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Background: Numerous studies have linked fine particulate matter (PM2.5) to increased cardiovascular mortality. Less is known how the PM2.5-cardiovascular mortality association varies by use of cardiovascular medications. This study sought to quantify effect modification by statin use status on the associations between long-term exposure to PM2.5 and mortality from any cardiovascular cause, coronary heart disease (CHD), and stroke., Methods: In this nested case-control study, we followed 1.2 million community-dwelling adults aged ≥66 years who lived in Ontario, Canada from 2000 through 2018. Cases were patients who died from the three causes. Each case was individually matched to up to 30 randomly selected controls using incidence density sampling. Conditional logistic regression models were used to estimate odds ratios (ORs) for the associations between PM2.5 and mortality. We evaluated the presence of effect modification considering both multiplicative (ratio of ORs) and additive scales (the relative excess risk due to interaction, RERI)., Results: Exposure to PM2.5 increased the risks for cardiovascular, CHD, and stroke mortality. For all three causes of death, compared with statin users, stronger PM2.5-mortality associations were observed among non-users [e.g. for cardiovascular mortality corresponding to each interquartile range increase in PM2.5, OR = 1.042 (95% CI, 1.032-1.053) vs OR = 1.009 (95% CI, 0.996-1.022) in users, ratio of ORs = 1.033 (95% CI, 1.019-1.047), RERI = 0.039 (95% CI, 0.025-0.050)]. Among users, partially adherent users exhibited a higher risk of PM2.5-associated mortality than fully adherent users., Conclusions: The associations of chronic exposure to PM2.5 with cardiovascular and CHD mortality were stronger among statin non-users compared to users., (© The Author(s) 2024. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: Epidemiological findings regarding the association of particulate matter ≤2.5 μm (PM2.5) exposure with hypertensive disorders in pregnancy (HDP) are inconsistent; evidence for HDP risk related to PM2.5 components, mixture effects, and windows of susceptibility is limited. We aimed to investigate the relationships between HDP and exposure to PM2.5 during pregnancy., Methods and Findings: A large retrospective cohort study was conducted among mothers with singleton pregnancies in Kaiser Permanente Southern California from 2008 to 2017. HDP were defined by International Classification of Diseases-9/10 (ICD-9/10) diagnostic codes and were classified into 2 subcategories based on the severity of HDP: gestational hypertension (GH) and preeclampsia and eclampsia (PE-E). Monthly averages of PM2.5 total mass and its constituents (i.e., sulfate, nitrate, ammonium, organic matter, and black carbon) were estimated using outputs from a fine-resolution geoscience-derived model. Multilevel Cox proportional hazard models were used to fit single-pollutant models; quantile g-computation approach was applied to estimate the joint effect of PM2.5 constituents. The distributed lag model was applied to estimate the association between monthly PM2.5 exposure and HDP risk. This study included 386,361 participants (30.3 ± 6.1 years) with 4.8% (17,977/373,905) GH and 5.0% (19,381/386,361) PE-E cases, respectively. In single-pollutant models, we observed increased relative risks for PE-E associated with exposures to PM2.5 total mass [adjusted hazard ratio (HR) per interquartile range: 1.07, 95% confidence interval (CI) [1.04, 1.10] p < 0.001], black carbon [HR = 1.12 (95% CI [1.08, 1.16] p < 0.001)] and organic matter [HR = 1.06 (95% CI [1.03, 1.09] p < 0.001)], but not for GH. The population attributable fraction for PE-E corresponding to the standards of the US Environmental Protection Agency (9 μg/m3) was 6.37%. In multi-pollutant models, the PM2.5 mixture was associated with an increased relative risk of PE-E ([HR = 1.05 (95% CI [1.03, 1.07] p < 0.001)], simultaneous increase in PM2.5 constituents of interest by a quartile) and PM2.5 black carbon gave the greatest contribution of the overall mixture effects (71%) among all individual constituents. The susceptible window is the late first trimester and second trimester. Furthermore, the risks of PE-E associated with PM2.5 exposure were significantly higher among Hispanic and African American mothers and mothers who live in low- to middle-income neighborhoods (p < 0.05 for Cochran's Q test). Study limitations include potential exposure misclassification solely based on residential outdoor air pollution, misclassification of disease status defined by ICD codes, the date of diagnosis not reflecting the actual time of onset, and lack of information on potential covariates and unmeasured factors for HDP., Conclusions: Our findings add to the literature on associations between air pollution exposure and HDP. To our knowledge, this is the first study reporting that specific air pollution components, mixture effects, and susceptible windows of PM2.5 may affect GH and PE-E differently., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Sun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Emissions of fine particulate matter (PM 2.5 ) from human activities have been linked to substantial disease burdens, but evidence regarding how reducing PM 2.5 at its sources would improve public health is sparse. We followed a population-based cohort of 2.7 million adults across Canada from 2007 through 2016. For each participant, we estimated annual mean concentrations of PM 2.5 and the fractional contributions to PM 2.5 from the five leading anthropogenic sources at their residential address using satellite observations in combination with a global atmospheric chemistry transport model. For each source, we estimated the causal effects of six hypothetical interventions on 10-y nonaccidental mortality risk using the parametric g-formula, a structural causal model. We conducted stratified analyses by age, sex, and income. This cohort would have experienced tangible health gains had contributions to PM 2.5 from any of the five sources been reduced. Compared with no intervention, a 10% annual reduction in PM 2.5 contributions from transportation and power generation, Canada's largest and fifth-largest anthropogenic sources, would have prevented approximately 175 (95%CI: 123-226) and 90 (95%CI: 63-117) deaths per million by 2016, respectively. A more intensive 50% reduction per year in PM 2.5 contributions from the two sources would have averted 360 and 185 deaths per million, respectively, by 2016. The potential health benefits were greater among men, older adults, and low-income earners. In Canada, where PM 2.5 levels are among the lowest worldwide, reducing PM 2.5 contributions from anthropogenic sources by as little as 10% annually would yield meaningful health gains.

Limited previous work has identified a relationship between exposure to ambient air pollution and aggressive somatic lung tumor mutations. More work is needed to confirm this relationship, especially using spatially resolved air pollution. We aimed to quantify the association between different air pollution metrics and aggressive tumor biology. Among patients treated at City of Hope Comprehensive Cancer Center in Duarte, CA (2013-2018), three non-small cell lung cancer somatic tumor mutations, TP53 , KRAS , and KRAS G12C / V , were documented. PM 2.5 exposure was assessed using state-of-the art ensemble models five and ten years before lung cancer diagnosis. We also explored the role of NO 2 using inverse-distance-weighting approaches. We fitted logistic regression models to estimate odds ratio (OR) and their 95% confidence intervals (CIs). Among 435 participants (median age: 67, female: 51%), an IQR increase in NO 2 exposure (3.5 μg/m 3 ) five years before cancer diagnosis was associated with an increased risk in TP53 mutation (OR, 95% CI: 1.30, 0.99-1.71). We found an association between highly-exposed participants to PM 2.5 (>12 μg/m 3 ) five and ten years before cancer diagnosis and TP53 mutation (OR, 95% CI: 1.61, 0.95-2.73; 1.57, 0.93-2.64, respectively). Future studies are needed to confirm this association and better understand how air pollution impacts somatic profiles and the molecular mechanisms through which they operate.

