Temporal cross-correlations between air pollutants and outpatient visits for respiratory and circulatory system diseases in Fuzhou, China

A quantitative assessment of natural and anthropogenic effects on the occurrence of high air pollution loading in Dhaka and neighboring cities and health consequences

Although existing studies mainly focused on the air quality status in Bangladesh, quantifying the natural and manmade effects, the frequency of high pollution levels, and the associated health risks remained beyond detailed investigation. Air quality and meteorological data from the Department of Environment for 2012-2019 were analyzed, attempting to answer those questions. Cluster analysis of PM2.5, PM10, and gaseous pollutants implied that Dhaka and neighboring cities, Narayangonj and Gazipur, are from similar sources compared to the other major cities in the country. Apart from the transboundary sources, land use types and climate parameters unevenly affected local pollution loadings across city domains. The particulate concentrations persistently remained above the national standard for almost half the year, with the peaks during the dry months. Even though nitrogen oxides remained high in all three cities, other gaseous pollutants, such as CO and O3, except SO2, showed elevated concentrations solely in Dhaka city. Concentrations of gaseous pollutants in Dhaka vary spatially, but no statistical differences could be discerned between the working days and holidays. Frequency analysis results and hazard quotients revealed the likelihood of adverse health outcomes in Narayangonj ensuing from particulate exposures surpasses the other cities for different age, gender, and occupation groups. Nonetheless, school-aged children and construction workers were most at risk from chronic exposure to gaseous pollutants mostly in Dhaka. One limitation of this study was that the routine air quality monitoring happens just from five sites, making the evidence-based study concerning health outcomes quite challenging.

Short-term effects of ambient ozone exposure on daily hospitalizations for circulatory diseases in Ganzhou, China: A time-series study

Adverse health effects of ambient ozone are getting widespread attention, but the evidence on the relationship between ozone levels and circulatory system diseases are limited and inconsistent. Daily data for ambient ozone levels and hospitalizations for total circulatory diseases and five subtypes in Ganzhou, China from January 1, 2016 to December 31, 2020 were collected. We constructed a generalized additive model with quasi-Poisson regression accounting for lag effects to estimate the associations between ambient ozone levels and the number of hospitalized cases of total circulatory diseases and five subtypes. The differences among gender, age, and season subgroups were furtherly assessed through stratified analysis. A total of 201,799 hospitalized cases of total circulatory diseases were included in the present study, including 94,844 hypertension (HBP), 28,597 coronary heart disease (CHD), 42,120 cerebrovascular disease (CEVD), 21,636 heart failure (HF), and 14,602 arrhythmia. Significantly positive associations were observed between ambient ozone levels and daily hospitalizations for total circulatory diseases and all subtypes except arrhythmia. Each 10 μg/m³ increase in ozone concentration, the risk of hospitalizations for total circulatory diseases, HBP, CHD, CEVD, and HF increased by 0.718% (95% confidence interval, 0.156%-1.284%), 0.956% (0.346%-1.570%), 0.499% (0.057%-0.943%), 0.386% (0.025%-0.748%), and 0.907% (0.118%-1.702%), respectively. The above associations remained significant after adjusting for other air pollutants. The risk of hospitalization for circulatory diseases was higher in warm season (May to October) and varied in gender and age subgroups. This study suggested that short-term exposure to ambient ozone may increase the risk of hospitalizations for circulatory diseases. Our findings reinforce the importance of reducing ambient ozone pollution levels for protecting public health.

A Multi-Pollutant and Meteorological Analysis of Cardiorespiratory Mortality among the Elderly in São Paulo, Brazil-An Artificial Neural Networks Approach

Traditionally, studies that associate air pollution with health effects relate individual pollutants to outcomes such as mortality or hospital admissions. However, models capable of analyzing the effects resulting from the atmosphere mixture are demanded. In this study, multilayer perceptron neural networks were evaluated to associate PM₁₀, NO₂, and SO₂ concentrations, temperature, wind speed, and relative air humidity with cardiorespiratory mortality among the elderly in São Paulo, Brazil. Daily data from 2007 to 2019 were considered and different numbers of neurons on the hidden layer, algorithms, and a combination of activation functions were tested. The best-fitted artificial neural network (ANN) resulted in a MAPE equal to 13.46%. When individual season data were analyzed, the MAPE decreased to 11%. The most influential variables in cardiorespiratory mortality among the elderly were PM₁₀ and NO₂ concentrations. The relative humidity variable is more important during the dry season, and temperature is more important during the rainy season. The models were not subjected to the multicollinearity issue as with classical regression models. The use of ANNs to relate air quality to health outcomes is still very incipient, and this work highlights that it is a powerful tool that should be further explored.

