Ambient Particulate Air Pollution Research Articles

Accumulation of Ambient Black Carbon Particles Within Key Memory-Related Brain Regions

Ambient air pollution is a worldwide problem, not only related to respiratory and cardiovascular diseases but also to neurodegenerative disorders. Different pathways on how air pollutants could affect the brain are already known, but direct evidence of the presence of ambient particles (or nanoparticles) in the human adult brain is limited. To examine whether ambient black carbon particles can translocate to the brain and observe their biodistribution within the different brain regions. In this case series a label-free and biocompatible detection technique of nonincandescence-related white light generation was used to screen different regions of biobanked brains of 4 individuals from Belgium with neuropathologically confirmed Alzheimer disease for the presence of black carbon particles. The selected biological specimens were acquired and subsequently stored in a biorepository between April 2013 and April 2017. Black carbon measurements and data analysis were conducted between June 2020 and December 2022. The black carbon load was measured in various human brain regions. A Kruskal-Wallis test was used to compare black carbon loads across these regions, followed by Dunn multiple comparison tests. Black carbon particles were directly visualized in the human brain of 4 individuals (3 women [75%]; mean [SD] age, 86 [13] years). Screening of the postmortem brain regions showed a significantly higher median (IQR) number of black carbon particles present in the thalamus (433.6 [289.5-540.2] particles per mm3), the prefrontal cortex including the olfactory bulb (420.8 [306.6-486.8] particles per mm3), and the hippocampus (364.7 [342.0-448.7] particles per mm3) compared with the cingulate cortex (192.3 [164.2-277.5] particles per mm3), amygdala (217.5 [147.3-244.5] particles per mm3), and the superior temporal gyrus (204.9 [167.9-236.8] particles per mm3). This case series provides evidence that ambient air pollution particles are able to translocate to the human brain and accumulate in multiple brain regions involved in cognitive functioning. This phenomenon may contribute to the onset and development of neurodegenerative disorders.

Improving consistency in estimating future health burdens from environmental risk factors: Case study for ambient air pollution

Future changes in exposure to risk factors should impact mortality rates and population. However, studies commonly use mortality rates and population projections developed exogenously to the health impact assessment model used to quantify future health burdens attributable to environmental risks that are therefore invariant to projected exposure levels. This impacts the robustness of many future health burden estimates for environmental risk factors. This work describes an alternative methodology that more consistently represents the interaction between risk factor exposure, population and mortality rates, using ambient particulate air pollution (PM2.5) as a case study. A demographic model is described that estimates future population based on projected births, mortality and migration. Mortality rates are disaggregated between the fraction due to PM2.5 exposure and other factors for a historic year, and projected independently. Accounting for feedbacks between future risk factor exposure and population and mortality rates can greatly affect estimated future attributable health burdens. The demographic model estimates much larger PM2.5-attributable health burdens with constant 2019 PM2.5 (∼10.8 million deaths in 2050) compared to a model using exogenous population and mortality rate projections (∼7.3 million), largely due to differences in mortality rate projection methods. Demographic model-projected PM2.5-attributable mortality can accumulate substantially over time. For example, ∼71 million more people are estimated to be alive in 2050 when WHO guidelines (5 µg m−3) are achieved compared to constant 2019 PM2.5 concentrations. Accounting for feedbacks is more important in applications with relatively high future PM2.5 concentrations, and relatively large changes in non-PM2.5 mortality rates.

Penalized distributed lag interaction model: Air pollution, birth weight, and neighborhood vulnerability

AbstractMaternal exposure to air pollution during pregnancy has a substantial public health impact. Epidemiological evidence supports an association between maternal exposure to air pollution and low birth weight. A popular method to estimate this association while identifying windows of susceptibility is a distributed lag model (DLM), which regresses an outcome onto exposure history observed at multiple time points. However, the standard DLM framework does not allow for modification of the association between repeated measures of exposure and the outcome. We propose a distributed lag interaction model that allows modification of the exposure‐time‐response associations across individuals by including an interaction between a continuous modifying variable and the exposure history. Our model framework is an extension of a standard DLM that uses a cross‐basis, or bi‐dimensional function space, to simultaneously describe both the modification of the exposure‐response relationship and the temporal structure of the exposure data. Through simulations, we showed that our model with penalization out‐performs a standard DLM when the true exposure‐time‐response associations vary by a continuous variable. Using a Colorado, USA birth cohort, we estimated the association between birth weight and ambient fine particulate matter air pollution modified by an area‐level metric of health and social adversities from Colorado EnviroScreen.