Background: Maternal gestational PM 2.5 exposure was associated with small for gestational age (SGA). Identifying potential mediating factors may help design preventive strategies to reduce this risk., Objective: This study aimed to explore the roles of maternal blood pressure and hemoglobin may play in the PM 2.5 exposure and SGA relationship among 117,162 births in 16 counties across China during 2014-2018., Methods: Daily PM 2.5 concentration was collected from China National Environmental Monitoring Center. According to maternal residency during pregnancy, the PM 2.5 exposure for each trimester and the whole pregnancy was assessed using an inverse-distance weighting approach. Repeated measurements of maternal blood pressure and hemoglobin during pregnancy were collected for each woman. We estimated the total effect of gestational PM 2.5 exposure on SGA, and further tested the mediation effects of maternal blood pressure and hemoglobin concentration during pregnancy., Results: Of 117,162 included mother-infant pairs, 11,361 (9.7 %) were SGA. The odds ratios of SGA associated with PM 2.5 exposure (per 10 μg/m 3 increase) in the second trimester and the whole pregnancy were 1.023 (95 % CI: 1.009, 1.037) and 1.024 (1.001, 1.048), respectively. We identified the independent mediating effect of blood pressure and hemoglobin in the second and third trimesters, with the proportion of mediation ranging from 1.64 % to 5.78 % and 2.40 % to 8.70 %, respectively. When considering the mediators jointly, we found a stronger mediating effect with a proportion of mediation ranging from 3.93 % to 13.69 %., Discussion: Increases in maternal blood pressure and hemoglobin in the second and third trimesters can independently and jointly mediate the effects of gestational PM 2.5 exposure on SGA. Monitoring and managing maternal blood pressure and hemoglobin during prenatal care may constitute a promising avenue to reducing SGA risk associated with gestational PM 2.5 exposure., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: To investigate the association between changes in long term residential exposure to ambient fine particulate matter (PM 2.5 ) and premature mortality in Canada., Design: Population based quasi-experimental study., Setting: Canada., Participants: 663 100 respondents to the 1996, 2001, and 2006 Canadian censuses aged 25-89 years who had consistently lived in areas with either high or low PM 2.5 levels over five years preceding census day and moved during the ensuing five years., Interventions: Changes in long term exposure to PM 2.5 arising from residential mobility., Main Outcome Measures: The primary outcome was deaths from natural causes. Secondary outcomes were deaths from any cardiometabolic cause, any respiratory cause, and any cancer cause. All outcomes were obtained from the national vital statistics database., Results: Using a propensity score matching technique with numerous personal, socioeconomic, health, and environment related covariates, each participant who moved to a different PM 2.5 area was matched with up to three participants who moved within the same PM 2.5 area. In the matched groups that moved from high to intermediate or low PM 2.5 areas, residential mobility was associated with a decline in annual PM 2.5 exposure from 10.6 μg/m 3 to 7.4 and 5.0 μg/m 3 , respectively. Conversely, in the matched groups that moved from low to intermediate or high PM 2.5 areas, annual PM 2.5 increased from 4.6 μg/m 3 to 6.7 and 9.2 μg/m 3 . Five years after moving, individuals who experienced a reduction in exposure to PM 2.5 from high to intermediate levels showed a 6.8% (95% confidence interval 1.7% to 11.7%) reduction in mortality (2510 deaths in 56 025 v 4925 deaths in 101 960). A greater decline in mortality occurred among those exposed to a larger reduction in PM 2.5 . Increased mortality was found with exposure to PM 2.5 from low to high levels, and to a lesser degree from low to intermediate levels. Furthermore, the decreases in PM 2.5 exposure were most strongly associated with reductions in cardiometabolic deaths, whereas the increases in PM 2.5 exposure were mostly related to respiratory deaths. No strong evidence was found for the changes in PM 2.5 exposure with cancer related deaths., Conclusions: In Canada, decreases in PM 2.5 were associated with lower mortality, whereas increases in PM 2.5 were associated with higher mortality. These results were observed at PM 2.5 levels considerably lower than many other countries, providing support for continuously improving air quality., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from funded by Health Canada for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background and Objectives: Exposure to airborne fine particles with diameters ≤2.5 μm (PM 2.5 ) pollution is a well-established cause of respiratory diseases in children; whether wildfire-specific PM 2.5 causes more damage, however, remains uncertain. We examine the associations between wildfire-specific PM 2.5 and pediatric respiratory health during the period 2011-2017 in San Diego County, California, and compare these results with other sources of PM 2.5 ., Methods: Visits to emergency and urgent care facilities of Rady's Children Hospital network in San Diego County, California, by individuals (aged ≤19 years) with ≥1 of the following respiratory conditions: difficulty breathing, respiratory distress, wheezing, asthma, or cough were regressed on daily, community-level exposure to wildfire-specific PM 2.5 and PM 2.5 from ambient sources (eg, traffic emissions)., Results: A 10-unit increase in PM 2.5 (from nonsmoke sources) was estimated to increase the number of admissions by 3.7% (95% confidence interval: 1.2% to 6.1%). In contrast, the effect of PM 2.5 attributable to wildfire was estimated to be a 30.0% (95% confidence interval: 26.6% to 33.4%) increase in visits., Conclusions: Wildfire-specific PM 2.5 was found to be ∼10 times more harmful on children's respiratory health than PM 2.5 from other sources, particularly for children aged 0 to 5 years. Even relatively modest wildfires and associated PM 2.5 resolved on our record produced major health impacts, particularly for younger children, in comparison with ambient PM 2.5 ., Competing Interests: POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose., (Copyright © 2021 by the American Academy of Pediatrics.)

Wildfires are becoming more frequent and destructive in a changing climate. Fine particulate matter, PM 2.5 , in wildfire smoke adversely impacts human health. Recent toxicological studies suggest that wildfire particulate matter may be more toxic than equal doses of ambient PM 2.5 . Air quality regulations however assume that the toxicity of PM 2.5 does not vary across different sources of emission. Assessing whether PM 2.5 from wildfires is more or less harmful than PM 2.5 from other sources is a pressing public health concern. Here, we isolate the wildfire-specific PM 2.5 using a series of statistical approaches and exposure definitions. We found increases in respiratory hospitalizations ranging from 1.3 to up to 10% with a 10 μg m -3 increase in wildfire-specific PM 2.5 , compared to 0.67 to 1.3% associated with non-wildfire PM 2.5 . Our conclusions point to the need for air quality policies to consider the variability in PM 2.5 impacts on human health according to the sources of emission.

Ambient air pollution exposure is associated with exacerbating respiratory illnesses. Race/ethnicity (R/E) have been shown to influence an individual's vulnerability to environmental health risks such as fine particles (PM 2.5). This study aims to assess the R/E disparities in vulnerability to air pollution with regards to respiratory hospital admissions in San Diego County, California where most days fall below National Ambient Air Quality Standards (NAAQS) for daily PM 2.5 concentrations. Daily PM 2.5 levels were estimated at the zip code level using a spatial interpolation using inverse-distance weighting from monitor networks. The association between daily PM 2.5 levels and respiratory hospital admissions in San Diego County over a 15-year period from 1999 to 2013 was assessed with a time-series analysis using a multi-level Poisson regression model. Cochran Q tests were used to assess the effect modification of race/ethnicity on this association. Daily fine particle levels varied greatly from 1 μg/m 3 to 75.86 μg/m 3 (SD = 6.08 μg/m 3 ) with the majority of days falling below 24-hour NAAQS for PM 2.5 of 35 μg/m 3 . For every 10 μg/m 3 increase in PM 2.5 levels, Black and White individuals had higher rates (8.6% and 6.2%, respectively) of hospitalization for respiratory admissions than observed in the county as a whole (4.1%). Increases in PM 2.5 levels drive an overall increase in respiratory hospital admissions with a disparate burden of health effects by R/E group. These findings suggest an opportunity to design interventions that address the unequal burden of air pollution among vulnerable communities in San Diego County that exist even below NAAQS for daily PM 2.5 concentrations.

The coronavirus disease 2019 (COVID-19) pandemic revealed and exacerbated existing social and economic health disparities, and actionable epidemiologic evidence is needed to identify potential vulnerability factors to help inform targeted responses. In this commentary, methodological challenges and opportunities regarding the links between air pollution and COVID-19 are discussed with a focus on 2 factors: 1) the role of differential exposure to air pollution across populations as an explanation for spatiotemporal variability of the epidemic spread and resultant mortality; and 2) the indirect impacts of interventions to control COVID-19 person-to-person spread treated as natural experiments on air pollution and population health. I first discuss the potential mechanisms between exposure to air pollution and COVID-19 and the opportunity to clearly formulate causal questions of interest through the target trial framework. Then, I discuss challenges regarding the use of quasiexperimental designs that capitalize on the differential timing of COVID-19 policies including the selection of control groups and potential violations of the common shock assumption. Finally, I discuss environmental justice implications of this many-headed beast of a crisis., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

This study aims to quantify changes in outdoor (ambient) air pollution exposure from different migration patterns within Peru and quantify its effect on premature mortality. Data on ambient fine particulate matter (PM 2.5 ) was obtained from the National Aeronautics and Space Administration (NASA). Census data was used to calculate rates of within-country migration at the district level. We calculated differences in PM 2.5 exposure between "current" (2016-2017) and "origin" (2012) districts for each migration patterns. Using an exposure-response relationship for PM 2.5 extracted from a meta-analysis, and mortality rates from the Peruvian Ministry of Health, we quantified premature mortality attributable to each migration pattern. Changes in outdoor PM 2.5 exposure were observed between 2012 and 2016 with highest levels of PM 2.5 in the Department of Lima. A strong spatial autocorrelation of outdoor PM 2.5 values (Moran's I = 0.847, p-value=0.001) was observed. In Greater Lima, rural-to-urban and urban-to-urban migrants experienced 10-fold increases in outdoor PM 2.5 exposure in comparison with non-migrants. Changes in outdoor PM 2.5 exposure due to migration drove 137.1 (95%CI: 93.2, 179.4) premature deaths related to air pollution, with rural-urban producing the highest risk of mortality from exposure to higher levels of ambient air pollution. Our results demonstrate that the rural-urban and urban-urban migrant groups have higher rates of air pollution-related deaths.

