Air Pollution and Long Term Mental Health

A pilot study on AI-driven approaches for classification of mental health disorders

The increasing prevalence of mental disorders among youth worldwide is one of society's most pressing issues. The proposed methodology introduces an artificial intelligence-based approach for comprehending and analyzing the prevalence of neurological disorders. This work draws upon the analysis of the Cities Health Initiative dataset. It employs advanced machine learning and deep learning techniques, integrated with data science, statistics, optimization, and mathematical modeling, to correlate various lifestyle and environmental factors with the incidence of these mental disorders. In this work, a variety of machine learning and deep learning models with hyper-parameter tuning are utilized to forecast trends in the occurrence of mental disorders about lifestyle choices such as smoking and alcohol consumption, as well as environmental factors like air and noise pollution. Among these models, the convolutional neural network (CNN) architecture, termed as DNN1 in this paper, accurately predicts mental health occurrences relative to the population mean with a maximum accuracy of 99.79%. Among the machine learning models, the XGBoost technique yields an accuracy of 95.30%, with an area under the ROC curve of 0.9985, indicating robust training. The research also involves extracting feature importance scores for the XGBoost classifier, with Stroop test performance results attaining the highest importance score of 0.135. Attributes related to addiction, namely smoking and alcohol consumption, hold importance scores of 0.0273 and 0.0212, respectively. Statistical tests on the training models reveal that XGBoost performs best on the mean squared error and R-squared tests, achieving scores of 0.013356 and 0.946481, respectively. These statistical evaluations bolster the models' credibility and affirm the best-fit models' accuracy. The proposed research in the domains of mental health, addiction, and pollution stands to aid healthcare professionals in diagnosing and treating neurological disorders in both youth and adults promptly through the use of predictive models. Furthermore, it aims to provide valuable insights for policymakers in formulating new regulations on pollution and addiction.

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.

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.

Air Quality Modeling with the Use of Regression Neural Networks

Air quality is assessed on the basis of air monitoring data. Monitoring data are often not complete enough to carry out an air quality assessment. To fill the measurement gaps, predictive models can be used, which enable the approximation of missing data. Prediction models use historical data and relationships between measured variables, including air pollutant concentrations and meteorological factors. The known predictive air quality models are not accurate, so it is important to look for models that give a lower approximation error. The use of artificial neural networks reduces the prediction error compared to classical regression methods. In previous studies, a single regression model over the entire concentration range was used to approximate the concentrations of a selected pollutant. In this study, it was assumed that not a single model, but a group of models, could be used for the prediction. In this approach, each model from the group was dedicated to a different sub-range of the concentration of the modeled pollutant. The aim of the analysis was to check whether this approach would improve the quality of modeling. A long-term data set recorded at two air monitoring stations in Poland was used in the examination. Hourly data of basic air pollutants and meteorological parameters were used to create predictive regression models. The prediction errors for the sub-range models were compared with the corresponding errors calculated for one full-range regression model. It was found that the application of sub-range models reduced the modeling error of basic air pollutants.

Multiple aspects of energy poverty are associated with lower mental health-related quality of life: A modelling study in three peri-urban African communities

Objective

Over 900 million people in sub-Saharan Africa (SSA) live in energy poverty, relying on cooking polluting fuels (e.g. wood, charcoal). The association between energy poverty and mental/physical health-related quality of life (HRQoL) among women in SSA, who are primarily tasked with cooking, is unknown.

Methods

Females (n ​= ​1,150) from peri-urban Cameroon, Kenya and Ghana were surveyed on their household energy use and mental/physical health status using the standardized Short-Form 36 (SF-36) questionnaire. Random effects linear regression linked household energy factors to SF-36 mental (MCS) and physical component summary (PCS) scores. A binary outcome of 'likely depression' was derived based on participants' MCS score. Random effects Poisson regression with robust error variance assessed the relationship between household energy factors and odds of likely depression.

Results

The prevalence of likely depression varied by a factor of four among communities (36%-Mbalmayo, Cameroon; 20%-Eldoret, Kenya; 9%-Obuasi, Ghana). In the Poisson model (coefficient of determination (R²) ​= ​0.28), females sustaining 2 or more cooking-related burns during the previous year had 2.7 (95%CI:[1.8,4.1]) times the odds of likely depression as those not burned. Females cooking primarily with charcoal and wood had 1.6 times (95%CI:[0.9,2.7]) and 1.5 times (95%CI:[0.8,3.0]) the odds of likely depression, respectively, as those primarily using liquefied petroleum gas. Women without electricity access had 1.4 (95%CI:[1.1,1.9]) times the odds of likely depression as those with access. In the MCS model (R² ​= ​0.23), longer time spent cooking was associated with a lower average MCS score in a monotonically increasing manner. In the PCS model (R² ​= ​0.32), women injured during cooking fuel collection had significantly lower (-4.8 95%CI:[-8.1,-1.4]) PCS scores.

Conclusion

The burden of energy poverty in peri-urban communities in SSA extends beyond physical conditions. Experiencing cooking-related burns, using polluting fuels for cooking or lighting and spending more time cooking are potential risk factors for lower mental HRQoL among women.

