People exposed to higher levels of air pollution are more likely to experience depression or die by suicide, finds a new analysis led by UCL.
The first systematic review and meta-analysis of evidence connecting air pollution and a range of mental health problems, published in Environmental Health Perspectives, reviewed study data from 16 countries.
The researchers found that, if the relationship with depression reported in some of these studies is causal, then reducing global average exposure to fine particulate matter (PM2.5) air pollution from 44 micrograms per metre cubed (μg/m3) to 25μg/m3 could result in a 15% reduction in depression risk worldwide.
The World Health Organization guidelines recommend that fine particulate matter pollution – small airborne particles that can include dust and soot – should be kept under 10μg/m3.
“We already know that air pollution is bad for people’s health, with numerous physical health risks ranging from heart and lung disease to stroke and a higher risk of dementia,” said the study’s lead author, Dr Isobel Braithwaite (UCL Psychiatry and UCL Institute of Health Informatics).
“Here, we’re showing that air pollution could be causing substantial harm to our mental health as well, making the case for cleaning up the air we breathe even more urgent.”
The research team searched for studies that had investigated the association between particulate matter pollution and five different adverse mental health outcomes in adults. They identified 25 studies that fitted their criteria, nine of which were included in the primary analyses.
Five studies looking at long-term particulate matter exposure and depression were included in one meta-analysis.
By pooling the results, they found that a 10μg/m3 (microgram per metre cubed) increase in the average level of fine particulate matter (PM2.5) air pollution people were exposed to over long periods was associated with an approximately 10% increase in their odds of depression.
“Here, we’re showing that air pollution could be causing substantial harm to our mental health as well, making the case for cleaning up the air we breathe even more urgent.”
“We found quite consistent results across the studies we reviewed that analysed the relationship between long-term air pollution exposure and depression, even after adjustment for many other factors which could explain the association. The association seems to be similar in magnitude to those that have been found for some physical health impacts of particulate matter, such as all-cause mortality,” Dr Braithwaite said.
Global city PM2.5 levels range from 114 and 97 in Delhi and Dhaka, to 6 in Ottawa and Wellington.
In UK cities, the average particulate matter level that people are exposed to is 12.8μg/m3.
The researchers estimate that lowering average air pollution levels to the WHO recommended limit of 10μg/m3 could reduce urban UK residents’ depression risk by roughly 2.5%.
The researchers also found evidence of a connection between short-term changes in coarse particulate air pollution (PM10)* exposure and the number of suicides, from pooling the results of four different studies in a meta-analysis.
The risk of suicide appears to be measurably higher on days when PM10 levels have been high over a three-day period than after less polluted periods.
The studies into short-term changes in suicide risk accounted for confounding factors such as weather changes, and day of the week.
The relationship is not affected by other neighbourhood or socioeconomic factors given that the comparisons being made are among the same individuals on days with different pollution levels.
The researchers say the evidence was particularly strong for the suicide risk link, but the effect was smaller than for depression (an increase in suicide risk of 2% for each 10μg/m3 increase in the average coarse particulate pollution level over a three-day period).
The researchers say they cannot yet confirm whether air pollution directly causes mental ill health, but say there is evidence to suggest possible causal mechanisms.
“We know that the finest particulates from dirty air can reach the brain via both the bloodstream and the nose, and air pollution has been implicated in increased neuroinflammation, damage to nerve cells and to changes in stress hormone production, which have been linked to poor mental health,” Dr Braithwaite said.
The study’s senior author, Dr Joseph Hayes (UCL Psychiatry and Camden and Islington NHS Foundation Trust), said: “Our findings correspond with other studies that have come out this year, with further evidence in young people and in other mental health conditions. While we cannot yet say that this relationship is causal, the evidence is highly suggestive that air pollution itself increases the risk of adverse mental health outcomes.”
“A lot of what we can do to reduce air pollution can also benefit our mental health in other ways, such as enabling people to cycle or walk rather than drive, and enhancing access to parks, so this adds support to the promotion of active travel and urban green spaces,” he added.
The researchers hope to follow up their study with further research into indoor air pollution and mental health risks.
Funding: The study was conducted by researchers at UCL, Camden and Islington NHS Foundation Trust and King’s College London, and supported by North Central Thames NHS Academic Foundation Programme, National Institute for Health Research University College London Hospitals Biomedical Research Foundation, Wellcome and the Royal Society.
*The two main types of particulate matter pollution are differentiated by being under 2.5 micrometres in diameter (fine particulate matter, or PM2.5), and between 2.5 and 10 micrometres in diameter (coarse particulate matter, or PM10).
Some, like smoke, are visible, while others are too small to be seen by the naked eye.