Rationale: There is significant evidence of increased healthcare utilization from cardiopulmonary causes in adults from exposure to wildfire smoke, but evidence in pediatric age groups is limited. Objectives: To quantify and examine the healthcare utilization effects of the December 2017 Lilac Fire in San Diego County among pediatric patients at the Rady Children's Hospital (RCH) emergency department and urgent care (UC) clinics. Methods: Using data from 2011 to 2017, including data on daily particulate matter <2.5 μm (PM 2.5 ) in an inverse-distance interpolation model and RCH electronic medical records, we retrospectively analyzed pediatric respiratory visits at the RCH emergency department and UC clinics during the Santa Ana wind (SAW)-driven Lilac Fire from December 7 to 16, 2017. An interrupted time series study design was applied as our primary analysis to compare the observed pediatric respiratory visits from December 7 to 16, 2017 to what would have occurred in a counterfactual situation, namely, if the Lilac Fire had not occurred. A complementary descriptive spatial analysis was also used to evaluate the geographic distribution of respiratory visits in relationship to satellite imaging of the Lilac Fire and the associated wind pattern. Results: The Lilac Fire was associated with 16.0 (95% confidence interval [CI], 11.2-20.9) excess respiratory visits per day at the RCH emergency department across all pediatric age groups. Children aged 0 to 5 years had the highest absolute excess respiratory visits per day with 7.3 (95% CI, 3.0-11.7), whereas those aged 6 to 12 years had the highest relative increase in visits, with 3.4 (95% CI, 2.3-4.6). RCH UC clinics had similar results. The top five ZIP codes in San Diego County with the highest standard deviations of age-adjusted respiratory visits were all located generally downwind of the fire perimeter, as expected for the SAW pattern. Conclusions: We have demonstrated an increase in pediatric respiratory visits during the SAW-driven Lilac Fire in San Diego County in a patterned geographic distribution that is attributable to an increase in PM 2.5 exposure. Younger children were particularly affected. Climate change is expected to result in more frequent and extensive wildfires in the region and will require greater preparedness and adaptation efforts to protect vulnerable populations, such as young children.

Background: Evidence suggests a link between air pollution and dementia. Cardiovascular disease (CVD) may be a potential determinant of dementia. This motivated us to quantify the contribution of CVD to the association between air pollution and dementia., Methods: A cohort of Canadian-born residents of Ontario, who participated in the 1996-2003 Canadian Community Health Surveys, was followed through 2013 or until dementia diagnosis. Exposure to nitrogen dioxide (NO2) and fine particulate matter (PM2.5) was estimated with a 3-year average and 5-year lag before dementia diagnosis. Incident CVD was evaluated as a mediator. We used multi-level Cox proportional and Aalen additive hazard regression models, adjusting for individual- and neighbourhood-level risk factors to estimate associations with NO2 and PM2.5. We estimated the total, direct and indirect effects of air pollution on dementia through cardiovascular disease., Results: This study included 34 391 older adults. At baseline, the mean age of this cohort was 59 years. The risk of dementia was moderately higher among those more exposed to NO2 (hazard ratio (HR) 1.10, 95% confidence interval (CI) 0.99-1.19; and 100 additional cases per 100 000 [standard error (SE) <100x10-5]) and PM2.5 [(HR 1.29, 95% CI 0.99-1.64; 200 additional cases per 100 000] [SE 100x10-5]) after adjusting for covariates; however, these estimates are imprecise. A greater proportion of the relationship between PM2.5 and dementia was mediated through CVD than NO2 for both scales., Conclusions: These results suggest some of the association between air pollution and dementia is mediated through CVD, indicating that improving cardiovascular health may prevent dementia in areas with higher exposure to air pollution., (© The Author(s) 2019; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: Both extreme heat and air pollution exposure during pregnancy have been associated with preterm birth; however, their combined effects are unclear., Objectives: Our goal was to estimate the independent and joint effects of heatwaves and fine particulate matter [PM < 2.5 μ m in aerodynamic diameter ( PM 2.5 )], exposure during the final gestational week on preterm birth., Methods: Using birth registry data from Guangzhou, China, we included 215,059 singleton live births in the warm season (1 May-31 October) between January 2015 and July 2017. Daily meteorological variables from 5 monitoring stations and PM 2.5 concentrations from 11 sites were used to estimate district-specific exposures. A series of cut off temperature thresholds and durations (2, 3, and 4 consecutive d) were used to define 15 different heatwaves. Cox proportional hazard models were used to estimate the effects of heatwaves and PM 2.5 exposures during the final week on preterm birth, and departures from additive joint effects were assessed using the relative excess risk due to interaction (RERI)., Results: Numbers of preterm births increased in association with heatwave exposures during the final gestational week. Depending on the heatwave definition used, hazard ratios (HRs) ranged from 1.10 (95% CI: 1.01, 1.20) to 1.92 (1.39, 2.64). Associations were stronger for more intense heatwaves. Combined effects of PM 2.5 exposures and heatwaves appeared to be synergistic ( RERIs > 0 ) for less extreme heatwaves (i.e., shorter or with relatively low temperature thresholds) but were less than additive ( RERIs < 0 ) for more intense heatwaves., Conclusions: Our research strengthens the evidence that exposure to heatwaves during the final gestational week can independently trigger preterm birth. Moderate heatwaves may also act synergistically with PM 2.5 exposure to increase risk of preterm birth, which adds new evidence to the current understanding of combined effects of air pollution and meteorological variables on adverse birth outcomes. https://doi.org/10.1289/EHP5117.

Background: Short-term exposure to PM 2.5 has been shown to be associated with changes in blood pressure. However, most of the evidence is based on PM 2.5 measurements from fixed stations and resting blood pressure measured at a regular time., Objectives: To evaluate the short-term daily and hourly effects of real-time personal PM 2.5 exposure on ambulatory blood pressure, and to compare the effects with those of PM 2.5 exposure from fixed stations., Methods: Between April 2017 and December 2017, 37 young adults were recruited in a panel study from a central urban area and a suburban area, to measure personal hourly PM 2.5 and ambulatory systolic blood pressure (SBP) as well as diastolic blood pressure (DBP) for three consecutive days. Hourly PM 2.5 concentrations were also obtained from the nearest monitoring station operated by Guangdong Environmental Monitoring Center. Generalized additive mixed model was employed to evaluate the effects of PM 2.5 on ambulatory blood pressure., Results: During the study period, the mean concentration of personal PM 2.5 exposure was 60.30 ± 52.14 μg/m 3 , while the value of PM 2.5 from fixed stations was 36.77 ± 21.52 μg/m 3 . Both personal PM 2.5 exposure and exposure from fixed stations averaged over the previous 1 to 3 days decreased blood pressure. During daytime, a 10 μg/m 3 increase in 1-day moving average of personal PM 2.5 was associated with a 0.54 mmHg (95% CI: -1.03, -0.05) and 0.22 mmHg (95% CI: -0.59, 0.15) decrease in SBP and DBP, respectively. When using PM 2.5 exposures from fixed stations, the decrease in SBP and DBP were 0.95 mmHg (95% CI: -1.82, -0.07) and 0.74 mmHg (95% CI: -1.46, -0.03). Stratified analysis showed stronger effects in the central urban area and among males., Conclusions: Both personal PM 2.5 exposure and exposure from fixed stations averaged over the previous 1 to 3 days decreased blood pressure. Stronger effects were found in a central urban area and among males., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Exposure to ambient fine particulate matter (particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5)) during pregnancy is associated with preterm birth (PTB), a leading cause of infant morbidity and mortality. Results from studies attempting to identify etiologically relevant exposure periods of vulnerability have been inconsistent, possibly because of failure to consider the time-to-event nature of the outcome and lagged exposure effects of PM2.5. In this study, we aimed to identify critical exposure windows for weekly PM2.5 exposure and PTB in California using California birth cohort data from 2005-2010. Associations were assessed using distributed-lag Cox proportional hazards models. We assessed effect-measure modification by race/ethnicity by calculating the weekly relative excess risk due to interaction. For a 10-μg/m3 increase in PM2.5 exposure over the entire period of gestation, PTB risk increased by 11% (hazard ratio = 1.11, 95% confidence interval: 1.09, 1.14). Gestational weeks 17-24 and 36 were associated with increased vulnerability to PM2.5 exposure. We find that non-Hispanic black mothers may be more susceptible to effects of PM2.5 exposure than non-Hispanic white mothers, particularly at the end of pregnancy. These findings extend our knowledge about the existence of specific exposure periods during pregnancy that have the greatest impact on preterm birth., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Athens, Greece has been in economic and social crises after the 2008 global recession, resulting in an increase in wood burning as a cheaper method of residential heating in the winter. Reducing wood burning emissions is a source-specific method to address air quality degradation, and indirectly climate change, through instituting policies aimed at human health co-benefits. In this work, we investigate and quantify the potential health co-benefits from policies reducing outdoor particulate matter (PM) pollution from residential wood burning by assessing the pollution conditions during the 2015 calendar year in Athens, Greece, emphasizing vulnerable populations. We conducted a systematic literature search to extract data regarding effective improvements to outdoor PM due to wood burning interventions, and get a range of potential ambient PM reduction estimates regarding realistic benefits from different interventions. We applied a health impact assessment methodology and used existing Athens specific data to calculate the preventable daily average non-accidental deaths associated with reducing PM, additionally considering low and high socioeconomic status (SES) groups. We found that the reduction in outdoor PM concentration showed the potential to benefit lower SES groups as much as 13.5 times more than the high SES group, representing an opportunity for policies to improve not only the health of the total population but also improve environmental equity and health disparities.