Analysis of seasonal and spatial distribution of particulate matters and gaseous pollutants around an open cast coal mining area of Odisha, India

Open cast mining - a predominant method of coal production in India (94.46% of total coal production) - has been found to be a major factor which is responsible for the emission of dust particles and gaseous pollutants, leading to the deterioration of air quality in the coal mining area. Considering the health concerns and environmental impacts of these pollutants, the inhabited villages of Ib valley coalfield area of Orisha, India, were selected for this study. In this regard, various researchers have performed the analysis of air quality data and modeling for the dispersion of pollutants. However, a long-term study on spatial and seasonal variations of air pollutants and their relationship with meteorological parameters were missing in the literature. Accordingly, the spatial and seasonal variations of air pollutants in the area were assessed for a period of six years (2014 - 2020), and concentrations of PM_2.5, PM₁₀, and SPM were found to be above the annual national ambient air quality standards (NAAQS) for all the three seasons. The overall mean concentrations of NO_x, PM₁₀, PM_2.5, SPM, and SO₂ during this period were found to be 17.2 ± 9.28, 152.5 ± 99.7, 53.27 ± 37.70, 268.5 ± 158.2, and 12.58 ± 7.47 μg/m³, respectively. The analysis of meteorological parameters showed a strong and significant negative correlation of relative humidity with PM_2.5 (r =  - 0.30, p-value = 5.659 × 10^-10), PM₁₀ (r =  - 0.36, p-value = 1.97 × 10^-13), and SPM (r =  - 0.45, p-value = 2.2 × 10^-16). Furthermore, the spatial distribution of pollutants was performed using the geographic information system (GIS) and inverse distance weighting (IDW) method, wherein the seasonal distribution of pollutants was shown through the bivariate polar plots. Therefore, the analyses and recommendations provided in this study can help the policymakers in developing a long-term air quality improvement strategy around a coal mining area, including the spatial and seasonal variations of air pollutants and their relationship with meteorological parameters.

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R² = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R² = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases.

Circulatory health risks from additive multi-pollutant models: short-term exposure to three common air pollutants in Canada

Numerous studies have reported adverse health effects of ambient air pollution on circulatory health outcomes mainly based on single-pollutant models. However, limited studies have focused on adjusted effect of multi-pollutant exposures on public health. This study aimed to examine short-term effects of three common air pollutants-ground-level ozone (ozone), nitrogen dioxide (NO2), and fine particulate matter (PM2.5)-through multi-pollutant models for mixed effect of adjustment. Daily data (circulatory hospitalization and mortality) and hourly data (air pollutants and temperature) were collected for 24 Canadian cities for 2001-2012. We applied generalized additive over-dispersion Poisson regression models with 1, 2, or 3 pollutants for city-specific risks, and Bayesian hierarchical models for national risks. This study found little mixed effect of adjustment through multi-pollutant models (ozone and/or NO2 and/or PM2.5) for circulatory hospitalization or mortality in Canada for 2001-2012, indicating that the 1-pollutant model did not result in considerable under- or over-estimates. It seemed weak-to-moderate correlations among air pollutants did not change the significant effect of one air pollutant after accounting for others. Inconsistent findings between other previous studies and this study indicate the need of comparable study design for multi-pollutant effect analysis.

Associations between short-term exposure to ambient PM2.5 and incident cases of cerebrovascular disease in Yantai, China

There are limited studies examining the association between PM_2.5 exposure and incident cerebrovascular disease (CD) cases in China. In this study, daily counts of incident CD cases and daily PM_2.5 concentrations were obtained in Yantai, Shandong Province, China from 2014 to 2019. We used a combination of the Poisson-distribution generalized linear model (GLM) and a distributed lag nonlinear model (DLNM) to examine the association of short-term exposure to ambient PM_2.5 and incident cases of CD. The results revealed that for every 10 μg/m³ increment of PM_2.5 would increase the incident CD cases by 0.216% (RR:1.00216, 95%CI:1.0016-1.0028) at lag4. The stratified analysis demonstrated that the females and residents aged 65 years or above presented higher short-term PM_2.5-associated CD risks than the males and aged below 65 years. Targeted prevention strategies should be adopted to reduce the PM_2.5-related CD burden, especially for the susceptible population in China.

