Health impacts of high temperatures on hospital emergency department visits (EDVs) have been less reported, especially from developing countries. To investigate high temperature-EDVs relationship in various regions with different climatic characteristics, to explore the regional differences, to identify vulnerable populations, and to provide scientific evidence for climate change adaptation strategies in China. Daily data on weather, air pollution and EDVs were collected from 18 sites in China from June to August during 2014-2017. A quasi-Poisson generalized additive regression model was applied to examine the high temperature-EDVs relationship in each site. Site-specific risks of EDVs were pooled using a random effect meta-analysis model. Stratified analyses were performed by gender, age-groups, cause-specific EDVs and regions. Attributable fractions of EDVs due to high temperatures were calculated in different regions. 1 °C increase in daily mean temperature was associated with 1.07% (95% CI, 0.46-1.67%) increase in EDVs across all study regions. The negative health effects from high temperatures were worse for the people living in southern China, in subtropical monsoon climate zone or in counties, with percentage change of 1.96% (95% CI, 0.92-3.02%), 1.35% (95% CI, 0.95-1.76%) and 1.41% (95% CI, 0.48-2.34%), respectively. People under 18 were more vulnerable to high temperatures. Exposure to high temperatures increased EDVs risks from endocrine, respiratory, and digestive diseases and injury. The attributable fraction due to high temperatures was 8.64% for overall EDVs, 11.70% for the people living in southern China, 10.80% for people living in subtropical monsoon climate zone and 12.65% for the county population. Exposure to high temperatures resulted in extra burden to China's already overloaded hospital emergency departments. More resources are needed to meet increasing demands and effective preventative measurements are warranted to tackle such a challenge. Further studies should pay more attention to both heat and cold-related EDVs risks and socioeconomic cost for better climate change adaptation.

Background: This study intends to identify the best model for predicting the incidence of hand, foot and mouth disease (HFMD) in Ningbo by comparing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) models combined and uncombined with exogenous meteorological variables. Methods: The data of daily HFMD incidence in Ningbo from January 2014 to November 2017 were set as the training set, and the data of December 2017 were set as the test set. ARIMA and LSTM models combined and uncombined with exogenous meteorological variables were adopted to fit the daily incidence of HFMD by using the data of the training set. The forecasting performances of the four fitted models were verified by using the data of the test set. Root mean square error (RMSE) was selected as the main measure to evaluate the performance of the models. Results: The RMSE for multivariate LSTM, univariate LSTM, ARIMA and ARIMAX (Autoregressive Integrated Moving Average Model with Exogenous Input Variables) was 10.78, 11.20, 12.43 and 14.73, respectively. The LSTM model with exogenous meteorological variables has the best performance among the four models and meteorological variables can increase the prediction accuracy of LSTM model. For the ARIMA model, exogenous meteorological variables did not increase the prediction accuracy but became the interference factor of the model. Conclusions: Multivariate LSTM is the best among the four models to fit the daily incidence of HFMD in Ningbo. It can provide a scientific method to build the HFMD early warning system and the methodology can also be applied to other communicable diseases.

Objectives: This study intends to build and compare two kinds of forecasting models at different time scales for hemorrhagic fever incidence in China. Methods: Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) were adopted to fit monthly, weekly and daily incidence of hemorrhagic fever in China from 2013 to 2018. The two models, combined and uncombined with rolling forecasts, were used to predict the incidence in 2019 to examine their stability and applicability. Results: ARIMA (2, 1, 1) (0, 1, 1)₁₂, ARIMA (1, 1, 3) (1, 1, 1)₅₂ and ARIMA (5, 0, 1) were selected as the best fitting ARIMA model for monthly, weekly and daily incidence series, respectively. The LSTM model with 64 neurons and Stochastic Gradient Descent (SGDM) for monthly incidence, 8 neurons and Adaptive Moment Estimation (Adam) for weekly incidence, and 64 neurons and Root Mean Square Prop (RMSprop) for daily incidence were selected as the best fitting LSTM models. The values of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the models combined with rolling forecasts in 2019 were lower than those of the direct forecasting models for both ARIMA and LSTM. It was shown from the forecasting performance in 2019 that ARIMA was better than LSTM for monthly and weekly forecasting while the LSTM was better than ARIMA for daily forecasting in rolling forecasting models. Conclusions: Both ARIMA and LSTM could be used to build a prediction model for the incidence of hemorrhagic fever. Different models might be more suitable for the incidence prediction at different time scales. The findings can provide a good reference for future selection of prediction models and establishments of early warning systems for hemorrhagic fever.

Background: Although exposure to air pollution has been linked to many health issues, few studies have quantified the modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China. Methods: The data of daily incidence of influenza and the relevant meteorological data and air pollution data in Ningbo from 2014 to 2017 were retrieved. Low, medium and high temperature layers were stratified by the daily mean temperature with 25th and 75th percentiles. The potential modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo was investigated through analyzing the effects of air pollutants stratified by temperature stratum using distributed lag non-linear model (DLNM). Stratified analysis by sex and age were also conducted. Results: Overall, a 10 μg/m³ increment of O₃, PM_2.5, PM₁₀ and NO₂ could increase the incidence risk of influenza with the cumulative relative risk of 1.028 (95% CI 1.007, 1.050), 1.061 (95% CI 1.004, 1.122), 1.043 (95% CI 1.003, 1.085), and 1.118 (95% CI 1.028, 1.216), respectively. Male and aged 7–17 years were more sensitive to air pollutants. Through the temperature stratification analysis, we found that temperature could modify the impacts of air pollution on daily incidence of influenza with high temperature exacerbating the impact of air pollutants. At high temperature layer, male and the groups aged 0–6 years and 18–64 years were more sensitive to air pollution. Conclusion: Temperature modified the relationship between air pollution and daily incidence of influenza and high temperature would exacerbate the effects of air pollutants in Ningbo.