Immortal time bias is a well-recognized bias in clinical epidemiology but is rarely discussed in environmental epidemiology. Under the target trial framework, this bias is formally conceptualized as a misalignment between the start of study follow-up (time 0) and treatment assignment. This misalignment can occur when attained duration of follow-up is encoded into treatment assignment using minimums, maximums, or averages. The bias can be exacerbated in the presence of time trends commonly found in environmental exposures. Using lung cancer cases from the California Cancer Registry (2000-2010) linked with estimated concentrations of particulate matter less than or equal to 2.5 μm in aerodynamic diameter (PM2.5), we replicated previous studies that averaged PM2.5 exposure over follow-up in a time-to-event model. We compared this approach with one that ensures alignment between time 0 and treatment assignment, a discrete-time approach. In the former approach, the estimated overall hazard ratio for a 5-μg/m3 increase in PM2.5 was 1.38 (95% confidence interval: 1.36, 1.40). Under the discrete-time approach, the estimated pooled odds ratio was 0.99 (95% confidence interval: 0.98, 1.00). We conclude that the strong estimated effect in the former approach was likely driven by immortal time bias, due to misalignment at time 0. Our findings highlight the importance of appropriately conceptualizing a time-varying environmental exposure under the target trial framework to avoid introducing preventable systematic errors.

BACKGROUND: This study capitalized on coal and oil facility retirements to quantify their potential effects on fine particulate matter (PM2.5) concentrations and cardiorespiratory hospitalizations in affected areas using a generalized synthetic control method. METHODS: We identified 11 coal and oil facilities in California that retired between 2006 and 2013. We classified zip code tabulation areas (ZCTA) as exposed or unexposed to a facility retirement using emissions information, distance, and a dispersion model. We calculated weekly ZCTA-specific PM2.5 concentrations based on previously estimated daily time-series PM2.5 concentrations from an ensemble model, and weekly cardiorespiratory hospitalization rates based on hospitalization data collected by the California Department of Health Care Access and Information. We estimated the average differences in weekly average PM2.5 concentrations and cardiorespiratory hospitalization rates in four weeks after each facility retirement between the exposed ZCTAs and the synthetic control using all unexposed ZCTAs (i.e., the average treatment effect among the treated [ATT]) and pooled ATTs using meta-analysis. We conducted sensitivity analyses to consider different classification schemes to distinguish exposed from unexposed ZCTAs, including aggregating outcomes with different time intervals and including a subset of facilities with reported retirement date confirmed via emission record. RESULTS: The pooled ATTs were 0.02 μg/m3 (95% confidence interval (CI): -0.25 to 0.29 μg/m3) and 0.34 per 10,000 person-weeks (95%CI: -0.08 to 0.75 per 10,000 person-weeks) following the facility closure for weekly PM2.5 and cardiorespiratory hospitalization rates, respectively. Our inferences remained the same after conducting sensitivity analyses. CONCLUSIONS: We demonstrated a novel approach to study the potential benefits associated with industrial facility retirements. The declining contribution of industrial emissions to ambient air pollution in California may explain our null findings. We encourage future research to replicate this work in regions with different industrial activities.

BACKGROUND: Significant mortality and morbidity in pregnant women and their offspring are linked to premature rupture of membranes (PROM). Epidemiological evidence for heat-related PROM risk is extremely limited. We investigated associations between acute heatwave exposure and spontaneous PROM. METHODS: We conducted this retrospective cohort study among mothers in Kaiser Permanente Southern California who experienced membrane ruptures during the warm season (May-September) from 2008 to 2018. Twelve definitions of heatwaves with different cut-off percentiles (75th, 90th, 95th, and 98th) and durations (≥ 2, 3, and 4 consecutive days) were developed using the daily maximum heat index, which incorporates both daily maximum temperature and minimum relative humidity in the last gestational week. Cox proportional hazards models were fitted separately for spontaneous PROM, term PROM (TPROM), and preterm PROM (PPROM) with zip codes as the random effect and gestational week as the temporal unit. Effect modification by air pollution (i.e., PM2.5 and NO2), climate adaptation measures (i.e., green space and air conditioning [AC] penetration), sociodemographic factors, and smoking behavior was examined. RESULTS: In total, we included 190,767 subjects with 16,490 (8.6%) spontaneous PROMs. We identified a 9-14% increase in PROM risks associated with less intense heatwaves. Similar patterns as PROM were found for TPROM and PPROM. The heat-related PROM risks were greater among mothers exposed to a higher level of PM2.5 during pregnancy, under 25 years old, with lower education and household income level, and who smoked. Even though climate adaptation factors were not statistically significant effect modifiers, mothers living with lower green space or lower AC penetration were at consistently higher heat-related PROM risks compared to their counterparts. CONCLUSION: Using a rich and high-quality clinical database, we detected harmful heat exposure for spontaneous PROM in preterm and term deliveries. Some subgroups with specific characteristics were more susceptible to heat-related PROM risk.

BackgroundNumerous epidemiological studies have documented the adverse health impact of long-term exposure to fine particulate matter [particulate matter ≤2.5μm in aerodynamic diameter (PM2.5)] on mortality even at relatively low levels. However, methodological challenges remain to consider potential regulatory intervention's complexity and provide actionable evidence on the predicted benefits of interventions. We propose the parametric g-computation as an alternative analytical approach to such challenges.MethodWe applied the parametric g-computation to estimate the cumulative risks of nonaccidental death under different hypothetical intervention strategies targeting long-term exposure to PM2.5 in the Canadian Community Health Survey cohort from 2005 to 2015. On both relative and absolute scales, we explored the benefits of hypothetical intervention strategies compared with the natural course that a) set the simulated exposure value at each follow-up year to a threshold value if exposure was above the threshold (8.8 μg/m3, 7.04 μg/m3, 5 μg/m3, and 4 μg/m3), and b) reduced the simulated exposure value by a percentage (5% and 10%) at each follow-up year. We used the 3-y average PM2.5 concentration with 1-y lag at the postal code of respondents' annual mailing addresses as their long-term exposure to PM2.5. We considered baseline and time-varying confounders, including demographics, behavior characteristics, income level, and neighborhood socioeconomic status. We also included the R syntax for reproducibility and replication.ResultsAll hypothetical intervention strategies explored led to lower 11-y cumulative mortality risks than the estimated value under the natural course without intervention, with the smallest reduction of 0.20 per 1,000 participants (95% CI: 0.06, 0.34) under the threshold of 8.8 μg/m3, and the largest reduction of 3.40 per 1,000 participants (95% CI: -0.23, 7.03) under the relative reduction of 10% per interval. The reductions in cumulative risk, or numbers of deaths that would have been prevented if the intervention was employed instead of maintaining the status quo, increased over time but flattened toward the end of the follow-up period. Estimates among those ≥65 years of age were greater with a similar pattern. Our estimates were robust to different model specifications.DiscussionWe found evidence that any intervention further reducing the long-term exposure to PM2.5 would reduce the cumulative mortality risk, with greater benefits in the older population, even in a population already exposed to low levels of ambient PM2.5. The parametric g-computation used in this study provides flexibilities in simulating real-world interventions, accommodates time-varying exposure and confounders, and estimates adjusted survival curves with clearer interpretation and more information than a single hazard ratio, making it a valuable analytical alternative in air pollution epidemiological research. https://doi.org/10.1289/EHP11095.

