Effects of ambient air pollution on psychological stress and anxiety disorder: a systematic review and meta-analysis of epidemiological evidence

Impact of air pollution and climate change on mental health outcomes: an umbrella review of global evidence

The impact of air pollution and climate change on mental health has recently raised strong concerns. However, a comprehensive overview analyzing the existing evidence while addressing relevant biases is lacking. This umbrella review systematically searched the PubMed/Medline, Scopus and PsycINFO databases (up to June 26, 2023) for any systematic review with meta-analysis investigating the association of air pollution or climate change with mental health outcomes. We used the R metaumbrella package to calculate and stratify the credibility of the evidence according to criteria (i.e., convincing, highly suggestive, suggestive, or weak) that address several biases, complemented by sensitivity analyses. We included 32 systematic reviews with meta-analysis that examined 284 individual studies and 237 associations of exposures to air pollution or climate change hazards and mental health outcomes. Most associations (n=195, 82.3%) involved air pollution, while the rest (n=42, 17.7%) regarded climate change hazards (mostly focusing on temperature: n=35, 14.8%). Mental health outcomes in most associations (n=185, 78.1%) involved mental disorders, followed by suicidal behavior (n=29, 12.4%), access to mental health care services (n=9, 3.7%), mental disorders-related symptomatology (n=8, 3.3%), and multiple categories together (n=6, 2.5%). Twelve associations (5.0%) achieved convincing (class I) or highly suggestive (class II) evidence. Regarding exposures to air pollution, there was convincing (class I) evidence for the association between long-term exposure to solvents and a higher incidence of dementia or cognitive impairment (odds ratio, OR=1.139), and highly suggestive (class II) evidence for the association between long-term exposure to some pollutants and higher risk for cognitive disorders (higher incidence of dementia with high vs. low levels of carbon monoxide, CO: OR=1.587; higher incidence of vascular dementia per 1 μg/m3 increase of nitrogen oxides, NOx: hazard ratio, HR=1.004). There was also highly suggestive (class II) evidence for the association between exposure to airborne particulate matter with diameter ≤10 μm (PM10) during the second trimester of pregnancy and the incidence of post-partum depression (OR=1.023 per 1 μg/m3 increase); and for the association between short-term exposure to sulfur dioxide (SO2) and schizophrenia relapse (risk ratio, RR=1.005 and 1.004 per 1 μg/m3 increase, respectively 5 and 7 days after exposure). Regarding climate change hazards, there was highly suggestive (class II) evidence for the association between short-term exposure to increased temperature and suicide- or mental disorders-related mortality (RR=1.024), suicidal behavior (RR=1.012), and hospital access (i.e., hospitalization or emergency department visits) due to suicidal behavior or mental disorders (RR=1.011) or mental disorders only (RR=1.009) (RR values per 1°C increase). There was also highly suggestive (class II) evidence for the association between short-term exposure to increased apparent temperature (i.e., the temperature equivalent perceived by humans) and suicidal behavior (RR=1.01 per 1°C increase). Finally, there was highly suggestive (class II) evidence for the association between the temporal proximity of cyclone exposure and severity of symptoms of post-traumatic stress disorder (r=0.275). Although most of the above associations were small in magnitude, they extend to the entire world population, and are therefore likely to have a substantial impact. This umbrella review classifies and quantifies for the first time the global negative impacts that air pollution and climate change can exert on mental health, identifying evidence-based targets that can inform future research and population health actions.

Airborne Exposure to Pollutants and Mental Health: A Review with Implications for United States Veterans

PURPOSE OF REVIEW

Inhalation of airborne pollutants in the natural and built environment is ubiquitous; yet, exposures are different across a lifespan and unique to individuals. Here, we reviewed the connections between mental health outcomes from airborne pollutant exposures, the biological inflammatory mechanisms, and provide future directions for researchers and policy makers. The current state of knowledge is discussed on associations between mental health outcomes and Clean Air Act criteria pollutants, traffic-related air pollutants, pesticides, heavy metals, jet fuel, and burn pits.

