Attributable risks associated with hospital outpatient visits for mental disorders due to air pollution: A multi-city study in China

Association of short-term exposure to ambient fine particulate matter and ozone with outpatient visits for anxiety disorders: A hospital-based case-crossover study in South China

BACKGROUND

The short-term adverse effects of ambient fine particulate matter (PM2.5) and ozone (O3) on anxiety disorders (ADs) remained inconclusive.

METHODS

We applied an individual-level time-stratified case-crossover study, which including 126,112 outpatient visits for ADs during 2019-2021 in Guangdong province, China, to investigate the association of short-term exposure to PM2.5 and O3 with outpatient visits for ADs, and estimate excess outpatient visits in South China. Daily residential air pollutant exposure assessments were performed by extracting grid data (spatial resolution: 1 km × 1 km) from validated datasets. We employed the conditional logistic regression model to quantify the associations and excess outpatient visits.

RESULTS

The results of the single-pollutant models showed that each 10 μg/m3 increase of PM2.5 and O3 exposures was significantly associated with a 3.14 % (95 % confidence interval: 2.47 %, 3.81 %) and 0.88 % (0.49 %, 1.26 %) increase in odds of outpatient visits for ADs, respectively. These associations remained robust in 2-pollutant models. The proportion of outpatient visits attributable to PM2.5 and O3 exposures was up to 7.20 % and 8.93 %, respectively. Older adults appeared to be more susceptible to PM2.5 exposure, especially in cool season, and subjects with recurrent outpatient visits were more susceptible to O3 exposure.

LIMITATION

As our study subjects were from one single hospital in China, it should be cautious when generalizing our findings to other regions.

CONCLUSION

Short-term exposure to ambient PM2.5 and O3 was significantly associated with a higher odds of outpatient visits for ADs, which can contribute to considerable excess outpatient visits.

Association of outdoor air and noise pollution with unprovoked seizures and new onset epilepsy: A systematic review and meta-analysis

Research has indicated that certain environmental exposures may increase the risk of unprovoked seizures and new onset epilepsy. This study aimed to synthesize the literature that has estimated the associations between short- and long-term exposure to outdoor air and noise pollution and the risk of unprovoked seizures and new onset epilepsy. We searched Embase, MEDLINE, Scopus, Web of Science, BIOSIS Previews, Latin American and Caribbean Health Sciences Literature, Proquest Dissertations and Theses, conference abstracts, and the gray literature and conducted citation tracing in June 2023. Observational and ecological studies assessing the associations of air and noise pollution with unprovoked seizures or new onset epilepsy were eligible. One reviewer extracted summary data. Using fixed and random effects models, we calculated the pooled risk ratios (RRs) for the studies assessing the associations between short-term exposure to air pollution and unprovoked seizures. Seventeen studies were included, 16 assessing the association of air pollution with seizures and one with epilepsy. Eight studies were pooled quantitatively. Ozone (O3; RR = .99, 95% confidence interval [CI] = .99-.99) and nitrogen dioxide (NO2) exposure adjusted for particulate matter (RR = 1.02, 95% CI = 1.01-1.02) on the same day, and carbon monoxide (CO) exposure 2 days prior (RR = 1.12, 95% CI = 1.02-1.22), were associated with seizure risk. A single study of air pollution and epilepsy did not report a significant association. The risk of bias and heterogeneity across studies was moderate or high. Short-term exposure to O3, NO2, and CO may affect the risk of seizures; however, the effect estimates for O3 and NO2 were minimal. Additional research should continue to explore these and the associations between outdoor air pollution and epilepsy and between noise pollution and seizures and epilepsy.

Short-term exposures to PM2.5, PM2.5 chemical components, and antenatal depression: Exploring the mediating roles of gut microbiota and fecal short-chain fatty acids

BACKGROUND

PM2.5 and its chemical components increase health risks and are associated with depression and gut microbiota. However, there is still limited evidence on whether gut microbiota and short-chain fatty acids (SCFAs) mediate the association between PM2.5, PM2.5 chemical components, and antenatal depression. The purpose of this study was to investigate the mediating role of maternal gut microbiota in correlations between short-term exposure to PM2.5, short-term exposure to PM2.5 chemical components, and antenatal depression.

METHODS

Demographic information and stool samples were collected from 75 pregnant women in their third trimester. Their exposure to PM2.5 and PM2.5 chemical components was measured. Participants were divided into the non-antenatal depression group or the antenatal depression group according to the cut-off of 10 points on the Edinburgh Postnatal Depression Scale (EPDS). The gut microbiota were analyzed using the 16 S rRNA-V3/V4 gene sequence, and the concentration of PM2.5 and its chemical components was calculated using the Tracking Air Pollution in China (TAP) database. Gas chromatography-mass spectrometry was used to analyze SCFAs in stool samples. In order to assess the mediating effects of gut microbiota and SCFAs, mediation models were utilized.

RESULTS

There were significant differences between gut microbial composition and SCFAs concentrations between the non-antenatal depression group and the antenatal depression group. PM2.5 and its chemical components were positively associated with EPDS scores and negatively associated with genera Enterococcus and Enterobacter. Genera Candidatus_Soleaferrea (β = -7.21, 95%CI -11.00 to -3.43, q = 0.01) and Enterococcus (β = -2.37, 95%CI -3.87 to -0.87, q = 0.02) were negatively associated with EPDS scores, indicating their potential protective effects against antenatal depression. There was no significant association between SCFAs and EPDS scores. The mediating role of Enterococcus between different lagged periods of PM2.5, PM2.5 chemical component exposure, and antenatal depression was revealed. For instance, Enterococcus explained 29.23% (95%CI 2.16-87.13%, p = 0.04) of associations between PM2.5 exposure level at the day of sampling (lag 0) and EPDS scores.

CONCLUSION

Our study highlights that Enterococcus may mediate the associations between PM2.5, PM2.5 chemical components, and antenatal depression. The mediating mechanism through which the gut microbiota influences PM2.5-induced depression in pregnant women still needs to be further studied.

A stronger association of mental disorders with smaller particulate matter and a modifying effect of air temperature

Mental disorders (MDs) can be triggered by adverse weather conditions and particulate matter (PM) such as PM2.5 and PM10 (aerodynamic diameter ≤2.5 μm and ≤10 μm). However, there is a dearth of evidence on the role of smaller PM (e.g. PM1, aerodynamic diameter ≤1 μm) and the potential modifying effects of weather conditions. We aimed to collect daily data on emergency department visits and hospitalisations for schizophrenia-, mood-, and stress-related disorders in a densely populated Chinese city (Hefei) between 2016 and 2019. A time-stratified case-crossover analysis was used to examine the short-term association of MDs with PM1, PM2.5, and PM10. The potential modifying effects of air temperature conditions (cold and warm days) were also explored. The three size-fractioned PMs were all associated with an increased risk of MDs; however, the association differed between emergency department visit and hospitalisation. Specifically, PM1 was primarily associated with an increased risk of emergency department visit, whereas PM2.5 was primarily associated with an increased risk of hospitalisation, and PM10 was associated with an increased risk of both emergency department visit and hospitalisation. The PM-MD association appeared to be greatest (although not significant) for PM1 (odds ratio range: 1.014-1.055), followed by PM2.5 (odds ratio range: 1.001-1.009) and PM10 (odds ratio range: 1.001-1.006). Furthermore, the PM-MD association was observed on cold days; notably, the association between PM and schizophrenia-related disorders was significant on both cold and warm days. Our results suggest that the smaller the PM, the greater the risk of MDs, and that the PM-MD association could be determined by air temperature conditions.

Effects of ambient air pollution on the hospitalization risk and economic burden of mental disorders in Qingdao, China

OBJECTIVE

The aim of this study was to examine the impacts of short-term exposure to air pollutants on hospitalizations for mental disorders (MDs) in Qingdao, a Chinese coastal city, and to assess the corresponding hospitalization risk and economic cost.

