Long-term exposure to ambient air pollution and incidence of depression: A population-based cohort study in China

Sufficient sleep and physical activity can relieve the effects of long-term exposure to particulate matter on depressive symptoms among 0.31 million children and adolescents from 103 counties in China

BACKGROUND

Although long-term exposures to air pollutants have been linked to mental disorders, existing studies remain limited and inconsistent. We investigated the relationship between exposure to particulate matter (PM) and depressive symptoms, as well as the potential role of sleep duration and physical activity.

METHOD

Using the surveillance data (2019 to 2022) of common diseases and risk factors among 312,390 students aged 10-25 years, logistic regression, generalized liner model (GLM) and restricted cubic spline (RCS) were employed to investigate the relationship between long-term exposure to PM and depressive symptoms.

RESULT

Significant associations were found between PM1 (OR = 1.21, 95 % CI: 1.12-1.32), PM2.5 (OR = 1.24, 95 % CI: 1.19-1.38), and PM10 (OR = 1.87, 95 % CI: 1.69-2.07) and increased risks of depressive symptoms. Sleep duration and physical activity relieved these associations. The odds ratios (ORs) of PM1, PM2.5, and PM10 on depressive symptoms were lower in group with sufficient sleep (1.02 vs. 1.49, 1.20 vs. 1.80, 2.15 vs. 2.23), lower in group with high level MVPA (1.13 vs. 1.48, 1.14 vs. 1.58, 1.85 vs. 2.38), and lower in group with high level outdoor activity (1.19 vs. 1.55, 1.23 vs. 1.63, 1.83 vs. 2.72).

LIMITATIONS

Conclusions about causality remain speculative because of the cross-sectional design.

CONCLUSION

Sufficient sleep duration and outdoor activity may mitigate the decline in mental health among adults in developing countries caused by long-term exposure to PM. This contribution enhanced our understanding of the mechanisms linking air pollution to mental health.

Fine particulate matter components associated with exacerbated depressive symptoms among middle-aged and older adults in China

Growing awareness acknowledges ambient fine particulate matter (PM2.5) as an environmental risk factor for mental disorders, especially among older people. However, there remains limited evidence regarding which specific chemical components of PM2.5 may be more detrimental. This nationwide prospective cohort study included 22,126 middle-aged and older adult participants of the China Health and Retirement Longitudinal Study (CHARLS, 2011-2016), to explore the individual and joint associations between long-term exposure to various PM2.5 components (sulfate, nitrate, ammonium, organic matter, and black carbon) and depressive symptoms. The depressive symptoms were assessed using the 10-item Center for Epidemiological Studies-Depression Scale (CES-D-10). Using the novel quantile-based g-computation for multi-pollutant mixture analysis, we found that exposure to the mixture of major PM2.5 components was significantly associated with aggravating depressive symptoms, with the exposure-response curve exhibiting consistent linear or supra-linear shape without a lower threshold. The estimated weight index indicated that, among major PM2.5 components, only nitrate, sulfate, and black carbon significantly contributed to the exacerbation of depressive symptoms. Given the expanding aging population, stricter regulation on the emissions of particularly toxic PM2.5 components may mitigate the escalating disease burden of depression.

Association of Residential Greenness Exposure with Depression Incidence in Adults 50 Years of Age and Older: Findings from the Cohort Study on Global AGEing and Adult Health (SAGE) in China

BACKGROUND

Depression is a social and public health problem of great concern globally. Identifying and managing the factors influencing depression are crucial for preventing and decreasing the burden of depression.

OBJECTIVES

Our objectives are to explore the association between residential greenness and the incidence of depression in an older Chinese population and to calculate the disease burden of depression prevented by greenness exposure.

METHODS

This study was the Chinese part of the World Health Organization Study on Global AGEing and Adult Health (WHO SAGE). We collected the data of 8,481 residents ≥ 50 years of age in China for the period 2007-2018. Average follow-up duration was 7.00 (± 2.51 ) years. Each participant was matched to the yearly maximum normalized difference vegetation index (NDVI) at their residential address. Incidence of depression was assessed using the Composite International Diagnostic Interview (CIDI), self-reports of depression, and/or taking depression medication. Association between greenness and depression was examined using the time-dependent Cox regression model with stratified analysis by sex, age, urbanicity, annual family income, region, smoking, drinking, and household cooking fuels. Furthermore, the prevented fraction (PF) and attributable number (AN) of depression prevented by exposure to greenness were estimated.

