Long-term Air Pollution Exposure Research Articles

Assessing the causal effect of long-term exposure to air pollution on cognitive decline in middle-aged and older adults – Empirical evidence from a nationwide longitudinal cohort

Air pollution remains a risk factor for the global burden of disease. Middle-aged and older people are more susceptible to air pollution because of their declining physical function and are more likely to develop diseases from long-term air pollution exposure. Studies of the effects of air pollution on cognitive function in middle-aged and older adults have been inconsistent. More representative and definitive evidence is needed. This study analysed data from the Chinese Family Panel Study, an ongoing nationwide prospective cohort study, collected in waves 2014, 2016 and 2018. Rigorously tested instrument was selected for analysis and participants’ PM2.5 and instrument exposures were assessed using high-precision satellite data. The causal relationship between long-term exposure to air pollution and poor cognitive function in middle-aged and older adults was investigated using the Correlated Random Effects Control Function (CRE-CF) method within a quasi-experimental framework. This study included a total of 7042 participants aged 45 years or older. A comparison of CRE-CF with other models (OLS model, ordered probit model, and ordered probit-CRE model) demonstrated the necessity of using CRE-CF given the endogeneity of air pollution. The credibility and validity of the instrumental variable were verified. In the CRE-CF model, long-term exposure to PM2.5 was found to accelerate cognitive decline in middle-aged and older adults (coefficients of −0.159, −0.336 and −0.244 for the total cognitive, verbal and mathematical scores, respectively). Taken together, these results suggest that chronic exposure to ambient air pollution is associated with cognitive decline in middle-aged and older adults, which highlights the need for appropriate protective policies.

Air pollution impacts on in-hospital case-fatality rate of ischemic stroke patients

BackgroundA growing body of evidence suggests that air pollution exposure is associated with an increased risk for cardiovascular diseases. Data regarding the impact of long-term air pollution exposure on ischemic stroke mortality are sparse. MethodsThe German nationwide inpatient sample was used to analyse all cases of hospitalized patients with ischemic stroke in Germany 2015–2019, which were stratified according to their residency. Data of the German Federal Environmental Agency regarding average values of air pollutants were assessed from 2015 to 2019 at district-level. Data were combined and the impact of different air pollution parameters on in-hospital case-fatality was analyzed. ResultsOverall, 1,505,496 hospitalizations of patients with ischemic stroke (47.7% females; 67.4 % ≥70 years old) were counted in Germany 2015–2019, of whom 8.2 % died during hospitalization. When comparing patients with residency in federal districts with high vs. low long-term air pollution, enhanced levels of benzene (OR 1.082 [95%CI 1.034–1.132],P = 0.001), ozone (O3, OR 1.123 [95%CI 1.070–1.178],P < 0.001), nitric oxide (NO, OR 1.076 [95%CI 1.027–1.127],P = 0.002) and PM2.5 fine particulate matter concentrations (OR 1.126 [95%CI 1.074–1.180],P < 0.001) were significantly associated with increased case-fatality independent from age, sex, cardiovascular risk-factors, comorbidities, and revascularization treatments. Conversely, enhanced carbon monoxide, nitrogen dioxide, PM10, and sulphur dioxide (SO2) concentrations were not significantly associated with stroke mortality. However, SO2−concentrations were significantly associated with stroke-case-fatality rate of >8 % independent of residence area-type and area use (OR 1.518 [95%CI 1.012–2.278],P = 0.044). ConclusionElevated long-term air pollution levels in residential areas in Germany, notably of benzene, O3, NO, SO2, and PM2.5, were associated with increased stroke mortality of patients. Research in contextEvidence before this study:Besides typical, established risk factors, increasing evidence suggests that air pollution is an important and growing risk factor for stroke events, estimated to be responsible for approximately 14 % of all stroke-associated deaths. However, real-world data regarding the impact of long-term exposure to air pollution on stroke mortality are sparse.Added value of this study:The present study demonstrates that the long-term exposure to the air pollutants benzene, O3, NO, SO2 and PM2.5 are independently associated with increased case-fatality of hospitalized patients with ischemic stroke in Germany.Implications of all the available evidence:The results of our study support the urgent need to reduce the exposure to air pollution by tightening emission controls to reduce the stroke burden and stroke mortality.

Comparing “causal” and “traditional” approaches in the association of long-term exposure to ambient air pollution on mortality: How sensitive are the results?