METHODS

Daily data on MD hospitalizations and environmental variables were collected from January 1, 2015, to December 31, 2019. An overdispersed generalized additive model was used to estimate the association between air pollution and MD hospitalizations. The cost of illness method was applied to calculate the corresponding economic burden.

RESULTS

With each 10 μg/m3 increase in the concentration of fine particulate matter (PM2.5) at lag05, inhalable particulate matter (PM10) at lag0, sulfur dioxide (SO2) at lag06 and ozone (O3) at lag0, the corresponding relative risks (RRs) and 95% confidence intervals (CIs) were 1.0182 (1.0035-1.0332), 1.0063 (1.0001-1.0126), 1.0997 (1.0200-1.1885) and 1.0099 (1.0005-1.0194), respectively. However, no significant effects of nitrogen dioxide (NO2) or carbon monoxide (CO) were found. Stratified analysis showed that males were susceptible to SO2 and O3, while females were susceptible to PM2.5. Older individuals (≥ 45 years) were more vulnerable to air pollutants (PM2.5, PM10, SO2 and O3) than younger individuals (< 45 years). Taking the Global Air Quality Guidelines 2021 as a reference, 8.71% (2,168 cases) of MD hospitalizations were attributable to air pollutant exposure, with a total economic burden of 154.36 million RMB.

CONCLUSION

Short-term exposure to air pollution was associated with an increased risk of hospitalization for MDs. The economic advantages of further reducing air pollution are enormous.

Short-term associations of particulate matter with different aerodynamic diameters with mortality due to mental disorders and dementia in Ningde, China

Limited evidence is available regarding the impact of ambient inhalable particulate matter (PM) on mental disorder (MD) or dementia-related deaths, particularly PM1, PM1-2.5, and coarse particles (PM2.5-10). Moreover, individual confounders have rarely been considered. In addition, evidence from low-pollution areas is needed but is inadequate. Using death records from the Death Registration System during 2015-2021 in Ningde, a coastal city in southeast China, we combined a conditional quasi-Poisson model with a distributed lag nonlinear model to estimate the nonlinear and lagged associations of PM exposure with MD or dementia-related deaths in Ningde, China, comprehensively controlling for individual time-invariant confounders using a time-stratified case-crossover design. The attributable fraction and number were calculated to quantify the burden of MD or dementia-related deaths that were related to PMs. We found J-shaped relationships between MD or dementia-related deaths and PMs, with different thresholds of 13, 9, 19, 33 and 12 μg/m3 for PM1, PM1-2.5, PM2.5, PM10 and PM2.5-10. An inter-quartile range increase for PM1, PM1-2.5, PM2.5, PM10 and PM2.5-10 above the thresholds led to an increase of 31.8% (95% confidence interval, 14.3-51.9%), 53.7% (22.4-93.1%), 32.6% (15.0-53.0%), 35.1% (17.7-55.0%) and 25.9% (13.0-40.3%) in MD-related deaths at lag 0-3 days, respectively. The associations were significant in the cool season rather than in the warm season and were significantly greater among people aged 75-84 years than in others. The fractions of MD-related deaths attributable to PM1, PM1-2.5, PM2.5, PM10 and PM2.5-10 were 5.55%, 6.49%, 7.68%, 10.66%, and 15.11%, respectively; however, only some of them could be protected by the concentrations recommended by the World Health Organisation or China grade I standard. Smaller associations and similar patterns were observed between PMs and dementia-related death. These findings suggest stricter standards, and provide evidence for the development of relevant policies and measures.

Short-term ambient ozone exposure increases the risk of hospitalization with depression: a multi-city time-stratified case-crossover study

BACKGROUND

Depression, the most common mental illness worldwide, has been studied and air pollution has been found to increase the risk of depression hospitalization, but research results on ozone (O3) remain limited. In this context, we investigated the relationship between short-term O3 exposure and depression-related hospital admissions (HAs).

METHODS

The 10,459 records of HAs for depression from medical institutions across in 9 cities, China, were collected between 1 January 2017, and 31 December 2018. Air pollutants and meteorological data was obtained from provincial ecological environment monitoring stations in the study area. Conditional Poisson regression was employed to estimate the association between O3 and hospitalizations for depression, with data stratification by sex, age, weather, and economic level.

RESULTS

Short-term O3 exposure was positively associated with the number of depression-related hospitalizations (Relative risk: 1.04 [95% CI: 1.02, 1.05]). O3 had a significant effect on the risk of depression-related hospitalizations on warm days (P = 0.021, Relative risk: 1.05 [1.03, 1.08]). The high gross domestic product group was more likely to be affected by O3 exposure-associated depression-related hospitalizations (P = 0.005, Relative risk: 1.03 [1.01, 1.05]).

CONCLUSIONS

Short-term changes to O3 exposure may increase the risk of depression related hospitalizations, especially on warm days.

Daytime and nighttime high temperatures differentially increased the risk of cardiovascular disease: A nationwide hospital-based study in China

Short-term exposure to ambient high temperature (heat) could increase the risk of cardiovascular disease (CVD). However, available evidence on the burden of daytime and nighttime heat on CVD is limited and vulnerable populations remain unknown so far. We aimed to examine and differentiate the impact of daytime and nighttime heat on CVD in China. Data on daily outpatient visits for CVD were collected from 15 Chinese cities spanning multiple geographical regions, climates, and socio-economic conditions. The population-weighted temperature was used to calculate excess heat exposure in warm seasons (June-September) from 2011 to 2015. Hot day excess (HDE) and hot night excess (HNE), the sum of temperature above the heat threshold during daytime and nighttime respectively, were used to represent daytime and nighttime excess heat. A distributed lag non-linear model was employed to estimate the city-level association between HDE/HNE and daily CVD cases. The city-level association was then pooled by multivariate meta-analysis. We further estimated the disease burden of CVD attributable to HDE and HNE by geographical regions, gender, and age. A total of 729,409 cases of CVD were included in this study. Both HDE and HNE were associated with an increased risk of CVD, with greater effects from nighttime heat (relative risk (RR): 1.38; 95% confidence interval (CI): 1.18-1.61) than daytime heat (RR: 1.10; 95% CI: 1.05-1.15). The proportion of CVD cases attributable to HNE was 15.7%, which was almost three times as high as HDE (4.6%, p for difference <0.05). Males, people living in northern cities, and those aged less than 45 years were more vulnerable to HNE. Our findings for the first time revealed an intra-day difference in the heat effect on CVD, with a greater impact from nighttime heat exposure, which should be considered to protect vulnerable populations on hot days.

The effects of long-term exposure to air pollution on incident mental disorders among patients with prediabetes and diabetes: Findings from a large prospective cohort

BACKGROUND

The association between air pollution and mental disorders has been widely documented in the general population. However, the evidence among susceptible populations, such as individuals with prediabetes or diabetes, is still insufficient.

METHODS

We analyzed data from 48,515 participants with prediabetes and 24,393 participants with diabetes from the UK Biobank. Annual pollution data were collected for fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide (NO2), and nitrogen dioxides (NOx) during 2006-2021. The exposure to air pollution and temperature for each participant were estimated by the bilinear interpolation approach and time-weighted method based on their geocoded home addresses and time spent at each address. We employed the generalized propensity score model based on the generalized estimating equation and the time-varying covariates Cox model to assess the effects of air pollution.

RESULTS

We observed causal links between air pollutants and mental disorders among both prediabetic and diabetic participants, with stronger effects among those with diabetes than prediabetes. The hazard ratios were 1.18 (1.12, 1.24), 1.15 (1.10, 1.20), 1.18 (1.13, 1.23), and 1.15 (1.11, 1.19) in patients with prediabetes, and 1.21 (1.13, 1.29), 1.17 (1.11, 1.24), 1.19 (1.13, 1.25), and 1.17 (1.12, 1.23) in patients with diabetes per interquartile range elevation in PM2.5, PM10, NO2, and NOx. Furthermore, the effects were more pronounced among people who were older, alcohol drinkers, and living in urban areas.