Though fine particulate matter (PM2.5) has decreased in the United States (U.S.) in the past two decades, the increasing frequency, duration, and severity of wildfires significantly (though episodically) impairs air quality in wildfire-prone regions and beyond. Increasing PM2.5 concentrations derived from wildfire smoke and associated impacts on public health require dedicated epidemiological studies. Main sources of PM2.5 data are provided by government-operated monitors sparsely located across U.S., leaving several regions and potentially vulnerable populations unmonitored. Current approaches to estimate PM2.5 concentrations in unmonitored areas often rely on big data, such as satellite-derived aerosol properties and meteorological variables, apply computationally-intensive deterministic modeling, and do not distinguish wildfire-specific PM2.5 from other sources of emissions such as traffic and industrial sources. Furthermore, modelling wildfire-specific PM2.5 presents a challenge since measurements of the smoke contribution to PM2.5 pollution are not available. Here, we aim to use statistical methods to isolate wildfire-specific PM2.5 from other sources of emissions. Our study presents an ensemble model that optimally combines multiple machine learning algorithms (including gradient boosting machine, random forest and deep learning), and a large set of explanatory variables to, first, estimate daily PM2.5 concentrations at the ZIP code level, a relevant spatiotemporal resolution for epidemiological studies. Subsequently, we propose a novel implementation of an imputation approach to estimate the wildfire-specific PM2.5 concentrations that could be applied geographical regions in the US or worldwide. Our ensemble model achieved comparable results to previous machine learning studies for PM2.5 prediction while avoiding processing larger, computationally intensive datasets. Our study is the first to apply a suite of statistical models using readily available datasets to provide daily wildfire-specific PM2.5 at a fine spatial scale for a 15-year period, thus providing a relevant spatiotemporal resolution and timely contribution for epidemiological studies.

Background Long-term exposure to outdoor fine particulate matter (PM2.5) is the leading environmental risk factor for premature mortality worldwide. Characterizing important pathways through which PM2.5 increases individuals' mortality risk can clarify the PM2.5-mortality relationship and identify possible points of interventions. Recent evidence has linked PM2.5 to the onset of diabetes and cardiovascular disease, but to what extent these associations contribute to the effect of PM2.5 on mortality remains poorly understood. Methods and Results We conducted a population-based cohort study to investigate how the effect of PM2.5 on nonaccidental mortality is mediated by its impacts on incident diabetes, acute myocardial infarction, and stroke. Our study population comprised ≈200 000 individuals aged 20 to 90 years who participated in population-based health surveys in Ontario, Canada, from 1996 to 2014. Follow-up extended until December 2017. Using causal mediation analyses with Aalen additive hazards models, we decomposed the total effect of PM2.5 on mortality into a direct effect and several path-specific indirect effects mediated by diabetes, each cardiovascular event, or both combined. A series of sensitivity analyses were also conducted. After adjusting for various individual- and neighborhood-level covariates, we estimated that for every 1000 adults, each 10 μg/m3 increase in PM2.5 was associated with ≈2 incident cases of diabetes, ≈1 major cardiovascular event (acute myocardial infarction and stroke combined), and ≈2 deaths annually. Among PM2.5-related deaths, 31.7% (95% CI, 17.2%-53.2%) were attributable to diabetes and major cardiovascular events in relation to PM2.5. Specifically, 4.5% were explained by PM2.5-induced diabetes, 22.8% by PM2.5-induced major cardiovascular events, and 4.5% through their interaction. Conclusions This study suggests that a significant portion of the estimated effect of long-term exposure to PM2.5 on deaths can be attributed to its effect on diabetes and cardiovascular diseases, highlighting the significance of PM2.5 on deteriorating cardiovascular health. Our findings should raise awareness among professionals that improving metabolic and cardiovascular health may reduce mortality burden in areas with higher exposure to air pollution.

IntroductionExposure to air pollution disproportionately affects racial/ethnic minorities that could contribute to health inequalities including metabolic disorders. However, most existing studies used a static assessment of air pollution exposure (mostly using the residential address) and do not account for activity space when modelling exposure to air pollution. The aim of this study is to understand how exposure to air pollution impacts metabolic disorders biomarkers, how this effect differs according to ethnicity, and for the first time compare these findings with two methods of exposure assessment: dynamic and static measures.MethodsAmong the Community of Mine study, a cross-sectional study conducted in San Diego County, insulin resistance, diabetes, hypertension, obesity, dyslipidemia, and metabolic syndrome (MetS) were assessed. Exposure to air pollution (PM2.5, NO2, traffic) was calculated using static measures around the home, and dynamic measures of mobility derived from Global Positioning Systems (GPS) traces using kernel density estimators to account for exposure variability across space and time. Associations of air pollution with metabolic disorders were quantified using generalized estimating equation models to account for the clustered nature of the data.ResultsAmong 552 participants (mean age 58.7 years, 42% Hispanic/Latino), Hispanics/Latinos had a higher exposure to PM2.5 compared to non-Hispanics using static measures. In contrast, Hispanics/Latinos had less exposure to PM2.5 using dynamic measures. For all participants, higher dynamic exposure to PM2.5 and NO2 was associated with increased insulin resistance and cholesterol levels, and increased risk of obesity, dyslipidemia and MetS (RR 1.17, 95% CI: 1.07-1.28; RR 1.21, 95% CI: 1.12-1.30, respectively). The association between dynamic PM2.5 exposure and MetS differed by Hispanic/Latino ethnicity.ConclusionThese results highlight the importance of considering people's daily mobility in assessing the impact of air pollution on health.

BackgroundThe tremendous global health burden related to COVID-19 means that identifying determinants of COVID-19 severity is important for prevention and intervention. We aimed to explore long-term exposure to ambient air pollution as a potential contributor to COVID-19 severity, given its known impact on the respiratory system.MethodsWe used a cohort of all people with confirmed SARS-CoV-2 infection, aged 20 years and older and not residing in a long-term care facility in Ontario, Canada, during 2020. We evaluated the association between long-term exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ground-level ozone (O3), and risk of COVID-19-related hospital admission, intensive care unit (ICU) admission and death. We ascertained individuals' long-term exposures to each air pollutant based on their residence from 2015 to 2019. We used logistic regression and adjusted for confounders and selection bias using various individual and contextual covariates obtained through data linkage.ResultsAmong the 151 105 people with confirmed SARS-CoV-2 infection in Ontario in 2020, we observed 8630 hospital admissions, 1912 ICU admissions and 2137 deaths related to COVID-19. For each interquartile range increase in exposure to PM2.5 (1.70 μg/m3), we estimated odds ratios of 1.06 (95% confidence interval [CI] 1.01-1.12), 1.09 (95% CI 0.98-1.21) and 1.00 (95% CI 0.90-1.11) for hospital admission, ICU admission and death, respectively. Estimates were smaller for NO2. We also estimated odds ratios of 1.15 (95% CI 1.06-1.23), 1.30 (95% CI 1.12-1.50) and 1.18 (95% CI 1.02-1.36) per interquartile range increase of 5.14 ppb in O3 for hospital admission, ICU admission and death, respectively.InterpretationChronic exposure to air pollution may contribute to severe outcomes after SARS-CoV-2 infection, particularly exposure to O3.