Association between Ambient Particulate Air Pollution and Soluble Biomarkers of Endothelial Function: A Meta-Analysis.

The burden of cardiovascular diseases caused by ambient particulate air pollution is universal. An increasing number of studies have investigated the potential effects of exposure to particulate air pollution on endothelial function, which is one of the important mechanisms for the onset and development of cardiovascular disease. However, no previous study has conducted a summary analysis of the potential effects of particulate air pollution on endothelial function. To summarize the evidence for the potential effects of short-term exposure to ambient particulate air pollution on endothelial function based on existing studies. A systematic literature search on the relationship between ambient particulate air pollution and biomarkers of endothelial function including endothelin-1 (ET-1), E-selectin, intercellular cell adhesion molecule-1 (ICAM-1), and vascular cell adhesion molecule-1 (VCAM-1) was conducted in PubMed, Scopus, EMBASE, and Web of Science up to 20 May 2023. Subsequently, a meta-analysis was conducted using a random effects model. A total of 18 studies were included in this meta-analysis. A 10 μg/m3 increase in short-term exposure to ambient PM2.5 was associated with a 1.55% (95% CI: 0.89%, 2.22%) increase in ICAM-1 and a 1.97% (95% CI: 0.86%, 3.08%) increase in VCAM-1. The associations of ET-1 (0.22%, 95% CI: -4.94%, 5.65%) and E-selectin (3.21%, 95% CI: -0.90% 7.49%) with short-term exposure to ambient PM2.5 were statistically insignificant. Short-term exposure to ambient PM2.5 pollution may significantly increase the levels of typical markers of endothelial function, including ICAM-1 and VCAM-1, suggesting potential endothelial dysfunction following ambient air pollution exposure.

Abstract 12441: Ambient Fine Particulate Matter Air Pollution but Not Ozone is Associated With Elevated Risk of All-Cause Hospitalization in Patients With a Prior Diagnosis of Atrial Fibrillation

Introduction: Short-term increases (days to weeks) in fine particulate matter (PM 2.5 ) air pollution are associated with atrial fibrillation (AF) episodes. We previously reported that PM 2.5 was not associated with severe AF measured as emergency or inpatient hospitalization for the primary diagnosis of AF. Mild AF, though, may influence all-cause hospitalizations. This study evaluated if short-term increases in PM 2.5 and ozone were associated with all-cause hospitalization at 11 Intermountain Health hospitals in patients with AF, and if this differed between wildfire (June-Oct.) and winter inversion (Nov.-March) seasons. Methods: Patients carrying an AF diagnosis (N=347,157) were evaluated for all-cause hospitalization and, secondarily, AF hospitalization (N=25,601) and first AF diagnosis (N=77,893). PM 2.5 levels were measured from January 1, 1999 to March 31, 2022 (ozone: 2006-2022) in subjects residing on Utah’s Wasatch Front. Using the case-crossover design that matched individual-level characteristics, Poisson regression also adjusted for weather variables. Results: Mean age was 72±15 years and 51.0% were female. Overall, PM 2.5 increases at a lag of 0 days, 1 day, 3-day moving average (mAvg) (days 0-2), and 7-day mAvg (days 0-6) were associated with all-cause hospitalization [respectively: odds ratio (OR)=1.010, 1.008, 1.011, 1.009 per +10 μg/m 3 ; p=0.0004, 0.006, 0.0004, 0.028]. Associations differed by seasons (Figure). Regressions entering both ozone and PM 2.5 mildly increased PM 2.5 ORs for all-cause hospitalization. PM 2.5 was not associated with first AF diagnosis. Increases in ozone were not associated with higher risk of any outcome. Conclusions: Short-term increases in PM 2.5 air pollution were associated with all-cause hospitalization in patients with a prior AF diagnosis. Lag structure varied for wildfire and inversion seasons, with greater effect sizes in wildfire season. Increases in ozone did not associate with elevated risk.

