PM2.5 exposure and anxiety in China: evidence from the prefectures

Analysis of the situations and influencing factors of public anxiety in China: based on Baidu index data

Background

Anxiety disorders have emerged as one of the most prevalent mental health problems and health concerns. However, previous research has paid limited attention to measuring public anxiety from a broader perspective. Furthermore, while we know many factors that influence anxiety disorders, we still have an incomplete understanding of how these factors affect public anxiety. We aimed to quantify public anxiety from the perspective of Internet searches, and to analyze its spatiotemporal changing characteristics and influencing factors.

Methods

This study collected Baidu Index from 2014 to 2022 in 31 provinces in mainland China to measure the degree of public anxiety based on the Baidu Index from 2014 to 2022. The spatial autocorrelation analysis method was used to study the changing trends and spatial distribution characteristics of public anxiety. The influencing factors of public anxiety were studied using spatial statistical modeling methods.

Results

Empirical analysis shows that the level of public anxiety in my country has continued to rise in recent years, with significant spatial clustering characteristics, especially in the eastern and central-southern regions. In addition, we constructed ordinary least squares (OLS) and geographically weighted regression (GWR) spatial statistical models to examine the relationship between social, economic, and environmental factors and public anxiety levels. We found that the GWR model that considers spatial correlation and dependence is significantly better than the OLS model in terms of fitting accuracy. Factors such as the number of college graduates, Internet traffic, and urbanization rate are significantly positively correlated with the level of public anxiety.

Conclusion

Our research results draw attention to public anxiety among policymakers, highlighting the necessity for a more extensive examination of anxiety issues, especially among university graduates, by the public and relevant authorities.

Chronic exposure to PM10 induces anxiety-like behavior via exacerbating hippocampal oxidative stress

As one of the most environmental concerns, inhaled particulate matter (PM10) causes numerous health problems. However, the associations between anxiety behavior and toxicity caused by PM10 have rarely been reported so far. To investigate the changes of behavior after PM10 exposure and to identify the potential mechanisms of toxicity, PM10 samples (with doses of 15 mg/kg and 30 mg/kg) were intratracheally instilled into rats to simulate inhalation of polluted air by the lungs. After instillation for eight weeks, anxiety-like behavior was evaluated, levels of oxidative stress and morphological changes of hippocampus were measured. The behavioral results indicated that PM10 exposure induced obvious anxiety-like behavior in the open field and elevated plus maze tests. Both PM10 concentrations tested could increase whole blood viscosity and trigger hippocampal neuronal damage and oxidative stress by increasing superoxide dismutase (SOD) activities and malondialdehyde levels, and decreasing the expressions of antioxidant-related proteins (e.g., nuclear factor erythroid 2-related factor 2 (Nrf2), SOD1 and heme oxygenase 1). Furthermore, through collecting and analyzing questionnaires, the data showed that the participants experienced obvious anxiety-related emotions and negative somatic responses under heavily polluted environments, especially PM10 being the main pollutant. These results show that PM10 exposure induces anxiety-like behavior, which may be related to suppressing the Nrf2/Keap1-SOD1 pathway.

A Fresh Perspective on Examining Population Emotional Well-Being Trends by Internet Search Engine: An Emerging Composite Anxiety and Depression Index

Traditional assessments of anxiety and depression face challenges and difficulties when it comes to understanding trends in-group psychological characteristics. As people become more accustomed to expressing their opinions online, location-based online media and cutting-edge algorithms offer new opportunities to identify associations between group sentiment and economic- or healthcare-related variables. Our research provides a novel approach to analyzing emotional well-being trends in a population by focusing on retrieving online information. We used emotionally enriched texts on social media to build the Public Opinion Dictionary (POD). Then, combining POD with the word vector model and search trend, we developed the Composite Anxiety and Depression Index (CADI), which can reflect the mental health level of a region during a specific time period. We utilized the representative external data by CHARLS to validate the effectiveness of CADI, indicating that CADI can serve as a representative indicator of the prevalence of mental disorders. Regression and subgroup analysis are employed to further elucidate the association between public mental health (measured by CADI) with economic development and medical burden. The results of comprehensive regression analysis show that the Import-Export index (-16.272, p < 0.001) and average cost of patients (4.412, p < 0.001) were significantly negatively associated with the CADI, and the sub-models stratificated by GDP showed the same situation. Disposable income (-28.389, p < 0.001) became significant in the subgroup with lower GDP, while the rate of unemployment (2.399, p < 0.001) became significant in the higher subgroup. Our findings suggest that an unfavorable economic development or unbearable medical burden will increase the negative mental health of the public, which was consistent across both the full and subgroup models.