Mineral dust is one of the largest natural constituents of coarse particulate matter (PM10). Most of these dust emissions originate from northern Africa, and several hundred tera-grams of dust are emitted annually from this region. Previous evidence has linked dust PM10 to adverse respiratory outcomes in children. However, most of these studies have been from high-income countries (HICs) or examined dust from other regions of the world, mainly Asia. Evidence from low-to-middle-income countries (LMICs) in Africa is scarce. Respiratory infections are one of the leading causes of under-five mortality across the globe. However, there is a poignant disparity in studies examining these outcomes in children in the region where most dust is emitted. This study linked remotely sensed satellite data to a nationally representative survey to examine acute exposure to dust in children living in Benin using a time-stratified case-crossover analysis. We identified acute effects of exposure to dust and increased risk of cough in children under five. The effect of increased risk is strongest within two weeks of exposure and dissipates by four weeks. Children living in rural areas and households with lower income had a greater risk of adverse respiratory outcomes when exposed to dust. We could elucidate the specific period and conditions of increased risk for respiratory problems in children living in Benin.

BackgroundEpidemiological findings are inconsistent regarding the associations between air pollution exposure during pregnancy and gestational diabetes mellitus (GDM). Several limitations exist in previous studies, including potential outcome and exposure misclassification, unassessed confounding, and lack of simultaneous consideration of air pollution mixtures and particulate matter (PM) constituents.ObjectivesTo assess the association between GDM and maternal residential exposure to air pollution, and the joint effect of the mixture of air pollutants and PM constituents.MethodsDetailed clinical data were obtained for 395,927 pregnancies in southern California (2008-2018) from Kaiser Permanente Southern California (KPSC) electronic health records. GDM diagnosis was based on KPSC laboratory tests. Monthly average concentrations of fine particulate matter

BackgroundHispanics/Latinos in the United States are more likely to live in neighborhoods with greater exposure to air pollution and are projected to have the largest increase in dementia among race/ethnic minority groups.ObjectiveWe examined the associations of air pollution with performance on cognitive function tests in Hispanic/Latino adults.MethodsWe used data from the San Diego site of the Hispanic Community Health Study/Study of Latinos, an ongoing cohort of Hispanics/Latinos. This analysis focused on individuals ≥45 years of age who completed a neurocognitive battery examining overall mental status, verbal learning, memory, verbal fluency, and executive function (n = 2,089). Air pollution (PM2.5 and O3) before study baseline was assigned to participants' zip code. Logistic and linear regression were used to estimate the associations of air pollution on overall mental status and domain-specific standardized test scores. Models accounted for complex survey design, demographic, and socioeconomic characteristics.ResultsWe found that for every 10μg/m3 increase in PM2.5, verbal fluency worsened (β: -0.21 [95%CI: -0.68, 0.25]). For every 10 ppb increase in O3, verbal fluency and executive function worsened (β: -0.19 [95%CI: -0.34, -0.03]; β: -0.01 [95%CI: -0.01, 0.09], respectively). We did not identify any detrimental effect of pollutants on other domains.ConclusionAlthough we found suggestions that air pollution may impact verbal fluency and executive function, we observed no consistent or precise evidence to suggest an adverse impact of air pollution on cognitive level among this cohort of Hispanic/Latino adults.

BACKGROUND: Recent studies have reported inconsistent associations between maternal residential green space and preterm birth (PTB, born

BackgroundRecent studies have reported inconsistent associations between maternal residential green space and preterm birth (PTB, born

BackgroundBoth extreme heat and air pollution exposure during pregnancy have been associated with preterm birth; however, their combined effects are unclear.ObjectivesOur goal was to estimate the independent and joint effects of heatwaves and fine particulate matter [PM 0) for less extreme heatwaves (i.e., shorter or with relatively low temperature thresholds) but were less than additive (RERIs

Background Numerous studies have linked fine particulate matter (PM2.5) to increased cardiovascular mortality. Less is known how the PM2.5-cardiovascular mortality association varies by use of cardiovascular medications. This study sought to quantify effect modification by statin use status on the associations between long-term exposure to PM2.5 and mortality from any cardiovascular cause, coronary heart disease (CHD), and stroke. Methods In this nested case-control study, we followed 1.2 million community-dwelling adults aged ≥66 years who lived in Ontario, Canada from 2000 through 2018. Cases were patients who died from the three causes. Each case was individually matched to up to 30 randomly selected controls using incidence density sampling. Conditional logistic regression models were used to estimate odds ratios (ORs) for the associations between PM2.5 and mortality. We evaluated the presence of effect modification considering both multiplicative (ratio of ORs) and additive scales (the relative excess risk due to interaction, RERI). Results Exposure to PM2.5 increased the risks for cardiovascular, CHD, and stroke mortality. For all three causes of death, compared with statin users, stronger PM2.5-mortality associations were observed among non-users [e.g. for cardiovascular mortality corresponding to each interquartile range increase in PM2.5, OR = 1.042 (95% CI, 1.032–1.053) vs OR = 1.009 (95% CI, 0.996–1.022) in users, ratio of ORs = 1.033 (95% CI, 1.019–1.047), RERI = 0.039 (95% CI, 0.025–0.050)]. Among users, partially adherent users exhibited a higher risk of PM2.5-associated mortality than fully adherent users. Conclusions The associations of chronic exposure to PM2.5 with cardiovascular and CHD mortality were stronger among statin non-users compared to users. [ABSTRACT FROM AUTHOR]

Wildfires have become more frequent and intense due to climate change and outdoor wildfire fine particulate matter (PM2.5) concentrations differ from relatively smoothly varying total PM2.5. Thus, we introduced a conceptual model for computing long-term wildfire PM2.5 and assessed disproportionate exposures among marginalized communities. We used monitoring data and statistical techniques to characterize annual wildfire PM2.5 exposure based on intermittent and extreme daily wildfire PM2.5 concentrations in California census tracts (2006 to 2020). Metrics included: 1) weeks with wildfire PM2.5 > 5 µg/m3; 2) days with non-zero wildfire PM2.5; 3) mean wildfire PM2.5 during peak exposure week; 4) smoke waves (= 2 consecutive days with > 15 µg/m3 wildfire PM2.5); and 5) mean annual wildfire PM2.5 concentration. We classified tracts by their racial/ethnic composition and CalEnviroScreen (CES) score, an environmental and social vulnerability composite measure. We examined associations of CES and racial/ethnic composition with the wildfire PM2.5 metrics using mixed-effects models. Averaged 2006 to 2020, we detected little difference in exposure by CES score or racial/ethnic composition, except for non-Hispanic American Indian and Alaska Native populations, where a 1-SD increase was associated with higher exposure for 4/5 metrics. CES or racial/ethnic × year interaction term models revealed exposure disparities in some years. Compared to their California-wide representation, the exposed populations of non-Hispanic American Indian and Alaska Native (1.68×, 95% CI: 1.01 to 2.81), white (1.13×, 95% CI: 0.99 to 1.32), and multiracial (1.06×, 95% CI: 0.97 to 1.23) people were over-represented from 2006 to 2020. In conclusion, during our study period in California, we detected disproportionate long-term wildfire PM2.5 exposure for several racial/ethnic groups. [ABSTRACT FROM AUTHOR]

Wildfires constitute a growing source of extremely high levels of particulate matter that is less than 2.5 microns in diameter (PM2.5). Recently, toxicologic and epidemiologic studies have shown that PM2.5 generated from wildfires may have a greater health burden than PM2.5 generated from other pollutant sources. This study examined the impact of PM2.5 on hospitalizations for respiratory diseases in California between 2006 and 2019 using a health impact assessment approach that considers differential concentration‐response functions (CRF) for PM2.5 from wildfire and non‐wildfire sources of emissions. We quantified the burden of respiratory hospitalizations related to PM2.5 exposure at the zip code level through two different approaches: (a) naïve (considering the same CRF for all PM2.5 emissions) and (b) nuanced (considering different CRFs for PM2.5 from wildfires and from other sources). We conducted a Geographically Weighted Regression to analyze spatially varying relationships between the delta (i.e., the difference between the naïve and nuanced approaches) and the Centers for Disease Control and Prevention's Social Vulnerability Index (SVI). A higher attributable number of respiratory hospitalizations was found when accounting for the larger health burden of wildfire PM2.5. We found that, between 2006 and 2019, the number of hospitalizations attributable to PM2.5 may have been underestimated by approximately 13% as a result of not accounting for the higher CRF of wildfire‐related PM2.5 throughout California. This underestimation was higher in northern California and areas with higher SVI rankings. The relationship between delta and SVI varied spatially across California. These findings can be useful for updating future air pollution guideline recommendations. Plain Language Summary: Climate change is leading to an increase in the frequency and intensity of extreme weather events, including wildfires. Wildfire smoke is a large source of particulate matter (i.e., fine inhalable particles) that could have a greater health burden than particulate matter from other sources (e.g., vehicle or industrial emissions). We aimed to quantify the burden of respiratory hospitalizations related to all particulate matter exposure in California for the years 2006–2019 with and without considering the differential health burden of wildfire smoke. We then compared these two approaches to quantify the number of respiratory hospitalizations that may have been underestimated in the past by not taking into account the higher health burden of wildfire smoke. We found that throughout California, between 2006 and 2019, we may have underestimated the number of respiratory hospitalizations attributable to particulate matter exposure by approximately 410,000 (13%). This underestimation was higher in northern California and in vulnerable areas. These results may be helpful for updating recommendations in upcoming air pollution guidelines and for protecting communities at higher risk from wildfire smoke. Key Points: By not considering the differential health burden of wildfire PM2.5, we underestimate the number of hospitalizations attributable to PM2.5In California, between 2006 and 2019, the number of hospitalizations attributable to PM2.5 may have been underestimated by approximately 13%Underestimation of PM2.5‐attributable hospitalizations was higher in vulnerable areas [ABSTRACT FROM AUTHOR]