Sources can include road transport, burning of fuels such as for heating or cooking, heavy industry and more.
Mental disorders, the second leading contributor to the global disease burden, accounts for 7~13% of disability-adjusted life-years1. With improved medical services, many epidemiological studies have suggested an increasing trend toward longevity, but also a higher prevalence of morbidity and disability among the global population2. As mental illness has been ranked as the top risk factor for years lived with disability (YLD), accounting for 21~32% of the global YLD1, it is among the major driver of the global disease burden, which is transferring from mortality to disability/morbidity2.
A comprehensive understanding of relevant risk factors is required to mitigate mental disorders. The roles of conventional factors, such as drug abuse, maternal infection, perinatal depression, physical inactivity, hormonal changes, lifestyle, urbanization, and so on, have been well studied3. The epidemiological links between mental health and environmental factors are being increasingly examined in the context of the global challenges associated with climate change4. However, most extant studies have been performed in developed countries4–11. Indeed, there is limited evidence, particularly on a national scale, about such associations in developing countries, including China, where the adjusted prevalence of mental disorders has been reported as high as 17.5%12.
There are many psychological mechanisms that also make an epidemiological linkage between environmental factors and mental health biologically plausible. First, lack of greenness has been widely linked to mental disorders, including depression and anxiety in adults5, and cognitive dysfunction in children6,7.
Many theories have been posited to explain these findings, including biogenics theory, the biodiversity hypothesis, restriction of physical activity, and social stressors13. Second, it has been shown that ambient pollutants, particularly fine particles, can cross the blood–brain barrier and thus damage the neurological system through introducing neuro-inflammation, neuronal signaling dysfunction, and immune responses14.
Third, the mechanism underpinning the maintenance of body temperature suggests that mental health may be affected by ambient temperature. As some neurotransmitters, such as biogenic amines, play roles in both emotional and thermal regulation15, patients with mental disorders (e.g., schizophrenia) are prone to disturbances in thermoregulation16 and thus may find it difficult to maintain body temperature when exposed to highly fluctuating temperatures.
Although recent epidemiological studies have associated risk of mental disorders with individual environmental variables including high temperature8,9, poor air quality17–20, and lack of residential greenness5–7, questions about whether these associations are confounded by collinearity between factors remain unanswered.
For instance, previous studies partially explained the link between mental health and residential greenness in terms of the superior air quality in greener places21. However, research that simultaneously incorporates multiple indicators is needed to identify the actual environmental risk factors.
In addition, the health effects of long-term level of temperature have been well studied, whereas the potential risks of increased variability in the temperature to the health of the general public have to date only been suggested, i.e., by a recent epidemiological study22 that linked temperature variability with total mortality; however, these relationships have not yet been examined from the perspective of mental health.
This study used self-rated mental health scores (MHSs) from the China Family Panel Studies (CFPS)23 to make individual-level comparisons of the mental health of 21,543 adults from 25 populous provinces in China between 2010 and 2014 (Supplementary Fig. 1); we then linked these data to multiple environmental factors, including long-term level of temperature (μT, annual mean of temperature), temperature variability (σT, SD of daily temperature within a calendar year), air quality (measured by annual mean of fine particles with diameters < 2.5 μm [PM2.5]), and residential greenness (measured by annual mean of normalized difference vegetation index, NDVI).
Specifically, the long-term exposures were evaluated in terms of the average annual values of the selected parameters within the county of residence of each individual (before the survey date), referring to previous studies on chronic environmental exposures24. This study, which used a difference-in-difference design25, is quasi-experimental in nature.
As we compared each subject with her/himself, the study design, itself, controlled unmeasured confounders that varied inter-individually but not longitudinally. The difference-in-difference models directly regressed changes in MHSs with environmental variations, after multiple adjustments.
Statistical examinations of our data suggest that MHS decrease is robustly related to increase in PM2.5 or σT, weakly related to NDVI decrease, and unrelated to μT, among Chinese adults. According to the findings, the efforts to mitigate climate change and air pollution can bring extra benefits in aspect of human mental health.
Results
Summary statistics
This study involved 9474 (44.0%) urban adults and 12,069 (56.0%) rural ones. We found that more adults (40.5%) reported poorer mental health than unchanged (23.0%) or improved (36.5%) mental health from 2010 to 2014 (Supplementary Table 1). Indeed, the statistics (Supplementary Table 2) indicate that the decreasing trend in mental health was correlated with the feeling of depressed (Q1), nervous (Q2), and upset (Q3).
