Influence of Indoor Temperature Exposure on Emergency Department Visits Due to Infectious and Non-Infectious Respiratory Diseases for Older People

Predicting Emergency Department Utilization among Older Hong Kong Population in Hot Season: A Machine Learning Approach

Previous evidence suggests that temperature is associated with the number of emergency department (ED) visits. A predictive system for ED visits, which takes local temperature into account, is therefore needed. This study aimed to compare the predictive performance of various machine learning methods with traditional statistical methods based on temperature variables and develop a daily ED attendance rate predictive model for Hong Kong. We analyzed ED utilization among Hong Kong older adults in May to September from 2000 to 2016. A total of 103 potential predictors were derived from 1- to 14-day lag of ED attendance rate and meteorological and air quality indicators and 0-day lag of holiday indicator and month and day of week indicators. LASSO regression was used to identify the most predictive temperature variables. Decision tree regressor, support vector machine (SVM) regressor, and random forest regressor were trained on the selected optimal predictor combination. Deep neural network (DNN) and gated recurrent unit (GRU) models were performed on the extended predictor combination for the previous 14-day horizon. Maximum ambient temperature was identified as a better predictor in its own value than as an indicator defined by the cutoff. GRU achieved the best predictive accuracy. Deep learning methods, especially the GRU model, outperformed conventional machine learning methods and traditional statistical methods.

Dynamic changes of emergency visits: a retrospective observational study

BACKGROUND

With more emergency visits, there is increasing pressure to provide emergency medical services globally and locally. This study aimed to investigate the epidemiological characteristics and the disease spectrum of patients presenting in the last three years to the Department of Emergency Medicine of Tianjin Medical University General Hospital, a tertiary hospital in Tianjin, China, to improve the services of the emergency medicine department.

METHODS

A retrospective study was conducted on all patients in the Department of Emergency Medicine of Tianjin Medical University General Hospital from Jan 1, 2017, 00:00:00 to Dec 31, 2020, 23:59:59, including variables like medical record number, gender, age, date of admission, principal diagnosis. The data were analyzed by SPSS statistical software; statistical charts were prepared by GraphPad Prism9.0 and SPSS 20.0; statistical tables were made by Microsoft Excel.

RESULTS

A total of 1,314,916 patients presented to the Department of Emergency Medicine of Tianjin Medical University General Hospital from Jan 1, 2017, 00:00:00 to Dec 31, 2020, 23:59:59. In terms of gender distribution, the male-female ratio was 0.78∶1. As for age distribution, patients aged 60-69 were the most (23.47%), and patients younger than 20 years were the least (2.80%). Concerning monthly data, the number of visits peaked during January and December. The distribution of daily visits showed the feature of three highs and a low. The top three prevalence diseases in the emergency disease spectrum were respiratory, cardiovascular, and digestive diseases. The respiratory system was the most common in patients with infectious diseases (200,912, accounting for 86.97%). Among the patients suffering from infectious diseases, the number of patients with respiratory infections peaked in 2019 (73,530) and was the lowest in 2020 (20,078).

CONCLUSIONS

From 2017 to 2019, the demand for emergency services in Tianjin Medical University General Hospital continued to increase, but it was greatly affected by COVID-19 in 2020. This emergency department is mainly for patients with respiratory system, circulatory system and digestive system diseases, and its treatment time is relatively centralized. The prevention of diseases for people of all ages, especially female patients and the elderly, should be strengthened, and emergency medical resources should be allocated reasonably according to the peak months and crowed periods of patients.

Nursing Students' Retention of Knowledge by Basic Knowledge Type: An Exploratory Study

Students’ interests help determine their learning effectiveness and knowledge acquisition and retention. It is necessary to confirm whether there is a difference in the way in which content being learned is remembered by the content type. In this study, we examined the characteristics of nursing students’ retention of physiological knowledge and environmental knowledge by utilizing scores obtained in class. The participants comprised 57 nursing students who had taken a class twice—once in their second year and once in their third year. Before and after each class, students completed an 11-question survey with human health and comfort items based on nursing core competencies and Sphere standards. The correct answer rate was calculated using a logistic regression model to account for inter- and intra-individual variations. The estimated correct answer rate per individual showed one of three trends: (1) increasing and decreasing depending on the lesson topic (knowledge type), (2) increasing overall after decreasing, and (3) increasing gradually. Physiological knowledge was retained well, whereas knowledge pertaining to the environment was retained poorly. Even with knowledge of the environment, the knowledge that students apply to their daily lives and social events was maintained.