BackgroundFew comparisons between causal inference and traditional approaches have been performed. We applied “causal” and “traditional” methods to investigate the association between long-term air pollution exposure (PM2.5 and NO2) and mortality. MethodsWe analyzed pooled data from eight well-characterized cohorts and one administrative cohort. We defined the generalized propensity score (GPS) as the conditional likelihood of exposure given confounders, and derived corresponding inverse-probability weights (IPW). We applied Cox-proportional hazard models weighted by IPW, adjusted for GPS, and directly adjusting for all confounders. ResultsIn IPW models, PM2.5 5 µg/m3 increases were associated with hazard ratios (HR) = 1.141 (95% confidence interval (CI): 1.107, 1.176) and 1.050 (1.014, 1.088) in the pooled and administrative cohorts. Corresponding estimates for traditional Cox models were 1.132 (1.107, 1.158) and 1.057 (1.025, 1.089). Almost identical results were found for all approaches and both pollutants, when unbalanced covariates were adjusted for in causal models. ConclusionsTraditional and causal approaches provided consistent associations between long-term exposure to air pollution and mortality.

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

Several studies on long-term air pollution exposure and sleep have reported inconsistent results. Large-scale studies on short-term air pollution exposures and sleep have not been conducted. We investigated the associations of long- and short-term exposure to ambient air pollutants with sleep in a Chinese population based on over 1 million nights of sleep data from consumer wearable devices. Air pollution data including particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) were collected from the Ministry of Ecology and Environment. Short-term exposure was defined as a moving average of the exposure level for different lag days from Lag0 to Lag0-6. A 365-day moving average of air pollution was regarded as long-term exposure. Sleep data were recorded using wearable devices from 2017 to 2019. The mixed-effects model was used to evaluate the associations. We observed that sleep parameters were associated with long-term exposure to all air pollutants. Higher levels of air pollutant concentrations were associated with longer total sleep and light sleep duration, shorter deep sleep duration, and decreases in wake after sleep onset (WASO), with stronger associations of exposures to NO2 and CO [a 1-interquartile range (IQR) increased NO2 (10.3 μg/m3) was associated with 8.7 min (95% CI: 8.08 to 9.32) longer sleep duration, a 1-IQR increased CO (0.3 mg/m3) was associated with 5.0 min (95% CI: − 5.13 to − 4.89) shorter deep sleep duration, 7.7 min (95% CI: 7.46 to 7.85) longer light sleep duration, and 0.5% (95% CI: − 0.5 to − 0.4%) lower proportion of WASO duration to total sleep]. The cumulative effect of short-term exposure on Lag0-6 is similar to long-term exposure but relatively less. Subgroup analyses indicated generally greater effects on individuals who were female, younger (< 45 years), slept longer (≥ 7 h), and during cold seasons, but the pattern of effects was mixed. We supplemented two additional types of stratified analyses to reduce repeated measures of outcomes and exposures while accounting for individual variation. The results were consistent with the overall results, proving the robustness of the overall results. In summary, both short- and long-term exposure to air pollution affect sleep, and the effects are comparable. Although people tend to have prolonged total sleep duration with increasing air pollutant concentrations, their sleep quality might remain poor because of the reduction in deep sleep.

High-resolution patterns and inequalities in ambient fine particle mass (PM2.5) and black carbon (BC) in the Greater Accra Metropolis, Ghana

Growing cities in sub-Saharan Africa (SSA) experience high levels of ambient air pollution. However, sparse long-term city-wide air pollution exposure data limits policy mitigation efforts and assessment of the health and climate effects. In the first study of its kind in West Africa, we developed high resolution spatiotemporal land use regression (LUR) models to map fine particulate matter (PM2.5) and black carbon (BC) concentrations in the Greater Accra Metropolitan Area (GAMA), one of the fastest sprawling metropolises in SSA. We conducted a one-year measurement campaign covering 146 sites and combined these data with geospatial and meteorological predictors to develop separate Harmattan and non-Harmattan season PM2.5 and BC models at 100 m resolution. The final models were selected with a forward stepwise procedure and performance was evaluated with 10-fold cross-validation. Model predictions were overlayed with the most recent census data to estimate the population distribution of exposure and socioeconomic inequalities in exposure at the census enumeration area level. The fixed effects components of the models explained 48–69 % and 63–71 % of the variance in PM2.5 and BC concentrations, respectively. Spatial variables related to road traffic and vegetation explained the most variability in the non-Harmattan models, while temporal variables were dominant in the Harmattan models. The entire GAMA population is exposed to PM2.5 levels above the World Health Organization guideline, including even the Interim Target 3 (15 μg/m3), with the highest exposures in poorer neighborhoods. The models can be used to support air pollution mitigation policies, health, and climate impact assessments. The measurement and modelling approach used in this study can be adapted to other African cities to bridge the air pollution data gap in the region.

