Severe Air Pollution Exposure and Long-Term Health Outcomes

Correlations between ambient air pollution and the prevalence of hospitalisations and emergency room visits for respiratory diseases in children: a systematic review

OBJECTIVE

It is known that exposure to air pollution is associated with an increased risk for cardiovascular and respiratory diseases. This review aimed to summarise observational studies on the impact of short and long-term exposure to ambient air pollution on prevalence of hospitalisations and/or emergency department visits caused by respiratory diseases in children and adolescents.

SOURCES

Pubmed, Scopus, Embase and Cochrane Library databases were searched for the years 2018 to December 2022, including studies in any language.

SUMMARY OF THE FINDINGS

A total of 15 studies published between 2018 and 15 January 2022 were included in this review. PM2.5 was the most type of particulate matter studied. Short-term exposure to PM2,5, PM10, NO2, SO2 and O3, even at concentrations less than the current health-based guidelines, was significantly correlated with increased risk of outpatient/hospital visits and hospitalisations for respiratory diseases by children.

CONCLUSIONS

Our findings emphasise the importance and urgency of long-term control of air pollution and pollution-related diseases, especially among children and adolescents. There is a need for further research employing more homogeneous methodologies for assessing exposure and outcome measurements, in order to enable systematic reviews with meta-analysis.

Determinants of Knowledge, Attitudes, Perceptions and Behaviors Regarding Air Pollution in Schoolchildren in Pristina, Kosovo

Air pollution poses a significant public health challenge, and Kosovo, a low-middle-income country in the Balkan peninsula, suffers from particularly poor air quality, especially around the area of the capital Pristina. The availability of accurate and timely information is crucial in mitigating the adverse effects of air pollution. This study aimed at evaluating the knowledge, attitudes, behaviors, and perceptions (KAPB) related to poor air quality in Pristina's low-middle schools. Furthermore, the study explored the connections between these factors and socio-demographic and health attributes and provided valuable inputs for the development of future strategies and policies in air pollution mitigation. Regression analysis provided insights into how these various factors interacted with KAPB scores. The results revealed limited knowledge about air pollution sources and risks among pupils, with insufficient awareness of reliable information sources. While attitudes were generally positive, they declined with higher grade levels. Parental education significantly influenced knowledge and attitudes, and better health correlated with more positive attitudes. Perceptions of air pollution risks were influenced by grade, gender, and parental education, with better-educated parents associated with improved perceptions. Overall behavior scores increased with higher levels of parental education. Understanding the factors that shape pupils' responses to air pollution is critical for strategy and policy development. These findings can guide strategies to enhance environmental awareness and promote healthy behavior, helping address the pressing issue of air pollution in the country.

The association of asthma and air pollution: Evidence from India

In the last two decades, air pollution has increased throughout India resulting in the deterioration of air quality. This paper estimates the prevalence of self-reported asthma in women aged 15-49 years and examines the link between outdoor air pollution and disease prevalence in India by combining satellite data on particulate matter (PM2.5) and the National Family Health Survey (NFHS-4), 2015-16. The results indicate that both indoor pollution as well as outdoor air pollution are important risk factors for asthma in women as both independently increase the probability of asthma among this group. Strategies around the prevention of asthma need to recognize the role of both indoor as well as outdoor air pollution. The other significant risk factors for asthma are smoking, second-hand smoking, type of diet and obesity.

Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants

BACKGROUND

Tuberculosis (TB) is a public health problem worldwide, and the influence of meteorological and air pollutants on the incidence of tuberculosis have been attracting interest from researchers. It is of great importance to use machine learning to build a prediction model of tuberculosis incidence influenced by meteorological and air pollutants for timely and applicable measures of both prevention and control.

METHODS

The data of daily TB notifications, meteorological factors and air pollutants in Changde City, Hunan Province ranging from 2010 to 2021 were collected. Spearman rank correlation analysis was conducted to analyze the correlation between the daily TB notifications and the meteorological factors or air pollutants. Based on the correlation analysis results, machine learning methods, including support vector regression, random forest regression and a BP neural network model, were utilized to construct the incidence prediction model of tuberculosis. RMSE, MAE and MAPE were performed to evaluate the constructed model for selecting the best prediction model.