Long-term exposure to air pollution and incidence of mental disorders. A large longitudinal cohort study of adults within an urban area

BackgroundRecent 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. MethodsWe 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 (PM2.5), 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. ResultsEach interquartile range (1.13 µg/m3) increase in PM2.5 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. ConclusionsLong-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.

Ambient particulate matter air pollution exposure and ovarian cancer incidence in the USA: An ecological study.

To investigate associations between air particulate matter of ≤2.5 μm in diameter (PM2.5 ) and ovarian cancer. County-level ecological study. Surveillance, epidemiology, and end results from a collection of state-level cancer registries across 744 counties. Data from the Environmental Protection Agency's network for PM2.5 monitoring was used to calculate trailing 5- and 10-year PM2.5 county-level values. County-level data on demographic characteristics were obtained from the American Community Survey. A total of 98 751 patients with histologically confirmed ovarian cancer as a primary malignancy from 2000 to 2016. Generalised linear regression models were developed to estimate the association between PM2.5 and PM10 levels, over 5- and 10-year periods of exposure, and ovarian cancer risk, after accounting for county-level covariates. Risk ratios for associations between ovarian cancer (both overall and specifically epithelial ovarian cancer) and PM2.5 levels. For the 744 counties included, the average PM2.5 level from 1990 through 2018 was 11.75 μg/m3 (SD = 3.7) and the average PM10 level was 22.7 μg/m3 (SD = 5.7). After adjusting for county-level covariates, the overall annualised ovarian cancer incidence was significantly associated with increases in 5-year PM2.5 (RR = 1.11 per 10 units (μg/m3 ) increase, 95% CI 1.06-1.16). Similarly, when the analysis was limited to epithelial cell tumours and adjusted for county-level covariates there was a significant association with trailing 5-year PM2.5 exposure models (RR = 1.12 per 10 units increase, 95% CI 1.08-1.17). Likewise, 10-year PM2.5 exposure was associated with ovarian cancer overall and with epithelial ovarian cancer. Higher county-level ambient PM2.5 levels are associated with 5- and 10-year incidences of ovarian cancer, as measurable in an ecological study.

Effects of long-term exposure to ambient fine particulate air pollution on all-cause mortality in Taiwan

ABSTRACT According to the US Environmental Protection Agency’s Integrated Science Assessment (ISA), there is a causal relationship between fine particulate matter (PM2.5) exposure and increased mortality rates. A similar association was also reported by the International Agency for Research on Cancer (IARC). While many studies are available on this relationship between PM exposure and elevated mortality frequency in Europe and North America, there are limited investigations in Asia. Thus, the aim of this study was to perform an ecological investigation to determine the relationship between exposure to ambient PM2.5 levels and all-cause mortality in 66 in Taiwan municipalities. To undertake this investigation, annual PM2.5 levels and age-standardized all-cause mortality rates were calculated for male and female residents of these areas from 2010 to 2020. Weighted-multiple regression analyses were used to obtain adjusted risk ratio (RR) controlling for possible confounding by urbanization level, physician density, and annual mean household income. Annual PM2.5 levels of each municipality were divided into tertiles. Data demonstrated that men residing in areas with intermediate tertile PM2.5 levels (21.06 to 27.29 µg/m3) and the highest tertiles levels (27.30–33.11 µg/m3) exhibited adjusted RRs of 1.06 (95% CI = 1.03–1.08) and 1.13 (95% CI = 1.10–1.16), respectively. Women in these locations displayed a similar risk, 1.03 (0.99–1.06) and 1.07 (1.04–1.11), respectively. These findings indicate that ambient exposure to PM2.5 increased risk for all-cause mortality rates in both men and women in Taiwan during this time period.