Association between exposure level of air pollutants and incidence rate of circulatory disease in residential and industrial areas of South Korea

This study investigated the correlation between the concentration of air pollutants in two metropolitan cities, Seoul and Incheon, located in South Korea with different urban characteristics and the number of patients with circulatory diseases among residents exposed to air pollution. The residential area was selected as Eunpyeong-gu of Seoul Metropolitan City and the industrial area as Jung-gu of Incheon Metropolitan City. The evaluation period is between 2015 and 2016. The relevant data provide by the Korea governmental agency were analysed to derive the purpose of this study. It was confirmed that PM₁₀, PM_2.5, nitrogen dioxide, carbon monoxide, and sulfur dioxide among air pollutants had an increasing impact on the incidence rate of circulatory diseases. The PM_2.5 was positively correlated with the incidence rate of all circulatory diseases in residential area (p < 0.05). The carbon monoxide showed a positive correlation with circulatory system diseases except for hypertension in residential area. (p < 0.05). The sulfur dioxide was positively correlated with all circulatory diseases in both residential and industrial area (p < 0.05). Based on the results obtained from this study, it was found that there are different types of air pollutants that affect circulatory diseases in residential and industrial areas.

Ambient air pollution, temperature and hospital admissions due to respiratory diseases in a cold, industrial city

Background

The influences of air pollution exposure and temperature on respiratory diseases have become major global health concerns. This study investigated the relationship between ambient air pollutant concentrations and temperature in cold industrial cities that have the risk of hospitalization for respiratory diseases.

Methods

A time-series study was conducted in Changchun, China, from 2015 to 2019 to analyse the number of daily admissions for respiratory diseases, air pollutant concentrations, and meteorological factors. Time-series decomposition was applied to analyse the trend and characteristics of the number of admissions. Generalized additive models and distributed lag nonlinear models were constructed to explore the effects of air pollutant concentrations and temperature on the number of admissions.

Results

The number of daily admissions showed an increasing trend, and the seasonal fluctuation was obvious, with more daily admissions in winter and spring than in summer and autumn. There were positive and gradually decreasing lag effects of PM10, PM2.5, NO₂, and CO concentrations on the number of admissions, whereas O₃ showed a J-shaped trend. The results showed that within the 7-day lag period, 0.5°C was the temperature associated with the lowest relative risk of admission due to respiratory disease, and extremely low and high temperatures (<-18°C, >27°C, respectively) increased the risk of hospitalization for respiratory diseases by 8.3% and 12.1%, respectively.

Conclusions

From 2015 to 2019, respiratory diseases in Changchun showed an increasing trend with obvious seasonality. The increased concentrations of SO₂, NO₂, CO, PM2.5, O₃ and PM10 lead to an increased risk of hospitalization for respiratory diseases, with a significant lag effect. Both extreme heat and cold could lead to increases in the risk of admission due to respiratory disease.

The lag-effects of meteorological factors and air pollutants on child respiratory diseases in Fuzhou, China

Background

The effects of meteorological factors and air pollutants on respiratory diseases (RDs) were various in different populations according to the demographic characteristics, and children were considered a vulnerable population. Previous studies were mainly based in cities with serious air pollution. This study aimed to qualify the lag effects of meteorological factors and air pollution on respiratory diseases among children under 18 years old in Fuzhou.

Methods

Meteorological data, air pollutants concentrations and hospital admission data of Fujian Maternity and Child Health Hospital between 2015 and 2019 were collected. A Distributed Lag Nonlinear Model (DLNM) was used to evaluate the nonlinear and lagged effect of meteorological factors and air pollutants on daily RDs number. A subgroup analysis was also conducted to evaluate the effect on different sex groups and age groups.

Results

A total number of 796 125 RDs visits was included during the study period. For meteorological factors, lower mean temperature and relative humidity were significantly associated with daily RDs number (peak relative risk (RR) = 1.032 (95% confidence interval (CI) = 1.011-1.053) and 1.021 (95% CI = 1.013-1.029)), while lower wind speed showed a significant association at low range (peak RR = 0.995 (95% CI = 0.992-0.999)). Temperature warming was a significant protective factor for RDs (peak RR = 0.989 (95% CI = 0.986-0.993)). For air pollutants, SO₂, NO₂, PM₁₀ and PM_2.5 were all significantly associated with RDs (peak RR = 1.028 (95% CI = 1.022-1.035), 1.024 (95% CI = 1.013-1.034), 1.036 (95% CI = 1.025-1.047), 1.028 (95% CI = 1.019-1.037)), and the relationship had no threshold. The estimated RR and peak lag day did not change extremely between subgroups.

Conclusions

The findings provide statistical evidence for the prevention of child RDs. In addition, our findings suggested that even at low concentrations, air pollutants still have negative effects on the respiratory system.