RESULTS

(1) From the year 2010 to 2021, the overall incidence of tuberculosis in Changde City showed a downward trend. (2) The daily TB notifications was positively correlated with average temperature (r = 0.231), maximum temperature (r = 0.194), minimum temperature (r = 0.165), sunshine duration (r = 0.329), PM_2.5 (r = 0.097), PM₁₀ (r = 0.215) and O₃ (r = 0.084) (p < 0.05). However, there was a significant negative correlation between the daily TB notifications and mean air pressure (r = -0.119), precipitation (r = -0.063), relative humidity (r = -0.084), CO (r = -0.038) and SO₂ (r = -0.034) (p < 0.05). (3) The random forest regression model had the best fitting effect, while the BP neural network model exhibited the best prediction. (4) The validation set of the BP neural network model, including average daily temperature, sunshine hours and PM₁₀, showed the lowest root mean square error, mean absolute error and mean absolute percentage error, followed by support vector regression.

CONCLUSIONS

The prediction trend of the BP neural network model, including average daily temperature, sunshine hours and PM₁₀, successfully mimics the actual incidence, and the peak incidence highly coincides with the actual aggregation time, with a high accuracy and a minimum error. Taken together, these data suggest that the BP neural network model can predict the incidence trend of tuberculosis in Changde City.

Evaluation of Health Economic Loss Due to Particulate Matter Pollution in the Seoul Subway, South Korea

Evaluating an illness's economic impact is critical for developing and executing appropriate policies. South Korea has mandatory national health insurance in the form of NHIS that provides propitious conditions for assessing the national financial burden of illnesses. The purpose of our study is to provide a comprehensive assessment of the economic impact of PM_2.5 exposure in the subway and a comparative analysis of cause-specific mortality outcomes based on the prevalent health-risk assessment of the health effect endpoints (chronic obstructive pulmonary disease (COPD), asthma, and ischemic heart disease (IHD)). We used the National Health Insurance database to calculate the healthcare services provided to health-effect endpoints, with at least one primary diagnosis in 2019. Direct costs associated with health aid or medicine, treatment, and indirect costs (calculated based on the productivity loss in health effect endpoint patients, transportation, and caregivers, including morbidity and mortality costs) were both considered. The total cost for the exposed population for these endpoints was estimated to be USD 437 million per year. Medical costs were the largest component (22.08%), followed by loss of productivity and premature death (15.93%) and other costs such as transport and caregiver costs (11.46%). The total incurred costs (per 1000 persons) were accounted to be USD 0.1771 million, USD 0.42 million, and USD 0.8678 million for COPD, Asthma, and IHD, respectively. Given that the economic burden will rise as the prevalence of these diseases rises, it is vital to adopt effective preventative and management methods strategies aimed at the appropriate population.

Air Quality Is Predictive of Mistakes in Professional Baseball and American Football

Air quality is a growing environmental concern that has implications for human physical and mental health. While air pollution has been linked to cognitive disease progression and declines in overall health, the impacts of air quality on athletic performance have not been extensively investigated. Much of the previous research focused on endurance sports indicates that air quality negatively impacts athletic performance; however, the effects of air quality on non-endurance elite team performance remains largely unknown. The purpose of this study was to examine the impact of air quality on errors committed by Major League Baseball (MLB) teams, interceptions thrown by quarterbacks in the National Football League (NFL), and overall quarterback performance in the NFL. Linear regression analysis was used to determine the impact of the median air quality index (AQI) of counties with MLB and NFL teams on errors, interceptions, and overall quarterback performance of players on those MLB and NFL teams. AQI was a significant positive predictor of errors and interceptions, indicating increased errors and interceptions with decreased air quality. Similarly, quarterback performance was significantly reduced for quarterbacks from teams in counties with worse air quality. These findings suggest that air quality has a significant impact on performance in the MLB and NFL, indicating impairments in physical and cognitive performance in professional athletes when competing in areas with poorer air quality. Hence, it is likely that air quality impacts athletic performance in numerous sports that have not yet been investigated.