Pediatric Emergency Response in a Non-Epicenter Hospital during the 2023 Turkey-Syria Earthquake: A Retrospective Study of 125 Cases in the First 20 Days.
Süeda ZamanSelahattin GürüPublished in: Medical science monitor : international medical journal of experimental and clinical research (2023)
BACKGROUND The 2023 Turkey-Syria earthquakes affected a large population. Ankara Mamak State Hospital, a non-epicenter hospital, was also making efforts to treat earthquake patients. This retrospective study was conducted from this non-epicenter hospital during the 2023 Turkey-Syria earthquake and aimed to evaluate the emergency response to 125 pediatric patients identified in the first 20 days. MATERIAL AND METHODS The cases were scanned from the hospital's electronic registry system by age and diagnosis code. We recorded the demographic data of patients under the age of 18 years, the day they arrived, the provinces they left, their diagnoses, treatments, consultations, characteristics of trauma in traumatic cases, and outcomes of all children in a non-epicenter hospital. We detected 125 pediatric cases in the first 20 days. RESULTS There were 125 pediatric patients under the age of 18 who arrived to the Emergency Department (ED). On the 6th day, the number of cases peaked. Their mean age was 7.9±5.6 years (minimum: 0, maximum: 18) and 52.8% were males. Most cases had non-traumatic internal disease (81.6%) and were most (97.6%) were discharged from the ED. While soft-tissue injury was the most common diagnosis in traumatic cases (69.9%), there were more (56.5%) extremity injuries according to the affected body zone. CONCLUSIONS After major disasters, there may be an increased number of pediatric patients taken to hospitals far from the disaster area. For this reason, non-epicenter hospitals should be prepared to provide an adequate number of health care workers and sufficient supplies and equipment.
Keyphrases
- emergency department
- healthcare
- end stage renal disease
- spinal cord injury
- adverse drug
- acute care
- ejection fraction
- newly diagnosed
- soft tissue
- chronic kidney disease
- public health
- prognostic factors
- primary care
- young adults
- machine learning
- metabolic syndrome
- big data
- adipose tissue
- deep learning
- general practice
- skeletal muscle