Analysis of the prevalence and influencing factors of anxiety and depression in the Chinese population: A cross-sectional survey

To explore the prevalence and influencing factors of anxiety and depression symptoms among Chinese people in 2021. Investigation teams were recruited in 120 cities across the country. Based on the data from "the Seventh National Population Census in 2021″, quota sampling was conducted on the residents of these cities to obtain samples that conformed to population characteristics. Next, baseline information on research objects was collected, and the questionnaire survey was conducted through the online questionnaire Wenjuanxing platform. The Patient Health Questionnaire-9 (PHQ-9) rating scale was used to evaluate the mental state of the subjects. The correlation between baseline information and different PHQ-9 risk intervals was analyzed using the Chi-square test and Logit model. The impact of relevant risk factors on PHQ-9 scores was analyzed using the decision tree. The Chi-square test results revealed that place of residence (p = 0.438) and obesity (p = 0.443) was not significantly correlated with PHQ-9 risk intervals. According to Logit model analysis, age (p = 0.001, 95%CI 0.84-0.96), marital status (p < 0.001, 95%CI 0.71-0.89), drinking (p < 0.001, 95%CI 1.07-1.18), diabetes or hypertension (p = 0.001, 95%CI 1.11-1.47), health care (p < 0.001, 95%CI 0.53-0.66), economic welfare (p = 0.022, 95%CI 0.85-0.99), COVID-19 vaccine (p < 0.001, 95%CI 1.28-1.72), and HPV vaccine (p < 0.001, 95%CI 0.46-0.57) were potential influencing factors of PHQ-9 risk intervals. Decision tree analysis results showed that the grouping strategy in the PHQ-9 two-side groups had a better classification effect on the questionnaire population according to the PHQ-9 score characteristics. The prevalence rate of moderate to severe depression among Chinese people was about 8.29%. Age, marital status, drinking, diabetes or hypertension, health care, economic well, COVID-19 vaccine, and HPV vaccine were potential influencing factors of anxiety and depression symptoms in Chinese people.

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.

Associations of PM2.5 and road traffic noise with mental health: Evidence from UK Biobank

BACKGROUND

The associations of atmospheric particulate matter with diameters of 2.5 μm or less (PM2.5) and road traffic noise with mental disorders in men and women are not well studied.

OBJECTIVES

We aim to examine the cross-sectional associations of PM2.5 and road traffic noise with mental disorders in men and women.

METHODS

The baseline data of the UK Biobank study (2006-2010) were used. Mental disorders including symptoms of nerves, anxiety, tension or depression (NATD), major depression, and bipolar disorder were assessed by validated questions. Verified models were used to estimate PM2.5 and road traffic noise.

RESULTS

A total of 334,986 participants with measurements of NATD and 90,706 participants with measurements of major depression and bipolar disorder were included in the analysis. After adjusting for covariates, the odds for the risk of NATD symptoms increased by 2.31 (95% CI: 2.15-2.50) times per 10 μg/m³ increase in PM2.5. The odds for the risk of major depression and bipolar disorder increased by 2.26 and 4.99 times per 10 μg/m³ increase in PM2.5. On the other hand, higher road traffic noise exposure was significantly associated with a higher risk of NATD symptoms (Decile 6-8 (54.9-57.8 dB), OR: 1.03, 95% CI: 1.01-1.06; Decile 9-10 (≥57.8 dB), OR: 1.04, 95% CI: 1.01-1.07) and bipolar disorder (Decile 2-5 (52.1-54.9 dB), OR: 1.26, 95% CI: 1.00-1.59; Decile 6-8 (54.9-57.8 dB), OR: 1.30, 95% CI: 1.02-1.65; Decile 9-10 (≥57.8 dB), OR: 1.54, 95% CI: 1.21-1.97). Interestingly, a negative association was observed between moderate road traffic noise and major depression (Decile 2-5 (52.1-54.9 dB), OR: 0.95, 95% CI: 0.90-1.00). Interactions between PM2.5 exposure with age, gender, and sleeplessness for NATD symptoms were observed (P < 0.05), while interactions between road traffic noise exposure with age and gender were observed (P < 0.05).

CONCLUSIONS

We found a positive association between PM2.5 and mental disorders. Meanwhile, we found a positive association of road traffic noise with NATD symptoms and bipolar disorder and a negative association of moderate road traffic noise with major depression. Also, the effect modifications of these associations by age, gender, or sleeplessness may exist.

Air pollution and mental health: associations, mechanisms and methods

PURPOSE OF REVIEW

There is increasing interest in the links between exposure to air pollution and a range of health outcomes. The association with mental health however is much less established. This article reviews developments in the field over the past 12 months, highlighting the evidence for causation, associations between multiple air pollutants and mental health outcomes, and assesses the challenges of researching this topic.

RECENT FINDINGS

Increasingly rigorous methods are being applied to the investigation of a broader range of mental health outcomes. These methods include basic science, neuroimaging, and observational studies representing diverse geographical locations. Cohort studies with linked high-resolution air pollutant exposure data are common, facilitating advanced analytic methods. To date, meta-analyses have demonstrated small and significant positive associations between long-term exposure to fine particulate matter and depressive symptoms and cognitive decline. Methodological complexities in measuring exposure and outcome pose ongoing difficulties for the field.

SUMMARY

Literature on this topic has recently seen an appreciable expansion. Work that better estimates daily exposure, controls for complex confounders, and is driven by hypotheses founded in candidate causal mechanisms would help clarify associations, and inform targeted interventions and policymakers.