Increased Risk of Hospital Admission for Asthma in Children From Short-Term Exposure to Air Pollution: Case-Crossover Evidence From Northern China

Short-term ambient ozone exposure increases the risk of hospitalization with depression: a multi-city time-stratified case-crossover study

BACKGROUND

Depression, the most common mental illness worldwide, has been studied and air pollution has been found to increase the risk of depression hospitalization, but research results on ozone (O3) remain limited. In this context, we investigated the relationship between short-term O3 exposure and depression-related hospital admissions (HAs).

METHODS

The 10,459 records of HAs for depression from medical institutions across in 9 cities, China, were collected between 1 January 2017, and 31 December 2018. Air pollutants and meteorological data was obtained from provincial ecological environment monitoring stations in the study area. Conditional Poisson regression was employed to estimate the association between O3 and hospitalizations for depression, with data stratification by sex, age, weather, and economic level.

RESULTS

Short-term O3 exposure was positively associated with the number of depression-related hospitalizations (Relative risk: 1.04 [95% CI: 1.02, 1.05]). O3 had a significant effect on the risk of depression-related hospitalizations on warm days (P = 0.021, Relative risk: 1.05 [1.03, 1.08]). The high gross domestic product group was more likely to be affected by O3 exposure-associated depression-related hospitalizations (P = 0.005, Relative risk: 1.03 [1.01, 1.05]).

CONCLUSIONS

Short-term changes to O3 exposure may increase the risk of depression related hospitalizations, especially on warm days.

The short-term association between environmental variables and daily pediatric asthma patient visits in Hangzhou, China: A time-series study

Background

To date, a large number of studies have shown correlations between environmental variables and pediatric asthma in short-term lag time. However, their results are inconsistent. Therefore, we aimed to evaluate the short-term impact of environmental variables on daily pediatric asthma patients' visits (DPAPV) in Hangzhou, China, and find the most important risk factor.

Methods

Generalized additive distribution lag non-linear model (GAM-DLNM) was applied to explore the effect of environmental variables on DPAPV in single- and multi-variable models in Hangzhou, China from 2014 to 2021. Then, risk factors of pediatric asthma were selected (p < 0.05 both in single- and multi-variable models) and used weighted quantile sum (WQS) regression model to evaluate their relative importance.

Results

There were 313,296 pediatric asthma patient records between 2014 and 2021. Both in single- and multi-variable models, PM2.5, PM10, and NO2 exhibited significant positive correlations in short-term lag time and these correlations reached their maximum in lag day 2 (RR = 1.00, 95%CI:1.00 to 1.01), lag day 2 (RR = 1.00, 95%CI:1.00 to 1.01), and lag day 3 (RR = 1.04, 95%CI:1.02 to1.05), respectively. The WQS index showed that NO2 had the greatest relative importance (weight over 70 %). The relative importance of NO2 increased with time passing. Males were more susceptible to the adverse effects of NO2.

Conclusion

PM2.5, PM10, and NO2 had significant adverse effects on pediatric asthma. Among them, NO2 presented the greatest and most important adverse effect on the disease. Therefore, parents could give priority to paying attention to NO2 to control children's asthma.

The Influence of Fine Particulate Matter and Cold Weather on Emergency Room Interventions for Childhood Asthma

(1) Background: Children are the most vulnerable to pollution due to their decreased stature, heightened respiratory rate, and frequent outdoor engagement. PM2.5, nitrogen dioxide (NO2), ozone, and cold weather are associated with pediatric asthma. In this study, we investigated the nexus between air pollution, climate factors, and pediatric asthma emergency room visits (ERVs). (2) Method: Pediatric asthma ERV data for healthcare quality from the Taiwanese National Insurance in the Taipei area were obtained from 2015 to 2019. Air pollution and climate factor data were also collected. Poisson regression was employed to determine the relationships with relative risks (RRs). (3) Results: The incidence of pediatric asthma ERVs decreased, with a crude RR of 0.983 (95% CI: 0.98-0.986, p < 0.001). Fine particulate matter (PM2.5) had an adjusted RR of 1.102 (95% CI: 1.037-1.172, p = 0.002) and a 7.7 µg/m3 increase, and air temperature had an adjusted RR of 0.813 (95% CI: 0.745-0.887, p < 0.001) comparing between the highest and lowest quarter air temperature associated with pediatric asthma ERVs. (4) Conclusions: This inquiry underscores the positive associations of PM2.5 and cold weather with pediatric asthma ERVs. The findings could guide the government to establish policies to reduce air pollution and promote children's health.

Prediction of Asthma Exacerbations in Children

Asthma exacerbations are common in asthmatic children, even among those with good disease control. Asthma attacks result in the children and their parents missing school and work days; limit the patient's social and physical activities; and lead to emergency department visits, hospital admissions, or even fatal events. Thus, the prompt identification of asthmatic children at risk for exacerbation is crucial, as it may allow for proactive measures that could prevent these episodes. Children prone to asthma exacerbation are a heterogeneous group; various demographic factors such as younger age, ethnic group, low family income, clinical parameters (history of an exacerbation in the past 12 months, poor asthma control, poor adherence to treatment, comorbidities), Th2 inflammation, and environmental exposures (pollutants, stress, viral and bacterial pathogens) determine the risk of a future exacerbation and should be carefully considered. This paper aims to review the existing evidence regarding the predictors of asthma exacerbations in children and offer practical monitoring guidance for promptly recognizing patients at risk.