CONCLUSIONS

Our study indicates the potential causal links between long-term exposure to air pollution and incident mental disorders among those with prediabetes and diabetes. Reducing air pollution levels would significantly benefit this vulnerable population by reducing the incidence of mental disorders.

Unexpected association between ambient ozone and adult insomnia outpatient visits: A large-scale hospital-based study

Growing evidence indicates that short-term ozone (O₃) exposure has substantial health consequences, but the relationship between short-term ambient O₃ and insomnia, a common sleep disorder, is not clear. This study aimed to investigate the short-term effects of ambient O₃ exposure on outpatient visits for adult insomnia and to explore the potential modifiers. A large-scale multihospital-based study was carried out in Chongqing, the largest city in Southwest China. Daily data on outpatient visits for adult insomnia, average concentrations of ambient air pollutants and meteorological factors were collected. We conducted quasi-Poisson regression with generalized additive model to assess the association between ambient O₃ and outpatient visits for adult insomnia in varied windows of exposure. Subgroup analyses were applied to identify its modifiers. Totally, 140,159 adult insomnia outpatient visits were identified. The daily maximum 8-h average concentration of O₃ was 69 μg/m³ during the study period, which greatly below the updated Chinese and WHO recommended limits (daily maximum 8-h average, O₃: 100 μg/m³). Short-term O₃ exposure was significantly negatively associated with outpatient visits for adult insomnia in different lag periods and the greatest decrease of outpatient visits for adult insomnia was found at lag 02 [0.93% (95% CI: 0.48%, 1.38%)]. Additionally, stronger links between O₃ and adult insomnia outpatient visits were presented in cool seasons, and we did not observe any significant modified effects of gender and age. Moreover, the negative O₃-insomnia association remained robust after controlling for other common air pollutants and comorbidities. In summary, short-term exposure to lower level of ambient O₃, was associated with reduced daily outpatient visits for adult insomnia and such association showed to be more obvious in cool seasons.

Occupational characteristics and incident anxiety and depression: A prospective cohort study of 206,790 participants

BACKGROUND

This study aimed to analyze the impact of a wide spectrum of occupational characteristics on the incidence of anxiety and depression, and to determine the features affecting adaptation to specific characteristics.

METHODS

Participants in paid employment or self-employed at baseline in UKB were included, with occupational characteristics extracted from O*NET. Cox-proportional-hazard models were conducted in the main analyses and subgroup analyses.

RESULTS

Direct work with the public and exposure to disease/infections were first time demonstrated to be risk factors for both anxiety and depression, along with occupations involving more physical activities and dealing with unpleasant/physically aggressive people. Protective factors for both: time spent sitting, communication, decision making, creativity and reasoning, and responsibility in work. Protective factors for anxiety only: Coordinating/leading, fluency of ideas, originality, problem sensitivity, decision latitude, and time pressure. Risk factor for depression only: Exposure to contaminants. Females were found more sensitive to dealing with unpleasant/physically aggressive people. The impact of exposure to disease/infections was more significant among those with lower education levels. Those with BMI over 24 were more sensitive to the risk factors.

LIMITATIONS

The short-term effect of the above exposures remained unclear. The scores of occupational characteristics were based on self-reported questionnaires. There was the potential for undiagnosed anxiety or depression events. The participants included only those aged from 40 to 69. Participants included in this cohort were mainly White British.

CONCLUSIONS

Our findings advocate closer monitoring of the mental health of workers with risk work-related factors.

Ambient ozone exposure and depression among middle-aged and older adults: Nationwide longitudinal evidence in China

BACKGROUND

Epidemiological studies have linked long-term ozone (O₃) exposure with depression in developed countries. However, available literature is sparse and exists great heterogeneities. We aimed to investigate the association of long-term O₃ exposure with depression among Chinese middle-aged and older adults.

METHODS

We designed a repeated measurement study based on longitudinal data from four waves (2011, 2013, 2015, and 2018) of the China Health and Retirement Longitudinal Study (CHARLS). Annual mean O₃ concentrations assessed through machine learning-based spatiotemporal models were assigned to each participant at city level. Depression score was measured using the 10-item Center for Epidemiologic Studies Depression scale (CES-D-10), with scores above the cut-off point of ten defined as depressive symptom. Mixed-effects models were used to evaluate the impact of O₃ on depression score and depressive symptom, and quantify the concentration-response (C-R) relationships. Subgroup analyses were performed to examine the potential effect modifications.

RESULTS

A total of 19,582 participants with 60,125 visits were included in our analysis, with mean depression score of 8.1 (standard deviation: 6.3). Multivariable-adjusted mixed-effects model estimated a 6.34% (95% confidence interval [CI]: 3.34%, 9.43%) increase in depression score and an odds ratio (OR) of 1.29 (95% CI: 1.16, 1.45) for depressive symptom associated with per 10-μg/m³ rise in annual mean O₃ exposure. Significantly elevated risks were identified only at high concentrations (approximately ≥90 μg/m³). Participants who suffered from chronic diseases had a significant increased risk of depression (% Change in depression score: 8.42% [95% CI: 4.79%, 12.17%], and OR: 1.42 [95% CI: 1.24, 1.62]), and an evident effect modification was identified for depressive symptom (P = 0.01).

FINDINGS

Our study provided novel evidence that long-term O₃ exposure could be a risk factor for depression among Chinese middle-aged and older adults. Our findings may have significant implications for formulating policies in reducing disease burden of depression by controlling air pollution.

Air pollution associated with hospital visits for mental and behavioral disorders in Northeast China

Background

Related studies have found that air pollution is an important factor affecting mental and behavioral disorders. Thus, we performed this time-series study to evaluate the relationship between short-term exposure to ambient air pollutants and visits to hospital by patients with mental and behavioral disorders in northeastern China.

Methods

We used quasi-Poisson regression models and generalized additive models to probe the links between air pollution and mental and behavioral disorders. The possible influences were also explored stratified by season, age and gender.

Results

We found that sulfur dioxide (SO2) had a cumulative effect on mental and behavioral disorders at lag04-lag07 and had the greatest effect at lag07 [Relative risk (RR) = 1.068, 95%CI = 1.021-1.117]. Particulate matter of size 2.5 μm (PM2.5) and SO2 had a cumulative effect on depression and both had the largest effect at lag07 (RR = 1.021, 95%CI = 1.002-1.041; RR = 1.103, 95%CI = 1.032-1.178); SO2 also had a cumulative effect on anxiety disorders, with the largest effect at lag06 (RR = 1.058, 95%CI = 1.009-1.110). In the stratified analysis, people are more susceptible in the cold season compared to the warm season and females and the 18-60-year age group are more sensitive to air pollutants. It is suggested to strengthen management and preventive measures to decrease air pollution exposure.

Conclusion

This study found an association between increased concentrations of air pollutants and increased outpatient visits for mental and behavioral disorders. We recommend that preventive and protective measures should be strengthened in an effort to reduce exposure to air pollution in order to maintain physical and mental health.

Air pollution, genetic factors and the risk of depression

Both genetics and ambient air pollutants contribute to depression, but the degree to which genetic susceptibility modifies the effect of air pollution on depression remains unknown. We aimed to investigate the effect of the modification of genetic susceptibility on depression. Notably, 490,780 participants who were free of depression at baseline in the UK Biobank study were recruited from 2006 to 2010. A land use regression (LUR) model was performed to estimate the concentrations of particulate matter with diameters ranging from ≤2.5-≤10 μm (PM_2.5, PM_2.5-10 and PM₁₀), nitrogen dioxide (NO₂), and nitrogen oxides (NO_x). The International Classification of Diseases 10th Revision (ICD-10) code was used to identify depression cases. Cox proportional hazard models adjusted for covariates were used to investigate the association between ambient air pollutants and depression. Moreover, the polygenic risk score (PRS) was calculated to evaluate cumulative genetic effects, and additive interaction models were established to explore whether genetic susceptibility modified the effects of air pollutants on depression. PM_2.5, PM₁₀, NO₂ and NO_x exposure were significantly positively associated with the risk of depression, and the hazard ratios and 95 % confidence intervals for a 10-μg/m³ increase in PM_2.5, PM₁₀, NO₂ and NO_x concentrations were 2.12 (1.82, 2.47), 1.12 (1.03, 1.23), 1.07 (1.05, 1.10) and 1.04 (1.03, 1.05), respectively. Air pollutants and genetic variants exerted significant additive effects on the risk of depression (relative excess risk due to the interaction [RERI]: 0.15 for PM_2.5, 0.12 for PM₁₀, 0.10 for NO₂, and 0.12 for NO_x; attributable proportion due to the interaction [AP]: 0.12 for PM_2.5, 0.10 for PM₁₀, 0.08 for NO₂, and 0.09 for NO_x). Air pollution exposure was significantly associated with the risk of depression, and participants with a higher genetic risk were more likely to develop depression when exposed to high levels of air pollution.

