Characteristics of In-Flight Medical Emergencies on a Commercial Airline in Mainland China: Retrospective Study
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».