Effect of heatwaves on mortality of Alzheimer's disease and other dementias among elderly aged 60 years and above in China, 2013-2020: a population-based study.

Background: China has the largest number of dementia patients in the world, posing a significant health and economic burden. Alzheimer's disease (AD) and other dementia patients face a higher risk of mortality during heatwaves, but relevant studies on this topic have been limited so far. Methods: The study extracted data from the China Cause of Death Reporting System (CDRS) on deaths of AD and other dementia patients aged 60 years and above between 2013 and 2020. Using an individual-level, time-stratified, and case-crossover study design, the effects of heatwaves across nine scenarios on dementia mortality were quantified by conditional logistic regression combined with distributed lag non-linear model (DLNM). Additionally, the attributable fractions (AFs) of deaths due to heatwaves were calculated. Findings: A total of 399,036 death cases were reported caused by AD and other dementias during the study period. It was found that heatwaves significantly increased the risk of death among people with AD and other dementias. As the intensities and durations of the heatwaves increased, the lag0-7 cumulative odds ratios (CORs) of mortality increased progressively from 1.140 (95% CI: 1.118, 1.163) under the mildest heatwave to 1.459 (95% CI: 1.403, 1.518) under the most severe one, across nine heatwave scenarios examined. Additionally, under specific heatwave scenarios, sex and regions modified the mortality risk, but no significant age differences were observed. The AFs of AD and other dementia mortality due to milder heatwaves were lower compared to more severe heatwaves, ranging from 12.281% (95% CI: 10.555%, 14.015%) to 31.460% (95% CI: 28.724%, 34.124%). Interpretation: The study provided critical insights into the substantial increase in heatwave-related mortality among AD and other dementia patients during and after heatwave events. The results from our quantitative analyses will provide needed scientific evidence for policymakers and practitioners to develop relevant policies and guidelines to protect the health and well-beings of vulnerable populations in future in the context of both seasonal changes and long-term climate change. Funding: This work was supported by the Project of Prevention and Intervention on Major Diseases for Elderly in China, NCNCD [00240201307], the National Key Research and Development Program of China [2022YFC2602301, 2023YFC2308703] and the Science and Technology Fundamental Resources Investigation Program of China [2017FY101201].

Epidemiological Characterization of Dengue Fever - Yunnan Province, China, 2010-2021.

Objective: The goal of this study is to analyze the epidemiological patterns of dengue fever across different districts and counties in Yunnan Province from 2010 to 2021. Methods: In this study, we employed joinpoint regression analysis, spatial autocorrelation analysis, and space-time scan analysis to illustrate the spatio-temporal propagation and demographic influence of dengue fever, using both graphical and tabular presentations to clearly demonstrate the findings. Results: Yunnan Province reported 14,098 cases of dengue fever during the period from 2010 to 2021. Of these, 11,513 cases were caused by local transmission, 2,566 were imported internationally, and 19 were inter-provincial imports. Seasonal trends emerged, revealing a surge in incidences during the summer and autumn months. The sex ratio of male to female cases was 1:0.88, with a significant majority of 82.00% of cases involving individuals belonging to the age group of 15-60. Commercial service workers constituted the most impacted occupational group, forming 20.96% of total cases. A spatio-temporal scan identified significant clustering of dengue fever cases across space and time, with the most pronounced cluster observed in southern Yunnan, primarily between 2015 and 2019. Conclusions: Dengue fever in Yunnan Province manifests as biennial outbreaks, underscoring the necessity for increased surveillance, particularly in counties bordering other regions.

A Scoping Review of Tools and Techniques on Evaluating Population Health and Healthy Life Expectancy.