Long-term exposure to air pollution and increased risk of atrial fibrillation prevalence in China

BackgroundAtrial fibrillation (AF) is the most common type of treated heart arrhythmia contributing to adverse cardiovascular events. The association between short-term air pollution exposure and AF episodes has been recognized. But the evidence of the association between long-term air pollution exposure and AF was limited, especially in developing countries. MethodsWe performed a nationwide cross-sectional study among 1,374,423 individuals aged ≥35 years from 13 health check-up centers. Using logistic regression models, we assessed the association between long-term exposure to single air pollution and AF prevalence, including particulate matter (PM2.5 and PM10), ozone (O3) and PM2.5 compositions, which were estimated by high-resolution and high-quality spatiotemporal datasets of ground-level air pollutants for China. The quantile g-computation model was used to explore the joint effect of all exposures to air pollution and the contribution of an individual component to the mixture. ResultsIn single-pollutant models, an increase of 10 μg/m3 in PM2.5 (OR 1.031[95%CI 1.010,1.053]) and PM10 (OR = 1.021 [95%CI 1.009,1.033]) was positively associated with AF prevalence. The stratified analyses revealed that these associations were significantly stronger in females, people <65 years old, and those with hypertension and diabetes. In the further exploration of the joint effect of PM2.5 compositions (OR 1.060 [95%CI 1.022,1.101]) per quintile increase in all five PM2.5 components), we found that PM2.5 sulfate contributed the most. ConclusionsThese findings provide important evidence for the positive relationship between long-term exposure to air pollution and AF prevalence in China and identify sulfate particles of PM2.5 as having the highest contribution to the overall mixture effects among all PM2.5 chemical constituents.

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

BackgroundEvidence linking air pollution to major depressive disorder (MDD) remains sparse and results are heterogeneous. In addition, the evidence about the interaction and joint associations of genetic risk and lifestyle with air pollution on incident MDD risk remains unclear. We aimed to examine the association of various air pollutants with the risk of incident MDD and assessed whether genetic susceptibility and lifestyle influence the associations.MethodsThis population-based prospective cohort study analyzed data collected between March 2006 and October 2010 from 354,897 participants aged 37 to 73 years from the UK Biobank. Annual average concentrations of PM2.5, PM10, NO2, and NOx were estimated using a Land Use Regression model. A lifestyle score was determined based on a combination of smoking, alcohol drinking, physical activity, television viewing time, sleep duration, and diet. A polygenic risk score (PRS) was defined using 17 MDD-associated genetic loci.ResultsDuring a median follow-up of 9.7 years (3,427,084 person-years), 14,710 incident MDD events were ascertained. PM2.5 (HR: 1.16, 95% CI: 1.07–1.26; per 5 μg/m3) and NOx (HR: 1.02, 95% CI: 1.01–1.05; per 20 μg/m3) were associated with increased risk of MDD. There was a significant interaction between the genetic susceptibility and air pollution for MDD (P-interaction < 0.05). Compared with participants with low genetic risk and low air pollution, those with high genetic risk and high PM2.5 exposure had the highest risk of incident MDD (PM2.5: HR: 1.34, 95% CI: 1.23–1.46). We also observed an interaction between PM2.5 exposure and unhealthy lifestyle (P-interaction < 0.05). Participants with the least healthy lifestyle and high air pollution exposures had the highest MDD risk when compared to those with the most healthy lifestyle and low air pollution (PM2.5: HR: 2.22, 95% CI: 1.92–2.58; PM10: HR: 2.09, 95% CI: 1.78–2.45; NO2: HR: 2.11, 95% CI: 1.82–2.46; NOx: HR: 2.28, 95% CI: 1.97–2.64).ConclusionsLong-term exposure to air pollution is associated with MDD risk. Identifying individuals with high genetic risk and developing healthy lifestyle for reducing the harm of air pollution to public mental health.

Long-term Exposure to Ambient Air Pollutants and Increased Risk of Pneumonia in the UK Biobank