The impacts of reduction in ambient fine particulate (PM2.5) air pollution on life expectancy in Taiwan

The impacts of reduction in ambient fine particulate (PM2.5) air pollution on life expectancy in Taiwan

Residential wood burning in two Swedish cohorts: Public health implications of source-specific particulate ambient air pollution

Residential wood burning in two Swedish cohorts: Public health implications of source-specific particulate ambient air pollution

Pre-admission ambient air pollution and blood soot particles predict hospitalisation outcomes in COVID-19 patients.

Air pollution exposure is one of the major risk factors for aggravation of respiratory diseases. We investigated whether exposure to air pollution and accumulated black carbon (BC) particles in blood were associated with coronavirus disease 2019 (COVID-19) disease severity, including the risk for intensive care unit (ICU) admission and duration of hospitalisation. From May 2020 until March 2021, 328 hospitalised COVID-19 patients (29% at intensive care) were recruited from two hospitals in Belgium. Daily exposure levels (from 2016 to 2019) for particulate matter with aerodynamic diameter <2.5 µm and <10 µm (PM2.5 and PM10, respectively), nitrogen dioxide (NO2) and BC were modelled using a high-resolution spatiotemporal model. Blood BC particles (internal exposure to nano-sized particles) were quantified using pulsed laser illumination. Primary clinical parameters and outcomes included duration of hospitalisation and risk of ICU admission. Independent of potential confounders, an interquartile range (IQR) increase in exposure in the week before admission was associated with increased duration of hospitalisation (PM2.5 +4.13 (95% CI 0.74-7.53) days, PM10 +4.04 (95% CI 1.24-6.83) days and NO2 +4.54 (95% CI 1.53-7.54) days); similar effects were observed for long-term NO2 and BC exposure on hospitalisation duration. These effect sizes for an IQR increase in air pollution on hospitalisation duration were equivalent to the effect of a 10-year increase in age on hospitalisation duration. Furthermore, for an IQR higher blood BC load, the OR for ICU admission was 1.33 (95% CI 1.07-1.65). In hospitalised COVID-19 patients, higher pre-admission ambient air pollution and blood BC levels predicted adverse outcomes. Our findings imply that air pollution exposure influences COVID-19 severity and therefore the burden on medical care systems during the COVID-19 pandemic.

The influence of ambient air pollution on the transmission of tuberculosis in Jiangsu, China.

In this paper, based on the statistical data, we investigate the effects of long-term exposure to ambient particulate air pollution on the transmission dynamics of tuberculosis (TB) in Jiangsu, China by studying the threshold dynamics of the TB epidemic model via the statistical data analytically and numerically. The basic reproduction number reveals that TB in Jiangsu, China is an endemic disease and will persist for a long time. And the numerical results show that, in order to control the TB in Jiangsu effectively, we must decrease the depuration coefficient of PM10 in the body, the proportion of TB symptomatic infectious by direct transmission, the reactivation rate of the pre-symptomatic infectious and the effect coefficient of PM10 and MTB inhaled of TB transmission, and increase the uptake coefficient, the recovery rate of the symptomatic/pre-symptomatic infectious and the influence coefficient of PM10 on the body of mortality. Our study shows that PM10 is closely related to the incidence of TB, and the effective control efforts are suggested to focus on increasing close-contact distance and wearing protective mask to decrease the influence of PM10 on the TB transmission, which may shed a new light on understanding the environmental drivers to TB.