RECENT FINDINGS

Although associations between airborne pollutants and negative physical health outcomes have been a topic of previous investigations, work highlighting associations between exposures and psychological health is only starting to emerge. Research on criteria pollutants and mental health outcomes has the most robust results to date, followed by traffic-related air pollutants, and then pesticides. In contrast, scarce mental health research has been conducted on exposure to heavy metals, jet fuel, and burn pits. Specific cohorts of individuals, such as United States military members and in-turn, Veterans, often have unique histories of exposures, including service-related exposures to aircraft (e.g. jet fuels) and burn pits. Research focused on Veterans and other individuals with an increased likelihood of exposure and higher vulnerability to negative mental health outcomes is needed. Future research will facilitate knowledge aimed at both prevention and intervention to improve physical and mental health among military personnel, Veterans, and other at-risk individuals.

How does air pollution threaten mental health? Protocol for a machine-learning enhanced systematic map

INTRODUCTION

Air pollution exposure has influenced a broad range of mental health conditions. It has attracted research from multiple disciplines such as biomedical sciences, epidemiology, neurological science, and social science due to its importance for public health, with implications for environmental policies. Establishing and identifying the causal and moderator effects is challenging and is particularly concerning considering the different mental health measurements, study designs and data collection strategies (eg, surveys, interviews) in different disciplines. This has created a fragmented research landscape which hinders efforts to integrate key insights from different niches, and makes it difficult to identify current research trends and gaps.

METHOD AND ANALYSIS

This systematic map will follow the Collaboration for Environmental Evidence's guidelines and standards and Reporting Standards for Systematic Evidence Syntheses guidelines. Different databases and relevant web-based search engines will be used to collect the relevant literature. The time period of search strategies is conducted from the inception of the database until November 2022. Citation tracing and backward references snowballing will be used to identify additional studies. Data will be extracted by combining of literature mining and manual correction. Data coding for each article will be completed by two independent reviewers and conflicts will be reconciled between them. Machine learning technology will be applied throughout the systematic mapping process. Literature mining will rapidly screen and code the numerous available articles, enabling the breadth and diversity of the expanding literature base to be considered. The systematic map output will be provided as a publicly available database.

ETHICS AND DISSEMINATION

Primary data will not be collected and ethical approval is not required in this study. The findings of this study will be disseminated through a peer-reviewed scientific journal and academic conference presentations.

Association between air pollution and sudden sensorineural hearing loss (SSHL): A systematic review and meta-analysis

Recent studies have indicated that air pollution (AP) has harmful effects on hearing and ear diseases such as Sudden Sensorineural Hearing Loss (SSHL). The purpose of this study was to evaluate the impact of exposure to AP on SSHL incidence. Valid electronic databases were searched to retrieve studies published until December 1, 2022, using appropriate keywords. The result of the search was 1146 studies, and after screening according to the defined criteria, in total 8 studies were obtained. The risk of bias (ROB) in the studies and their quality were assessed. Finally, the meta-analysis with a significance level of 5% was performed. The findings revealed that the mean level of SO2, CO, NO2, and PM10 in the patient group was more than that of the control group, and p-values were 0.879, 0.144, 0.077, and 0.138, respectively. There was an indirect relation between air pollutants and SSHL, and PM2.5 showed a significant effect (p < 0.05). Given the limited research and the use of different statistical methods, more research is suggested to confirm this association and to determine the mechanisms by which AP exposure may cause SSHL.

Long-term exposure to air pollution and incidence of mental disorders. A large longitudinal cohort study of adults within an urban area

BACKGROUND

Recent epidemiological evidence suggests associations between air pollution exposure and major depressive disorders, but the literature is inconsistent for other mental illnesses. We investigated the associations of several air pollutants and road traffic noise with the incidence of different categories of mental disorders in a large population-based cohort.

METHODS

We enrolled 1,739,277 individuals 30 + years from the 2011 census in Rome, Italy, and followed them up until 2019. In detail, we analyzed 1,733,331 participants (mean age 56.43 +/- 15.85 years; 54.96 % female) with complete information on covariates of interest. We excluded subjects with prevalent mental disorders at baseline to evaluate the incidence (first hospitalization or co-pay exemption) of schizophrenia spectrum disorders, bipolar, anxiety, personality, or substance use disorders. In addition, we studied subjects with first prescriptions of antipsychotics, antidepressants, and mood stabilizers. Annual average concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO₂), Black Carbon (BC), ultrafine particles (UFP), and road traffic noise were assigned to baseline residential addresses. We applied Cox regression models adjusted for individual and area-level covariates.