Short-term exposure to air pollution has been linked to pneumonia risk. However, evidence on the long-term effects of air pollution on pneumonia morbidity is scarce and inconsistent. We investigated the associations of long-term air pollutant exposure with pneumonia and explored the potential interactions with smoking. Is long-term exposure to ambient air pollution associated with the risk of pneumonia, and does smoking modify the associations? We analyzed data in 445,473 participants without pneumonia within 1 year before baseline from the UK Biobank. Annual average concentrations of particulate matter (particulate matter with a diameter< 2.5 μm [PM2.5] and particulate matter with a diameter< 10 μm [PM10]), nitrogen dioxide (NO2), and nitrogen oxides (NOx) were estimated using land-use regression models. Cox proportional hazards models were used to assess the associations between air pollutants and pneumonia incidence. Potential interactions between air pollution and smoking were examined on both additive and multiplicative scales. The hazard ratios of pneumonia for each interquartile range increase in PM2.5, PM10, NO2, and NOx concentrations were 1.06 (95%CI, 1.04-1.08), 1.10 (95%CI, 1.08-1.12), 1.12 (95%CI, 1.10-1.15), and 1.06 (95%CI, 1.04-1.07), respectively. There were significant additive and multiplicative interactions between air pollution and smoking. Compared with individuals who had never smoked with low air pollution exposure, individuals who had ever smoked with high air pollution exposure had the highest pneumonia risk (PM2.5: hazard ratio [HR], 1.78; 95%CI, 1.67-1.90; PM10: HR, 1.94; 95%CI, 1.82-2.06; NO2: HR, 2.06; 95%CI, 1.93-2.21; NOx: HR, 1.88; 95%CI, 1.76-2.00). The associations between air pollutants and pneumonia risk persisted in participants exposed to air pollutants concentrations meeting the European Union limits. Long-term exposure to air pollutants was associated with an increased risk of pneumonia, especially in individuals who smoke.

Short-term association of air pollution with lung cancer mortality in Osaka, Japan

Long-term air pollution exposure has been linked to increased lung cancer mortality. However, little is known about whether day-to-day fluctuations in air pollution levels are in relation to lung cancer mortality, particularly in low-exposure settings. This study aimed to evaluate the short-term associations between air pollution and lung cancer mortality. Daily data on lung cancer mortality, fine particulate matter (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and weather conditions were collected from Osaka Prefecture, Japan, from 2010 to 2014. Generalized linear models were combined with quasi-Poisson regression were applied to evaluate the associations between each air pollutant and lung cancer mortality after adjusting for potential confounders. Mean (standard deviation) concentrations of PM2.5, NO2, SO2, and CO were 16.7 (8.6) μg/m3, 36.8 (14.2) μg/m3, 11.1 (4.0) μg/m3, and 0.51 (0.16) mg/m3, respectively. Interquartile range increases in concentrations of PM2.5, NO2, SO2, and CO (2-d moving average) were associated with 2.65% (95% confidence intervals [CIs]: 0.96%–4.37%), 4.28% (95% CIs: 2.24%–6.36%), 3.35% (95% CIs: 1.03%–5.73%), and 4.60% (95% CIs: 2.19%–7.05%) increased risk of lung cancer mortality, respectively. Stratified analyses showed that the associations were strongest in the older population and men. Exposure‒response curves showed a continuously increasing mortality risk from lung cancer with elevation of air pollution levels, without discernible thresholds. In summary, we found evidence of increased lung cancer mortality in relation to short-term elevations in ambient air pollution. These findings may merit further research to better understand this issue.

Relationship of long-term air pollution exposure with asthma and rhinitis in Italy: an innovative multipollutant approach

Backgroundair pollution is a complex mixture; novel multipollutant approaches could help understanding the health effects of multiple concomitant exposures to air pollutants. Aimto assess the relationship of long-term air pollution exposure with the prevalence of respiratory/allergic symptoms and diseases in an Italian multicenter study using single and multipollutant approaches. Methods14420 adults living in 6 Italian cities (Ancona, Pavia, Pisa, Sassari, Turin, Verona) were investigated in 2005–2011 within 11 different study cohorts. Questionnaire information about risk factors and health outcomes was collected. Machine learning derived mean annual concentrations of PM10, PM2.5, NO2 and mean summer concentrations of O3 (μg/m3) at residential level (1-km resolution) were used for the period 2013–2015. The associations between the four pollutants and respiratory/allergic symptoms/diseases were assessed using two approaches: a) logistic regression models (single-pollutant models), b) principal component logistic regression models (multipollutant models). All the models were adjusted for age, sex, education level, smoking habits, season of interview, climatic index and included a random intercept for cohorts. Resultsthe three-year average (± standard deviation) pollutants concentrations at residential level were: 20.3 ± 6.8 μg/m3 for PM2.5, 29.2 ± 7.0 μg/m3 for PM10, 28.0 ± 11.2 μg/m3 for NO2, and 70.9 ± 4.3 μg/m3 for summer O3. Through the multipollutant models the following associations emerged: PM10 and PM2.5 were related to 14–25% increased odds of rhinitis, 23–34% of asthma and 30–33% of night awakening; NO2 was related to 6–9% increased odds of rhinitis, 7–8% of asthma and 12% of night awakening; O3 was associated with 37% increased odds of asthma attacks. Overall, the Odds Ratios estimated through the multipollutant models were attenuated when compared to those of the single-pollutant models. Conclusionsthis study enabled to obtain new information about the health effects of air pollution on respiratory/allergic outcomes in adults, applying innovative methods for exposure assessment and multipollutant analyses.