Objective: To evaluate the association of short‐term exposure to overall fine particulate matter of <2.5 μm (PM2.5) and wildfire‐specific PM2.5 with emergency department (ED) visits for headache. Background: Studies have reported associations between PM2.5 exposure and headache risk. As climate change drives longer and more intense wildfire seasons, wildfire PM2.5 may contribute to more frequent headaches. Methods: Our study included adult Californian members (aged ≥18 years) of a large de‐identified commercial and Medicare Advantage claims database from 2006 to 2020. We identified ED visits for primary headache disorders (subtypes: tension‐type headache, migraine headache, cluster headache, and "other" primary headache). Claims included member age, sex, and residential zip code. We linked daily overall and wildfire‐specific PM2.5 to residential zip code and conducted a time‐stratified case‐crossover analysis considering 7‐day average PM2.5 concentrations, first for primary headache disorders combined, and then by headache subtype. Results: Among 9898 unique individuals we identified 13,623 ED encounters for primary headache disorders. Migraine was the most frequently diagnosed headache (N = 5534/13,623 [47.6%]) followed by "other" primary headache (N = 6489/13,623 [40.6%]). For all primary headache ED diagnoses, we observed an association of 7‐day average wildfire PM2.5 (odds ratio [OR] 1.17, 95% confidence interval [CI] 0.95–1.44 per 10 μg/m3 increase) and by subtype we observed increased odds of ED visits associated with 7‐day average wildfire PM2.5 for tension‐type headache (OR 1.42, 95% CI 0.91–2.22), "other" primary headache (OR 1.40, 95% CI 0.96–2.05), and cluster headache (OR 1.29, 95% CI 0.71–2.35), although these findings were not statistically significant under traditional null hypothesis testing. Overall PM2.5 was associated with tension‐type headache (OR 1.29, 95% CI 1.03–1.62), but not migraine, cluster, or "other" primary headaches. Conclusions: Although imprecise, these results suggest short‐term wildfire PM2.5 exposure may be associated with ED visits for headache. Patients, healthcare providers, and systems may need to respond to increased headache‐related healthcare needs in the wake of wildfires and on poor air quality days. [ABSTRACT FROM AUTHOR]

Emissions of fine particulate matter (PM2.5) from human activities have been linked to substantial disease burdens, but evidence regarding how reducing PM2.5 at its sources would improve public health is sparse. We followed a population-based cohort of 2.7 million adults across Canada from 2007 through 2016. For each participant, we estimated annual mean concentrations of PM2.5 and the fractional contributions to PM2.5 from the five leading anthropogenic sources at their residential address using satellite observations in combination with a global atmospheric chemistry transport model. For each source, we estimated the causal effects of six hypothetical interventions on 10-y nonaccidental mortality risk using the parametric g-formula, a structural causal model. We conducted stratified analyses by age, sex, and income. This cohort would have experienced tangible health gains had contributions to PM2.5 from any of the five sources been reduced. Compared with no intervention, a 10% annual reduction in PM2.5 contributions from transportation and power generation, Canada's largest and fifth-largest anthropogenic sources, would have prevented approximately 175 (95%CI: 123-226) and 90 (95%CI: 63-117) deaths per million by 2016, respectively. A more intensive 50% reduction per year in PM2.5 contributions from the two sources would have averted 360 and 185 deaths per million, respectively, by 2016. The potential health benefits were greater among men, older adults, and low-income earners. In Canada, where PM2.5 levels are among the lowest worldwide, reducing PM2.5 contributions from anthropogenic sources by as little as 10% annually would yield meaningful health gains. [ABSTRACT FROM AUTHOR]

Introduction: No evidence exists about the impact of air pollution reduction on incidence of dementia. The aim of this study was to quantify how air quality improvement leads to dementia‐incidence benefits. Methods: In the French Three‐City cohort (12 years of follow‐up), we used parametric g‐computation to quantify the expected number of prevented dementia cases under different hypothetical interventions with particulate matter measuring <2.5 μm (PM2.5) reductions. Results: Among 7051 participants, 789 participants developed dementia. The median PM2.5 reduction between 1990 and 2000 was 12.2 (μg/m3). Such a reduction reduced the risk of all‐cause dementia (hazard ratio [HR], 0.85; 95% confidence interval [CI], 0.76 to 0.95). If all study participants were enjoying a hypothetical reduction of more than 13.10 μg/m3 (median reduction observed in the city of Montpellier), the rate difference was −0.37 (95% CI, −0.57 to −0.17) and the rate ratio was 0.67 (95% CI, 0.50 to 0.84). Discussion: These findings highlight the possible substantial benefits of reducing air pollution in the prevention of dementia. [ABSTRACT FROM AUTHOR]

Lower respiratory tract infections disproportionately affect children and are one of the main causes of hospital referral and admission. COVID‐19 stay‐at‐home orders in early 2020 led to substantial reductions in hospital admissions, but the specific contribution of changes in air quality through this natural experiment has not been examined. Capitalizing on the timing of the stay‐at‐home order, we quantified the specific contribution of fine‐scale changes in PM2.5 concentrations to reduced respiratory emergency department (ED) visits in the pediatric population of San Diego County, California. We analyzed data on pediatric ED visits (n = 72,333) at the ZIP‐code level for respiratory complaints obtained from the ED at Rady Children's Hospital in San Diego County (2015–2020) and ZIP‐code level PM2.5 from an ensemble model integrating multiple machine learning algorithms. We examined the decrease in respiratory visits in the pediatric population attributable to the stay‐at‐home order and quantified the contribution of changes in PM2.5 exposure using mediation analysis (inverse of odds ratio weighting). Pediatric respiratory ED visits dropped during the stay‐at‐home order (starting on 19 March 2020). Immediately after this period, PM2.5 concentrations, relative to the counterfactual values based in the 4‐year baseline period, also decreased with important spatial variability across ZIP codes in San Diego County. Overall, we found that decreases in PM2.5 attributed to the stay‐at‐home order contributed to explain 4% of the decrease in pediatric respiratory ED visits. We identified important spatial inequalities in the decreased incidence of pediatric respiratory illness and found that brief decline in air pollution levels contributed to a decrease in respiratory ED visits. Plain Language Summary: Children are commonly affected by respiratory tract infections, which can lead to hospitalization in some cases. During the COVID‐19 lockdown in early 2020, pediatric cases of respiratory illnesses decreased substantially. We examined the contribution in the lower levels of fine particle pollution observed in San Diego County, over the lockdown period, on the decreased number of visits to pediatric Emergency Department facilities. Key Points: We assessed changes in pediatric respiratory emergency department (ED) visits and PM2.5 in 2020 with respect to the preceding 4 yearsWe observed a sustained decline in pediatric respiratory ED visits, while decreases in PM2.5 concentrations were short‐livedWe estimated a 4% contribution of PM2.5 changes on the reduction in pediatric respiratory ED visits during the stay‐at‐home period [ABSTRACT FROM AUTHOR]

