Risks of hospital admissions from a spectrum of causes associated with particulate matter pollution

Zhu, Xiaojuan; Qiu, Hang; Wang, Li Ya; Duan, Zongshuan; Yu, Haiyan; Deng, Ren; Zhang, Yanlong; Zhou, Li

doi:10.1016/j.scitotenv.2018.11.240

Cited by 55 publications

(42 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, visits for NCDs accounted for the majority of visits in our study. The fact that the proportions of visits for cardiovascular and circulatory diseases and chronic respiratory diseases were the highest may be associated with air pollution in Shanghai, as it has been proposed by many researchers that exposure to high levels of ambient particulate matter (PM) is related to the incidence of these diseases in China [11,12]. Among cardiovascular diseases, hypertension and chronic ischaemic heart disease were the most common reasons outpatients sought care in community healthcare centres, especially among outpatients older than 18 years.…”

Section: Discussionmentioning

confidence: 99%

Examining health disparities and characteristics in general practice utilization: based on outpatient data from 2014 - 2018 in Shanghai

et al. 2020

View full text Add to dashboard Cite

Background: Since 2000, China has been developing primary care institutions to serve as the gateway to the healthcare system. However, the investment of resources in primary care institutions is not based on the actual medical demands of the public. This study analysed primary care utilization to provide targeted guidance for the improvement of primary healthcare delivery in China. Methods: We extracted outpatient visit data from all community healthcare centres in Shanghai from 2014 to 2018. Diseases were then classified according to ICD-10 codes. The disease spectrum (frequency, proportion, rank) was stratified by sex, age, and region. Results: Most primary care outpatients were female (58.20%), 60-79 years old (57.91%), and in suburban regions (62.18%). Chronic diseases accounted for the majority (91.41%). Hypertension, chronic ischaemic heart disease, diabetes, and acute upper respiratory tract infections were the top four disorders for primary care visits regardless of sex. In the group aged 0-18 years, symptoms, signs and abnormal clinical and laboratory findings not elsewhere classified accounted for 37.96% of the top 20 reasons. Acute upper respiratory tract infections were the most common diseases in the groups aged 0-18 (11.20%) and 19-39 (11.14%) years. However, hypertension was the most common disease in the group aged > 39 years old (> 20%). There were more outpatients with respiratory and digestive diseases in suburban areas than in urban areas. In addition, problems associated with medical equipment and other healthcare deficiencies were relatively more common in suburban areas (suburban: 4.13%, rank 5; urban: 2.29%, rank 10). Conclusions:To meet the patients' needs and to develop the primary care system, the Shanghai government should focus on diseases with regionally high proportions. Disease diagnosis and treatment should be improved in the younger and suburban populations.

show abstract

Section: Discussionmentioning

confidence: 99%

Examining health disparities and characteristics in general practice utilization: based on outpatient data from 2014 - 2018 in Shanghai

et al. 2020

View full text Add to dashboard Cite

show abstract

“…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%

“…Accumulating epidemiological studies have suggested that the effect of meteorological conditions and air pollutants on CVDs admissions is delayed, and the lag effect is related to the regional environment [8,12,37]. Hence, we employed an over-dispersed GAM, which allowed the quasi-Poisson distribution to analyze the lag effects of daily meteorological conditions and air pollutants on CVDs admissions, and chose the lag day based on the minimum Generalized Cross-Validation (GCV) values which 7 measure models fit [5,34]. The lag effects of single day lags (from lag0 to lag6) and cumulative day lags (from lag01 to lag06) were taken into consideration.…”

Section: Feature Extractionmentioning

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.…”

mentioning

confidence: 99%

See 1 more Smart Citation

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

Qiu

Luo

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Health effects of PM pollution are well-documented and include both short-term [1][2][3][4] and long-term effects [5]. In addition, PM 10 is associated with respiratory effects such as hospitalizations [6], outpatient visits [7] and emergency department admissions for chronic obstructive pulmonary disease (COPD) [8]. Compared to occupational settings, environmental effects are modest, however, millions of people are affected in large cities, and the overall number of victims may be huge, such as in China [9].…”

Section: Introductionmentioning

confidence: 99%

Occupational exposure to particulate matter from air pollution in the outdoor workplaces in Almaty during the cold season

2020

View full text Add to dashboard Cite

Background A large fraction of population in Almaty and other Kazakhstan cities is employed in the outdoor jobs and likely exposed to high levels of particulate matter (PM) during the cold season. The magnitude of such occupational exposure remains unknown; therefore, the aim was to quantify the levels of exposure to PM 10 in the outdoor workplaces in Almaty in order to guide future interventions of primary prevention. Methods Outdoor security non-smoking guards (N = 12) wore TSI DustTrack AM520 aerosol monitors with a 10-μm impactor for 8 hours of outdoor shift. Ten samples (k = 10) from each worker were obtained for the cold season (November-March) from various locations across Almaty. Total sampling time was 57600 minutes. We compared normalized time-weighted average (TWA) concentrations for 8-hour shifts within and between workers using analysis of variance (ANOVA) and assessed compliance with environmental exposure limit (EEL) (0.060 mg/m 3) via exceedance (γ) and probability of overexposure (θ). Results PM 10 TWA ranged from 0.050 to 2.075 mg/m 3 with the geometric mean 0.366 and median 0.352 mg/m 3. PM 10 TWA distribution was left-skewed with large variation. The fold-range of within-person variability, containing 95% of the exposure concentration (w R 0.95) was 13, whereas between-person fold-range (b R 0.95) was 3. However, between-person variance exceeded the one within with F-ratio 2.797 (p = 0.003) with statistical power 97% at α = 0.05. Only two of 120 samples had TWA below EEL, yielding γ = 0.995 and θ = 1. Conclusions Outdoor workers in polluted cities like Almaty are exposed to very high levels of PM 10 during the cold season. Urgent action should be taken to regulate such occupational exposure and to raise awareness of workers and employers on hazards associated with it.

show abstract

Risks of hospital admissions from a spectrum of causes associated with particulate matter pollution

Cited by 55 publications

References 80 publications

Examining health disparities and characteristics in general practice utilization: based on outpatient data from 2014 - 2018 in Shanghai

Examining health disparities and characteristics in general practice utilization: based on outpatient data from 2014 - 2018 in Shanghai

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

Occupational exposure to particulate matter from air pollution in the outdoor workplaces in Almaty during the cold season

Contact Info

Product

Resources

About