1990 to 2019. Of note, the ASIR showed an uptrend during 2010-2019, especially in high, high-middle, and middle SDI regions. Stratified analysis by age group showed that ASIR increased in each age group in recent years. From 1990 to 2019, the ASDR in the over-70 age group showed an uptrend worldwide, especially in high and high-middle SDI regions. In different types of FBs, the ASDR of PAFBA was the highest. The death burden of PAFBA was mainly clustered in 82 countries during 1993-2007, such as Canada, Cuba, and Mexico. CONCLUSION: The most important goal is to improve public awareness and emergency knowledge of FBs through publicity methods, such as the internet or offline activities, and to improve laws and regulations. Additionally, different age groups need different targeted measures, such as strengthening the care of children, caring for elderly individuals, improving necessary monitoring programs and reporting systems, conducting effective hazard assessments, and publicity and education activities.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".