RESULTS

Residential greenness was negatively associated with depression. Each interquartile range (IQR) increase in NDVI 500 -m buffer was associated with a 40% decrease [hazard ratio  ( HR ) = 0.60 ; 95% confidence interval (CI): 0.37, 0.97] in the risk of depression incidence among the total participants. Subgroup analyses showed negative associations in urban residents (HR = 0.32 ; 95% CI: 0.12, 0.86) vs. rural residents, in high-income residents (HR = 0.28 ; 95% CI: 0.11, 0.71) vs. low-income residents, and in southern China (HR = 0.50 ; 95% CI: 0.26, 0.95) vs. northern China. Over 8.0% (PF = 8.69 % ; 95% CI: 1.38%, 15.40%) and 1,955,199 (95% CI: 310,492; 3,464,909) new cases of depression may be avoided by increasing greenness exposures annually across China.

DISCUSSION

The findings suggest protective effects of residential greenness exposure on depression incidence in the older population, particularly among urban residents, high-income residents, and participants living in southern China. The construction of residential greenness should be included in community planning. https://doi.org/10.1289/EHP13947.

PM2.5 exposure contributes to anxiety and depression-like behaviors via phenyl-containing compounds interfering with dopamine receptor

As a global problem, fine particulate matter (PM2.5) really needs local fixes. Considering the increasing epidemiological relevance to anxiety and depression but inconsistent toxicological results, the most important question is to clarify whether and how PM2.5 causally contributes to these mental disorders and which components are the most dangerous for crucial mitigation in a particular place. In the present study, we chronically subjected male mice to a real-world PM2.5 exposure system throughout the winter heating period in a coal combustion area and revealed that PM2.5 caused anxiety and depression-like behaviors in adults such as restricted activity, diminished exploratory interest, enhanced repetitive stereotypy, and elevated acquired immobility, through behavioral tests including open field, elevated plus maze, marble-burying, and forced swimming tests. Importantly, we found that dopamine signaling was perturbed using mRNA transcriptional profile and bioinformatics analysis, with Drd1 as a potential target. Subsequently, we developed the Drd1 expression-directed multifraction isolating and nontarget identifying framework and identified a total of 209 compounds in PM2.5 organic extracts capable of reducing Drd1 expression. Furthermore, by applying hierarchical characteristic fragment analysis and molecular docking and dynamics simulation, we clarified that phenyl-containing compounds competitively bound to DRD1 and interfered with dopamine signaling, thereby contributing to mental disorders. Taken together, this work provides experimental evidence for researchers and clinicians to identify hazardous factors in PM2.5 and prevent adverse health outcomes and for local governments and municipalities to control source emissions for diminishing specific disease burdens.

Trajectories of depressive symptom and its association with air pollution: evidence from the Mr. OS and Ms. OS Hong Kong cohort study

BACKGROUND

Depression is a global health priority. Maintaining and delaying depressive symptoms in older adults is a key to healthy aging. This study aimed to identify depressive symptom trajectories, predictors and mortality, while also exploring the relationship between air quality and depressive symptoms in older adults in the Hong Kong community over 14 years.

METHODS

This study is a longitudinal study in Hong Kong. The target population was community-dwelling older adults over age 65. Depressive symptoms were measured by the Geriatric Depression Scale (GDS-15). Group-based trajectory model was used to identify heterogeneity in longitudinal changes over 14 years and examine the associations between baseline variables and trajectories for different cohort members using multinomial logistic regression. The Kaplan-Meier method was employed to conduct survival analysis and explore the variations in survival probabilities over time among different trajectory group. Linear mixed model was used to explore the relationship between air quality and depressive symptoms.