Long-term associations of air pollution exposure with liver enzymes among adult employees of the Electricity Generating Authority of Thailand: A longitudinal cohort study

Exposure to particulate air pollution may induce hepatotoxic effects via low-grade inflammation. However, only a few longitudinal epidemiological studies have investigated the long-term effects of air pollution on liver enzyme levels. The present study examined associations between long-term air pollution exposure and serum liver enzymes [alkaline phosphatase (ALP), aspartate aminotransferase (AST), alanine aminotransferase (ALT)] in employees of the Electricity Generating Authority of Thailand (EGAT) in Thailand's Bangkok Metropolitan Region (BMR). We conducted secondary analyses using the data obtained from 2022 subjects (mean age, 41.2 years as of 2009) of the EGAT3 cohort study (2009–2019). The daily mean concentration of each air pollutant (PM10, O3, NO2, SO2, and CO) was estimated at the sub-district level in BMR from 2008 to 2019 using the Kriging approach. Long-term exposure for each subject was defined as the 1-year average concentrations before the date of the physical examination in 2009, 2014, and 2019. We used linear mixed effects models to assess associations between air pollution and serum liver enzymes. Interquartile range increases in PM10 and O3 were related to decreased ALP [-2.4% (95%CI: -3.0, −1.9) and −6.6% (−7.3, −5.9), respectively] after potential confounders were adjusted. Positive associations between NO2, SO2, and CO with ALP were found [5.7% (4.8, 6.5), 4.5% (4.0, 5.0), and 0.8 (0.3, 1.4), respectively]. Elevated AST was also associated with higher PM10 [1.7% (0.8, 2.5)] and O3 [5.7% (4.5, 6.9)]. However, AST was negatively associated with NO2 [-2.9% (−4.0, −1.8)] and SO2 [-1.7% (−2.3, −1.1)]. In contrast to other air pollutants, we only observed a positive association between O3 and ALT [3.4% (1.5, 5.3)]. The alteration in liver enzyme levels was significantly associated with long-term exposure to air pollution, which may raise the possibility of hepatocellular damage.

Associations Between Long-Term Air Pollutant Exposure and 30-Day All-Cause Hospital Readmissions in US Patients With Stroke.

Long-term exposure to air pollutants is associated with increased stroke incidence, morbidity, and mortality; however, research on the association of pollutant exposure with poststroke hospital readmissions is lacking. We assessed associations between average annual carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), particulate matter 2.5, and sulfur dioxide (SO2) exposure and 30-day all-cause hospital readmission in US fee-for-service Medicare beneficiaries age ≥65 years hospitalized for ischemic stroke in 2014 to 2015. We fit Cox models to assess 30-day readmissions as a function of these pollutants, adjusted for patient and hospital characteristics and ambient temperature. Analyses were then stratified by treating hospital performance on the Centers for Medicare and Medicaid Services risk-standardized 30-day poststroke all-cause readmission measure to determine if the results were independent of performance: low (Centers for Medicare and Medicaid Services rate for hospital <25th percentile of national rate), high (>75th percentile), and intermediate (all others). Of 448 148 patients with stroke, 12.5% were readmitted within 30 days. Except for tropospheric NO2 (no national standard), average 2-year CO, O3, particulate matter 2.5, and SO2 values were below national limits. Each one SD increase in average annual CO, NO2, particulate matter 2.5, and SO2 exposure was associated with an adjusted 1.1% (95% CI, 0.4-1.9%), 3.6% (95% CI, 2.9%-4.4%), 1.2% (95% CI, 0.2%-2.3%), and 2.0% (95% CI, 1.1%-3.0%) increased risk of 30-day readmission, respectively, and O3 with a 0.7% (95% CI, 0.0%-1.5%) decrease. Associations between long-term air pollutant exposure and increased readmissions persisted across hospital performance categories. Long-term air pollutant exposure below national limits was associated with increased 30-day readmissions after stroke, regardless of hospital performance category. Whether air quality improvements lead to reductions in poststroke readmissions requires further research.

