Impact of ambient temperature on clinical visits for cardio-respiratory diseases in rural villages in northwest China

Zhao, Qi; Zhao, Yi; Li, Shanshan; Zhang, Yajuan; Wang, Qingan; Zhang, Huiling; Qiao, Hui; Li, Wuping; Huxley, Rachel R.; Williams, Gail M.; Zhang, Yuhong; Lee, Hyewon

doi:10.1016/j.scitotenv.2017.08.244

Cited by 79 publications

(67 citation statements)

References 42 publications

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“…A possible explanation for this may be due to poorer Irish housing standards than those in Norway and that the indoor temperature was greatly impacted by falls of outdoor temperature. Zhao et al [32] recently studied the effect of cold temperatures on clinical visits for cardiorespiratory diseases in the Ningxia Hui Autonomous Region, the same region as this study. They collected cardiovascular and respiratory illness data from 203 villages between 1 January 2012 and 31 December 2015.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Heat Transfer and Thermal Environment in a Rural Residential Building for Addressing Energy Poverty

et al. 2018

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Reducing energy consumption and creating a comfortable thermal indoor environment in rural residential buildings can play a key role in fighting global warming in China. As a result of economic development, rural residents are building new houses and modernizing existing buildings. This paper investigated and analyzed a typical rural residential building in the Ningxia Hui Autonomous Region in Northwest China through field measurements and numerical simulation. The results showed that making full use of solar energy resources is an important way to improve the indoor temperature. Reasonable building layout and good thermal performance of the building envelope can reduce wind velocities and convective heat loss. Insulation materials and double-glazed windows should be used to reduce energy loss in new buildings, although it is an evolution process in creating thermally efficient buildings in rural China. This research provides a reference for the design and construction of rural residential buildings in Northwest China and similar areas for addressing energy poverty.

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Section: Discussionmentioning

confidence: 99%

Analysis of Heat Transfer and Thermal Environment in a Rural Residential Building for Addressing Energy Poverty

et al. 2018

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“…Environmental exposure, such as ambient air pollution and extreme temperatures, is an important but underappreciated risk factor contributing to the development and severity of CVDs [4]. Accumulating evidence from epidemiological studies has linked environmental exposure to increased risk of CVDs morbidity [5][6][7][8][9][10][11][12]. However, evidence of the effect of a complex mixture of environmental exposure on…”

Section: Discussionmentioning

confidence: 99%

“…Cardiovascular diseases (CVDs) are the leading cause of death worldwide; about 17.9 million deaths were attributable to CVDs in 2016, representing approximately 31% of all global deaths in that year [1]. Even though behavioral factors, including physical inactivity, smoking, unhealthy diets and obesity, are well-known risk factors for CVDs, a large body of studies have indicated that environmental exposure [2][3][4], such as ambient air pollution [5][6][7][8][9] and temperature variability [10][11][12], also makes a significant contribution to CVDs, resulting in increased risk of morbidity. For example, using conditional logistic regression models, Liu et al [13] conducted a multi-city study in 26 Chinese cities, and the results showed that elevated concentrations of sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), carbon monoxide (CO), and ozone (O 3 ) were associated with increased risk of hospitalization for heart failure.…”

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confidence: 99%

Machine Learning Approaches to Predict Peak Demand Days of Cardiovascular Admissions Considering Environmental Exposure

Qiu

Luo

et al. 2020

Preprint

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Background: Accumulating evidence has linked environmental exposure, such as ambient air pollution and meteorological factors, to the development and severity of cardiovascular diseases (CVDs), resulting in increased healthcare demand. Effective prediction of demand for healthcare services, particularly those associated with peak events of CVDs, can be useful in optimizing the allocation of medical resources. However, few studies have attempted to adopt machine learning approaches with excellent predictive abilities to forecast the healthcare demand for CVDs. This study aims to develop and compare several machine learning models in predicting the peak demand days of CVDs admissions using the hospital admissions data, air quality data and meteorological data in Chengdu, China from 2015 to 2017.Methods: Six machine learning algorithms, including logistic regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were applied to build the predictive models with a unique feature set. The area under a receiver operating characteristic curve (AUC), logarithmic loss function, accuracy, sensitivity, specificity, precision, and F1 score were used to evaluate the predictive performances of the six models.Results: The LightGBM model exhibited the highest AUC (0.940, 95% CI: 0.900-0.980), which was significantly higher than that of LR (0.842, 95% CI: 0.783-0.901), SVM (0.834, 95% CI: 0.774-0.894) and ANN (0.890, 95% CI: 0.836-0.944), but did not differ significantly from that of RF (0.926, 95% CI: 0.879-0.974) and XGBoost (0.930, 95% CI: 0.878-0.982). In addition, the LightGBM has the optimal logarithmic loss function (0.218), accuracy (91.3%), specificity (94.1%), precision (0.695), and F1 score (0.725). Feature importance identification indicated that the contribution rate of meteorological conditions and air pollutants for the prediction was 32% and 43%, respectively.Conclusion: This study suggests that ensemble learning models, especially the LightGBM model, can be used to effectively predict the peak events of CVDs admissions, and therefore could be a very useful decision-making tool for medical resource management.

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“…Extreme temperature has a critical effect on human health [1], numerous studies have investigated the risk of extreme temperature exposure on respiratory diseases [2][3][4][5][6][7]. People spend over 80% of their time indoors [8,9], and several factors affect indoor temperature change, such as building materials, air conditioning usage, and human activities [10].…”

Section: Introductionmentioning

confidence: 99%

Indoor temperature and respiratory disease–related emergency department visits: a study of cumulative effects among older adults in Taiwan

Chien-Cheng¹,

Hsia²,

Hsu³

et al. 2020

Preprint

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Background Studies have demonstrated that exposure to extreme outdoor temperatures increases respiratory diseases related mortality and morbidity, especially in older adults. However, older adults spend over 80% of their time indoors, and the cumulative effects of exposure to indoor temperature on the risk of respiratory diseases have not been investigated. The objective of this study was to study the cumulative effect of indoor temperature exposure on emergency department visit due to respiratory diseases among older adults. Methods Participant data were collected from the Longitudinal Health Insurance Database from 2000 to 2014 in Taiwan. The cumulative degree hour was used to reflect the cumulative effect of indoor temperature exposure. A distributed lag nonlinear model was used to analyze the association between cumulative degree hour and emergency department visit due to respiratory diseases. Results Our findings revealed a significant risk of emergency department visits due to respiratory diseases at 27, 28, 29, 30, and 31 °C when cooling cumulative degree hours exceeded 60, 30, 19, 1, and 1, respectively, during the hot season (May to October) and at 19, 20, 21, 22, and 23 °C when heating cumulative degree hours exceeded 1, 1, 1, 39, and 49, respectively, during the cold season (November to April). Conclusions We conclude that the cumulative effects of indoor temperature exposure should be considered to reduce respiratory disease risk under climate change.

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Impact of ambient temperature on clinical visits for cardio-respiratory diseases in rural villages in northwest China

Cited by 79 publications

References 42 publications

Analysis of Heat Transfer and Thermal Environment in a Rural Residential Building for Addressing Energy Poverty

Analysis of Heat Transfer and Thermal Environment in a Rural Residential Building for Addressing Energy Poverty

Machine Learning Approaches to Predict Peak Demand Days of Cardiovascular Admissions Considering Environmental Exposure

Indoor temperature and respiratory disease–related emergency department visits: a study of cumulative effects among older adults in Taiwan

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