RESULTS

A total of 2828 older adults were included. Three different trajectories of depressive symptoms in older people were identified: relatively stable (15.4%), late increase (67.1%) and increase (17.5%). Female, more number of chronic diseases, poor cognitive function, and poor health-related quality of life (HRQOL) were significantly associated with other less favorable trajectories compared with participants with stable levels of depressive symptoms. The late increase group had a lower mortality rate than the relatively stable and increased groups. Lower baseline ambient air pollutant exposure to NO2 over 14 years was significantly associated with fewer depressive symptoms.

CONCLUSIONS

In this study, we found that a late increase in depressive symptoms was the predominant trend in older Chinese people in Hong Kong. Poorer HRQOL was predictive of less favorable trajectories of depressive symptoms. Ambient air pollution was associated with depressive symptoms. This novel observation strengthens the epidemiological evidence of longitudinal changes in depressive symptoms and associations with late-life exposure to air pollution.

The relationship between air pollution and depression and anxiety disorders - A systematic evaluation and meta-analysis of a cohort-based study

OBJECTIVE

To explore the correlation between air pollution and the onset of depression and anxiety disorders, to draw more comprehensive and integrated conclusions, and to provide recommendations for maintaining mental health and developing policies to reduce mental health risks caused by air pollution.

METHODS

Meta-analysis of cohort study articles exploring the relationship between air pollution and depression or anxiety disorders included in Pubmed, Web Of Science, CNKI, and Wan Fang database before October 31, 2022, and subgroup analysis of the association between air pollution and depression and anxiety disorders regarding the air pollutants studied, the study population, and Publication bias analysis and sensitivity analysis.

RESULTS

A total of 25 articles meeting the criteria were included in this study, including 23 articles examining the relationship between air pollution and depression and 5 articles examining the relationship between air pollution and anxiety disorders. The results of the meta-analysis were based on the type of pollutant and showed that there was a high degree of heterogeneity among the studies on the relationship between air pollution and depression and a significant heterogeneity among the studies on PM2.5 and the risk of anxiety disorders (I2 = 71%, p < .01), so a random-effects model was selected for the analysis. CO, O3, and SO2 and depression onset had combined RR values of 1.10 (1.00, 1.20), 1.06 (0.87, 1.29), 1.17 (1.06, 1.31), 1.19 (0.90, 1.58), 1.03 (0.99, 1.07), and 1.09 (0.97, 1.24), respectively, and PM2.5 and anxiety The combined RR value for morbidity was 1.10 (0.99, 1.22). The results of sensitivity analysis showed that the combined results were stable and reliable. The results of Egger regression method test showed that none of them had significant publication bias (p > .05).

LIMITATION

Combined exposure to air pollutants on depression and anxiety, further studies by other researchers are needed in the future.

CONCLUSIONS

PM2.5 and NO2 exposure, especially long-term exposure, may be associated with the onset of depression, and no association was found for the time being between PM10, CO, O3, SO2 exposure and depression and PM2.5 exposure and anxiety disorders.

Role of metabolic risk factors in the relationship between ambient fine particulate matter and depressive symptoms: Evidence from a longitudinal population study

BACKGROUND

There is growing evidence indicating a connection between fine particulate matter (PM2.5) and depressive symptoms. Metabolic risk factors are critical determinants of depressive symptoms. However, the mediating role of these factors on the association between PM2.5 and depressive symptoms remains elusive. We aimed to investigate whether and to what extent metabolic risk factors mediated the link between long-term PM2.5 exposure and depressive symptoms.

METHODS

This study comprised 7794 individuals aged between 30 and 79 years who participated in two waves of the on-site surveys in the China Multi-Ethnic Cohort. Ambient PM2.5 concentrations were assessed utilizing a random forest method based on satellite data. We employed the Patient Health Questionnaire-9 to assess depressive symptoms at wave 2, and the overall as well as three sub-domain symptom scores (emotional, neurovegetative, and neurocognitive symptoms) were calculated. Three metabolic risk factors, including hypertension, diabetes, and dyslipidemia, were considered. Mediation analyses were conducted to assess the indirect effects of PM2.5 on depressive symptoms through metabolic risk factors.