Abstract WMP98: Associations Between Long-term Co, No 2 , O 3 , Pm 2.5 , And So 2 Exposure And 30-day All-cause Hospital Readmission Among Stroke Patients In The Us: 2014-2015

Objective: There are known associations between long-term air pollution exposure and greater stroke incidence, morbidity, and mortality; however, there is little research on the association of pollutant exposure with poststroke hospital readmission. Methods: We assessed associations of average annual CO, NO 2 , O 3 , PM 2.5 , and SO 2 exposure with 30-day all-cause hospital readmission in US fee-for-service Medicare beneficiaries age ≥65 years hospitalized for ischemic stroke in 2014-2015. We fit Cox models to assess 30-day readmission as a function of these pollutants, adjusted for patient and hospital factors and temperature. We repeated the models stratified by performance of the treating hospital on the Centers for Medicare &amp; Medicaid Services (CMS) risk-standardized 30-day all-cause readmission for stroke measure to determine if the results were independent of performance: Low (CMS rate for hospital &lt;25th percentile of national rate), High (&gt;75 th percentile), and Middle (all others). Results: There were 448148 patients discharged with stroke in 2014-2015 of whom 12.5% were readmitted. Average 2-year CO, O 3 , PM 2.5 , and SO 2 values during the study were below national standards. In adjusted analyses, each 1 standard deviation increase in average annual NO 2 and SO 2 exposure was associated with a 3.6% (95% CI 2.9%-4.4%) and a 2.0% (95% CI 1.1%-3.0%) increased readmission risk within 30-days, respectively ( Table ). Associations between long-term air pollution exposure and increased readmissions persisted across performance categories. Conclusion: Long-term air pollution exposure was associated with increased 30-day readmissions after stroke at pollutant levels below national standards across hospital performance categories. Additional research is needed to determine whether improvements in air quality lead to reductions in poststroke hospital readmissions.

Association between long-term air pollution exposure and COVID-19 mortality in Latin America.

Recent studies have shown a relationship between air pollution and increased vulnerability and mortality due to COVID-19. Most of these studies have looked at developed countries. This study examines the relationship between long-term exposure to air pollution and COVID-19-related deaths in four countries of Latin America that have been highly affected by the pandemic: Brazil, Chile, Colombia, and Mexico. Our results suggest that an increase in long-term exposure of 1 μg/m3 of fine particles is associated with a 2.7 percent increase in the COVID-19 mortality rate. This relationship is found primarily in municipalities of metropolitan areas, where urban air pollution sources dominate, and air quality guidelines are usually exceeded. By focusing the analysis on Latin America, we provide a first glimpse on the role of air pollution as a risk factor for COVID-19 mortality within a context characterized by weak environmental institutions, limited health care capacity and high levels of inequality.

Long-term air pollution exposure and diabetes risk in American older adults: A national secondary data-based cohort study.

Type 2 diabetes is a major public health concern. Several studies have found an increased diabetes risk associated with long-term air pollution exposure. However, most current studies are limited in their generalizability, exposure assessment, or the ability to differentiate incidence and prevalence cases. We assessed the association between air pollution and first documented diabetes occurrence in a national U.S. cohort of older adults to estimate diabetes risk. We included all Medicare enrollees 65 years and older in the fee-for-service program, part A and part B, in the contiguous United States (2000–2016). Participants were followed annually until the first recorded diabetes diagnosis, end of enrollment, or death (264, 869, 458 person-years). We obtained annual estimates of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and warm-months ozone (O3) exposures from highly spatiotemporally resolved prediction models. We assessed the simultaneous effects of the pollutants on diabetes risk using survival analyses. We repeated the models in cohorts restricted to ZIP codes with air pollution levels not exceeding the national ambient air quality standards (NAAQS) during the study period. We identified 10, 024, 879 diabetes cases of 41, 780, 637 people (3.8% of person-years). The hazard ratio (HR) for first diabetes occurrence was 1.074 (95% CI 1.058; 1.089) for 5 μg/m3 increase in PM2.5, 1.055 (95% CI 1.050; 1.060) for 5 ppb increase in NO2, and 0.999 (95% CI 0.993; 1.004) for 5 ppb increase in O3. Both for NO2 and PM2.5 there was evidence of non-linear exposure-response curves with stronger associations at lower levels (NO2 ≤ 36 ppb, PM2.5 ≤ 8.2 μg/m3). Furthermore, associations remained in the restricted low-level cohorts. The O3-diabetes exposure-response relationship differed greatly between models and require further investigation. In conclusion, exposures to PM2.5 and NO2 are associated with increased diabetes risk, even when restricting the exposure to levels below the NAAQS set by the U.S. EPA.

