Air pollution and outpatient visits for conjunctivitis: A case-crossover study in Hangzhou, China

Fu, Qiang; Mo, Zhe; Lyu, Danni; Zhang, Lifang; Qin, Zhenwei; Tang, Qiaomei; Yin, Houfa; Xu, Pingwei; Wu, Lizhi; Lou, Xiaoming; Chen, Zhijian; Yao, Ke

doi:10.1016/j.envpol.2017.08.109

Cited by 91 publications

(64 citation statements)

References 32 publications

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“…The results show that Random forest algorithm had the highest predictive accuracy, and that the most important variables affecting the predictive accuracy were O 3 , CO, SO 2 , NO 2 , and air temperature. Our results, corroborate with previous studies, showing that the same important environmental factors affecting conjunctivitis [9]. Strati ed analyses by gender and age showed that environmental factors affect the number of conjunctivitis patients differed between genders and age groups.…”

Section: Discussionsupporting

confidence: 93%

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Machine learning prediction on number of patient due to conjunctivitis based on air pollutants: A preliminary study

Cheng

Feng

Zhou

et al. 2020

Preprint

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Background Accurate prediction of the number of patients with conjunctivitis plays an important role in providing adequate treatment at the hospital, but such accurate predictive model currently does not exist. The current study sought to use machine learning (ML) prediction based on past patient for conjunctivitis and several air pollutants. The optimal machine learning prediction model was selected to predict conjunctivitis-related number patients. Methods The average daily air pollutants concentrations (CO, O3, NO2, SO2, PM10, PM2.5) and weather data (highest and lowest temperature) were collected. Data were randomly divided into training dataset and test dataset, and normalized mean square error (NMSE) was calculated by 10 fold cross validation, comparing between the ability of seven ML methods to predict the number of patient due to conjunctivitis (Lasso penalized liner model, Decision tree, Boosting regression, Bagging regression, Random forest, Support vector, and Neural network). According to the accuracy of impact prediction, the important air and weather factors that affect conjunctivitis were identified. Results A total of 84977 cases to treat conjunctivitis were obtained from the ophthalmology center of the Affiliated Hospital of Hangzhou Normal University. For all patients together, the NMSE of the different methods were as follows: Lasso penalized liner regression: 0.755, Decision tree: 0.710, Boosting regression: 0.616, Bagging regression: 0.615, Random forest: 0.392, Support vectors: 0.688, and Neural network: 0.476. Further analyses, stratified by gender and age at diagnosis, supported Random forest as being superior to others ML methods. The main factors affecting conjunctivitis were: O3, NO2, SO2 and air temperature. Conclusion Machine learning algorithm can predict number of patients due to conjunctivitis, among which, the Random forest algorithm had the highest accuracy. Machine learning algorithm could provide accurate information for hospitals dealing with conjunctivitis caused by air factors.

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Section: Discussionsupporting

confidence: 93%

“…A meta-analysis study has shown that females and youth are more vulnerable to PM2.5, NO 2 , and O 3 [7]. Several studies have further provided signi cant evidence that, in China, patient due to conjunctivitis are associated with air pollution [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Machine learning prediction on number of patient due to conjunctivitis based on air pollutants: A preliminary study

Cheng

Feng

Zhou

et al. 2020

Preprint

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“…It was stated that exposure to exhaust from diesel engines increases expression of cytokines, chemokines and growth factors in the conjunctiva's epithelium's cells, meaning it activates inflammation in the conjunctiva [6]. A study conducted in China [7] included 9737 patients who visited an ophthalmologist because of conjunctivitis and it compared the number of visits with the concentration of CO, NO 2 , SO 2 , ozone, PM 2,5 and PM 10 in the air on the day of the visit and on the days before it. It was demonstrated that an increase of the concentration of all above gases by 10 μg/m 3 in the air on the day of the admission or on days directly before was associated with an increase of the number of patients checking in with conjunctivitis.…”