RESULTS

We found a positive association between chronic exposure to ambient PM2.5 and overall depressive symptoms as well as the three sub-domains. In mediation analyses, metabolic risk factors partially mediated the associations of PM2.5 on depressive symptoms. The natural indirect effects (RR, 95% CI) of PM2.5 on overall, emotional, neurovegetative, and neurocognitive symptoms mediated through metabolic risk factors were 1.004(1.001, 1.007), 1.004 (1.001, 1.008), 1.004 (1.001, 1.007), and 1.003(0.999, 1.007), respectively. Larger indirect effects were found in elderly participants (mediated proportion, 29.3%), females (13.3%), and people who did not consume alcohol (19.6%).

CONCLUSIONS

Metabolic risk factors may act as mediators in the relationship between chronic PM2.5 exposure and depression. Treatment of metabolic risk factors may be an opportunity to reduce the burden of depression caused by long-term exposure to PM2.5.

Particulate matters (PM2.5, PM10) and the risk of depression among middle-aged and older population: analysis of the Korean Longitudinal Study of Aging (KLoSA), 2016-2020 in South Korea

BACKGROUND

There is a growing concern that particulate matter (PM) such as PM2.5 and PM10 has contributed to exacerbating psychological disorders, particularly depression. However, little is known about the roles of these air pollutants on depression in elderly. Therefore, this study aimed to examine the association between PM2.5 and PM10, and depression in the elderly population in South Korea.

METHODS

We used panel survey data, the Korean Longitudinal Study of Aging (KLoSA), administered by the Labor Institute during the study period of 2016, 2018, and 2020 covering 217 districts in South Korea (n = 7674). Annual district-specific PM2.5 and PM10 concentrations were calculated for the study period from the monthly prediction concentrations produced by a machine-learning-based ensemble model (cross-validated R2: 0.87), then linked to the people matching with year and their residential district. We constructed a generalized estimating equation (GEE) model with a logit link to identify the associations between each of the long-term PM2.5 and PM10 exposures and depression (CES-D 10) after adjusting for individual and regional factors as confounders.

RESULTS

In single-pollutant models, we found that long-term 10 [Formula: see text] increments in PM2.5 (OR 1.36, 95% CI 1.20-1.56) and PM10 (OR 1.19, 95% CI 1.10-1.29) were associated with an increased risk of depression in the elderly. Associations were consistent after adjusting for other air pollutants (NO2 and O3) in two-pollutant models. In addition, the impacts substantially differed by regions grouped by the tertile of the population density, for which the risks of particulate matters on depression were substantial in the middle- or high-population-density areas in contrast to the low-population-density areas.

CONCLUSIONS

Long-term exposure to PM2.5 and PM10 was associated with a higher risk of developing depression in elderly people. The impact was modified by the population density level of the region where they reside.

Exposure to Neighborhood Walkability and Residential Greenness and Incident Fracture

Importance

Emerging studies have suggested that environmental factors are associated with fracture. However, little is known about the association of neighborhood walkability and residential greenness with fracture.

Objective

To investigate the association of long-term exposure to walkability and greenness with incident fracture and explore the potential interaction effect.

Design, Setting, and Participants

This cohort study recruited participants aged 40 years or older in Ningbo, China from June 2015 to January 2018. Participants were observed for outcomes through February 2023, with data analysis conducted in March 2023.

Exposures

Neighborhood walkability was measured by a modified walkability calculation method according to a walk score tool. Residential greenness was assessed by satellite-derived normalized difference vegetation index (NDVI) within a 1000-m buffer.

Main Outcomes and Measures

Incident fracture was ascertained according to International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes via the Yinzhou Health Information System. Cox proportional hazards models were fit, with age as time scale to estimate the associations of walkability and greenness with fracture. Potential effect modification was explored by covariates, as well as the interactive effect of walkability and greenness.

