Department of Case Statistics, Second Affiliated Hospital, Zhejiang University School of Medicine, Linping Campus, Hangzhou, 311199, China.
Department of Quality Management, Second Affiliated Hospital, Zhejiang University School of Medicine, Linping Campus, Hangzhou, 311199, China.
BMC Public Health. 2024 Sep 19;24(1):2549. doi: 10.1186/s12889-024-20033-7.
By analysing the deaths of inpatients in a tertiary hospital in Hangzhou, this study aimed to understand the epidemiological distribution characteristics and the composition of the causes of death. Additionally, this study aimed to predict the changing trend in the number of deaths, providing valuable insights for hospitals to formulate relevant strategies and measures aimed at reducing mortality rates.
In this study, data on inpatient mortality at a tertiary hospital in Hangzhou from 2015 to 2022 were obtained via the population information registration system of the Chinese Center for Disease Control and Prevention. The death data of inpatients were described and analysed through a retrospective study. Excel 2016 was utilized for data sorting, and SPSS 22.0 software was employed for data analysis. The statistical inference of single factor differences was conducted via χ2 tests. The SARIMA model was established via the forecast, aTSA, and tseries software packages (version 4.3.0) to forecast future changes in the number of deaths.
A total of 1938 inpatients died at the tertiary hospital in Hangzhou, with the greatest number of deaths occurring in 2022 (262, 13.52%). The sex ratio was 2.22:1, and there were significant differences between sexes in terms of age, marital status, educational level, and place of residence (P < 0.05). The percentage of males in the groups aged of 20 to 29 and 30 to 39 years was significantly greater than that of females (χ = 46.905, P < 0.001). More females than males died in the widowed group, and divorced and married males experienced a greater number of deaths than divorced and married females did (χ = 61.130, P < 0.001). The proportions of male students with a junior college and senior high school education were significantly greater than that of female students (χ = 12.310, P < 0.05). The primary causes of mortality within the hospital setting included circulatory system diseases, injury, poisoning, tumours, and respiratory system diseases. These leading factors accounted for 86.12% of all recorded deaths. Finally, the SARIMA (2, 1, 1) (1, 1, 1) model was determined to be the optimal model, with an AIC of 380.23, a BIC of 392.79, and an AICc of 381.81. The MAPE was 14.99%, indicating a satisfactory overall fit of this model. The relative error between the predicted and actual number of deaths in 2022 was 8.02%. Therefore, the SARIMA (2, 1, 1) (1, 1, 1) model demonstrates good predictive performance.
Hospitals should enhance the management of sudden cardiac death, acute myocardial infarction, severe craniocerebral injury, lung cancer, and lung infection to reduce the mortality rate. The SARIMA model can be employed for predicting the number of deaths.
本研究通过分析杭州市某三级医院住院患者的死亡情况，旨在了解其流行病学分布特征和死因构成，预测未来死亡人数的变化趋势，为医院制定降低病死率的相关策略和措施提供有价值的参考。
本研究通过中国疾病预防控制中心人口信息登记系统获取杭州市某三级医院 2015 年至 2022 年住院患者的死亡数据，采用回顾性研究对住院患者的死亡数据进行描述性分析。运用 Excel 2016 进行数据整理，采用 SPSS 22.0 软件进行数据分析。采用卡方检验进行单因素差异的统计学推断。利用 forecast、aTSA 和 tseries 软件包（版本 4.3.0）中的 SARIMA 模型进行预测，建立未来死亡人数的变化趋势模型。
杭州市某三级医院共死亡 1938 例，2022 年死亡人数最多（262 例，13.52%）。男女病死率之比为 2.22∶1，不同性别在年龄、婚姻状况、文化程度和户籍所在地方面差异有统计学意义（P<0.05）。2029 岁和 3039 岁组男性比例显著高于女性（χ=46.905，P<0.001）。丧偶组女性死亡率高于男性，离异组和已婚组男性死亡率均高于女性（χ=61.130，P<0.001）。男性中专和高中文化程度学生比例显著高于女性（χ=12.310，P<0.05）。院内死亡的主要病因包括循环系统疾病、损伤和中毒、肿瘤和呼吸系统疾病，这些主要因素占所有死亡病例的 86.12%。最终确定 SARIMA（2，1，1）（1，1，1）模型为最优模型，AIC 为 380.23，BIC 为 392.79，AICc 为 381.81。MAPE 为 14.99%，表明该模型总体拟合度较好。2022 年实际死亡人数与预测死亡人数的相对误差为 8.02%。因此，SARIMA（2，1，1）（1，1，1）模型具有良好的预测性能。
医院应加强对心源性猝死、急性心肌梗死、重型颅脑损伤、肺癌和肺部感染的管理，以降低病死率。SARIMA 模型可用于预测死亡人数。
