Factors associated with prolonged hospitalization among patients transported by emergency medical services A population-based study in Osaka, Japan

MEDICINE(2021)

引用 1|浏览1
暂无评分
摘要
The emergency medical system, one of the essential elements of public health, has been around for more than 50 years. Although many studies have assessed the factors associated with overcrowding and prolonged length of stay in emergency departments, whether the clinical characteristics and background of a patient are associated with prolonged hospitalization among patients transported by ambulance is unknown. The purpose of this study was to reveal factors associated with the continuation of hospitalization at 21 days after hospital admission among patients transported by ambulance using a population-based patient registry in Osaka, Japan. This was a retrospective observational study whose study period was the three years from January 2016 to December 2018. In this study, we included patients who were hospitalized after transportation by ambulance in Osaka, Japan. The main outcome was continuation of hospitalization at 21 days after hospital admission. We calculated the adjusted odds ratios (AOR) and 95% confidence interval (CI) with a multivariable logistic regression model to assess factors associated with the outcome. We included 481,886 patients in this study, of whom 158,551 remained hospitalized at 21 days after hospital admission and 323,335 had been discharged home by 21 days after hospital admission. Factors associated with prolonged hospitalization were elderly (AOR: 1.767 [95% CI: 1.730-1.805]), traffic accident (AOR: 1.231 [95% CI: 1.183-1.282]), no fixed address (AOR: 4.494 [95% CI: 3.632-5.314]), need for nursing care (AOR: 1.420 [95% CI: 1.397-1.443]) and solitary person (AOR: 1.085 [95% CI: 1.050-1.120]). In this study, the elderly, traffic accidents, no fixed address, need for nursing care, and solitary person were associated with prolonged hospitalization of patients transported by ambulance in Japan.
更多
查看译文
关键词
ambulance transport, emergency medical service, epidemiology, prolonged hospitalisation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要