Exposure to ultrafine particles and cognitive decline among older people in the United States

BackgroundSome studies suggest that ambient particulate air pollution is associated with cognitive decline. However, the findings are mixed, and there is no relevant research examining the influences of ultrafine particles (UFP), which may have more toxicity than larger particles. We therefore conducted this study to investigate whether residential UFP exposure is associated with cognitive decline using data from the Alzheimer's Disease Research Centers in the United States. MethodsThis is a longitudinal study of participants who were aged 65 years and older and had normal cognitive status at baseline. Residential UFP exposure, expressed as particle number concentrations (PNC), was assessed in 2016–2017 using a nationwide land use regression model, and was assigned to each participant using their 3-digit residential ZIP codes. Cognitive functions including memory, attention, language, executive function, and global function were assessed annually using 15 neuropsychological tests from March 2015 to February 2022. Linear mixed-effects models were used to examine the associations after adjustment for covariates including baseline age, sex, APOE ε4 status, race, education, smoking status, history of diabetes, quartiles of neighborhood median household income, and interaction terms of follow-up time with each covariate. ResultsThis study included 5646 participants (mean age 76 years, 65% female). On average, each participant had 4 annual visits. When PNC was treated as a continuous variable, there were no statistically or clinically significant changes in annual decline of each cognitive function in relation to an interquartile range elevation in PNC (4026 particles/cm3). Similarly, when PNC was treated as a categorical variable including five exposure groups, there were no linear exposure-response trends in annual decline of each cognitive function across the five exposure groups. ConclusionsThis study found no meaningful associations between residential UFP exposure and cognitive decline in global and domain-specific functions. There is a need for further research that assigns UFP exposure at a finer geographic scale.

Global burden of type 2 diabetes in adolescents and young adults, 1990-2019: systematic analysis of the Global Burden of Disease Study 2019

ObjectiveTo estimate the global burden of type 2 diabetes in adolescents and young adults (aged 15-39 years) from 1990 to 2019.DesignSystematic analysis.Data sourceGlobal Burden of Disease Study 2019. Participants aged...

State-level disparities in burden of ischemic heart diseases mortality attributable to ambient fine particulate matter in the United States, 1990–2019: Observational analysis for the Global Burden of Disease (2019) study

Background: ambient fine particulate air pollution (PM2.5) is an important yet often overlooked risk factor for cardiovascular disease in the United States. However, epidemiologic evidence suggests that current knowledge does not comprehensively capture state-level disparities in mortality for ambient PM2.5-related ischemic heart diseases. MethodsI performed serial cross sectional analysis with ambient PM2.5-attributable ischemic heart diseases decedents between 1990 and 2019. I used the Global Health Data Exchange to extract age adjusted mortality rates in each state, and used Joinpoint software to calculate average annual percentage change.Results: the average age adjusted mortality rates (AAMR) for ischemic heart diseases attributable to ambient PM2.5 for both sex ranged from 1.5 (1.3–1.8) per 100,000 in Hawaii to 1.1 (0.9–1.3) per 100,000 in Mississippi, respectively. The rate of change in AAMR varied widely across states. Compared with the national level, the number of states with smaller decline has increased from 3 before 2002 to 10 after 2011. Oregon, Idaho, and Montana showed an upward trend in AAMR, which was independent of sex after 2011. Between 1990 and 2019, average AAMR for ischemic heart diseases attributable to ambient PM2.5 was higher for male than female in all states. But the number of states with sex disparities in the rate of change has decreased, from 13 in 1990–2002 to 5 after 2011.Conclusions: wide state-level disparities exist in burden of ischemic heart diseases attributable to ambient PM2.5. After 2011, the association between rate of AAMR decline and sex weakened across states. State-level policies and programs are needed to address the avoidable burden of ischemic heart diseases deaths attributable to ambient particulate pollution, with priority for states with a higher burden.

Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods

Air pollution exposure has been linked to various diseases, including dementia. However, a novel method for investigating the associations between air pollution exposure and disease is lacking. The objective of this study was to investigate whether long-term exposure to ambient particulate air pollution increases dementia risk using both the traditional Cox model approach and a novel machine learning (ML) with random forest (RF) method. We used health data from a national population-based cohort in Taiwan from 2000 to 2017. We collected the following ambient air pollution data from the Taiwan Environmental Protection Administration (EPA): fine particulate matter (PM2.5) and gaseous pollutants, including sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3), nitrogen oxide (NOx), nitric oxide (NO), and nitrogen dioxide (NO2). Spatiotemporal-estimated air quality data calculated based on a geostatistical approach, namely, the Bayesian maximum entropy method, were collected. Each subject's residential county and township were reviewed monthly and linked to air quality data based on the corresponding township and month of the year for each subject. The Cox model approach and the ML with RF method were used. Increasing the concentration of PM2.5 by one interquartile range (IQR) increased the risk of dementia by approximately 5% (HR = 1.05 with 95% CI = 1.04–1.05). The comparison of the performance of the extended Cox model approach with the RF method showed that the prediction accuracy was approximately 0.7 by the RF method, but the AUC was lower than that of the Cox model approach. This national cohort study over an 18-year period provides supporting evidence that long-term particulate air pollution exposure is associated with increased dementia risk in Taiwan. The ML with RF method appears to be an acceptable approach for exploring associations between air pollutant exposure and disease.

Long-term Exposure to Ambient PM2.5 and Self-Reported Health: Evidence from Longitudinally-linked Census Data.

ObjectivesThis paper estimates associations between long-term exposure to ambient particulate air pollution and 13 self-reported health outcomes including poor general health, chronic illness, experiencing long-running difficulties with breathing, and experiencing frequent periods of confusion or memory loss. It examines the extent to which these associations are explained by confounding factors.&#x0D; ApproachLongitudinally-linked Census data from the Northern Ireland Longitudinal Study linked to data on annual average particulate (PM2.5) concentrations at the 1km grid-square level over the period 2002-2010, exploiting complete residential histories, are used. The paper controls for potentially confounding factors at the neighbourhood, household and individual level, including for prior health, in a multivariate regression framework. Robustness to the presence of remaining unobserved confounders is assessed in two extensions, first by assuming selection on unobservables is proportional to selection on observables, and second through inclusion of an extensive set of fixed effects in the model.&#x0D; ResultsThere are strong statistical associations between long-term exposure to ambient particulate pollution and all 13 health outcomes measured by the 2011 Northern Ireland Census. Most of these estimated associations survive conditioning on an extensive set of observable controls. Of these, however, only two associations – with chronic illness and with long-running breathing difficulties, where we might expect the strongest causal effects – survive further analysis designed to elicit robustness to selection on unobservables. The estimated magnitudes of these remaining effects are non-trivial. For example, a 5 µg/m3 difference in particulate exposure averaged over 9 years has a similar magnitude effect on the probability of reporting long-running breathing difficulties as the difference between those aged in their 20s and those in their 40s.&#x0D; ConclusionsThis study provides evidence of substantial effects of long-term exposure to ambient particulates on the probabilities of experiencing chronic illness and long-running breathing difficulties. These are qualitatively robust to both observed and unobserved confounders. Associations between particulate exposure and other health outcomes in the study are shown to reflect confounders.

The impacts of reduction in ambient fine particulate (PM2.5) air pollution on life expectancy in Taiwan

ABSTRACT Fine particulate matter, particles less than 2.5 um in diameter (PM2.5), is an important environmental human health factor to consider. The long- and short-term influence of PM2.5 on health has been extensively studied in relation to many health outcomes, although few investigations examined the consequences of chronic ambient PM2.5 on life expectancy, which constitutes an important gauge of public human health status. Therefore, the aim of this study was to investigate the effects of reducing ambient PM2.5 levels in Taiwan on life expectancy there from 2000 to 2020. Officially reported island-wide annually average concentrations of ambient PM2.5, county-level life expectancies, and demographic and socioeconomic and proxy variable were collected for the prevalence of smoking from various national public agencies and organizations, since variables these might potentially confound life expectancy results. The relationship between changes in ambient PM2.5 levels and life expectancy were determined using linear regression. Data demonstrated that counties with greater reductions in ambient PM2.5 concentrations were associated with higher life expectancies. Adjusting for alterations in demographic and socioeconomic variables and proxy parameter, the prevalence of smoking data from a multiple regression model, it was found that a 0.3-year rise in life expectancy was noted for each 10 ug/m3 decrease in PM2.5 in those counties. Our findings show that reducing ambient PM2.5 levels play an important role for prolongation of life expectancy in Taiwan.