The concept of healthy life expectancy (HLE) integrates the ideas of life expectancy and health status, providing a valuable metric to evaluate both the length and quality of life. This paper seeks to aid policymakers in creating an inclusive HLE indicator system through a systematic review of methodologies for defining and measuring HLE, along with relevant published studies' descriptions. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews statement, two English language literature databases were researched from January 2020 to April 2023. Findings from empirical HLE-related studies were analyzed by extracting data on the study area, design, population, healthy state measurement tools, and results of studies using HLE indicators. The current analysis encompassed 48 empirical studies. Researchers discerned 11 unique HLE indicators within this corpus, each concentrating on a particular aspect. Furthermore, the analysis revealed 18 diverse instruments for evaluating health statuses, each varying in its definition of a healthy state, dimensions of measurement, and the categories of data employed. Therefore, merging global health concepts, HLE indicators, methodologies for assessing healthy states, and applied research demonstrations are essential for a consolidated HLE indicator system creation.

Seasonal associations between air pollutants and influenza in 10 cities of southern China.

Few studies have explored the associations between air pollutants and influenza across seasons, especially at large scales. This study aimed to evaluate seasons' modifying effects on associations between air pollutants and influenza from 10 cities of southern China. Through scientific evidence, it provides mitigation and adaptation strategies with practical guidelines to local health authorities and environmental protection agencies. Daily influenza incidence, meteorological, and air pollutants data from 2016 to 2019 were collected. Quasi-Poisson regression with a distributed lag nonlinear model was used to evaluate city-specific air pollutants and influenza associations. Meta-analysis was used to pool site-specific estimates. Attributable fractions (AFs) of influenza incidence due to pollutants were calculated. Stratified analyses were conducted by season, sex, and age. Overall, the cumulative relative risk (CRR) of influenza incidence for a 10-unit increase in PM2.5, PM10, SO2, NO2, and CO was 1.45 (95% CI: 1.25, 1.68), 1.53 (95% CI: 1.29, 1.81), 1.87 (95% CI: 1.40, 2.48), 1.74 (95% CI: 1.49, 2.03), and 1.19 (95% CI: 1.04, 1.36), respectively. Children aged 0-17 were more sensitive to air pollutants in spring and winter. PM10 had greater effect on influenza than PM2.5 in autumn, winter, and overall, lesser in spring. The overall AF due to PM2.5, PM10, SO2, NO2, and CO was 4.46% (95% eCI: 2.43%, 6.43%), 5.03% (95% eCI: 2.33%, 7.56%), 5.36% (95% eCI: 3.12%, 7.58%), 24.88% (95% eCI: 18.02%, 31.67%), and 23.22% (95% eCI: 17.56%, 28.61%), respectively. AF due to O3 was 10.00% (95% eCI: 4.76%, 14.95%) and 3.65% (95% eCI: 0.50%, 6.59%) in spring and summer, respectively. The seasonal variations in the associations between air pollutants and influenza in southern China would provide evidence to service providers for tailored intervention, especially for vulnerable populations.

The effect of Cr doping on the magnetic and magnetocaloric properties of Ni45Co5Mn36.5In13.5 Heusler alloys.

Herein, Ni45-xCrxCo5Mn36.5In13.5 (x = 0, 0.2, 0.4, and 0.6 at%) and Ni45Co5Mn36.5-yCryIn13.5 (y = 0.2, 0.4, and 0.6 at%) polycrystalline Heusler alloys are prepared by arc melting and then characterized using X-ray diffraction and a vibrating sample magnetometer. A single L21 austenitic phase is confirmed at room temperature. Meanwhile, we studied the effect of Cr doping on the magnetic properties of Ni45Co5Mn36.5In13.5 alloys. It is observed that, with the incorporation of Cr atoms, both the lattice constant and valence electron concentration of the alloys have changed, resulting in the phase transition temperature, saturation magnetization and magnetic entropy changing significantly. In addition, when Cr is replaced by Mn, the change of phase transition temperature (ΔT) induced by the magnetic field decreases; therefore, in the Ni45Co5Mn36.1Cr0.4In13.5 samples, the magnetic entropy change reaches a maximum value of up to 37.1 J kg-1 K-1 under an external magnetic field of 3T, which is more than 50% higher than that of other Ni-Mn based Heusler alloys reported in the literature.