Results

A total of 23 940 participants were included in this study with 13 735 being female (57.4%). The mean (SD) age at baseline was 63.4 (9.4) years. During a follow-up period of 134 638 person-years, 3322 incident fractures were documented. In the full adjusted model, every IQR increment in neighborhood walkability and residential greenness was associated with a hazard ratio (HR) of 0.88 (95% CI, 0.83-0.92) and 0.84 (95% CI, 0.80-0.89), respectively, for fracture. Furthermore, the association of greenness and fracture was greater with an increase in walkability. The HR (Q4 vs Q1) for greenness was 0.62 (95% CI, 0.46-0.82) in neighborhoods with the highest quartile of walkability.

Conclusions and Relevance

This population cohort study suggested that long-term exposure to neighborhood walkability and residential greenness were both associated with lower risk of incident fracture. The benefits of greenness increased in more walkable areas.

Health Impacts of Long-Term NO2 Exposure and Inequalities among the Chinese Population from 2013 to 2020

Nitrogen dioxide (NO₂) is associated with mortality and many other adverse health outcomes. In 2021, the World Health Organization established a new NO₂ air quality guideline (AQG) (annual average <10 μg/m³). However, the burden of diseases attributable to long-term NO₂ exposure above the AQG is unknown in China. Nitrogen oxide is a major air pollutant in populous cities, which are disproportionately impacted by NO₂; this represents a form of environmental inequality. We conducted a nationwide risk assessment of premature deaths attributable to long-term NO₂ exposure from 2013 to 2020 based on the exposure-response relationship, high-resolution annual NO₂ concentrations, and gridded population data (considering sex, age, and residence [urban vs rural]). We calculated health metrics including attributable deaths, years of life lost (YLL), and loss of life expectancy (LLE). Inequality in the distribution of attributable deaths and YLLs was evaluated by the Lorenz curve and Gini index. According to the health impact assessments, in 2013, long-term NO₂ exposure contributed to 315,847 (95% confidence interval [CI]: 306,709-319,269) premature deaths, 7.90 (7.68-7.99) million YLLs, and an LLE of 0.51 (0.50-0.52) years. The high-risk subgroup (top 20%) accounted for 85.7% of all NO₂-related deaths and 85.2% of YLLs, resulting in Gini index values of 0.81 and 0.67, respectively. From 2013 to 2020, the estimated health impact from NO₂ exposure was significantly reduced, but inequality displayed a slightly increasing trend. Our study revealed a considerable burden of NO₂-related deaths in China, which were disproportionally frequent in a small high-risk subgroup. Future clean air initiatives should focus not only on reducing the average level of NO₂ exposure but also minimizing inequality.

Long-term Exposure to Multiple Ambient Air Pollutants and Association With Incident Depression and Anxiety

Importance

Air pollution is increasingly recognized as an important environmental risk factor for mental health. However, epidemiologic evidence on long-term exposure to low levels of air pollutants with incident depression and anxiety is still very limited.

Objectives

To investigate the association of long-term joint exposure to multiple air pollutants with incident depression and anxiety.

Design, Setting, and Participants

This prospective, population-based cohort study used data from the UK Biobank. The participants were recruited between March 13, 2006, and October 1, 2010, and included individuals who had never been diagnosed with depression or anxiety at baseline and had full information on exposure and covariates. Data were analyzed from May 1 to October 10, 2022.

Exposures

Annual mean air pollution concentrations of particulate matter (PM) with aerodynamic diameter of 2.5 μm or less (PM2.5) and PM with aerodynamic diameter between 2.5 μm and 10 μm (PM2.5-10). Nitrogen dioxide (NO2) and nitric oxide (NO) were estimated for each participant's residential address using the land use regression model, and joint exposure to air pollution reflected by air pollution score was calculated by principal components analysis.

Main Outcomes and Measures

Incidence of diagnosed depression (F32-F33) and anxiety (F40-F48) were ascertained with International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes.