Severe Air Pollution Exposure and Long-Term Health Outcomes

BACKGROUND

There is a large literature that documents the negative health implications of exposure to air pollution, particularly PM2.5. Much of this literature, however, relies on short-term cross-sectional data, which cannot establish a true causal link between pollution and health. There are also very few studies that document long- and very long-term effects.

PURPOSE

This study intends to estimate a causal relationship between exposure to severe air pollution and negative health outcomes that persist over long periods of time.

METHODS

We use a large longitudinal dataset that spans almost 2 decades and that allows us to not only document the persistence of negative health effects, but also a pattern of recovery from a severe pollution episode. We use multivariate regression methods to estimate a causal link between air pollution and health over time. A large pollution shock that occurred in 1997 in Indonesia is used as a natural experiment to pinpoint the true causal effects of pollution exposure and not mere correlations.

RESULTS

Exposure to an additional unit of pollution in 1997 leads to a loss of roughly six units of lung capacity and to an increase of 4.3% in the probability of being in poor general health, as measured ten years after the pollution exposure. These effects somewhat diminish over time, to a loss of roughly three units of lung capacity and to an increase of only about 3% in the probability of being in poor general health, as measured 17 years after exposure.

CONCLUSIONS

Our study finds significant health consequences of exposure to air pollution, which persist over long periods of time, with some patterns of recovery. Policymakers should pay special attention to such massive sources of pollution and try to mitigate these negative health consequences.

Inequality of Low Air Quality-Related Health Impacts among Socioeconomic Groups in the World of Work

This research aimed to assess the perceptions of air quality and health symptoms caused by low urban air quality among vulnerable socio-economic groups in the world of work in Bangkok, Thailand through a questionnaire survey of 400 workers of both formal and informal sectors in the five districts with different socio-economic characteristics and levels of air pollution. The findings showed symmetry between air quality-monitoring data and health symptoms of different socio-economic groups but asymmetry between air quality-monitoring data and people's perceptions of air quality in their areas. It also showed inequalities of low air quality-related health impacts on socio-economic groups in the world of work. People working near the streets, highways, and industrial zones tended to have more health symptoms related to low air quality, and informal sector workers faced more health risks than formal sector workers. The study appeals for effective air pollution communication to enhance the public and informal sector worker population's literacy of air pollution, the sources of air pollution and its critical health impacts, and the available and sufficient primary care organizations and community health care centers to address work-related health needs to reach the informal sector worker population.

Impact of Environmental Quality on Healthcare Expenditures in Developing Countries: A Panel Data Approach

Objective: The deterioration in environmental quality has an economic and social cost. The aim of this study is to analyze the impact of environmental factors on health expenditures in developing countries. Method: To analyze the relationship between environmental quality (air pollution and temperature) and health expenditure in thirty-three developing countries, the study uses system generalized method of moments (GMM) using data from 2000 to 2017. Results: The results suggest a positive effect of both air pollution and temperature on health expenditure. However, the effect is highest for government health expenditure, followed by private and total health expenditure in the studied countries. The results further suggest that the impact of environmental factors is greater in higher-income countries when we divide the studied countries into two groups, i.e., higher- and lower-income countries. Conclusion: Our results are interesting and informative for the policy makers to design such policies to attain better environmental quality and social well-being. The increased healthcare expenditures due to increased air pollution and climate change necessitate for an efficient, reliable, affordable and modern energy policy by emphasizing the use of clean and renewable energy in these countries that ensure better health for the masses. Furthermore, a smart and sustainable environmentally friendly economic growth policy is necessary to ensure better health for the masses.

Airborne fungi in Universiti Sains Malaysia: knowledge, density and diversity

Airborne fungi are among common contaminants in indoor and outdoor environments, leading to poor indoor air quality (IAQ), and to some extent, implicate health risks to humans worldwide. In Malaysia, fungal contamination in institutional buildings is rarely documented although these places are frequently visited by many. This study was conducted to assess the density and diversity of airborne fungi in Universiti Sains Malaysia (USM) main campus, Penang. A total of 11 sampling sites were assessed. Fungi were collected by using Andersen Single Stage Impact Air Sampler N-6 and MEA plates. Two separate trials, namely Trial 1 and Trial 2, were conducted in 2008 and 2019, respectively. The recovered fungi were identified up to the genus level-based morphological features. A survey involving 400 respondents among USM staff and students in relation to fungal contamination in indoor air environment was also conducted to evaluate the knowledge on indoor fungi among USM community. The densities of indoor air fungi in Trial 1 were higher; ranging from 81 to 1743 CFU/m³, exceeding the recommended level set by the Malaysia Industry Code of Practice (MCPIAQ) in some sampling sites, compared to that of in Trial 2 where the densities ranged from 229 to 699 CFU/m³. A total of 154 isolates and 230 isolates of airborne fungi were recovered in Trial 1 and Trial 2, respectively. In total, 11 fungal genera were identified in both trials, and three genera were predominant: Aspergillus, Penicillium, and Cladosporium. The survey also revealed that knowledge of IAQ among staff and students was limited and that they were unaware of fungal contamination and IAQ. A continuous and wide-spread awareness should be implemented at USM main campus for safer and healthier indoor air environments, particularly university students where productivity and efficiency are of the utmost importance.

Contribution of Aerosol Sources to Health Impacts

Atmospheric aerosol is one of the major leading environmental risk factors for human health worldwide, potentially causing several million premature deaths per year [...]