Effects of ambient temperature on stroke hospital admissions: Results from a time-series analysis of 104,432 strokes in Guangzhou, China

Guo, Pi; Zheng, Murui; Feng, Weiyue; Wu, Jiagang; Deng, Chao; Luo, Ganfeng; Wang, Li; Pan, Bingying; Liu, Huazhang

doi:10.1016/j.scitotenv.2016.11.093

Cited by 43 publications

(36 citation statements)

References 40 publications

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“…Distributed lag nonlinear model has been widely used in the analysis of the burden of ambient air pollution on years of life lost, effects of ambient temperature on stroke hospital admissions, short-term effect of ambient temperature on mortality, and so on. [23][24][25] Furthermore, DLNM has also been used in the study of associations between meteorological factors and communicable diseases. Xiang et al 26 found reversed U-shaped nonlinear associations between ambient temperature, relative humidity, extreme wind velocity, and dengue.…”

Section: Discussionmentioning

confidence: 99%

Association between Severe Fever with Thrombocytopenia Syndrome Incidence and Ambient Temperature

Sun

Lu²,

Yang³

et al. 2018

The American Journal of Tropical Medicine and Hygiene

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Severe fever with thrombocytopenia syndrome (SFTS) is emerging in China. To explore the lagged effects and nonlinear association between temperature and SFTS, we collected data on ambient temperature and SFTS cases and analyzed the data using a distributed lag nonlinear model. A total of 1,933 SFTS cases were reported in the study area from 2011 to 2015. Our study revealed a nonlinear relationship between weekly temperature and SFTS. The exposure-response curve was an approximately reversed U-shaped peak at 23°C. High temperatures had acute and short-term effects, whereas low temperatures had persistent and long-term effects. The effects of lower temperatures (1.62°C and 6.97°C) could last 24 weeks, but the effect of 29.30°C was not significant at lag 8 weeks. Our results provide information to better understand the effect of temperature variation on SFTS and may have policy implications for disease prevention and control.

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Section: Discussionmentioning

confidence: 99%

Association between Severe Fever with Thrombocytopenia Syndrome Incidence and Ambient Temperature

Sun

Lu²,

Yang³

et al. 2018

The American Journal of Tropical Medicine and Hygiene

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“…Then the goodness of fit of the models was assessed by means of the R-squared statistic [ 36 ], where the higher R-squared indicates the greater explanatory power in dengue incidence predicting. Moreover, to examine the adequacy of the models, we applied an autocorrelation function (ACF) and a partial autocorrelation function (PACF) to check if the residuals from the established models were independent and randomly distributed over time [ 37 ]. These analyses were performed for each of the twenty cities in Guangdong.…”

Section: Methodsmentioning

confidence: 99%

Developing a dengue forecast model using machine learning: A case study in China

et al. 2017

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BackgroundIn China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue.Methodology/Principal findingsWeekly dengue cases, Baidu search queries and climate factors (mean temperature, relative humidity and rainfall) during 2011–2014 in Guangdong were gathered. A dengue search index was constructed for developing the predictive models in combination with climate factors. The observed year and week were also included in the models to control for the long-term trend and seasonality. Several machine learning algorithms, including the support vector regression (SVR) algorithm, step-down linear regression model, gradient boosted regression tree algorithm (GBM), negative binomial regression model (NBM), least absolute shrinkage and selection operator (LASSO) linear regression model and generalized additive model (GAM), were used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed using the root-mean-square error (RMSE) and R-squared measures. The residuals of the models were examined using the autocorrelation and partial autocorrelation function analyses to check the validity of the models. The models were further validated using dengue surveillance data from five other provinces. The epidemics during the last 12 weeks and the peak of the 2014 large outbreak were accurately forecasted by the SVR model selected by a cross-validation technique. Moreover, the SVR model had the consistently smallest prediction error rates for tracking the dynamics of dengue and forecasting the outbreaks in other areas in China.Conclusion and significanceThe proposed SVR model achieved a superior performance in comparison with other forecasting techniques assessed in this study. The findings can help the government and community respond early to dengue epidemics.

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“…Recorded demographic information, medical history, and clinical information on stroke symptoms were obtained. If there were recurrent strokes occurring within 28 days of a previous admission, they were treated as a single event of stroke [ 18 ]. All the strokes were coded under the World Health Organization’s International Classification of Diseases, the 10th version (ICD-10) from I60 to I64, and the subgroup of ischemic strokes corresponds to the code of I63 [ 18 ].…”

Section: Methodsmentioning

confidence: 99%

Ambient Air Pollution and Risk for Ischemic Stroke: A Short-Term Exposure Assessment in South China

Guo

Wang

Feng

et al. 2017

IJERPH

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Data on the association between air pollution and risk of ischemic stroke in China are still limited. This study aimed to investigate the association between short-term exposure to ambient air pollution and risk of ischemic strokes in Guangzhou, the most densely-populated city in south China, using a large-scale multicenter database of stroke hospital admissions. Daily counts of ischemic stroke admissions over the study years 2013-2015 were obtained from the Guangzhou Cardiovascular and Cerebrovascular Disease Event Surveillance System. Daily particulate matter <2.5 µm in diameter (PM 2.5 ), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), ozone (O 3 ), and meteorological data were collected. The associations between air pollutants and hospital admissions for stroke were examined using relative risks (RRs) and their corresponding 95% confidence intervals (CIs) based on time-series Poisson regression models, adjusting for temperature, public holiday, day of week, and temporal trends in stroke. Ischemic stroke admissions increased from 27,532 to 35,279 through 2013 to 2015, increasing by 28.14%. Parameter estimates for NO 2 exposure were robust regardless of the model used. The association between same-day NO 2 (RR = 1.0509, 95% CI: 1.0353-1.0668) exposure and stroke risk was significant when accounting for other air pollutants, day of the week, public holidays, temperature, and temporal trends in stroke events. Overall, we observed a borderline significant association between NO 2 exposure modeled as an averaged lag effect and ischemic stroke risk. This study provides data on air pollution exposures and stroke risk, and contributes to better planning of clinical services and emergency contingency response for stroke.

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Effects of ambient temperature on stroke hospital admissions: Results from a time-series analysis of 104,432 strokes in Guangzhou, China

Cited by 43 publications

References 40 publications

Association between Severe Fever with Thrombocytopenia Syndrome Incidence and Ambient Temperature

Association between Severe Fever with Thrombocytopenia Syndrome Incidence and Ambient Temperature

Developing a dengue forecast model using machine learning: A case study in China

Ambient Air Pollution and Risk for Ischemic Stroke: A Short-Term Exposure Assessment in South China

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