Association between air pollution and telomere length: A study of 471,808 UK Biobank participants

&lt;p&gt;Previous research suggested an association between air pollution and shortened telomere length (TL), a biomarker of oxidative stress and inflammation. However, supporting results are challenged by the small sample size and heterogeneity in participant characteristics. To comprehensively evaluate the association of long-term exposure to air pollution with telomere length, we studied 471,808 participants from UK Biobank with measurements on leukocyte telomere length (LTL). Air pollution data on PM&lt;sub&gt;2.5&lt;/sub&gt;, PM&lt;sub&gt;10&lt;/sub&gt;, NO&lt;sub&gt;2&lt;/sub&gt;, NO&lt;sub&gt;x&lt;/sub&gt;, SO&lt;sub&gt;2&lt;/sub&gt;, and CO before baseline at 1 km spatial resolution were collected and linked to each participant��s residential address. We applied mixed-effects linear regression models to examine the association between long-term air pollution exposure and LTL. Compared to the lowest quartile (Q1) of air pollutants, the estimated percentage changes of age-corrected LTL were -2.71% [95% confidence interval (CI): -3.78, -1.63] for SO&lt;sub&gt;2&lt;/sub&gt;, -0.82% (95% CI: -1.87, 0.23) for NO&lt;sub&gt;2&lt;/sub&gt;, -1.17% (95% CI: -2.23, -0.11) for NO&lt;sub&gt;x&lt;/sub&gt;, and -0.47% (95% CI: -1.45, 0.53) for CO in the highest quartile groups (Q4). Decreasing trends in age-corrected LTL following the increase in PM&lt;sub&gt;2.5&lt;/sub&gt; and PM&lt;sub&gt;10&lt;/sub&gt; leveled off during high levels of air pollutants. Among participants with lower household income, lower educational attainment, and higher BMI, a stronger association was found between air pollution and LTL. Our findings suggest a negative association between air pollution and LTL and provide insights into the potential pathways linking air pollution to age-related diseases.&lt;/p&gt;

Long-Term Air Pollution, Genetic Susceptibility, and the Risk of Depression and Anxiety: A Prospective Study in the UK Biobank Cohort.

Depression and anxiety are two mental disorders that are often comorbid. However, the associations of long-term air pollution exposure with depression and anxiety remain inconclusive. We conducted a cross-sectional and prospective study to examine the associations of ambient exposure to particulate matter (PM) with a diameter of (), (), and (), nitrogen oxides (), and nitrogen dioxide () with the risk of depression and anxiety in the UK Biobank. This study included 398,241 participants from the UK Biobank, 128,456 of whom participated the 7-y online mental health survey. A total of 345,876 individuals were free of depression and anxiety at baseline; of those, 16,185 developed incident mental disorders during a median of 8.7 y of follow-up. Depression and anxiety were assessed using hospital admission records and mental health questionnaires. Associations of air pollution with prevalent and incident mental disorders were examined using logistic regression and Cox regression models, respectively. Elevated levels of the five air pollutants were associated with higher odds of mental disorders at baseline. Levels of four pollutants but not were also associated with higher odds and risks of mental disorders during follow-up; specifically, hazard ratios [HR, 95% confidence interval (CI)] of an interquartile range increase in , , , and for incident mental disorders were 1.03 (95% CI: 1.01, 1.05), 1.06 (95% CI: 1.04, 1.08), 1.03 (95% CI: 1.01, 1.05), and 1.06 (95% CI: 1.04, 1.09), respectively. An air pollution index reflecting combined effects of pollutants also demonstrated a positive association with the risk of mental disorders. HR (95% CI) of incident mental disorders were 1.11 (95% CI: 1.05, 1.18) in the highest quintile group in comparison with the lowest quintile of the air pollution index. We further observed that the associations between air pollution and mental disorders differed by a genetic risk score based on single nucleotide polymorphisms previously associated with genetic susceptibility to mental disorders in the UK Biobank cohort. To our knowledge, this research is one of the largest cohort studies that demonstrates an association between mental health disorders and exposure to long-term air pollution, which could be further enhanced by genetic predisposition. https://doi.org/10.1289/EHP10391.

Residential exposure to air pollution and adverse respiratory and allergic outcomes in children and adolescents living in a chipboard industrial area of Northern Italy