Results

During a median (IQR) follow-up of 10.9 (10.1-11.6) years, among 389 185 participants (mean [SD] age, 56.7 [8.1] years, 205 855 female individuals [52.9%]), a total of 13 131 and 15 835 patients were diagnosed with depression and anxiety, respectively. The median (IQR) concentration of pollutants was as follows: PM2.5, 9.9 (9.3-10.6) μg/m3; PM2.5-10, 6.1 (5.8-6.6) μg/m3; NO2, 26.0 (21.3-31.1) μg/m3; and NO, 15.9 (11.6-20.6) μg/m3. Long-term estimated exposure to multiple air pollutants was associated with increased risk of depression and anxiety, and the exposure-response curves were nonlinear, with steeper slopes at lower concentrations and plateauing trends at higher exposure. The hazard ratios (HRs) and 95% CIs for depression and anxiety were 1.16 (95% CI, 1.09-1.23; P < .001) and 1.11 (95% CI, 1.05-1.17; P < .001) in the highest quartile compared with the lowest quartile of air pollution score, respectively. Similar trends were shown for PM2.5, NO2, and NO. Subgroup analysis showed the association between PM2.5 and anxiety tended to be higher in male individuals than in female individuals (quartile 4: male individuals, 1.18; 95% CI, 1.08-1.29; female individuals, 1.07; 95% CI, 1.00-1.14; P = .009).

Conclusions and Relevance

Study results suggest that estimates of long-term exposure to multiple air pollutants was associated with increased risk of depression and anxiety. The nonlinear associations may have important implications for policy making in air pollution control. Reductions in joint exposure to multiple air pollutants may alleviate the disease burden of depression and anxiety.

The long-term and short-term effects of ambient air pollutants on sleep characteristics in the Chinese population: big data analysis from real world by sleep records of consumer wearable devices

Several studies on long-term air pollution exposure and sleep have reported inconsistent results. Large-scale studies on short-term air pollution exposures and sleep have not been conducted. We investigated the associations of long- and short-term exposure to ambient air pollutants with sleep in a Chinese population based on over 1 million nights of sleep data from consumer wearable devices. Air pollution data including particulate matter (PM_2.5, PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and ozone (O₃) were collected from the Ministry of Ecology and Environment. Short-term exposure was defined as a moving average of the exposure level for different lag days from Lag0 to Lag0-6. A 365-day moving average of air pollution was regarded as long-term exposure. Sleep data were recorded using wearable devices from 2017 to 2019. The mixed-effects model was used to evaluate the associations. We observed that sleep parameters were associated with long-term exposure to all air pollutants. Higher levels of air pollutant concentrations were associated with longer total sleep and light sleep duration, shorter deep sleep duration, and decreases in wake after sleep onset (WASO), with stronger associations of exposures to NO₂ and CO [a 1-interquartile range (IQR) increased NO₂ (10.3 μg/m³) was associated with 8.7 min (95% CI: 8.08 to 9.32) longer sleep duration, a 1-IQR increased CO (0.3 mg/m³) was associated with 5.0 min (95% CI: - 5.13 to - 4.89) shorter deep sleep duration, 7.7 min (95% CI: 7.46 to 7.85) longer light sleep duration, and 0.5% (95% CI: - 0.5 to - 0.4%) lower proportion of WASO duration to total sleep]. The cumulative effect of short-term exposure on Lag0-6 is similar to long-term exposure but relatively less. Subgroup analyses indicated generally greater effects on individuals who were female, younger (< 45 years), slept longer (≥ 7 h), and during cold seasons, but the pattern of effects was mixed. We supplemented two additional types of stratified analyses to reduce repeated measures of outcomes and exposures while accounting for individual variation. The results were consistent with the overall results, proving the robustness of the overall results. In summary, both short- and long-term exposure to air pollution affect sleep, and the effects are comparable. Although people tend to have prolonged total sleep duration with increasing air pollutant concentrations, their sleep quality might remain poor because of the reduction in deep sleep.

Genetic susceptibility and lifestyle modify the association of long-term air pollution exposure on major depressive disorder: a prospective study in UK Biobank

BACKGROUND

Evidence linking air pollution to major depressive disorder (MDD) remains sparse and results are heterogeneous. In addition, the evidence about the interaction and joint associations of genetic risk and lifestyle with air pollution on incident MDD risk remains unclear. We aimed to examine the association of various air pollutants with the risk of incident MDD and assessed whether genetic susceptibility and lifestyle influence the associations.

