Recent epidemiological evidence suggests associations between air pollution exposure and major depressive disorders, but the literature is inconsistent for other mental illnesses. We investigated the associations of several air pollutants and road traffic noise with the incidence of different categories of mental disorders in a large population-based cohort. We enrolled 1,739,277 individuals 30 + years from the 2011 census in Rome, Italy, and followed them up until 2019. In detail, we analyzed 1,733,331 participants (mean age 56.43 +/- 15.85 years; 54.96 % female) with complete information on covariates of interest. We excluded subjects with prevalent mental disorders at baseline to evaluate the incidence (first hospitalization or co-pay exemption) of schizophrenia spectrum disorders, bipolar, anxiety, personality, or substance use disorders. In addition, we studied subjects with first prescriptions of antipsychotics, antidepressants, and mood stabilizers. Annual average concentrations of fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), Black Carbon (BC), ultrafine particles (UFP), and road traffic noise were assigned to baseline residential addresses. We applied Cox regression models adjusted for individual and area-level covariates. Each interquartile range (1.13 µg/m³) increase in PM_2.5 was associated with a hazard ratio (HR) of 1.070 (95 % confidence interval [CI]: 1.017, 1.127) for schizophrenia spectrum disorder, 1.135 (CI: 1.086, 1.186) for depression, 1.097 (CI: 1.030, 1.168) for anxiety disorders. Positive associations were also detected for BC and UFP, and with the three categories of drug prescriptions. Bipolar, personality, and substance use disorders did not show clear associations. The effects were highest in the age group 30-64 years, except for depression. Long-term exposure to ambient air pollution, especially fine and ultrafine particles, was associated with increased risks of schizophrenia spectrum disorder, depression, and anxiety disorders. The association of the pollutants with the prescriptions of specific drugs increases the credibility of the results.

The complex interplay of multiple environmental factors and cardiovascular has scarcely been studied. Within the EXPANSE project, we evaluated the association between long-term exposure to multiple environmental indices and stroke incidence across Europe. Participants from three traditional adult cohorts (Germany, Netherlands and Sweden) and four administrative cohorts (Catalonia [region Spain], Rome [city-wide], Greece and Sweden [nationwide]) were followed until incident stroke, death, migration, loss of follow-up or study end. We estimated exposures at residential addresses from different exposure domains: air pollution (nitrogen dioxide (NO₂), particulate matter < 2.5 μm (PM_2.5), black carbon (BC), ozone), built environment (green/blue spaces, impervious surfaces) and meteorology (seasonal mean and standard deviation of temperatures). Associations between environmental exposures and stroke were estimated in single and multiple-exposure Cox proportional hazard models, and Principal Component (PC) Analyses derived prototypes for specific exposures domains. We carried out random effects meta-analyses by cohort type. In over 15 million participants, increased levels of NO₂ and BC were associated with increased higher stroke incidence in both cohort types. Increased Normalized Difference Vegetation Index (NDVI) was associated with a lower stroke incidence in both cohort types, whereas an increase in impervious surface was associated with an increase in stroke incidence. The first PC of the air pollution domain (PM_2.5, NO₂ and BC) was associated with an increase in stroke incidence. For the built environment, higher levels of NDVI and lower levels of impervious surfaces were associated with a protective effect [%change in HR per 1 unit = -2.0 (95 %CI, -5.9;2.0) and -1.1(95 %CI, -2.0; -0.3) for traditional adult and administrative cohorts, respectively]. No clear patterns were observed for distance to blue spaces or temperature parameters. We observed increased HRs for stroke with exposure to PM_2.5, NO₂ and BC, lower levels of greenness and higher impervious surface in single and combined exposure models.

Chronic exposure to ambient air pollution has been related to increased mortality in the general population [1]. After the outbreak of the SARS-CoV-2 pandemic in 2019, there has been a fast proliferation of epidemiological studies linking ambient air pollution to coronavirus disease 2019 (COVID-19) incidence or adverse prognosis [2]. It has been hypothesised that ambient air pollution might increase human vulnerability to viruses by reducing immune defences, promoting a low-level chronic inflammatory state, or leading to chronic diseases [3]. Most studies have applied ecological designs, and failed to account for key individual-level or area-level determinants of COVID-19 spread or severity, such as demographic characteristics of the studied populations, socioeconomic or clinical susceptibility, and area-level proxies of disease spread such as mobility or population density [4].

