Ambient air pollution and hospital admission in Shanghai, China

Chen, Renjie; Chu, Chen; Tan, Jianwei; Cao, Junshan; Song, Weimin; Xu, Xiaohui; Jiang, Cheng; Ma, Wenjuan; Yang, Changyuan; Chen, Bingheng; Gui, Yonghao; Kan, Haidong

doi:10.1016/j.jhazmat.2010.05.002

Cited by 175 publications

(91 citation statements)

References 38 publications

(51 reference statements)

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“…Over the past two decades, a number of studies have evaluated the associations between outdoor air pollution and adverse health effects such as cardiovascular and respiratory diseases in different parts of the world including North America (Li et al 2011;Lin et al 2008;Rodopoulou et al 2014;Wilson et al 2004), Europe (Kalantzi et al 2011;Di Ciaula 2012;Rushworth et al 2014), Australia (Hansen et al 2012), and some Asian countries including China, Taiwan, and India (Cao et al 2009;Chang et al 2005;Chen et al 2010;Ge et al 2011;Gurjar et al 2010;Lin et al 2013;Tao et al 2014). These studies have shown an increase in mortality and/or hospital admissions for cardiovascular and respiratory diseases due to short-and long-term exposure to air pollutants.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike many studies that have focused on mortality compared with hospitalization prior to 2005 (Chang et al 2005), there are an increasing number of studies that have been conducted to evaluate the association between air pollution and hospital admissions during the recent decade. These epidemiological studies have reported associations between daily variations in air pollution and hospital admissions for cardiovascular and respiratory diseases (Cao et al 2009;Chang et al 2005;Chen et al 2010;Di Ciaula 2012;Ge et al 2011;Gurjar et al 2010;Hansen et al 2012;Kalantzi et al 2011;Li et al 2011;Lin et al 2013Lin et al , 2008Rodopoulou et al 2014;Tao et al 2014;Wilson et al 2004;Liu et al 2013). …”

Section: Introductionmentioning

confidence: 99%

“…Additionally, conditional or mixed model logistic regressions, accounting for correlation between case and control records, are commonly used to model the relation between air pollution and meteorological data with adverse health effects (Chan and Ng 2011;Chang et al 2005;Chen et al 2010;Gleason et al 2014;Guo et al 2009;Hansen et al 2012;Kan and Chen 2003;Lee et al 2008;Li et al 2011;Lin et al 2013;Qorbani et al 2012;Tobías et al 2011;Villeneuve et al 2012;Wendt et al 2014;Wong et al 2014;Yang et al 2004). However, the artificial neural networks (ANNs) were also used for modeling in the present study.…”

Section: Introductionmentioning

confidence: 99%

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Air pollution and hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus conditional logistic regression

Shakerkhatibi

Dianat

Jafarabadi

et al. 2015

Int. J. Environ. Sci. Technol.

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This study was conducted to evaluate the relationship between air pollutants (including nitrogen oxides [NO, NO 2 ]) and hospital admissions for cardiovascular and respiratory diseases. The study had a case-crossover design which was conducted in Tabriz, Iran. Daily hospital admissions and air quality data from March 2009 to March 2011 were analyzed using the artificial neural networks (ANNs) and conditional logistic regression modeling. The results showed significant associations between gaseous air pollutants including NO 2 , O 3 , and NO and hospital admissions for cardiovascular disease. Gaseous air pollutants of NO 2 , NO, and CO were associated with hospital admissions for chronic obstructive pulmonary disease, while PM 10 was associated with hospitalizations due to respiratory infections. PM 10 and O 3 were also associated with asthmatic hospital admissions. There was no significant association between SO 2 and studied health outcomes. Comparing the results of logistic regressions and ANNs confirmed the optimality of the ANNs for detection of the best predictors of hospital admissions caused by air pollution. Further research is required to investigate the effects of seasonal variations on air pollution-related health outcomes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Air pollution and hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus conditional logistic regression

Shakerkhatibi

Dianat

Jafarabadi

et al. 2015

Int. J. Environ. Sci. Technol.