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.

The impact of cold spells on schizophrenia admissions and the synergistic effect with the air quality index

OBJECTIVES

Under current global climate conditions, there are insufficient studies on the health influences of cold spells, especially on mental health. This study aimed to examine the effect of cold spells on schizophrenia admissions and to analyze the potential interaction effect with the air quality index (AQI).

METHODS

Daily data on schizophrenia admissions and climatic variables in Hefei were collected from 2013 to 2019. Based on 20 definitions, the impacts of cold spells were quantified separately to find the most appropriate definition for the region, and meta-regression was used to explore the different effect sizes of the different days in a cold spell event. In addition, the potential interaction effect was tested by introducing a categorical variable, CSH, reflecting the cold spell and AQI level.

RESULTS

The cold spell defined by temperature below the 6th centile while lasting for at least three days produced the optimum model fit performance. In general, the risk of schizophrenia admissions increased on cold spell days. The largest single-day effect occurred on the 12th day with RR = 1.081 (95% CI: 1.044, 1.118). In a single cold spell event, the effect of the 3rd and subsequent days of a cold spell (RR = 1.082, 95% CI: 1.036, 1.130) was higher than that on the 2nd day (RR = 1.054, 95% CI: 1.024, 1.085). Similarly, the effect of the 2nd day was also higher than that of the 1st day (RR = 1.027, 95% CI: 1.012, 1.042). We found a synergistic effect between cold spells and high AQI in the male group, and the relative excess risk due to interaction (RERI) was 0.018 (95% CI: 0.005-0.030).

CONCLUSIONS

This study suggested that the impacts of cold spells should be considered based on the definition of the most appropriate for the region when formulating targeted measures of schizophrenia. The discovery of the synergistic effect was referred to help the selection of the timing of precautions for susceptible people.

Co-exposure to multiple air pollutants and sleep disordered breathing in patients with or without obstructive sleep apnea: A cross-sectional study

BACKGROUND

Air pollution may be a contributing risk factor for obstructive sleep apnea (OSA). However, the health effects of co-exposure to multiple air pollutants on OSA patients remain unclear.

OBJECTIVES

To assess the joint effect of multi-pollutant on sleep disordered breathing (SDB) parameters in patients with or without OSA and identify the dominant pollutants.

METHODS

A total of 2524 outpatients from April 2020 to May 2021 were recruited in this cross-sectional study. Ambient air pollutant data were obtained from the nearest central monitoring stations to participants' residential address. SDB parameters were measured by the ApneaLink devices, including apnea-hypopnea index (AHI), hypopnea index (HI), oxygen desaturation index (ODI), average oxygen saturation (SpO₂), percentage sleep time with <90% saturation (T90), and desaturation. Bayesian kernel machine regression (BKMR) was applied to evaluate the effects of multiple pollutants.

RESULTS

Significant associations were observed between air pollutants and SDB parameters (including increases in AHI, HI, ODI, and desaturation) among patients with OSA. Co-exposure to air pollutants was positively correlated with AHI, HI, and ODI. PM₁₀ and O₃ dominated the effects of pollutant mixtures on OSA, with the highest posterior inclusion probability (PIP) values of 0.592 and 0.640, respectively. Stratified analysis showed that, compared to male patients with OSA, stronger effects on the SDB parameters were observed in female patients. Stronger associations were also found in the warm season than those in the cold season.

CONCLUSION

Co-exposure to air pollutants was associated with SDB parameters among patients with OSA, PM₁₀ and O₃ might play the dominant roles.

New Insights into Prenatal NO2 Exposure and Behavioral Abnormalities in Male Offspring: Disturbed Serotonin Metabolism and Delayed Oligodendrocyte Development

Epidemiological studies show that prenatal exposure to nitrogen dioxide (NO₂) might cause behavioral abnormalities in childhood. However, toxicological mechanisms for such effects remain unclear, and it is still difficult to define adverse outcome pathways linking exposures to behavioral phenotypes. In this study, by exposing pregnant mice to NO₂ (2.5 ppm, 5 h/day) throughout gestation, we provided the first experimental evidence that prenatal NO₂ exposure did cause anxiety- and depression-like behaviors in weaning male offspring but not females. Specifically, the behavioral abnormalities were associated with abnormal myelination and the alterations attributed to the delayed oligodendrocyte (OL) development in the fetus and the early stage after birth. The expression of platelet-derived growth factor receptor α (Pdgfr-α) and Olig2 significantly decreased in the NO₂ group at E13.5 and E15.5, and the expression of Olig2, adenomatous polyposis coli colon (Cc1), and myelin basic protein (Mbp) was reduced in offspring at PNDs 1, 7, and 21. We performed the targeted metabolomic analysis of neurotransmitters in the placenta and found that prenatal exposure to NO₂ disturbed the metabolism of placental neurotransmitters. Serotonin (5-HT) was transferred from the placenta to the fetus at E10.5, and its accumulation in the fetal forebrain might affect oligodendrocyte progenitor cell (OPC) differentiation and OL maturation and eventually be involved in behavioral abnormalities. Our findings provide new insights into the association between prenatal NO₂ exposure with anxiety- and depression-like behaviors in male offspring.

An Evaluation of Risk Ratios on Physical and Mental Health Correlations due to Increases in Ambient Nitrogen Oxide (NOx) Concentrations

Nitrogen oxides (NOx) are gaseous pollutants contributing to pollution in their primary form and are also involved in reactions forming ground-level ozone and fine particulate matter. Thus, NOx is of great interest for targeted pollution reduction because of this cascade effect. Primary emissions originate from fossil fuel combustion making NOx a common outdoor and indoor air pollutant. Numerous studies documenting the observed physical health impacts of NOx were reviewed and, where available, were summarized using risk ratios. More recently, the literature has shifted to focus on the mental health implications of NOx exposure, and a review of the current literature found five main categories of mental health-related conditions with respect to NOx exposure: common mental health disorders, sleep, anxiety, depression, and suicide. All the physical and mental health effects with available risk ratios were organized in order of increasing risk. Mental health concerns emerged as those most influenced by NOx exposure, with physical health impacts, such as asthma, only beginning to surface as the fourth highest risk. Mental health conditions occupied seven of the top ten highest risk health ailments. The results summarized in this narrative review show that there are clear positive correlations between NOx and negative physical and mental health manifestations, thus strengthening the argument in support of the reduction in ambient NOx levels.