Section: Review Of the Literature Influence On Conjunctivamentioning

confidence: 99%

How air pollution affects the eyes — a review

et al. 2018

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The aim of this study is to present scientific reports concerning the influence of air pollution on eyes. Air pollution is nowadays a common problem. The most significant pollutants are carbon monoxide (CO), nitrogen dioxide (NO 2 ), ozone (O 3 ), sulphur dioxide (SO 2 ), particulate matters -PM 10 and PM 2.5 . The main source of toxins in the air are cars, burning of the fuels and burning of the charcoal in household stoves. Most attention is paid to negative effects of air pollution on respiratory system, such as asthma and lung cancer, however, it is worth remembering that influence on the eyes is equally important. Children are especially vulnerable to the harmful effects of air pollution. Anterior segment of the eye is the most exposed. Substances, which are part of the smog have an irritating effect on the surface of the eye, cause disturbances in the tear film and an inflammation. Medical conditions associated with increased air pollution are mainly eye irritation, conjunctivitis, dry eye syndrome (DES), meibomian gland dysfunction (MGB) and blepharitis.

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“…Fu et al studied the potential correlation between conjunctivitis and air pollution based on the air pollutant data from the Environmental Protection Department of Zhejiang Province from 1 July 2014 to 30 June 2016 and data of 9737 outpatient visits for conjunctivitis at the Eye Center of the Second Affiliated Hospital of Zhejiang University School of Medicine. Their results indicated significant correlations between the number of conjunctival outpatient visits and air pollution in Zhejiang [29]. Xu et al conducted a sample survey on air pollutants in Ningbo, Zhejiang Province from 3 December 2012 to 27 June 2013 in order to study the chemical characteristics of highly polluting aerosols in the air of Zhejiang Province.…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy Optimization Model Based on Entropy Weight Method in Atmospheric Quality Evaluation: A Case Study of Zhejiang Province, China

Wang

Yang

2019

Sustainability

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A fuzzy optimization model based on the entropy weight method for investigating air pollution problems in various cities of Zhejiang Province, China has been proposed in this paper. Meanwhile, the air quality comprehensive evaluation system has been constructed based on the six major pollutants (SO2, NO2, CO, PM10, PM2.5 and O3) involved in China’s current air quality national standards. After analyzing the monthly data of six pollutants in 11 cities of Zhejiang Province from January 2015 to April 2018 by the above method, the authors found that, although the air quality of cities in Zhejiang Province did not reach the long-term serious pollution of Beijing, Tianjin, and Hebei, the air quality changes in the northern cities of Zhejiang were worse than those in southern Zhejiang. For example, the air quality of Shaoxing in northern Zhejiang has dropped by 14.85% in the last study period when compared with that of the beginning period, and Hangzhou, the provincial capital of Zhejiang, has also seen a decrease of 6.69% in air quality. The air quality of Lishui, Zhoushan and Wenzhou in southern Zhejiang has improved by 8.04%, 4.67% and 4.22% respectively. Apart from the geographical influence, the industrial structure of these cities is also an important cause for worse air quality. From the local areas in southern Zhejiang, cities have developed targeted air pollution control measures according to their own characteristics, including adjusting the industrial structure, changing the current energy consumption structure that heavily relies on coal, and improving laws and regulations on air pollution control, etc. In the four cities in central Zhejiang, the air quality at the end of the period (April 2018) has decreased from the beginning of the period (January 2015), given that there were no fundamental changes in their industrial structure and energy pattern.

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Air pollution and outpatient visits for conjunctivitis: A case-crossover study in Hangzhou, China

Cited by 91 publications

References 32 publications

Machine learning prediction on number of patient due to conjunctivitis based on air pollutants: A preliminary study

Machine learning prediction on number of patient due to conjunctivitis based on air pollutants: A preliminary study

How air pollution affects the eyes — a review

Application of Fuzzy Optimization Model Based on Entropy Weight Method in Atmospheric Quality Evaluation: A Case Study of Zhejiang Province, China

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