The relationship between PM2.5 and the onset and exacerbation of childhood asthma: a short communication

Much is known about the link between air pollution and asthma in adults, particularly fine particulate matter (PM2.5). Studies have found that certain levels of fine PM2.5 can increase airway responsiveness and worsen asthma. PM2.5 may play a role in the onset and exacerbation of childhood asthma. However, there is little in the literature on how PM2.5 affects asthma attacks and exacerbations in children. Asthma is a common chronic disease in children, and air pollution can aggravate it. The effect of PM2.5 on childhood asthma needs further research. By evaluating, reviewing, and collating existing results in this area, this paper aims to explore the relationship between PM2.5 and asthma onset and exacerbation in children.

Air pollution and childhood asthma

PURPOSE OF REVIEW

Asthma is the most common chronic disease of childhood. Environmental exposures, such as allergens and pollutants, are ubiquitous factors associated with asthma development and asthma morbidity. In this review, we highlight the most recent studies relevant to childhood asthma risk, onset, and exacerbation related to air pollution exposure.

RECENT FINDINGS

In this article, we review current research that has been published between 2021 and 2022, demonstrating the effects of early-life exposure to key air pollutants (e.g., particulate matter (PM), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ) and ground-level ozone (O 3 ), environmental tobacco smoke, radon, and volatile organic compounds (VOC) on respiratory health.

SUMMARY

Air pollution continues to be a global burden with serious consequences related to respiratory health. Interventions aimed at reducing air pollution in the environment must be achieved in an effort to improve asthma outcomes and pediatric health.

Causal relationship between outdoor atmospheric quality and pediatric asthma visits in hangzhou

Many air pollutants and climate variables have proven to be significantly associated with pediatric asthma and have worsened asthma symptoms. However, their exact causal effects remain unclear. We explored the causality between air pollutants, climate, and daily pediatric asthma patient visits with a short-term lag effect. Based on eight years of daily environmental data and daily pediatric asthma patient visits, Spearman correlation analysis was used to select the air pollutants and climate variables that correlated with daily pediatric asthma patient visits at any time (with a lag of 1-6 days). We regarded these environmental variables as treatments and built multiple- and single-treatment causal inference models using the Dowhy library (a Python library for causal inference by graphing the model, quantitatively evaluating causal effects, and validating the causal assumptions) to estimate the quantitative causal effect between these correlated variables and daily pediatric asthma patient visits in lag time. The multiple-treatment causal inference model was a model with 8 treatments (Visibility, Precipitation, PM₁₀, PM_2.5, SO₂, NO₂, AQI and CO), 1 outcome (daily pediatric asthma patients visits), and 5 confounders (Humidity, Temperature, Sea level pressure, wind speed and unobserved confounders "U"). Single-treatment causal inference models were 8 models, and each model has 1 treatment, 1 outcome and 12 confounders. Spearman correlation analysis showed that precipitation, wind speed, visibility, air quality index, PM_2.5, PM₁₀, SO₂, NO₂, and CO were significantly associated variables at all times (p < 0.05). The multiple-treatment model showed that pooled treatments had significant causality for the short-term lag (lag1-lag6; p < 0.05). Causality was mainly due to SO₂. In the single-treatment models, visibility, SO₂, NO₂, and CO exhibited significant causal effects at any one time (p < 0.05). SO₂ and CO exhibited stronger positive causal effects. The causal effect of SO₂ reached its maxima (causal effect = 11.41, p < 0.05) at lag5. The greatest causal effect of CO appeared at lag3 (causal effect = 10.67, p < 0.05). During the eight year-period, the improvements in SO₂, CO, and NO₂ in Hangzhou were estimated to reduce asthma visits by 8478.03, 3131.08, and 1341.39 per year, respectively. SO₂, NO₂, CO, and visibility exhibited causal effects on daily pediatric asthma patient visits; SO₂ was the most crucial causative variable with a relatively higher causal effect, followed by CO. Improvements in atmospheric quality in the Hangzhou area have effectively reduced the incidence of asthma.

Associations between air pollutant and pneumonia and asthma requiring hospitalization among children aged under 5 years in Ningbo, 2015-2017

Introduction

Exposure to ambient air pollutants is associated with an increased incidence of respiratory diseases such as pneumonia and asthma, especially in younger children. We investigated the relationship between rates of hospitalization of children aged under 5 years for pneumonia and asthma and the concentration of air pollutants in Ningbo between January 1, 2015 and August 29, 2017.

Methods

Data were obtained from the Ningbo Air Quality Data Real-time Publishing System and the big data platform of the Ningbo Health Information Center. A generalized additive model was established via logarithmic link function and utilized to evaluate the effect of pollutant concentration on lag dimension and perform sensitivity analysis.