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.

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.

Bibliometric Analysis on Utilization of New Information Technology in the Prevention and Control of COVID-19 - China, 2020.

INTRODUCTION: New information technology (IT) has been applied to prevent and control coronavirus disease 2019 (COVID-19) in many regions of China since the outbreak of COVID-19. This study aims to illustrate the current status and key areas of the application of new IT in the prevention and control of COVID-19. METHODS: Literature related to the prevention and control of COVID-19 with new IT since 2020 was retrieved from China National Knowledge Internet and Wanfang Literature databases, the two most authoritative databases in China. CiteSpace 5.7.R2 was used to analyze the institutions, authors, and keywords of the articles. The application of new IT is determined by keywords and highly cited documents. RESULTS: A total of 1,711 articles were included, as the number of publications has been continually increasing over the investigation period. The six hot topics of new IT applied in COVID-19 were big data, artificial intelligence, Internet+, blockchain, Internet of Things, and 5G. The most productive institution is University of Chinese Academy of Sciences, and the most productive author in this field is Tao Pei, whose article, "Multi-level Spatial Distribution Estimation Model of the Inter-Regional Migrant Population Using Multi-Source Spatio-Temporal Big Data: A Case Study of Migrants from Wuhan During the Spread of COVID-19," was highly cited. DISCUSSION: This study could help medical professionals understand the application status and research trends of new IT in the prevention and control of COVID-19. This paper also helps researchers find potential co-operative institutions and partners.

Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China.

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.

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.

Daily mean temperature and HFMD: risk assessment and attributable fraction identification in Ningbo China.

BACKGROUND: Hand, foot, and mouth disease (HFMD) remains a significant public health issue, especially in developing countries. Many studies have reported the association between environmental temperature and HFMD. However, the results are highly heterogeneous in different regions. In addition, there are few studies on the attributable risk of HFMD due to temperature. OBJECTIVES: The study aimed to assess the association between temperature and HFMD incidence and to evaluate the attributable burden of HFMD due to temperature in Ningbo China. METHODS: The research used daily incidence of HFMD from 2014 to 2017 and distributed lag non-linear model (DLNM) to investigate the effects of daily mean temperature (Tmean) on HFMD incidence from lag 0 to 30 days, after controlling potential confounders. The lag effects and cumulative relative risk (CRR) were analyzed. Attributable fraction (AF) of HFMD incidence due to temperature was calculated. Stratified analysis by gender and age were also conducted. RESULTS: The significant associations between Tmean and HFMD incidence were observed in Ningbo for lag 0-30. Two peaks were observed at both low (5-11 °C) and high (16-29 °C) temperature scales. For low temperature scale, the highest CRR was 2.22 (95% CI: 1.61-3.07) at 7 °C on lag 0-30. For high temperature scale, the highest CRR was 3.54 (95% CI: 2.58-4.88) at 24 °C on lag 0-30. The AF due to low and high temperature was 5.23% (95% CI: 3.10-7.14%) and 39.55% (95% CI: 30.91-45.51%), respectively. There was no significant difference between gender- and age-specific AFs, even though the school-age and female children had slightly higher AF values. CONCLUSIONS: The result indicates that both high and low temperatures were associated with daily incidence of HFMD, and more burdens were caused by heat in Ningbo.

Lessons Learned in the Development of a Web-based Surveillance Reporting System and Dashboard to Monitor Acute Febrile Illnesses in Guangdong and Yunnan Provinces, China, 2017-2019.