Facing the Impact of the COVID-19 Pandemic: How Can We Allocate Outpatient Doctor Resources More Effectively?

The COVID-19 pandemic caused significant damage to global healthcare systems. Previous studies regarding COVID-19’s impact on outpatient numbers focused only on a specific department, lacking research data for multiple departments in general hospitals. We assessed differences in COVID-19’s impact on outpatient numbers for different departments to help hospital managers allocate outpatient doctor resources more effectively during the pandemic. We compared the outpatient numbers of 24 departments in a general hospital in Beijing in 2019 and 2020. We also examined an indicator not mentioned in previous studies, monthly departmental patient reservation rates. The results show that, compared with 2019, 2020 outpatient numbers decreased overall by 33.36%. Ten departments’ outpatient numbers decreased >33.36%; however, outpatient numbers increased in two departments. In 2020, the overall patient reservation rate in 24 departments was 82.22% of the 2019 reservation rate; the rates in 14 departments were <82.22%. Moreover, patient reservation rates varied across different months. Our research shows that COVID-19’s impact on different departments also varied. Additionally, our research suggests that well-known departments will be less affected by COVID-19, as will departments related to tumor treatment, where there may also be an increase in patient numbers. Patient reservation rates are an indicator worthy of attention. We suggest that hospital managers classify departments according to changes in outpatient numbers and patient reservation rates and adopt accurate, dynamic, and humanized management strategies to allocate outpatient doctor resources.

Short-term effects of gaseous air pollutants on outpatient visits for respiratory diseases: a case-crossover study in Baotou, China

Air pollution is a major public health problem throughout the world. Although there have been several studies in this field, most of them have focused on particulate matter and only covered a few key cities. This study aimed to assess a potential association between exposure to gaseous air pollutants and outpatient visits for respiratory diseases in Baotou, China. Daily outpatient visits for respiratory diseases and daily averages of air pollutants and meteorological parameters from 2015 to 2020 were obtained. Time-stratified case-crossover design and restricted cubic splines were used to perform the analyses. Stratified analyses were performed in different hospital departments and districts. Significant association between the concentrations of air pollutants and outpatient visits for respiratory diseases was observed. The odds ratios of outpatient visits for respiratory diseases associated with per 10 μg/m³ increases in concentrations of NO₂ and SO₂, and per 10 mg/m³ increases in concentrations of CO were 1.033 (95% CI: 1.018 to 1.049), 0.965 (95% CI: 0.954 to 0.976), and 1.038 (95% CI: 1.006 to 1.071), respectively. Short-term exposure to NO₂, SO₂, and CO was positively associated with outpatient visits for respiratory diseases, with stronger effects among children. The relationship between O₃ and respiratory diseases varied at different concentrations.

Association Between the Ambient Fine Particulate Pollution and the Daily Internal Medicine Outpatient Visits in Zhoushan, China: A Time-Series Study

Background: There has been a recent worsening of air pollution in China, which poses a huge threat to public health by inducing and promoting circulatory and respiratory diseases. This study aimed to explore the association between the concentration of air pollution and daily internal medicine outpatient visits registered for the treatment of circulatory and respiratory symptoms in Zhoushan, China using a time-series method.

Methods: We validated and acquired the daily internal medicine outpatient visits records between January 1, 2014, and December 31, 2019, from the Zhoushan Center for Disease Control and Prevention in Zhejiang, China. Further, we collected the daily average records of the ambient air pollutants from the Zhoushan Environmental Monitoring Centre within the same duration. A generalized additive model with the natural splines was constructed to explore the association between the ambient air pollutants and daily internal medicine outpatient visits. Further, we conducted a lag analysis by using the distributed lag non-linear model to estimate the time-delayed effects of the air pollutants on the daily internal medicine outpatient visits.

Results: A total of 2,190,258 daily internal medicine outpatient visits with a mean of 202.4 visits per day were recorded. The non-linear relationships were found among particulate matter_2.5 (PM_2.5), sulfur dioxide (SO₂), and the daily internal medicine outpatient visits. Overall, PM_2.5 was positively correlated with the daily internal medicine outpatient visits. Both ozone (O₃) and SO₂ had significant delayed effects on the daily internal medical outpatient numbers; however, PM_2.5 only showed a short-term risk.

Conclusion: Short-term exposure to PM_2.5 was associated with an increase in the daily internal medicine outpatient visits for circulatory and respiratory diseases/symptoms in Zhoushan, China. SO₂ and O₃ were shown to induce significant effects after a concentration-dependent time lag.