Heatwave and urinary hospital admissions in China: Disease burden and associated economic loss, 2014 to 2019.

BACKGROUND: Many studies have shown that heatwaves are associated with an increased prevalence of urinary diseases. However, few national studies have been undertaken in China, and none have considered the associated economic losses. Such information would be useful for health authorities and medical service providers to improve their policy-making and medical resource allocation decisions. OBJECTIVES: To explore the association between heatwaves and hospital admissions for urinary diseases and assess the related medical costs and indirect economic losses in China from 2014 to 2019. METHODS: Daily meteorological and hospital admission data from 2014 to 2019 were collected from 23 study sites with different climatic characteristics in China. We assessed the heatwave-hospitalization associations and evaluated the location-specific attributable fractions (AFs) of urinary-related hospital admissions due to heatwaves by using a time-stratified case-crossover method with a distributed lag nonlinear model. We then pooled the AFs in a meta-analysis and estimated the national excess disease burden and associated economic losses. We also performed stratified analyses by sex, age, climate zone, and urinary disease subtype. RESULTS: A significant association between heatwaves and urinary-related hospital admissions was found with a relative risk of 1.090 (95 % confidence interval (CI): 1.050, 1.132). The pooled AF was 8.27 % (95%CI: 4.77 %, 11.63 %), indicating that heatwaves during the warm season (May to September) caused 248,364 urinary-related hospital admissions per year, with 2.42 (95%CI: 1.35, 3.45) billion CNY in economic losses, including 2.23 (95%CI: 1.29, 3.14) billion in direct losses and 0.19 (95%CI, 0.06, 0.31) billion in indirect losses, males, people aged 15-64 years, residents of temperate continental climate zones, and patients with urolithiasis were at higher risk. CONCLUSION: Tailored community health campaigns should be developed and implemented to reduce the adverse health effects and economic losses of heatwave-related urinary diseases, especially in the context of climate change.

Heat and outpatient visits of skin diseases - A multisite analysis in China, 2014-2018.

Background: Many studies have shown that various kinds of diseases were associated with the variation of ambient temperature. However, there's only a scrap of evidence paying attention to the link between temperature and skin diseases, and no relevant national research was performed in China. Objective: This study aimed to quantify the effect of heat on skin diseases and identify the vulnerable populations and areas in China. Methods: Daily meteorological data, air pollutant data and outpatient data were collected from in 18 sites of China during 2014-2018. A time-series study with distributed lag nonlinear model and multivariate meta-analysis was applied to analyze the site-specific and pooled associations between daily mean temperature and daily outpatient visits of skin diseases by using the data of warm season (from June to September). Stratified analysis by age, sex and climate zones and subtypes of skin diseases were also conducted. Results: We found a positive linear relationship between the ambient temperature and risk of skin diseases, with a 1.25% (95%CI: 0.34%, 2.16%) increase of risk of outpatient visits for each 1 °C increase in daily mean temperature during the warm season. In general, groups aged 18-44 years, males and people living in temperate climate regions were more susceptible to high temperature. Immune dysfunction including dermatitis and eczema were heat-sensitive skin diseases. Conclusions: Our findings suggested that people should take notice of heat-related skin diseases and also provided some references about related health burden for strategy-makers. Targeted measures for vulnerable populations need to be taken to reduce disease burden, including monitoring and early warning systems, and sun-protection measures.

Temperature and influenza transmission: Risk assessment and attributable burden estimation among 30 cities in China.

BACKGROUND: Many studies have explored the epidemiological characteristics of influenza. However, most previous studies were conducted in a specific region without a national picture which is important to develop targeted strategies and measures on influenza control and prevention. OBJECTIVES: To explore the association between ambient temperature and incidence of influenza, to estimate the attributable risk from temperature in 30 Chinese cities with different climatic characteristics for a national picture, and to identify the vulnerable populations for national preventative policy development. METHODS: Daily meteorological and influenza incidence data from the 30 Chinese cities over the period 2016-19 were collected. We estimated the city-specific association between daily mean temperature and influenza incidence using a distributed lag non-linear model and evaluated the pooled effects using multivariate meta-analysis. The attributable fractions compared with reference temperature were calculated. Stratified analyses were performed by region, sex and age. RESULTS: Overall, an N-shape relationship between temperature and influenza incidence was found in China. The cumulative relative risk of the peak risk temperature (5.1 °C) was 2.13 (95%CI: 1.41, 3.22). And 60% (95%eCI: 54.3%, 64.3%) of influenza incidence was attributed to ambient temperature during the days with sensitive temperatures (1.6°C-14.4 °C). The ranges of sensitive temperatures and the attributable disease burden due to temperatures varied for different populations and regions. The residents in South China and the children aged ≤5 and 6-17 years had higher fractions attributable to sensitive temperatures. CONCLUSIONS: Tailored preventions targeting on most vulnerable populations and regions should be developed to reduce influenza burden from sensitive temperatures.

Effects of Cold Spells on Mortality - Ningbo City, Zhejiang Province, China, 2014-2018.

What is already known about this topic?: In recent years, climate change may lead to an increase in cold spells in the middle latitudes, and there is a positive correlation between cold spells and population mortality. What is added by this report?: The acute response period and the vulnerable population were identified under the optimal definition of cold spells, and the mortality burden caused by cold spells was estimated. What are the implications for public health practice?: This research would provide evidence on the acute mortality effects of cold spells in southern China. Therefore, vulnerable populations, especially the elderly, should take timely measures to reduce the health damage caused by cold spells, especially in the first week after cold waves.

Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China.

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.

The mortality burden of nervous system diseases attributed to ambient temperature: A multi-city study in China.

BACKGROUNDS: Studies on the association between ambient temperature and human mortality have been widely reported, focusing on common diseases such as cardiopulmonary diseases. However, multi-city studies on the association between both high and low temperatures and mortality of nervous system diseases were scarce, especially on the evidence of vulnerable populations. METHODS: Weekly meteorological data, air pollution data and mortality data of nervous system were collected in 5 cities in China. A quasi-Poisson regression with distributed lag non-linear model (DLNM) was applied to quantify the association between extreme temperatures and mortality of nervous system diseases. Multivariate meta-analysis was applied to estimate the pooled effects at the overall levels. The attributable fractions (AFs) were calculated to assess the mortality burden attributable to both high and low temperatures. Stratified analyses were also performed by gender and age-groups through the above steps. RESULTS: A total of 12,132 deaths of nervous system diseases were collected in our study. The overall minimum mortality temperature was 23.9 °C (61.9th), the cumulative relative risks of extreme heat and cold for nervous system diseases were 1.33(95%CI: 1.10, 1.61) and 1.47(95%CI: 1.27, 1.71). The mortality burden attributed to non-optimal temperatures accounted for 29.54% (95%eCI: 13.45%, 40.52%), of which the mortality burden caused by low temperature and high temperature accounted for 25.89% (95%eCI: 13.03%, 34.36%) and 3.65% (95%eCI: 0.42%, 6.17%), respectively. The mortality burden attributable to ambient temperature was higher in both males and the elderly (>74 years old), with the AF of 31.85% (95%eCI: 20.68%, 39.88%) and 31.14% (95%eCI: -6.83%, 49.51%), respectively. CONCLUSIONS: The non-optimal temperature can increase the mortality of nervous system diseases and the males and the elderly over 74 years have the highest attributable burden. The findings add the evidence of vulnerable populations of nervous system diseases against ambient temperatures.

The modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China.

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, PM2.5, 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.