Global spread of Zika virus in 2015 and 2016 highlighted the importance of surveillance to rapidly detect, report, and respond to emerging infections. We describe the lessons learned from the development and deployment of a web-based surveillance reporting system for Zika virus and other acute febrile illnesses (AFI) in Guangdong and Yunnan provinces, China. In less than 2 months, we customized the China Epidemiologic Dynamic Data Collection (EDDC) platform to collect, manage, and visualize data in close to real time. According to provincial and sentinel hospital staff requirements, the customized platform provided specific user authorization management, online/offline data collection, data quality control, and secure data transmission and protection and visualization tools. AFI case data and laboratory test results were entered through a web-based interface by hospital and provincial-level staff and saved on a China CDC server in Beijing. The dashboard visualized aggregate data by hospital, age, onset date, and laboratory test results. From June 2017 to December 2018, data from 768 patients with AFI were entered into the web-based surveillance system and visualized by hospital and provincial-level decision makers. Input from provincial and hospital staff was essential for developing the AFI case-reporting and feedback tools appropriate for specific settings and decision-making requirements. Web-based systems (eg, EDDC, data collection, and visualization tools that can be easily adapted to meet local surveillance needs) can help shorten time for system deployment, thereby strengthening global health security to rapidly detect and respond to emerging infections and outbreaks.

Spatial distribution of tuberculosis and its association with meteorological factors in mainland China.

BACKGROUND: The incidence of tuberculosis (TB) remains high worldwide. Current strategies will not eradicate TB by 2035; instead, by 2182 is more likely. Therefore, it is urgent that new risk factors be identified. METHODS: An ecological study was conducted in 340 prefectures in China from 2005 to 2015. The spatial distribution of TB incidence was shown by clustering and hotspot analysis. The relationship between the distribution patterns and six meteorological factors was evaluated by the geographically weighted regression (GWR) model. RESULTS: During the 11 years of the study period, TB incidence was persistently low in the east and high in the west. Local coefficients from the GWR model showed a positive correlation between TB incidence and yearly average rainfall (AR) but a negative correlation with other meteorological factors. Average relative humidity (ARH) was negatively correlated with the incidence of TB in all prefectures (p < 0.05). CONCLUSION: Meteorological factors may play an important role in the prevention and control of TB.

Impacts of tropical cyclones and accompanying precipitation on infectious diarrhea in cyclone landing areas of Zhejiang Province, China.

BACKGROUND: Zhejiang Province, located in southeastern China, is frequently hit by tropical cyclones. This study quantified the associations between infectious diarrhea and the seven tropical cyclones that landed in Zhejiang from 2005-2011 to assess the impacts of the accompanying precipitation on the studied diseases. METHOD: A unidirectional case-crossover study design was used to evaluate the impacts of tropical storms and typhoons on infectious diarrhea. Principal component analysis (PCA) was applied to eliminate multicollinearity. A multivariate logistic regression model was used to estimate the odds ratios (ORs) and the 95% confidence intervals (CIs). RESULTS: For all typhoons studied, the greatest impacts on bacillary dysentery and other infectious diarrhea were identified on lag 6 days (OR = 2.30, 95% CI: 1.81-2.93) and lag 5 days (OR = 3.56, 95% CI: 2.98-4.25), respectively. For all tropical storms, impacts on these diseases were highest on lag 2 days (OR = 2.47, 95% CI: 1.41-4.33) and lag 6 days (OR = 2.46, 95% CI: 1.69-3.56), respectively. The tropical cyclone precipitation was a risk factor for both bacillary dysentery and other infectious diarrhea when daily precipitation reached 25 mm and 50 mm with the largest OR = 3.25 (95% CI: 1.45-7.27) and OR = 3.05 (95% CI: 2.20-4.23), respectively. CONCLUSIONS: Both typhoons and tropical storms could contribute to an increase in risk of bacillary dysentery and other infectious diarrhea in Zhejiang. Tropical cyclone precipitation may also be a risk factor for these diseases when it reaches or is above 25 mm and 50 mm, respectively. Public health preventive and intervention measures should consider the adverse health impacts from tropical cyclones.