Short-term effects and economic burden assessment of ambient air pollution on hospitalizations for schizophrenia

The evidence on the health and economic impacts of air pollution with schizophrenia is scarce, especially in developing countries. In this study, we aimed to systemically examine the short-term effects of PM_2.5 (particulate matter ≤ 2.5 μm in diameter), PM₁₀ (≤ 10 μm in diameter), NO₂ (nitrogen dioxide), SO₂ (sulfur dioxide), CO (carbon monoxide), and O₃ (ozone) on hospital admissions for schizophrenia in a Chinese coastal city (Qingdao) and to further assess the corresponding attributable risk and economic burden. A generalized additive model (GAM) was applied to model the impact of air pollution on schizophrenia, and the corresponding economic burden including the direct costs (medical expenses) and indirect costs (productivity loss). Stratified analyses were also performed by age, gender, and season (warm or cold). Our results showed that for a 10 μg/m³ increase in the concentrations of PM_2.5, PM₁₀, SO₂, and CO at lag5, the corresponding relative risks (RRs) were 1.0160 (95% CI: 1.0038-1.0282), 1.0097 (1.0018-1.0177), 1.0738 (1.0222-1.01280), and 1.0013 (1.0001-1.0026), respectively. However, no significant effect of NO₂ or O₃ on schizophrenia admissions was found. The stratified analysis indicated that females and younger individuals (< 45 years old) appeared to be more vulnerable, but no significant difference was found between seasons. Furthermore, 12.41% of schizophrenia hospitalizations were attributable to exposure to air pollution exceeding the World Health Organization (WHO) air quality standard, with a total economic burden of 89.67 million RMB during the study period. At the individual level, excessive air pollution exposure resulted in an economic burden of 8232.08 RMB per hospitalization. Our study found that short-term exposure to air pollutants increased the risk of hospital admissions for schizophrenia and resulted in a substantial economic burden. Considerable health benefits can be achieved by further reducing air pollution.

Association between traffic-related air pollution and anxiety hospitalizations in a coastal Chinese city: are there potentially susceptible groups?

Motor vehicle exhaust emissions have become the main source of urban air pollution in China, but few studies have explored the association of short-term exposure to traffic-related air pollutants (TRAPs) with anxiety disorders. Thus, we used an overdispersed, generalized additive model (GAM) to investigate the association between TRAPs and hospital admissions (HAs) for anxiety in Qingdao, a coastal Chinese city with high vehicle ownership. In addition, stratified analyses were performed by gender, age, season and hospitalization frequency (first admission and readmission). A positive association between TRAPs and HAs for anxiety was observed. Both inhalable particulate matter (PM₁₀) and nitrogen dioxide (NO₂) showed significant effects at lag 3 in the single-day lag structure, and each 10 μg/m³ increase in the concentrations was significantly associated with increases of 0.88% [95% confidence interval (CI): 0.04%, 1.72%] for PM₁₀ and 2.74% (0.45%, 5.08%) for NO₂ on anxiety hospitalizations. For fine particulate matter (PM_2.5) and carbon monoxide (CO), the strongest effects were found at lag05 and lag04 [2.67% (0.77%, 4.62%) and 0.19% (0.04%, 0.34%), respectively] in the multiday lag structure. The estimates of PM_2.5 were relatively robust after adjusting for other pollutants in the two-pollutant model. Stratified analyses indicated that the associations were stronger in females and younger individuals (<45 in age) than in males and elderly individuals (≥45 in age). Furthermore, the effects of PM_2.5 and CO were most obvious during the cold season. Regarding hospitalization frequency, only PM_2.5 was found to have a significant effect in the first-admission group. The results showed that short-term exposure to TRAPs, especially to PM_2.5, was significantly associated with the increased risk of daily HAs for anxiety, which can help clinicians and policymakers better understand the effects of TRAPs to implement targeted interventions.

Short-term NO2 exposure and cognitive and mental health: A panel study based on a citizen science project in Barcelona, Spain

BACKGROUND

The association between short-term exposure to air pollution and cognitive and mental health has not been thoroughly investigated so far.

OBJECTIVES

We conducted a panel study co-designed with citizens to assess whether air pollution can affect attention, perceived stress, mood and sleep quality.

METHODS

From September 2020 to March 2021, we followed 288 adults (mean age = 37.9 years; standard deviation = 12.1 years) for 14 days in Barcelona, Spain. Two tasks were self-administered daily through a mobile application: the Stroop color-word test to assess attention performance and a set of 0-to-10 rating scale questions to evaluate perceived stress, well-being, energy and sleep quality. From the Stroop test, three outcomes related to selective attention were calculated and z-score-transformed: response time, cognitive throughput and inhibitory control. Air pollution was assessed using the mean nitrogen dioxide (NO₂) concentrations (mean of all Barcelona monitoring stations or using location data) 12 and 24 h before the tasks were completed. We applied linear regression with random effects by participant to estimate intra-individual associations, controlling for day of the week and time-varying factors such as alcohol consumption and physical activity.

RESULTS

Based on 2,457 repeated attention test performances, an increase of 30 μg/m³ exposure to NO₂ 12 h was associated with lower cognitive throughput (beta = -0.08, 95% CI: -0.15, -0.01) and higher response time (beta = 0.07, 95% CI: 0.01, 0.14) (increase inattentiveness). Moreover, an increase of 30 μg/m³ exposure to NO₂ 12 h was associated with higher self-perceived stress (beta = 0.44, 95% CI: 0.13, 0.77). We did not find statistically significant associations with inhibitory control and subjective well-being.

CONCLUSIONS

Our findings suggest that short-term exposure to air pollution could have adverse effects on attention performance and perceived stress in adults.

Attributable risk and economic cost of hospital admissions for depression due to short-exposure to ambient air pollution: A multi-city time-stratified case-crossover study

BACKGROUND

Depression has become the most common mental disease globally and is a strong predictor for suicide. Studies have indicated that exposure to high levels of air pollution increased the risk of depression, but evidence in human populations is still limited. At present, a few studies estimated the impact of multi-pollutants on hospitalization for depression in multi-city in areas with severe air pollution. We aimed to examine the association between short-term exposure to common ambient air pollutants and hospital admissions (HAs) for depression based on statistics of inpatients with depression in multi-city.

METHODS

The 10,459 records of HAs for depression from medical institutions in nine cities/prefectures, Sichuan Province, China, between January 1, 2017 and December 31, 2018 were collected. Air pollutant data including PM_2.5, PM₁₀, SO₂ and NO₂ from provincial ecological environment monitoring stations were obtained. Based on a time-stratified case-crossover design, we estimated the impact on relative risk (RR) of short-term exposure to air pollutants on hospitalization for depression, with stratification by sex, age, and economic level. The cost of illness method was used to further assess hospitalization costs.

RESULTS

The short-term exposure to air pollutants was positively associated with hospitalization for depression. The increase of air particulate matter (PM) had the strongest effect on lag 0 day (PM_2.5:1.037 (95% CI:1.022,1.052), PM₁₀:1.024 (95% CI:1.013,1.036)). The effects of SO₂ reached the peak on lag 2 day (1.317 (95% CI:1.151,1.507)). Women and older people were more likely to be affected by air pollutants and prone to depression (P = 0.013, P = 0.006). During the study period, the economic cost of hospitalization for depression caused by PM pollution was US$ 8.36 million.

LIMITATIONS

The air pollutant concentration level of the monitoring stations in the study area was regarded as personal pollutant exposure, which may not accurately reflect the patient's exposure level, resulting in a certain measurement error.

CONCLUSIONS

Short-term changes to ambient air pollution exposure may increase the risk of hospital admissions for depression and cause economic costs due to hospitalization.

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.

Interactive effects of cold spell and air pollution on outpatient visits for anxiety in three subtropical Chinese cities

BACKGROUND

Although low temperature and air pollution exposures have been associated with the risk of anxiety, their combined effects remain unclear.

OBJECTIVE

To investigate the independent and interactive effects of low temperature and air pollution exposures on anxiety.

METHOD

Using a case-crossover study design, the authors collected data from 101,636 outpatient visits due to anxiety in three subtropical Chinese cities during the cold season (November to April in 2013 through 2018), and then built conditional logistic regression models based on individual exposure assessments [temperature, relative humidity, particulate matter (PM_2.5, PM₁₀), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂)] and twelve cold spell definitions. Additive-scale interactions were assessed using the relative excess risk due to interaction (RERI).