RESULTS

Each interquartile range (1.13 µg/m3) increase in PM2.5 was associated with a hazard ratio (HR) of 1.070 (95 % confidence interval [CI]: 1.017, 1.127) for schizophrenia spectrum disorder, 1.135 (CI: 1.086, 1.186) for depression, 1.097 (CI: 1.030, 1.168) for anxiety disorders. Positive associations were also detected for BC and UFP, and with the three categories of drug prescriptions. Bipolar, personality, and substance use disorders did not show clear associations. The effects were highest in the age group 30-64 years, except for depression.

CONCLUSIONS

Long-term exposure to ambient air pollution, especially fine and ultrafine particles, was associated with increased risks of schizophrenia spectrum disorder, depression, and anxiety disorders. The association of the pollutants with the prescriptions of specific drugs increases the credibility of the results.

The association between short-term exposure to nitrogen dioxide and hospital admission for schizophrenia: A systematic review and meta-analysis

BACKGROUND

Ambient air pollution has been identified as a primary risk factor for mental disorders. In recent years, the relationship between exposure to ambient nitrogen dioxide (NO2) and the risk of hospital admissions (HAs) for schizophrenia has garnered increasing scientific interest, but evidence from epidemiological studies has been inconsistent. Therefore, a systematic review and meta-analysis were conducted to comprehensively identify potential correlations.

METHODS

A literature search in 3 international databases was conducted before December 31, 2022. Relative risk (RR) and corresponding 95% confidence intervals (CI) were calculated to evaluate the strength of the associations. Summary effect sizes were calculated using a random-effects model due to the expected heterogeneity (I2 over 50%).

RESULTS

A total of ten eligible studies were included in the meta-analysis, including 1,412,860 participants. The pooled analysis found that an increased risk of HAs for schizophrenia was associated with exposure to each increase of 10 μg/m3 in NO2 (RR = 1.029, 95% CI = 1.016-1.041, P < .001). However, the heterogeneity was high for the summary estimates, reducing the credibility of the evidence. In 2-pollutant models, results for NO2 increased by 0.3%, 0.2% and 2.3%, respectively, after adjusting for PM2.5, PM10 and SO2.

CONCLUSIONS

This study provides evidence that NO2 exposure significantly increases the risk of hospital admission for schizophrenia. Future studies are required to clarify the potential biological mechanism between schizophrenia and NO2 exposure to provide a more definitive result.

Neurotoxicity and endocrine disruption caused by polystyrene nanoparticles in zebrafish embryo

Nanoplastics (NP) are present in aquatic and terrestrial ecosystems. Humans can be exposed to them through contaminated water, food, air, or personal care products. Mechanisms of NP toxicity are largely unknown and the Zebrafish embryo poses an ideal model to investigate them due to its high homology with humans. Our objective in the present study was to combine a battery of behavioral assays with the study of endocrine related gene expression, to further explore potential NP neurotoxic effects on animal behavior. Polystyrene nanoplastics (PSNP) were used to evaluate NP toxicity. Our neurobehavioral profiles include a tail coiling assay, a light/dark activity assay, two thigmotaxis anxiety assays (auditory and visual stimuli), and a startle response - habituation assay in response to auditory stimuli. Results show PSNP accumulated in eyes, neuromasts, brain, and digestive system organs. PSNP inhibited acetylcholinesterase and altered endocrine-related gene expression profiles both in the thyroid and glucocorticoid axes. At the whole organism level, we observed altered behaviors such as increased activity and anxiety at lower doses and lethargy at a higher dose, which could be due to a variety of complex mechanisms ranging from sensory organ and central nervous system effects to others such as hormonal imbalances. In addition, we present a hypothetical adverse outcome pathway related to these effects. In conclusion, this study provides new understanding into NP toxic effects on zebrafish embryo, emphasizing a critical role of endocrine disruption in observed neurotoxic behavioral effects, and improving our understanding of their potential health risks to human populations.

Air pollution, depressive and anxiety disorders, and brain effects: A systematic review