Consistent with the trend toward global warming, the average μT increased by 0.98 °C, whereas the σT decreased by 0.55 °C. Probably benefiting from the land-use management26, the indicator of residential greenness, NDVI ( ∈ [−1, 1]) increased by 0.03. Co-determined by meteorological changes and the reduction in anthropogenic emissions resulting from China’s Clean Air Act27, the major species of ambient pollutant, PM2.5, decreased by 0.66 μg m−3.
Mean temperature
Our results revealed a weak and complex association between μT and mental health. The nonlinear effect model indicated that either increased μT or decreased μT was associated to MHS decrease (Fig. 1).
However, the pointwise confidence intervals (CIs) suggested the association was not statistically significant, which was consistent with the results of linear models (Fig. 2 and Supplementary Table 3). According to the fully adjusted model (i.e., model 5 in Supplementary Table 3), a 1 °C increase in μT was associated with a 3% (−11%, 15%) extra risk of MHS decrease. Both subregion and subgroup analyses (Supplementary Fig. 2) suggested the homogeneity of the weak association.
Exposure–response curves. The curves (the solid lines) with 95% confidence intervals (dashed lines) are estimated by the fully adjusted nonlinear effect models. The covariates include changes in alcohol consumption, education, migration, obesity, physical activity, and smoking status, as well as baseline age, alcohol consumption, education, diet type, gender, income, marital status, nationality, physical activity status, obesity status, area of residence, and smoking status in 2013. The histograms (gray bars) present the distributions of the environmental changes among the studied adults. For the exposure–response curves, please refer to the left y-axis; for the distributions, please refer to right y-axis
Environmental effects on different dimensions of mental health. The effects are evaluated by fully adjusted associations between the question-specific mental health scores and the four environmental factors. Black dots and black solid polygons: estimated odds ratios (ORs); black dashed polygons: corresponding 95% confidence intervals; gray polygons: references of no effect (OR = 1); gray radial lines: different dimensions of mental health; Q1: feeling depressed and incapability to cheer up no matter what you are doing; Q2: feeling nervous; Q3: feeling upset; Q4: feeling hopeless about the future; Q5: feeling that everything is difficult; Q6: thinking life is meaningless. Along a gray radial line, its interaction with a polygon presents the corresponding estimate or no-effect reference, for the dimension of mental health
Temperature variability
We found a significant association between the σT increment and MHS reduction, which remained robust after various adjustments (Supplementary Table 3) or model settings (Supplementary Table 4). The data showed that a 15% (3%, 25%) risk of MHS decrease was correlated with a 1 °C increase in σT (fully adjusted model; Supplementary Table 3). The nonlinear model further confirmed the negative association between changes in σT and changes in mental health status (Fig. 1).
Based on the question-specific models, incremental changes in σT tended to be strongly linked to a higher probability of feeling nervous (Q2), upset (Q3), hopelessness (Q4), and meaninglessness (Q6) (Fig. 2). Although neither subregion nor subpopulation analyses revealed significant heterogeneity in the effect of σT, this association may nonetheless vary slightly by geographical region (e.g., it may be weaker in Northern China than in Southeastern China) or inter-individually (e.g., it may be weaker in urban than in rural residents); these differences may be attributable to socioeconomic factors related to temperature maintenance facilities (e.g., owning an air conditioner). The results of double-exposure models indicated that the effect of σT was not considerably confounded by other environmental factors.
Greenness
Our results are comparable to previous findings on the association between NDVI and mental health5. According to the fully adjusted models, every 0.05 decrease in the NDVI was associated to 19% (8%, 30%) risk of MHS decrease. Although this association was not considerably affected by adjustments for other environmental factors (Fig. 3), its significant level was sensitive to model settings, including adjusted covariates (Supplementary Table 3) and model assumptions (Supplementary Table 4).
In addition, subgroup analyses suggested that some individual-level factors can modify the effect of the NDVI. Specifically, physical activity significantly enhanced this association (Supplementary Fig. 2), possibly because physically inactive adults may be relatively unaffected by the outdoor environment. Similarly, the question-specific results (Fig. 2) showed that increases in the NDVI may significantly alleviate feelings of depressed (Q1) and nervous (Q2).
Results of the double-exposure models. In each panel, the fully adjusted odds ratios of an environmental factor with their 95% confidence intervals (black dots with error bars) estimated by the double-exposure models are compared with the estimate of the corresponding single-exposure model (black circles with error bars)
Air quality
Consistent with the existing evidences10,11, we found a significant association between higher levels of PM2.5 and MHS decrease. A 28% (16%, 39%) extra risk of reduction in MHS was associated with a 10 μg m−3 increase in PM2.5 (Supplementary Table 3, the fully adjusted model) and this effect remained robust after adjustment for different sets of covariates (Supplementary Table 3) and other environmental parameters (Fig. 3).