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show abstract

“…The approach of a simple comparison of routinely collected data on population health status with concentrations of air pollutants used in the past does not meet current requirements for the evaluation of air pollution impacts on health and was replaced by the methods of health risk assessment that is based on qualitative or quantitative estimates of the impact of air pollution on health. As demonstrated by numerous studies this quantification is biased by relatively large uncertainties regarding the level of specific estimates of air pollution impacts on health (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). Theoretically estimated numbers of deaths and hospitalizations of persons cannot in fact correspond with real numbers of deceased or hospitalized persons with respect to a simplification that represents a theoretical calculation.…”

Section: Discussionmentioning

confidence: 99%

“…More accurate estimates modified this range. The US studies (12)(13)(14)(15) found an increase in the number of hospital admissions for cardiovascular reasons from 0.1 to 2.4%, the European studies from 0.3 to 0.9% (16)(17)(18)(19)(20)(21)(22), and the Asian studies from 0.15 to 0.6% per 10 µg.m −3 PM 10 (23)(24)(25)(26). In case of hospitalization for respiratory causes the American studies showed an increase from 0.4 to 2.3% (12), the European studies from 0.6 to 1.1% (16)(17)(18)(19)27), and the Asian studies from 0.12 to 1.6% per each 10 µg.m −3 PM 10 (23-26).…”

Section: Introductionmentioning

confidence: 99%

PM10 Air Pollution and Acute Hospital Admissions for Cardiovascular and Respiratory Causes in Ostrava

Tomášková

Tomášek

Šlachtová

et al. 2016

Cent Eur J Public Health

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SUMMARY Background and Aim:The city of Ostrava and its surroundings belong to the most long-therm polluted areas in the Czech Republic and Europe. For identification of health risk, the World Health Organization recommends a theoretical estimation of increased short-term PM 10 concentrations effect on hospital admissions for cardiac complaints based on a 0.6% increase per 10 µg.m −3 PM 10 and 1.14% increase for respiratory causes. The goal of the present study is to verify the percentage increase of morbidity due to cardiovascular and respiratory causes, as per WHO recommendations for health risk assessment, in the population of Ostrava.Method: The input data include data on PM 10 air pollution, meteorologi cal data, the absolute number of hospital admissions for acute cardiovascular and respiratory diseases in the period 2010-2012. To examine the association between air pollution and health outcomes the time series Poisson regression adjusted for covari ates was used.Results: A significant relationship was found between the cardiovascular hospital admissions (percentage increase of 1.24% per 10 µg.m −3 ) and values of PM 10 less than 150 μg.m −3 in the basic model, although after adjustment for other factors, this relationship was no longer significant. A significant relationship was also observed for respiratory causes of hospital admissions in the basic model. Contrary to cardiovascular hospitalization, the relationship between respiratory hospital admissions and PM 10 values below 150 μg.m −3 (percentage increase of 1.52%) remained statistically significant after adjustment for other factors.Conclusions: The observed significant relationship between hospital admissions for respiratory causes was consistent with the results of large European and American studies.

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On Generalized Additive Models with Dependent Time Series Covariates

Ispány

Reisen

Franco

et al. 2018

Contributions to Statistics

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The generalized additive model (GAM) is a standard statistical methodology and is frequently used in various fields of applied data analysis where the response variable is non-normal, e.g., integer valued, and the explanatory variables are continuous, typically normally distributed. Standard assumptions of this model, among others, are that the explanatory variables are independent and identically distributed vectors which are not multicollinear. To handle the multicollinearity and serial dependence together a new hybrid model, called GAM-PCA-VAR model, was proposed in [17] which is the combination of GAM with the principal component analysis (PCA) and the vector autoregressive (VAR) model. In this paper, some properties of the GAM-PCA-VAR model are discussed theoretically and verified by simulation. A real data set is also analysed with the aim to describe the association between respiratory disease and air pollution concentrations.

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Ambient air pollution and hospital admission in Shanghai, China

Cited by 175 publications

References 38 publications

Air pollution and hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus conditional logistic regression

Air pollution and hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus conditional logistic regression

PM10 Air Pollution and Acute Hospital Admissions for Cardiovascular and Respiratory Causes in Ostrava

On Generalized Additive Models with Dependent Time Series Covariates

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