RESULTS

Both cold spell and air pollution were significantly associated with outpatients for anxiety. The effects of cold spell increased with its intensity, ranging from 8.98% (95% CI: 2.02%, 16.41%) to 15.24% (95% CI: 6.75%, 24.39%) in Huizhou. Additionally, each 10 μg/m³ increase of PM_2.5, PM₁₀, NO₂ and SO₂ was associated with a 1.51% (95% CI: 0.61%, 2.43%), 1.58% (95% CI: 0.89%, 2.28%), 13.95% (9.98%, 18.05%) and 11.84% (95% CI: 8.25%, 15.55%) increase in outpatient visits for anxiety. Synergistic interactions (RERI >0) of cold spell with all four air pollutants on anxiety were observed, especially for more intense cold spells. For particulate matters, these interactions were found even under mild cold spell definitions [RERI: 0.11 (95% CI: 0.02, 0.21) for PM_2.5, and 0.24 (95% CI: 0.14, 0.33) for PM₁₀]. Stratified analyses yielded a pronounced results in people aged 18-65 years.

CONCLUSIONS

These findings indicate that both cold spell and air pollution are important drivers of the occurrence of anxiety, and simultaneous exposure to these two factors might have synergistic effects on anxiety. These findings highlight the importance of controlling air pollution and improving cold-warning systems.

Ambient air pollution and epileptic seizures: a panel study in Australia

OBJECTIVE

Emerging evidence has shown that ambient air pollution affects brain health, but little is known about its effect on epileptic seizures. This work aimed to assess the association between daily exposure to ambient air pollution and the risk of epileptic seizures.

METHODS

This study used epileptic seizure data from two independent data sources (NeuroVista and Seer App seizure diary). In the NeuroVista dataset, 3273 seizures were recorded using intracranial electroencephalography (iEEG) from 15 participants with refractory focal epilepsy in Australia in 2010-2012. In the seizure diary dataset, 3419 self-reported seizures were collected through a mobile application from 34 participants with epilepsy in Australia in 2018-2021. Daily average concentrations of carbon monoxide (CO), nitrogen dioxide (NO₂ ), ozone (O₃ ), particulate matter ≤10 μm in diameter (PM₁₀ ), and sulfur dioxide (SO₂ ) were retrieved from the Environment Protection Authority (EPA) based on participants' postcodes. A patient-time-stratified case-crossover design with the conditional Poisson regression model was used to determine the associations between air pollutants and epileptic seizures.

RESULTS

A significant association between CO concentrations and epileptic seizure risks was observed, with an increased seizure risk of 4% (relative risk [RR]: 1.04, 95% confidence interval [CI]: 1.01-1.07) for an interquartile range (IQR) increase of CO concentrations (0.13 parts per million), while no significant associations were found for the other four air pollutants in the whole study population. Females had a significantly increased risk of seizures when exposing to elevated CO and NO₂ , with RR of 1.05 (95% CI: 1.01-1.08) and 1.09 (95% CI: 1.01-1.16), respectively. Additionally, a significant association was observed between CO and the risk of subclinical seizures (RR: 1.20, 95% CI: 1.12-1.28).

SIGNIFICANCE

Daily exposure to elevated CO concentrations may be associated with the increased risk of epileptic seizures, especially for subclinical seizures.

Association of residential greenness and incident depression: Investigating the mediation and interaction effects of particulate matter

BACKGROUND

Growing evidence has linked residential greenness to depression, the results from prospective cohort study are still limited. And it remains unclear whether particulate matter (PM) modify, mediate, or interact the greenness-depression relationship.

METHODS

We collected data from Yinzhou Cohort(N = 47,516) which was recruited between June 2015 and December 2017. Depression cases before April 2020 were ascertained from local Health Information System covered all residents' health care records. Residential greenness (the Normalized Difference Vegetation Index, NDVI, and the Enhanced Vegetation Index, EVI) and PM (particulate matters with diameters≤2.5 μm, PM_2.5 and particulate matters with diameters≤10 μm, PM₁₀) were estimated based on participants' residential coordinates. We conducted Cox models employing age as timescale to estimate the association between residential greenness within different buffers and incident depression. Furthermore, we explored the potential confounding, mediation and interaction relationship between greenness and PM.

RESULTS

During the 99,556 person-years of follow-up, 1043 incident depression cases occurred. In single exposure models, residential greenness was inversely associated with depression incidence (e.g. Hazard Ratio (HR) = 0.86, 95% confidence interval (CI): 0.79, 0.94 for per interquartile range (IQR) increase NDVI 250 m). The protective association between greenness was attenuated after introducing PM_2.5 and PM₁₀ into the models. We identified multiplicative interactions between greenness and PM exposure for depression (e.g. HR _interaction = 0.91, 95%CI: 0.85, 0.98 for per IQR decrease NDVI 250 m and per IQR increase PM_2.5). Besides, we found the protective association of greenness was partly mediated by PM (e.g. mediation proportion = 52.9% between NDVI 250 m and PM_2.5).

CONCLUSIONS

In this longitudinal cohort study, residents living in greener neighborhoods had a lower risk of depression incidence and the benefits were interacted and partly mediated by PM. Improvement in residential greenness could be an actionable and planning intervention to prevent depression.

Deep Ensemble Machine Learning Framework for the Estimation of PM2.5 Concentrations

BACKGROUND

Accurate estimation of historical PM2.5 (particle matter with an aerodynamic diameter of less than 2.5μm) is critical and essential for environmental health risk assessment.

OBJECTIVES

The aim of this study was to develop a multiple-level stacked ensemble machine learning framework for improving the estimation of the daily ground-level PM2.5 concentrations.

METHODS

An innovative deep ensemble machine learning framework (DEML) was developed to estimate the daily PM2.5 concentrations. The framework has a three-stage structure: At the first stage, four base models [gradient boosting machine (GBM), support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost)] were used to generate a new data set of PM2.5 concentrations for training the next-stage learners. At the second stage, three meta-models [RF, XGBoost, and Generalized Linear Model (GLM)] were used to estimate PM2.5 concentrations using a combination of the original data set and the predictions from the first-stage models. At the third stage, a nonnegative least squares (NNLS) algorithm was employed to obtain the optimal weights for PM2.5 estimation. We took the data from 133 monitoring stations in Italy as an example to implement the DEML to predict daily PM2.5 at each 1km×1km grid cell from 2015 to 2019 across Italy. We evaluated the model performance by performing 10-fold cross-validation (CV) and compared it with five benchmark algorithms [GBM, SVM, RF, XGBoost, and Super Learner (SL)].

RESULTS

The results revealed that the PM2.5 prediction performance of DEML [coefficients of determination (R2)=0.87 and root mean square error (RMSE)=5.38μg/m3] was superior to any benchmark models (with R2 of 0.51, 0.76, 0.83, 0.70, and 0.83 for GBM, SVM, RF, XGBoost, and SL approach, respectively). DEML displayed reliable performance in capturing the spatiotemporal variations of PM2.5 in Italy.

DISCUSSION

The proposed DEML framework achieved an outstanding performance in PM2.5 estimation, which could be used as a tool for more accurate environmental exposure assessment. https://doi.org/10.1289/EHP9752.