Accumulating data suggest that air pollution increases the risk of internalizing psychopathology, including anxiety and depressive disorders. Moreover, the link between air pollution and poor mental health may relate to neurostructural and neurofunctional changes. We systematically reviewed the MEDLINE database in September 2021 for original articles reporting effects of air pollution on 1) internalizing symptoms and behaviors (anxiety or depression) and 2) frontolimbic brain regions (i.e., hippocampus, amygdala, prefrontal cortex). One hundred and eleven articles on mental health (76% human, 24% animals) and 92 on brain structure and function (11% human, 86% animals) were identified. For literature search 1, the most common pollutants examined were PM2.5 (64.9%), NO2 (37.8%), and PM10 (33.3%). For literature search 2, the most common pollutants examined were PM2.5 (32.6%), O3 (26.1%) and Diesel Exhaust Particles (DEP) (26.1%). The majority of studies (73%) reported higher internalizing symptoms and behaviors with higher air pollution exposure. Air pollution was consistently associated (95% of articles reported significant findings) with neurostructural and neurofunctional effects (e.g., increased inflammation and oxidative stress, changes to neurotransmitters and neuromodulators and their metabolites) within multiple brain regions (24% of articles), or within the hippocampus (66%), PFC (7%), and amygdala (1%). For both literature searches, the most studied exposure time frames were adulthood (48% and 59% for literature searches 1 and 2, respectively) and the prenatal period (26% and 27% for literature searches 1 and 2, respectively). Forty-three percent and 29% of studies assessed more than one exposure window in literature search 1 and 2, respectively. The extant literature suggests that air pollution is associated with increased depressive and anxiety symptoms and behaviors, and alterations in brain regions implicated in risk of psychopathology. However, there are several gaps in the literature, including: limited studies examining the neural consequences of air pollution in humans. Further, a comprehensive developmental approach is needed to examine windows of susceptibility to exposure and track the emergence of psychopathology following air pollution exposure.

Is ambient air pollution a risk factor for Parkinson's disease? A meta-analysis of epidemiological evidence

Current evidence shows inconsistencies about ambient air pollution (AAP) exposure as a risk factor for Parkinson's disease (PD). We performed meta-analyses to estimate the pooled risk of PD due to AAP exposure. We performed a systematic search in PubMed, Google Scholar, The Cochrane Library, and J-GATEPLUS databases for peer-reviewed epidemiological studies reporting the risk of PD due to exposure to PM_2.5, PM₁₀, O₃, CO, NO₂, NO_X and SO₂; from the beginning until October 2021. The pooled odds ratio (OR) for the effect of NO₂ (per 1 μg/m³) and O₃ (per 1 ppb) on PD was 1.01[95% CI: 1.00,1.02; I² = 69% (p = .01)] and 1.01 [95% CI: 1.00,1.02; I² = 66% (p = .03)], respectively. The ORs for the effects of PM_2.5 (per 1 µg/m³) and CO (per 1 ppm) on PD were 1.01 [95% CI: .99,1.03; I² = 40%] and 1.64 [95% CI: .96,2.78; I² = 75% (p = .01)], respectively. The study showed the adverse roles of NO₂, O₃, PM_2.5, and CO in increasing the risk for PD.

Panic attack prediction using wearable devices and machine learning: Development and cohort study (Preprint)

Background

A panic attack (PA) is an intense form of anxiety accompanied by multiple somatic presentations, leading to frequent emergency department visits and impairing the quality of life. A prediction model for PAs could help clinicians and patients monitor, control, and carry out early intervention for recurrent PAs, enabling more personalized treatment for panic disorder (PD).

Objective

This study aims to provide a 7-day PA prediction model and determine the relationship between a future PA and various features, including physiological factors, anxiety and depressive factors, and the air quality index (AQI).

Methods

We enrolled 59 participants with PD (Diagnostic and Statistical Manual of Mental Disorders, 5th edition, and the Mini International Neuropsychiatric Interview). Participants used smartwatches (Garmin Vívosmart 4) and mobile apps to collect their sleep, heart rate (HR), activity level, anxiety, and depression scores (Beck Depression Inventory [BDI], Beck Anxiety Inventory [BAI], State-Trait Anxiety Inventory state anxiety [STAI-S], State-Trait Anxiety Inventory trait anxiety [STAI-T], and Panic Disorder Severity Scale Self-Report) in their real life for a duration of 1 year. We also included AQIs from open data. To analyze these data, our team used 6 machine learning methods: random forests, decision trees, linear discriminant analysis, adaptive boosting, extreme gradient boosting, and regularized greedy forests.

Results

For 7-day PA predictions, the random forest produced the best prediction rate. Overall, the accuracy of the test set was 67.4%-81.3% for different machine learning algorithms. The most critical variables in the model were questionnaire and physiological features, such as the BAI, BDI, STAI, MINI, average HR, resting HR, and deep sleep duration.

Conclusions

It is possible to predict PAs using a combination of data from questionnaires and physiological and environmental data.