METHODS

This population-based prospective cohort study analyzed data collected between March 2006 and October 2010 from 354,897 participants aged 37 to 73 years from the UK Biobank. Annual average concentrations of PM_2.5, PM₁₀, NO₂, and NO_x were estimated using a Land Use Regression model. A lifestyle score was determined based on a combination of smoking, alcohol drinking, physical activity, television viewing time, sleep duration, and diet. A polygenic risk score (PRS) was defined using 17 MDD-associated genetic loci.

RESULTS

During a median follow-up of 9.7 years (3,427,084 person-years), 14,710 incident MDD events were ascertained. PM_2.5 (HR: 1.16, 95% CI: 1.07-1.26; per 5 μg/m³) and NO_x (HR: 1.02, 95% CI: 1.01-1.05; per 20 μg/m³) were associated with increased risk of MDD. There was a significant interaction between the genetic susceptibility and air pollution for MDD (P-interaction < 0.05). Compared with participants with low genetic risk and low air pollution, those with high genetic risk and high PM_2.5 exposure had the highest risk of incident MDD (PM_2.5: HR: 1.34, 95% CI: 1.23-1.46). We also observed an interaction between PM_2.5 exposure and unhealthy lifestyle (P-interaction < 0.05). Participants with the least healthy lifestyle and high air pollution exposures had the highest MDD risk when compared to those with the most healthy lifestyle and low air pollution (PM_2.5: HR: 2.22, 95% CI: 1.92-2.58; PM₁₀: HR: 2.09, 95% CI: 1.78-2.45; NO₂: HR: 2.11, 95% CI: 1.82-2.46; NO_x: HR: 2.28, 95% CI: 1.97-2.64).

CONCLUSIONS

Long-term exposure to air pollution is associated with MDD risk. Identifying individuals with high genetic risk and developing healthy lifestyle for reducing the harm of air pollution to public mental health.

Effects of pain and depression on the relationship between household solid fuel use and disability among middle-aged and older adults

Household air pollution (HAP) is suggested to increases people's risk of disability, but mediating mechanisms between HAP and disability remains under-investigated. The aim of this study was to investigate the underlying mechanisms between household air pollution and disability in middle-aged and older adults (i.e., older than 45 years) using a nationally representative prospective cohort. In total, 3754 middle-aged and older adults were selected from the China Health and Retirement Longitudinal Study. Correlation analysis and logistic regression analysis were employed to estimate the association between HAP, pain, depression and disability. Finally, three significant mediation pathways through which HAP directly impacts disability were found: (1) pain (B = 0.09, 95% CI 0.01, 0.02), accounting for 15.25% of the total effect; (2) depression (B = 0.07, 95% CI 0.004, 0.02), accounting for 11.86% of the total effect; (3) pain and depression (B = 0.04, 95% CI 0.003, 0.01), accounting for 6.78% of the total effect. The total mediating effect was 33.89%. This study clarified that HAP can indirectly affect disability through the respective and serial mediating roles of pain and depression. These findings potentially have important implications for national strategies concerning the widespread use of clean fuels by citizens.

Association of NO2 with daily hospital admissions for mental disorders: Investigation of the modification effects of green spaces and long-term NO2 exposure

Air pollution is a risk factor for increased hospital admissions due to mental disorders, while green spaces have been linked with better mental health. We linked daily hospital admission records from Wuhan's 74 municipal hospitals from 2017 to 2019 with modeled annual average NO₂ concentrations and added data on the residential surrounding green spaces with 250 m and 500 m buffers based on the normalized difference vegetation index (NDVI) using a land use regression model (LUR). The conditional logistic regression model was used to estimate the acute effect of short-term NO₂ exposure, and stratification analyses were applied to explore the modification effect of long-term NO₂ exposure and green spaces by estimating the odds ratios in the single- and dual-environmental factor groups. A total of 42,705 hospital admissions for mental disorders were identified. Short-term exposure to NO₂ was associated with an increased risk of hospital admission for mental disorders. A 10 μg/m³ increase in NO₂ (lag01 day) was associated with an increase in hospital admissions of 2.86% (95% CI, 2.05-3.68) for the total mental disorders. Compared with patients in the "low-NDVI/low-NO₂" group (ER = 2.27%, 95% CI, 0.27-4.31), patients in the "high-NDVI/low-NO₂" group (ER = 1.93%, -0.10-3.99) showed a lower and insignificant increase in hospitalizations for the total mental disorders, while greenness had a slight moderating effect in the high-level long-term NO₂ exposure areas. This study suggested that green spaces may moderate the acute effect of NO₂ exposure for mental disorder hospitalizations, especially in low-level long-term NO₂ exposure areas.