Gaseous air pollutants and hospitalizations for mental disorders in 17 Chinese cities: Association, morbidity burden and economic costs

The short-term morbidity effects of gaseous air pollutants on mental disorders (MDs), and the corresponding morbidity and economic burdens have not been well studied. We aimed to explore the associations of ambient sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃) and carbon monoxide (CO) with MDs hospitalizations in 17 Chinese cities during 2015-2018, and estimate the attributable risk and economic costs of MDs hospitalizations associated with gaseous pollutants. City-specific relationships between gaseous pollutants and MDs hospitalizations were evaluated using over-dispersed generalized additive models, then combined to obtain the pooled effect. Concentration-response (C-R) curves of gaseous pollutants with MDs from each city were pooled to allow regional estimates to be derived. The morbidity and economic burdens of MDs hospitalizations attributable to gaseous pollutants were further assessed. A total of 171,939 MDs hospitalizations were included. We observed insignificant association of O₃ with MDs. An interquartile range increase in SO₂ at lag0 (9.1 μg/m³), NO₂ at lag0 (16.7 μg/m³) and CO at lag2 (0.4 mg/m³) corresponded to a 3.02% (95%CI: 0.72%, 5.38%), 5.03% (95%CI: 1.84%, 8.32%) and 2.18% (95%CI: 0.40%, 4.00%) increase in daily MDs hospitalizations, respectively. These effects were modified by sex, season and cause-specific MDs. The C-R curves of SO₂ and NO₂ with MDs indicated nonlinearity and the slops were steeper at lower concentrations. Overall, using current standards as reference concentrations, 0.27% (95%CI: 0.07%, 0.48%) and 0.06% (95%CI: 0.02%, 0.10%) of MDs hospitalizations could be attributable to extra SO₂ and NO₂ exposures, and the corresponding economic costs accounted for 0.34% (95%CI: 0.08%, 0.60%) and 0.07% (95%CI: 0.03%, 0.11%) of hospitalization expenses, respectively. Moreover, using threshold values detected from C-R curves as reference concentrations, the above mentioned morbidity and economic burdens increased a lot. These findings suggest more strict emission control regulations are needed to protect mental health from gaseous pollutants.

Short-term exposure to ambient air pollution and risk of daily hospital admissions for anxiety in China: A multicity study

The potential impact of short-term exposure to ambient air pollution on risk of anxiety remains uncertain. We performed a detailed evaluation based on data from national insurance databases in China. Daily hospital admissions for anxiety disorders were identified in 2013-2017 from the national insurance databases covering up to 261 million urban residents in 56 cities in China. A two-stage time-series study was conducted to evaluate the associations between short-term exposure to major ambient air pollutants, including fine particles, inhalable particles, nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone, and carbon monoxide, and risk of daily hospital admissions for anxiety. Significant associations between short-term exposures to ambient NO₂ and SO₂ and risk of daily hospital admissions for anxiety were found in the overall analysis. Per 10 μg/m³ increases in NO₂ at lag0 and SO₂ at lag6 were associated with significant increases of 1.37% (95% CI: 0.14%, 2.62%) and 1.53% (95% CI: 0.59%, 2.48%) in anxiety admissions, respectively. Stronger associations were found in the southern region and patients <65 years for SO₂. Short-term exposure to ambient air pollution is associated with increased risk of anxiety admissions, which may provide important implications for promotion of mental health in the public.

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

BACKGROUND

Long-term exposure to ambient air pollution was linked to depression incidence, although the results were limited and inconsistent.

OBJECTIVES

To investigate the effects of long-term air pollution exposure on depression risk prospectively in China.

METHODS

The present study used data from Yinzhou Cohort on adults without depression at baseline, and followed up until April 2020. Two-year moving average concentrations of particulate matter with a diameter ≤ 2.5 μm (PM_2.5), ≤10 μm (PM₁₀) and nitrogen dioxide (NO₂) were measured using land-use regression (LUR) models for each participant. Depression cases were ascertained using the Health Information System (HIS) of the local health administration by linking the unique identifiers. We conducted Cox regression models with time-varying exposures to estimate the hazard ratios (HRs) and 95% confidence intervals (95% CIs) of depression with each pollutant, after adjusting for a sequence of individual covariates as demographic characteristics, lifestyles, and comorbidity. Besides, physical activity, baseline potential depressive symptoms, cancer status, COVID-19 pandemic, different outcome definitions and air pollution exposure windows were considered in sensitivity analyses.

RESULTS

Among the 30,712 adults with a mean age of 62.22 ± 11.25, 1024 incident depression cases were identified over totaling 98,619 person-years of observation. Interquartile range increments of the air pollutants were associated with increased risks of depression, and the corresponding HRs were 1.59 (95%CI: 1.46, 1.72) for PM_2.5, 1.49 (95%CI: 1.35, 1.64) for PM₁₀ and 1.58 (95%CI: 1.42, 1.77) for NO₂. Subgroup analyses suggested that participants without taking any protective measures towards air pollution were more susceptible. The results remained robust in all sensitivity analyses.

CONCLUSIONS

Long-term exposure to ambient air pollution was identified as a risk factor for depression onset. Strategies to reduce air pollution are necessary to decrease the disease burden of depression.

Air pollution exposure and depression: A comprehensive updated systematic review and meta-analysis

We provide a comprehensive and updated systematic review and meta-analysis of the association between air pollution exposure and depression, searching PubMed, Embase, and Web of Sciences for relevant articles published up to May 2021, and eventually including 39 studies. Meta-analyses were performed separately according to pollutant type [particulate matter with diameter ≤10 μm (PM₁₀) and ≤2.5 μm (PM_2.5), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and carbon monoxide (CO)] and exposure duration [short- (<30 days) and long-term (≥30 days)]. Test for homogeneity based on Cochran's Q and I² statistics were calculated and the restricted maximum likelihood (REML) random effect model was applied. We assessed overall quality of pooled estimates, influence of single studies on the meta-analytic estimates, sources of between-study heterogeneity, and publication bias. We observed an increased risk of depression associated with long-term exposure to PM_2.5 (relative risk: 1.074, 95% confidence interval: 1.021-1.129) and NO₂ (1.037, 1.011-1.064), and with short-term exposure to PM₁₀ (1.009, 1.006-1.012), PM_2.5 (1.009, 1.007-1.011), NO₂ (1.022, 1.012-1.033), SO₂ (1.024, 1.010-1.037), O₃ (1.011, 0.997-1.026), and CO (1.062, 1.020-1.105). The publication bias affecting half of the investigated associations and the high heterogeneity characterizing most of the meta-analytic estimates partly prevent to draw very firm conclusions. On the other hand, the coherence of all the estimates after excluding single studies in the sensitivity analysis supports the soundness of our results. This especially applies to the association between PM_2.5 and depression, strengthened by the absence of heterogeneity and of relevant publication bias in both long- and short-term exposure studies. Should further investigations be designed, they should involve large sample sizes, well-defined diagnostic criteria for depression, and thorough control of potential confounding factors. Finally, studies dedicated to the comprehension of the mechanisms underlying the association between air pollution and depression remain necessary.

Explainable deep learning predictions for illness risk of mental disorders in Nanjing, China

Epidemiological studies have revealed the associations of air pollutants and meteorological factors with a range of mental health conditions. However, little is known about local explanations and global understanding on the importance and effect of input features in the complex system of environmental stressors - mental disorders (MDs), especially for exposure to air pollution mixture. In this study, we combined deep learning neural networks (DLNNs) with SHapley Additive exPlanation (SHAP) to predict the illness risk of MDs on the population level, and then provided explanations for risk factors. The modeling system, which was trained on day-by-day hospital outpatient visits of two major hospitals in Nanjing, China from 2013/07/01 through 2019/02/28, visualized the time-varying prediction, contributing factors, and interaction effects of informative features. Our results suggested that NO₂, SO₂, and CO made outstanding contributions in magnitude of feature attributions under circumstances of mixed air pollutants. In particular, NO₂ at high concentration level was associated with an increase in illness risk of MDs, and the maximum and mean absolute SHAP value were approximated to 10 and 2 as a local and global measure of feature importance, respectively. It presented a marginally antagonistic effect for two pairs of gaseous pollutants, i.e., NO₂ vs. SO₂ and CO vs. NO₂. In contrast, CO and SO₂ displayed the opposite direction of feature effects to the rise of observed concentrations, but an apparent synergistic effect was obviously captured. The primary risk factors driving a sharp increase in acute attack or exacerbation of MDs were also identified by depicting prediction paths of time-series samples. We believe that the significance of coupling accurate predictions from DLNNs with interpretable explanations of why a prediction is completed has broad applicability throughout the field of environmental health.

