Characteristics of In-Flight Medical Emergencies on a Commercial Airline in Mainland China: Retrospective Study
Bibliographic record
Abstract
Background: In-flight medical emergencies (IMEs) can have severe outcomes, including the deaths of passengers and aircraft diversions. Information is lacking regarding the incidence rate and characteristics of IMEs in most countries, especially in mainland China. Objective: The objective of this study was to investigate the incidence, patterns, and associated risk factors of IMEs in mainland China and to provide medical suggestions for the evaluation and management of IMEs. Methods: This population-based retrospective study examined electronic records for all IME reports between January 1, 2018, and December 31, 2022, from a major airline company in mainland China. Outcome variables included the medical category of the IMEs, the outcomes of first aid, and whether or not the IMEs led to a flight diversion. We calculated the incidence rate and death rate of IMEs based on the number of passengers and flights, respectively. A logistic regression model was used to investigate the factors associated with aircraft diversions. Results: A total of 199 IMEs and 24 deaths occurred among 447.2 million passengers, yielding an incidence rate of 0.44 (95% CI 0.39-0.51) events per million passengers and 66.56 (95% CI 50.55-86.04) events per million flights, and an all-cause mortality rate of 0.05 (95% CI 0.03-0.07) events per million passengers and 7.50 (95% CI 4.81-11.16) events per million flights. From 2018 to 2022, the highest incidence and mortality rates were observed in 2019 and 2020, respectively, while the lowest were in 2020 and 2021, respectively. Additionally, the highest incidence and mortality rates were observed between 6 PM to 6 AM and noon to 6 PM, respectively. There was a higher incidence rate of IMEs in the winter months. Moreover, the highest case-fatality rates were observed in 2019 (12/74, 16.2%), on flights traveling ≥4000 km (9/43, 20.9%), and on wide-body planes (10/52, 19.2%). Seizures (29/199, 14.6%), cardiac symptoms (25/199, 12.6%), and syncope or presyncope (19/199, 9.6%) were the most common medical problems and main reasons for aircraft diversion. The incidence of aircraft diversion was 42.50 (95% CI 37.02-48.12) events per million flights. Narrow-body planes (odds ratio [OR] 5.69, 95% CI 1.05-30.90), flights ≥4000 km (OR 16.40, 95% CI 1.78-151.29), and the months of December to February (OR 12.70, 95% CI 3.09-52.23), as well as the months of March to May (OR 23.21, 95% CI 3.75-143.43), were significantly associated with a higher risk of diversion. Conclusions: The occurrence of and deaths associated with IMEs are rare in mainland China, but a temporal trend shows higher incidence rates at night and in winter. The leading IMEs are cardiac symptoms, seizures, and syncope. The establishment of a unified reporting system for IMEs and ground-to-air medical support are of great value for reducing IMEs and deaths in the global community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".