BackgroundChipboard production is a source of wood dust, formaldehyde, and combustion-related pollutants such as nitrogen dioxide (NO2) and particulate matter (PM). In this cohort study, we assessed whether exposures to NO2, formaldehyde, PM10, PM2.5, and black carbon were associated with adverse respiratory and allergic outcomes among all 7525 people aged 0–21 years residing in the Viadana district, an area in Northern Italy including the largest chipboard industrial park in the country. MethodsData on hospitalizations, emergency room (ER) admissions, and specialist visits in pneumology, allergology, ophthalmology, and otorhinolaryngology were obtained from the Local Health Unit. Residential air pollution concentrations in 2013 (baseline) were derived using local (Viadana II), national (EPISAT), and continental (ELAPSE) exposure models. Associations were estimated using negative binomial regression models for counts of events occurred during 2013–2017, with follow-up time as an offset term and adjustment for sex, age, nationality, and a census-block socio-economic indicator. ResultsMedian annual exposures to NO2, PM10, and PM2.5 were below the European Union annual air quality standards (40, 40, and 25 μg/m3) but above the World Health Organization 2021 air quality guideline levels (10, 15, and 5 μg/m3). Exposures to NO2 and PM2.5 were significantly associated with higher rates of ER pneumology admissions (13 to 30 % higher rates per interquartile range exposure differences, all p < 0.01). Higher rates of allergology and ophthalmology visits were found for participants exposed to higher pollutants' concentrations. When considering the 4-km buffer around the industries, associations with respiratory hospitalizations became significant, and associations with ER pneumology admissions, allergology and ophthalmology visits became stronger. Formaldehyde was not associated with the outcomes considered. ConclusionUsing administrative indicators of health effects a priori attributable to air pollution, we documented the adverse impact of long-term air pollution exposure in residential areas close to the largest chipboard industries in Italy. These findings, combined with evidence from previous studies, call for an action to improve air quality through preventive measures especially targeting emissions related to the industrial activities.

Impact of Mobile Monitoring Network Design on Air Pollution Exposure Assessment Models.

Short-term mobile monitoring campaigns are increasingly used to assess long-term air pollution exposure in epidemiology. Little is known about how monitoring network design features, including the number of stops and sampling temporality, impacts exposure assessment models. We address this gap by leveraging an extensive mobile monitoring campaign conducted in the greater Seattle area over the course of a year during all days of the week and most hours. The campaign measured total particle number concentration (PNC; sheds light on ultrafine particulate (UFP) number concentration), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2). In Monte Carlo sampling of 7327 total stops (278 sites × 26 visits each), we restricted the number of sites and visits used to estimate annual averages. Predictions from the all-data campaign performed well, with cross-validated R2s of 0.51-0.77. We found similar model performances (85% of the all-data campaign R2) with ∼1000 to 3000 randomly selected stops for NO2, PNC, and BC, and ∼4000 to 5000 stops for PM2.5 and CO2. Campaigns with additional temporal restrictions (e.g., business hours, rush hours, weekdays, or fewer seasons) had reduced model performances and different spatial surfaces. Mobile monitoring campaigns wanting to assess long-term exposure should carefully consider their monitoring designs.

Associations between air pollution and multimorbidity in the UK Biobank: A cross-sectional study.

Long-term exposure to air pollution concentrations is known to be adversely associated with a broad range of single non-communicable diseases, but its role in multimorbidity has not been investigated in the UK. We aimed to assess associations between long-term air pollution exposure and multimorbidity status, severity, and patterns using the UK Biobank cohort. Multimorbidity status was calculated based on 41 physical and mental conditions. We assessed cross-sectional associations between annual modeled particulate matter (PM)2.5, PMcoarse, PM10, and nitrogen dioxide (NO2) concentrations (μg/m3-modeled to residential address) and multimorbidity status at the baseline assessment (2006-2010) in 364,144 people (mean age: 52.2 ± 8.1 years, 52.6% female). Air pollutants were categorized into quartiles to assess dose-response associations. Among those with multimorbidity (≥2 conditions; n = 156,395) we assessed associations between air pollutant exposure levels and multimorbidity severity and multimorbidity patterns, which were identified using exploratory factor analysis. Associations were explored using generalized linear models adjusted for sociodemographic, behavioral, and environmental indicators. Higher exposures to PM2.5, and NO2 were associated with multimorbidity status in a dose-dependent manner. These associations were strongest when we compared the highest air pollution quartile (quartile 4: Q4) with the lowest quartile (Q1) [PM2.5: adjusted odds ratio (adjOR) = 1.21 (95% CI = 1.18, 1.24); NO2: adjOR = 1.19 (95 % CI = 1.16, 1.23)]. We also observed dose-response associations between air pollutant exposures and multimorbidity severity scores. We identified 11 multimorbidity patterns. Air pollution was associated with several multimorbidity patterns with strongest associations (Q4 vs. Q1) observed for neurological (stroke, epilepsy, alcohol/substance dependency) [PM2.5: adjOR = 1.31 (95% CI = 1.14, 1.51); NO2: adjOR = 1.33 (95% CI = 1.11, 1.60)] and respiratory patterns (COPD, asthma) [PM2.5: adjOR = 1.24 (95% CI = 1.16, 1.33); NO2: adjOR = 1.26 (95% CI = 1.15, 1.38)]. This cross-sectional study provides evidence that exposure to air pollution might be associated with having multimorbid, multi-organ conditions. Longitudinal studies are needed to further explore these associations.