Ambient NO2 exposure sex-specifically impairs myelin and contributes to anxiety and depression-like behaviors of C57BL/6J mice

NO₂ is a common indoor and outdoor air pollutant, but its health effects are still controversial. Beside respiratory injury, more epidemiological studies show that inhalation of NO₂ is associated with an increased risk of anxiety and depression. However, the causal relationship at the molecular level remains unclear. In the present study, we exposed adult C57BL/6J mice to NO₂ (2.5 ppm, 5 h/day) for four weeks, and found anxiety and depression-like behaviors in male mice, but not female mice. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment indicated that differentially expressed genes (DEGs) in the prefrontal cortex and cerebellum were closely associated with signal transduction pathways, such as axon guidance. Importantly, NO₂ inhalation damaged the ultrastructure of myelin sheath and caused the abnormal expression of related genes in males, which partially contributed to mental disorders. We also found that prolactin (Prl), through its anti-inflammatory activity and remyelination, might play a major role in the sex-specific neurobehavioral disorder in male mice caused by NO₂ exposure.

Effect of extreme temperatures on daily emergency room visits for mental disorders

Relatively few studies investigated the effects of extreme temperatures (both heat and cold) on mental health (ICD-9: 290-319; ICD-10: F00-F99) and the potential effect modifications by individuals' age, sex, and race. We aimed to explore the effect of extreme temperatures of both heat and cold on the emergency room (ER) visits for mental health disorders, and conducted a stratified analysis to identify possible susceptible population in Erie and Niagara counties, NY, USA. To assess the short-term impacts of daily maximum temperature on ER visits related to mental disorders (2009-2015), we applied a quasi-Poisson generalized linear model combined with a distributed lag non-linear model (DLNM). The model was adjusted for day of the week, precipitation, long-term time trend, and seasonality. We found that there were positive associations between short-term exposure to extreme ambient temperatures and increased ER visits for mental disorders, and the effects can vary by individual factors. We found heat effect (relative risk (RR) = 1.16; 95% confidence intervals (CI), 1.06-1.27) on exacerbated mental disorders became intense in the study region and subgroup of population (the elderly) being more susceptible to extreme heat than any other age group. For extreme cold, we found that there is a substantial delay effect of 14 days (RR = 1.25; 95% CI = 1.08-1.45), which is particularly burdensome to the age group of 50-64 years old and African-Americans. Our findings suggest that there is a positive association between short-term exposure to extreme ambient temperature (heat and cold) and increased ER visits for mental disorders, and the effects vary as a function of individual factors, such as age and race.

Role of atmospheric particulate matter exposure in COVID-19 and other health risks in human: A review

Due to intense industrialization and urbanization, air pollution has become a serious global concern as a hazard to human health. Epidemiological studies found that exposure to atmospheric particulate matter (PM) causes severe health problems in human and significant damage to the physiological systems. In recent days, PM exposure could be related as a carrier for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus transmission and Coronavirus disease 2019 (COVID-19) infection. Hence, it is important to understand the adverse effects of PM in human health. This review aims to provide insights on the detrimental effects of PM in various human health problems including respiratory, circulatory, nervous, and immune system along with their possible toxicity mechanisms. Overall, this review highlights the potential relationship of PM with several life-limiting human diseases and their significance for better management strategies.

Particulate matter pollution and risk of outpatient visits for psychological diseases in Nanjing, China

Exposure to air pollution has been associated with increased risk for a range of adverse mental health conditions. Less is known about whether air pollution is also associated with increases in the utilization of mental health services, especially outpatient mental health service utilization. This study aimed to examine the association between the number of daily outpatient visits at the psychological disease departments of two major hospitals (PSYC) and daily average concentrations of PM_2.5 and PM₁₀ in a heavily polluted city in China, Nanjing, from 2013/7/1 to 2019/2/28, using generalized additive models with a quasi-Poisson regression. Results showed that each 10 μg/m³ increase in PM_2.5 concentration on lag0 day was associated with a 0.40% increase (95% CI: 0.07-0.72) in PSYC visits, and each 10 μg/m³ increase in PM₁₀ concentration on the same day a 0.31% increase (95% CI: 0.09-0.54) in PSYC visits. Exposure-response curves suggested linear relationships between PM concentration and daily PSYC outpatient visits, without evidence of a threshold. Associations remained positive, but were non-significant, with adjustment for co-pollutants, SO₂, NO₂ and CO. Significantly larger effects were found for older and male participants, vs. their counterparts. These findings add to the growing literature linking air pollution to mental health service utilization, demonstrating the critical need for both air pollution mitigation measures and increased capacity of the mental health system in China.

The association between ambient air pollution and blood lipids: A longitudinal study in Shijiazhuang, China

BACKGROUND

Few studies have explored the associations between ambient air pollution and blood lipid levels. This study aimed to fill this knowledge gap based on a routine health examination cohort in Shijiazhuang, China.

METHODS

We included 7063 participants who took the routine health examination for 2-3 times at Hebei General Hospital from January 2016 to December 2018. Individual serum levels of cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were measured. Their three-month average exposure to air pollution prior to the routine health examinations was estimated using inverse distance weighted method. We used linear mixed-effects regression models to examine the associations between air pollution and levels of blood lipids while controlling for age, gender, body mass index (BMI), smoking, alcohol drinking, temperature, humidity, with a random effect for each individual.

RESULTS

Particles with diameters ≤2.5 μm and ≤10 μm (PM_2.5 and PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂) and ozone (O₃) were all positively associated with TC, TG, and LDL-C and negatively associated with HDL-C, in single pollutant models. Each 10 μg/m³ increment of 3-month average PM_2.5 was associated with 0.65% [95% confidence interval (CI): 0.03%-1.28%], 0.56% (95%CI: 0.33%-0.79%) and 0.63% (95%CI: 0.35%-0.91%) increment in TG, TC, and LDL-C, and 0.91% (95%CI: 0.68%-1.13%) decrease in HDL-C. In two-pollutant models, the effects of gaseous pollutants on blood lipids were weakened, while those of PMs were strengthened. Stronger associations were presented in the elderly (≥60 years) and overweight/obese (BMI ≥ 24) participants.

CONCLUSIONS

Ambient air pollution had significantly adverse effects on blood lipid levels, especially in overweight/obese and elderly individuals.

CAPSULE

Significant associations between increased air pollution and worse blood lipid levels were found, especially in overweight/obese and elderly individuals.

Short-term effects of air pollution on cause-specific mental disorders in three subtropical Chinese cities

BACKGROUND

The effects of ambient air pollution on specific mental disorders are rarely studied, and the reported results are inconsistent.

OBJECTIVE

To assess the short-term effect of ambient air pollution on the morbidity of mental disorders in three subtropical Chinese cities.

METHODS

Daily concentrations of air pollution were averaged from 19 fixed monitoring stations across each city, and data on patients were collected from three psychiatric specialty hospitals. A time-series study combined with a generalized additive Poisson model was conducted to investigate the association between air pollution and mental disorders. The exposure-response relationships were explored and stratified analyses by age and sex were conducted.

RESULTS

A total of 1,133,220 outpatient visits were recorded in three subtropical cities (Huizhou, Shenzhen, and Zhaoqing). The number of daily outpatient visits for mental disorders increased with higher air pollutant (PM_2.5, PM₁₀, SO₂ and NO₂) concentrations, and the effect of NO₂ appeared to be consistently significant across the three cities, with excess risk (ER) of 4.45% (95% CI: 2.90%, 6.04%) in Huizhou, 7.94% (95% CI: 6.28%, 9.62%) in Shenzhen, and 2.19% (95% CI: 0.51%, 3.89%) in Zhaoqing, respectively, at lag03. We also observed significant effect of PM_2.5 at lag0 (ER = 1.20%, 95% CI: 0.28%, 2.13%), PM₁₀ at lag0 (ER = 0.99%, 95% CI: 0.36%, 1.62%), and SO₂ at lag0 (ER = 10.74%, 95% CI: 3.20%, 18.84%) in Shenzhen. For specific mental disorders, significant associations were found in all the air pollutants except between SO₂ and affective disorder and between PM_2.5 and schizophrenia. In addition, we found that air pollution exhibited stronger effects for males and adults (≥18 years).

CONCLUSION

Acute exposure to air pollution, especially NO₂, might be an important trigger of mental disorders.