Prevalence of Mental Health Problems among Patients Treated by Emergency Medical Teams: Findings from J-SPEED Data Regarding the West Japan Heavy Rain 2018.
Yui YumiyaOdgerel Chimed-OchirAkihiro TajiEisaku KishitaKouki AkahoshiHisayoshi KondoAkinori WakaiKayoko ChishimaYoshiki ToyokuniYuichi KoidoHirokazu TachikawaSho TakahashiSayaka GomeiYuzuru KawashimaTatsuhiko KuboPublished in: International journal of environmental research and public health (2022)
It is crucial to provide mental health care following a disaster because the victims tend to experience symptoms such as anxiety and insomnia during the acute phase. However, little research on mental health during the acute phase has been conducted, and reported only in terms of the temporal transition of the number of consultations and symptoms. Thus, the aim of the study was to examine how mental health care needs are accounted for in the overall picture of disaster relief and how they change over time. Using data from the Japanese version of Surveillance in Post-Extreme Emergencies and Disasters (J-SPEED), we assessed the mental health of injured and ill patients to whom Emergency Medical Teams (EMTs) were providing care during the acute period of a disaster. Approximately 10% of all medical consultations were for mental health issues, 83% of which took place within the first 2 weeks after the disaster. The findings showed that, from the start of the response period to the 19th response day, the daily proportion of mental health problems declined substantially, and then gradually increased. Such a V-shaped pattern might be helpful for identifying phase changes and supporting the development of EMT exit strategies.
Keyphrases
- mental health
- emergency medical
- mental illness
- healthcare
- sleep quality
- end stage renal disease
- electronic health record
- public health
- palliative care
- newly diagnosed
- chronic kidney disease
- ejection fraction
- big data
- liver failure
- general practice
- prognostic factors
- machine learning
- epithelial mesenchymal transition
- artificial intelligence
- intensive care unit
- affordable care act