Results

A total of 10,301 cases of pneumonia and 115 cases of asthma were identified over the course of this study. Results revealed that PM2.5, PM10, SO2 and NO2 were significantly associated with hospitalization for pneumonia and asthma in children under 5 years of age. For every 10-unit increase in lag03 air pollutant concentration, hospitalization for pneumonia and asthma due to PM2.5, PM10, SO2 and NO2 increased by 2.22% (95%CI: 0.64%, 3.82%), 1.94% (95%CI: 0.85%, 3.04%), 11.21% (95%CI: 4.70%, 18.10%) and 5.42% (95%CI: 3.07%, 7.82%), respectively.

Discussion

Adverse effects of air pollutants were found to be more severe in children aged 1 to 5 years and adverse effects due to PM2.5, PM10 and SO2 were found to be more severe in girls. Our findings underscore the need for implementation of effective public health measures to urgently improve air quality and reduce pediatric hospitalizations due to respiratory illness.

Spatial autocorrelation may bias the risk estimation: An application of eigenvector spatial filtering on the risk of air pollutant on asthma

Air pollutants are major risk factors for respiratory diseases, particularly asthma, socially and spatially correlated. Many existing environment-asthma-related studies, however, have evaluated the impact of crude trends at the largest district level, which accounts only for temporal effects and may produce biased results with spatial autocorrelation. This study aimed to investigate how the spatial autocorrelation affects the air pollution effect estimations (sulfur dioxide [SO₂], nitrogen dioxide [NO₂], carbon monoxide [CO], and particulate matter [PM₁₀]) on daily asthma emergency department (ED) visits in two metropolitan areas in Korea (Seoul Metropolitan Area [SMA] and Busan Metropolitan City, Ulsan Metropolitan City, Gyeongsangnamdo [BUG]). We applied eigenvector spatial filter (ESF) to the spatio-temporal model to remove spatial autocorrelation and distributed lag nonlinear model (DLNM) to explore nonlinear patterns between air pollutant concentration and lagged days on the three models including aggregated model (a temporal model), spatial model without ESF, and spatial model with ESF (both are spatio-temporal models). The effect of SO₂ was not statistically significant for asthma ED visits in the aggregated model for SMA (cumulative relative risks [CRR] = 0.99, confidence intervals [CI]: 0.93-1.05), while the effect was statistically significant in the spatial model with ESF (CRR = 1.10, CI: 1.08-1.12). NO₂ and CO were positively correlated to asthma ED visits in the spatial model without ESF (CRR = 0.84, CI: 0.81-0.86; 0.91, 0.89-0.94, respectively), but the spatial model with ESF showed significant risks (CRR = 1.21, CI: 1.18-1.24; 1.13, 1.11-1.16). Moreover, the spatial model with ESF successfully removed spatial autocorrelation (P-values for Moran's I 0.83-0.98) and demonstrated the highest model fit (McFadden's pseudo R² 0.42-0.43 for SMA and 0.26-0.27 for BUG) among the three models. Our findings demonstrate how ESF can be introduced into spatial correlation to remove bias and construct more reliable models.

A Framework for Characterizing the Multilateral and Directional Interaction Relationships Between PM Pollution at City Scale: A Case Study of 29 Cities in East China, South Korea and Japan

Transboundary particulate matter (PM) pollution has become an increasingly significant public health issue around the world due to its impacts on human health. However, transboundary PM pollution is difficult to address because it usually travels across multiple urban jurisdictional boundaries with varying transportation directions at different times, therefore posing a challenge for urban managers to figure out who is potentially polluting whose air and how PM pollution in adjacent cities interact with each other. This study proposes a statistical analysis framework for characterizing directional interaction relationships between PM pollution in cities. Compared with chemical transport models (CTMs) and chemical composition analysis method, the proposed framework requires less data and less time, and is easy to implement and able to reveal directional interaction relationships between PM pollution in multiple cities in a quick and computationally inexpensive way. In order to demonstrate the application of the framework, this study applied the framework to analyze the interaction relationships between PM_2.5 pollution in 29 cities in East China, South Korea and Japan using one year of hourly PM_2.5 measurement data in 2018. The results show that the framework is able to reveal the significant multilateral and directional interaction relationships between PM_2.5 pollution in the 29 cities in Northeast Asia. The analysis results of the case study show that the PM_2.5 pollution in China, South Korea and Japan are linked with each other, and the interaction relationships are mutual. This study further evaluated the framework's validity by comparing the analysis results against the wind vector data, the back trajectory data, as well as the results extracted from existing literature that adopted CTMs to study the interaction relationships between PM pollution in Northeast Asia. The comparisons show that the analysis results produced by the framework are consistent with the wind vector data, the back trajectory data as well as the results using CTMs. The proposed framework provides an alternative for exploring transportation pathways and patterns of transboundary PM pollution between cities when CTMs and chemical composition analysis would be too demanding or impossible to implement.