Spatial environmental factors predict cardiovascular and all-cause mortality: Results of the SPACE study.

BackgroundEnvironmental exposures account for a growing proportion of global mortality. Large cohort studies are needed to characterize the independent impact of environmental exposures on mortality in low-income settings.MethodsWe collected data on individual and environmental risk factors for a multiethnic cohort of 50,045 individuals in a low-income region in Iran. Environmental risk factors included: ambient fine particular matter air pollution; household fuel use and ventilation; proximity to traffic; distance to percutaneous coronary intervention (PCI) center; socioeconomic environment; population density; local land use; and nighttime light exposure. We developed a spatial survival model to estimate the independent associations between these environmental exposures and all-cause and cardiovascular mortality.FindingsSeveral environmental factors demonstrated associations with mortality after adjusting for individual risk factors. Ambient fine particulate matter air pollution predicted all-cause mortality (per μg/m3, HR 1.20, 95% CI 1.07, 1.36) and cardiovascular mortality (HR 1.17, 95% CI 0.98, 1.39). Biomass fuel use without chimney predicted all-cause mortality (reference = gas, HR 1.23, 95% CI 0.99, 1.53) and cardiovascular mortality (HR 1.36, 95% CI 0.99, 1.87). Kerosene fuel use without chimney predicted all-cause mortality (reference = gas, HR 1.09, 95% CI 0.97, 1.23) and cardiovascular mortality (HR 1.19, 95% CI 1.01, 1.41). Distance to PCI center predicted all-cause mortality (per 10km, HR 1.01, 95% CI 1.004, 1.022) and cardiovascular mortality (HR 1.02, 95% CI 1.004, 1.031). Additionally, proximity to traffic predicted all-cause mortality (HR 1.13, 95% CI 1.01, 1.27). In a separate validation cohort, the multivariable model effectively predicted both all-cause mortality (AUC 0.76) and cardiovascular mortality (AUC 0.81). Population attributable fractions demonstrated a high mortality burden attributable to environmental exposures.InterpretationSeveral environmental factors predicted cardiovascular and all-cause mortality, independent of each other and of individual risk factors. Mortality attributable to environmental factors represents a critical opportunity for targeted policies and programs.

Ambient particulate matter air pollution and stillbirth in Ghana: A difference-in-differences approach

Sub-Saharan African countries, including Ghana, are known hotspots for fine particulate matter air pollution (PM2.5) and stillbirths but lacked epidemiologic evidence. We investigated the association between PM2.5 and stillbirth in Ghana. District-level stillbirth data were obtained from the Ghana Health Service for all 260 local districts from 2012 to 2019 for a total of 5,229,338 births, including 81,611 stillbirths. Spatiotemporal datasets, including satellite-derived PM2.5, temperature, population density, and gross domestic product were linked with the birth data. We applied a variant difference-in-differences design with conditional quasi-Poisson regression to estimate the risk of stillbirth associated with annual PM2.5 concentrations. We adjusted for relevant environmental and sociodemographic factors and performed subgroup analyses by population density and household air pollution. The average district-level annual stillbirth incidence was 29 (standard deviation = 55) per 1000 births. The annual average PM2.5 concentration was 59.97 μg/m3 (standard deviation = 9.75). Every 10 μg/m3 increment in annual average PM2.5 was associated with a 3% risk of stillbirth (RR); 1.03 (95% CI: 0.97, 1.09) for all-source PM2.5 and 2% risk each for anthropogenic (RR = 1.02, 95% CI: 0.96, 1.07) and natural (RR = 1.02, 95% CI: 0.94, 1.11) sources. The association was higher for moderate or high subgroup, relative to low subgroup and higher in natural than anthropogenic sources of PM2.5 exposures. Thus, there was some evidence for an adverse association between PM2.5 exposure and stillbirth but estimates were less precise. Given that the district-level variation may be underpowered, stronger risk is expected in future high-quality individual-level longitudinal cohort studies in Ghana.