Exposure to indoor air pollution from solid fuel and its effect on depression: a systematic review and meta-analysis

A growing body of research has investigated the relationship between indoor air pollution from solid fuel and depression risk. Our study aimed to elucidate the relationship between indoor air pollution from solid fuel and depression in observational studies. The effect of indoor air pollution on depression was estimated using pooled odds ratios (ORs) with 95% confidence intervals (CIs). Heterogeneity was evaluated by the I-squared value (I²), and the random-effects model was adopted as the summary method. We finalized nine articles with 70,214 subjects. The results showed a statistically positive relationship between the use of household solid fuel and depression (OR = 1.22, 95% CI = 1.09-1.36). Subgroup analysis based on fuel type groups demonstrated that indoor air pollution from solid fuel was a higher risk to depression (OR = 1.24, 95% CI = 1. 10-1.39; I² = 67.0%) than that from biomass (OR = 1.18, 95% CI = 0.96-1.45; I² = 66.5%). In terms of fuel use, the use of solid fuel for cooking and heating increased depression risk, and the pooled ORs were 1.21 (95% CI = 1.08-1.36) and 1.23 (95% CI = 1.13-1.34). Exposure to indoor air pollution from solid fuel might increase depression risk.

Air Pollution, Foreign Direct Investment, and Mental Health: Evidence From China

Recently, there has been interest in the relationship between mental health and air pollution; however, the results are inconsistent and the contribution of foreign direct investment (FDI) has received little attention. This article studies the effects of air pollution on mental health and the moderating role of FDI based on the China Health and Retirement Longitudinal Study (CHARLS) data in 2015 and 2018 applying the fixed effects panel regression approach and the threshold model. The results show that mental health is adversely affected by air pollution, especially PM_2.5, PM₁₀, sulfur dioxide (SO₂), carbon monoxide (CO), and nitrogen dioxide (NO₂). Second, FDI has an alleviating influence on the negative relationship. Third, the effects of air pollution and FDI are heterogeneous based on regional characteristics, including location, medical resource and investment in science and technology, and individual characteristics covering education level, age, income, and physical health. Finally, the threshold effects show that FDI has a moderating effect when it is >1,745.59 million renminbi (RMB). There are only 11.19% of cities exceeding the threshold value in China. When the value of air quality index (AQI) exceeds 92.79, air pollution is more harmful to mental health. Government should actively introduce high-quality FDI at the effective level and control air pollution to improve mental health.

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.

Population Health Effects of Air Pollution: Fresh Evidence From China Health and Retirement Longitudinal Survey

The effects of air pollution on population health are currently a hot topic. However, few studies have examined the physical and mental health effects of air pollution jointly in China. Using data from the China Health and Retirement Longitudinal Study (CHARLS) in 2015 and 2018, this study explores how air pollution affects the physical and mental health of middle-aged and elderly residents. The empirical results highlight that air pollution can negatively affect both physical and mental health. In terms of physical health, those exposed to chronic shock are likely to suffer more adverse effects from air pollution than those exposed to acute shock. In terms of mental health, those exposed to depression suffer greater adverse effects than those exposed to episodic memory and mental cognition. Besides, heterogeneity analysis also shows that air pollution affects the mental and physical health of males more than females. Furthermore, the increase in air pollution is expected to result in huge hospitalization costs. Therefore, the Chinese government should formulate differentiated public health policies to reduce the effects of air pollution on the health of middle-aged and elderly residents.