Analogously, the association was not sensitive to different regression presumptions (Supplementary Table 4). Meanwhile, nonlinear analysis revealed a complex association for PM2.5 (Fig. 1). We found an effect threshold of ~ 5 μg m−3 for every increment in PM2.5 and the PM2.5 changes from 2010 to 2014 were above 5 μg m−3 for 8.5% of the study population. Subgroup analyses also reflected the complex effect of PM2.5 (Supplementary Fig. 2). For instance, our results suggest that PM2.5 had a significantly higher effect among the physically active adults. Potential heterogeneity in health effects of ambient particles has also been reported by previous studies18 and may be caused by variation in toxicity among different species of PM2.5, which may partially explain the apparent geographic variation in the effect (Supplementary Fig. 2).
Discussion
In summary, according to our quasi-experimental population-level study on the effects of multiple environmental changes, declines in mental health of Chinese adults was strongly and robustly associated with increased σT or PM2.5, and plausibly related to decreased NDVI. Environmental changes have been evidenced as additional risk factors, which can impact on mental health, together with the well-studied factors, such as lifestyle and urbanization3.
From 2010 to 2014, the overall trend of poorer mental health suggested that benefits from less variability of temperature and improved air quality did not offset the negative impacts from changes in other factors. For instance, the association between obesity and mental disorders is well known28, and there was an increased trend of obesity among our study population. The level of body mass index increased for 10.3% subjects, decreased for 5.6% subjects, and remained unchanged for the rest (Supplementary Table 1).
However, the continuing efforts to mitigate environmental changes, such as clean air action27 and land-use management26 in China, is expected to improve mental health considerably. For instance, during 2013–2015, the national average of PM2.5 exposure was reported to decrease by 4.51 (3.12, 5.90) μg m−3 year−1, which was remarkable, compared with the PM2.5 reduction (0.66 μg m−3, Supplementary Table 1) in this study29.
The associations between mental health and environmental indicators in China have been explored. However, previous studies are based on data from local areas14,17–20,30–32 and their results have been mixed. For instance, a statistically significant association between hospital admissions for mental disorders and ambient exposure to PM2.5 was identified in Shijiazhuang18 but not in Shanghai19 or Beijing20.
This divergence may derive from the heterogeneity of study populations, the use of different epidemiologic designs or statistical models, differences in the quality of the data, and so on. A national study, like this one, is needed to reevaluate the representative exposure–response curves among the general population. Taken together with these existing evidences, our findings confirm the epidemiological link between environmental changes and human mental health.
However, our findings are not conclusive because of the following limitations. First, mental health status was evaluated using a simple self-report questionnaire, which may call the quality of the data into question. The health outcome (MHS decrease or not) might be misclassified due to the potential errors in the questionnaire. Moreover, health outcome misclassification has been reported to bias the estimated association33.
Analogously, misclassification may also have arisen from our approximation of long-term exposure levels according to annual and county-level averages. Any such exposure misclassification could lead to underestimation of the associations33. For instance, although the averaged exposure during the previous year might be representative to capture the environmental effects on mental health according to a sensitivity analysis (Supplementary Fig. 3), we might still ignore some risks from environmental changes in a longer term (e.g., lifelong exposure).
Furthermore, although the difference-in-difference design could control unmeasured confounders, it has limited statistical power to detect weak associations, because variations in environmental changes (e.g., SD of ΔPM2.5: 4.0 μg m−3) were smaller than the overall spatiotemporal variations of those factors (e.g., SD of PM2.5 = 19.8 μg m−3). Finally, although this study examined and compared the psychological effects of four well-studied environmental factors, we may nonetheless have overlooked other associations between climate change and mental health.
Based on a nation-scale quasi-experimental study of Chinese adults, we derive representative exposure–response functions for indicators of air pollution and temperature variability, which can support the public health interventions for better mental health in China. Our results also reveal complexities underlying the epidemiological linkage between mental health and environmental changes, in the aspects of inter-individual susceptibilities, mutual confounders, and nonlinear curvatures, which should be explored by future studies.
Source:
UCL
Media Contacts:
Jake Hawkes – UCL
Original Research: Open access
“Air Pollution (Particulate Matter) Exposure and Associations with Depression, Anxiety, Bipolar, Psychosis and Suicide Risk: A Systematic Review and Meta-Analysis”. Isobel Braithwaite, Shuo Zhang, James B. Kirkbride, David P. J. Osborn, and Joseph F. Hayes.
Environmental Health Perspectives doi:10.1289/EHP4595.