Importance: The association between short-term exposure to air pollution and mortality has been widely documented worldwide; however, few studies have applied causal modeling approaches to account for unmeasured confounders that vary across time and space.Objective: To estimate the association between short-term changes in fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) concentrations and changes in daily all-cause mortality rates using a causal modeling approach.Design, Setting, and Participants: This cross-sectional study used air pollution and mortality data from Jiangsu, China; California; central-southern Italy; and Germany with interactive fixed-effects models to control for both measured and unmeasured spatiotemporal confounders. A total of 8 963 352 deaths in these 4 regions from January 1, 2015, to December 31, 2019, were included in the study. Data were analyzed from June 1, 2021, to October 30, 2023.Exposure: Day-to-day changes in county- or municipality-level mean PM_2.5 and NO₂ concentrations.Main Outcomes and Measures: Day-to-day changes in county- or municipality-level all-cause mortality rates.Results: Among the 8 963 352 deaths in the 4 study regions, a 10-μg/m³ increase in daily PM_2.5 concentration was associated with an increase in daily all-cause deaths per 100 000 people of 0.01 (95% CI, 0.001-0.01) in Jiangsu, 0.03 (95% CI, 0.004-0.05) in California, 0.10 (95% CI, 0.07-0.14) in central-southern Italy, and 0.04 (95% CI, 0.02- 0.05) in Germany. The corresponding increases in mortality rates for a 10-μg/m³ increase in NO₂ concentration were 0.04 (95% CI, 0.03-0.05) in Jiangsu, 0.03 (95% CI, 0.01-0.04) in California, 0.10 (95% CI, 0.05-0.15) in central-southern Italy, and 0.05 (95% CI, 0.04-0.06) in Germany. Significant effect modifications by age were observed in all regions, by sex in Germany (eg, 0.05 [95% CI, 0.03-0.06] for females in the single-pollutant model of PM_2.5), and by urbanicity in Jiangsu (0.07 [95% CI, 0.04-0.10] for rural counties in the 2-pollutant model of NO₂).Conclusions and Relevance: The findings of this cross-sectional study contribute to the growing body of evidence that increases in short-term exposures to PM_2.5 and NO₂ may be associated with increases in all-cause mortality rates. The interactive fixed-effects model, which controls for unmeasured spatial and temporal confounders, including unmeasured time-varying confounders in different spatial units, can be used to estimate associations between changes in short-term exposure to air pollution and changes in health outcomes.

Evidence available on the associations between urban greenness and mental health is mainly based on cross-sectional studies and has relied on 2D indicators of greenness. This longitudinal study aimed at investigating the association between 2D and 3D indicators of green and grey spaces and incident mental health-related outcomes in a large population-based cohort. Our study used data from 593,894 Italian adults (≥30 years) from the Rome Longitudinal Study. Mental health outcomes were defined using either drug prescriptions (antidepressants, antipsychotics, lithium and other mood stabilisers, and anxiolytics, hypnotics and sedatives), or hospitalisation records (for schizophrenia spectrum disorder, depression, anxiety, stress-related and somatoform, or substance use disorders). We obtained 2D and 3D indicators of green and grey exposures including Normalized Difference Vegetation Index (NDVI), green volume, grey volume, number of trees, and Normalized Difference Green-Grey Volume Index around participants' homes. Cox proportional hazards regression models were developed to estimate the association of green and grey space exposure and psychiatric conditions and medicine use, adjusted for relevant covariates. We found beneficial associations of NDVI and the number of trees with antipsychotic and lithium and other mood stabiliser drugs. We also observed detrimental associations between grey volume and lithium and other mood stabilisers and anxiolytic, hypnotic and sedative drugs. Finally, we found a protective association of the NDGG with lithium and other mood stabilisers (HR: 0.977; 95% CI: 0.965-0.990) and anxiolytic, hypnotic and sedative drugs (HR: 0.851; 95% CI: 0.762-0.950). The associations for hospitalisation for psychiatric conditions were less consistent and generally not statistically significant. Findings suggested that higher greenness areas around residential addresses are associated with reduced use of drugs for psychiatric conditions, while the opposite is true for higher grey space exposure. The study highlights the importance of accurately characterising green and grey spaces, using novel exposure indicators.