A growing literature in economics and epidemiology has exploited changes in wind patterns as a source of exogenous variation to better measure the acute health effects of air pollution. Since the distribution of wind components is not randomly distributed over time and related to other weather parameters, multivariate regression models are used to adjust for these confounding factors. However, this type of analysis relies on its ability to correctly adjust for all confounding factors and extrapolate to units without empirical counterfactuals. As an alternative to current practices and to gauge the extent of these issues, we propose to implement a causal inference pipeline to embed this type of observational study within an hypothetical randomized experiment. We illustrate this approach using daily data from Paris, France, over the 2008–2018 period. Using the Neyman–Rubin potential outcomes framework, we first define the treatment of interest as the effect of North-East winds on particulate matter concentrations compared to the effects of other wind directions. We then implement a matching algorithm to approximate a pairwise randomized experiment. It adjusts nonparametrically for observed confounders while avoiding model extrapolation by discarding treated days without similar control days. We find that the effective sample size for which treated and control units are comparable is surprisingly small. It is however reassuring that results on the matched sample are consistent with a standard regression analysis of the initial data. We finally carry out a quantitative bias analysis to check whether our results could be altered by an unmeasured confounder: estimated effects seem robust to a relatively large hidden bias. Our causal inference pipeline is a principled approach to improve the design of air pollution studies based on wind patterns. [ABSTRACT FROM AUTHOR]

Key Points: Santa Ana winds have a predominant ventilation effect as background PM2.5 is transported offshore from highly polluted areasA polluting effect occurs when SAWs spread smoke PM2.5 from wildfires inland toward the coastal regionStatistical approaches that relate surface wind and PM2.5 over space and time can help in identifying wildfire PM2.5 Fine particulate matter (PM2.5) raises human health concerns since it can deeply penetrate the respiratory system and enter the bloodstream, thus potentially impacting vital organs. Strong winds transport and disperse PM2.5, which can travel over long distances. Smoke from wildfires is a major episodic and seasonal hazard in Southern California (SoCal), where the onset of Santa Ana winds (SAWs) in early fall before the first rains of winter is associated with the region's most damaging wildfires. However, SAWs also tend to improve visibility as they sweep haze particles from highly polluted areas far out to sea. Previous studies characterizing PM2.5 in the region are limited in time span and spatial extent, and have either addressed only a single event in time or short time series at a limited set of sites. Here we study the space‐time relationship between daily levels of PM2.5 in SoCal and SAWs spanning 1999–2012 and also further identify the impact of wildfire smoke on this relationship. We used a rolling correlation approach to characterize the spatial‐temporal variability of daily SAW and PM2.5. SAWs tend to lower PM2.5 levels, particularly along the coast and in urban areas, in the absence of wildfires upwind. On the other hand, SAWs markedly increase PM2.5 in zip codes downwind of wildfires. These empirical relationships can be used to identify windows of vulnerability for public health and orient preventive measures. [ABSTRACT FROM AUTHOR]

Wildfire smoke has been associated with adverse respiratory outcomes, but the impacts of wildfire on other health outcomes and sensitive subpopulations are not fully understood. We examined associations between smoke events and emergency department visits (EDVs) for respiratory, cardiovascular, diabetes, and mental health outcomes in California during the wildfire season June–December 2016–2019. Daily, zip code tabulation area-level wildfire-specific fine particulate matter (PM 2.5) concentrations were aggregated to air basins. A "smoke event" was defined as an air basin-day with a wildfire-specific PM 2.5 concentration at or above the 98th percentile across all air basin-days (threshold = 13.5 μg/m3). We conducted a two-stage time-series analysis using quasi-Poisson regression considering lag effects and random effects meta-analysis. We also conducted analyses stratified by race/ethnicity, age, and sex to assess potential effect modification. Smoke events were associated with an increased risk of EDVs for all respiratory diseases at lag 1 [14.4%, 95% confidence interval (CI): (6.8, 22.5)], asthma at lag 0 [57.1% (44.5, 70.8)], and chronic lower respiratory disease at lag 0 [12.7% (6.2, 19.6)]. We also found positive associations with EDVs for all cardiovascular diseases at lag 10. Mixed results were observed for mental health outcomes. Stratified results revealed potential disparities by race/ethnicity. Short-term exposure to smoke events was associated with increased respiratory and schizophrenia EDVs. Cardiovascular impacts may be delayed compared to respiratory outcomes. • WF-specific PM 2.5 concentrations were used to identify smoke events in California. • Smoke events were associated with increased respiratory and schizophrenia EDVs. • Delayed positive associations observed for cardiovascular EDVs after smoke event. • Results were robust to co-pollutants adjustment and smoke event definition. • Higher risk of diabetes EDVs for people who are Hispanic than people who are White. [ABSTRACT FROM AUTHOR]

• NO 2 , O 3 , and PM 2.5 exposure can increase premature rupture of membranes (PROM) risk. • Maternal PM 2.5 nitrate, sulfate, and organic matter exposure can increase PROM risk. • The effect of air pollution mixture on PROM may be mainly driven by O 3 exposure. • Underweight mothers can be more vulnerable to PROM associated with air pollution. There is minimal evidence of relationships between maternal air pollution exposure and spontaneous premature rupture of membranes (SPROM), a critical obstetrical problem that can significantly increase maternal and fetal mortality and morbidity. No prior study has explored the PROM risk related to specific components of particulate matter with aerodynamic diameters of ≤ 2.5 µm (PM 2.5). We examined associations between maternal exposure to nitrogen dioxide (NO 2), ozone (O 3), PM 2.5 , PM 10 , and PM 2.5 constituents and SPROM. A large retrospective cohort study was conducted and included 427,870 singleton live births from Kaiser Permanente Southern California during 2008–2018. Monthly averages of NO 2 , O 3 (8-h daily maximum), PM 2.5 , and PM 10 were measured using empirical Bayesian kriging based on measurements from monitoring stations. Data on PM 2.5 sulfate, nitrate, ammonium, organic matter, and black carbon were obtained from a fine-resolution model. A discrete time approach with pooled logistic regressions was used to estimate associations throughout the pregnancy and based on trimesters and gestational months. The quantile-based g-computation models were fitted to examine the effects of 1) the air pollution mixture of four pollutants of interest and 2) the mixture of PM 2.5 components. There were 37,857 SPROM cases (8.8%) in our study population. We observed relationships between SPROM and maternal exposure to NO 2 , O 3 , and PM 2.5. PM 2.5 sulfate, nitrate, ammonium, and organic matter were associated with higher SPROM risks in the single-pollutant model. Mixture analyses demonstrated that the overall effects of the air pollution mixture and PM 2.5 mixture in this study were mainly driven by O 3 and PM 2.5 nitrate, respectively. Underweight mothers had a significantly higher risk of SPROM associated with NO 2. Our findings add to the literature on associations between air pollution exposure and SPROM. This is the first study reporting the impact of PM 2.5 constituents on SPROM. [ABSTRACT FROM AUTHOR]

Though fine particulate matter (PM 2.5) has decreased in the United States (U.S.) in the past two decades, the increasing frequency, duration, and severity of wildfires significantly (though episodically) impairs air quality in wildfire-prone regions and beyond. Increasing PM 2.5 concentrations derived from wildfire smoke and associated impacts on public health require dedicated epidemiological studies. Main sources of PM 2.5 data are provided by government-operated monitors sparsely located across U.S., leaving several regions and potentially vulnerable populations unmonitored. Current approaches to estimate PM 2.5 concentrations in unmonitored areas often rely on big data, such as satellite-derived aerosol properties and meteorological variables, apply computationally-intensive deterministic modeling, and do not distinguish wildfire-specific PM 2.5 from other sources of emissions such as traffic and industrial sources. Furthermore, modelling wildfire-specific PM 2.5 presents a challenge since measurements of the smoke contribution to PM 2.5 pollution are not available. Here, we aim to use statistical methods to isolate wildfire-specific PM 2.5 from other sources of emissions. Our study presents an ensemble model that optimally combines multiple machine learning algorithms (including gradient boosting machine, random forest and deep learning), and a large set of explanatory variables to, first, estimate daily PM 2.5 concentrations at the ZIP code level, a relevant spatiotemporal resolution for epidemiological studies. Subsequently, we propose a novel implementation of an imputation approach to estimate the wildfire-specific PM 2.5 concentrations that could be applied geographical regions in the US or worldwide. Our ensemble model achieved comparable results to previous machine learning studies for PM 2.5 prediction while avoiding processing larger, computationally intensive datasets. Our study is the first to apply a suite of statistical models using readily available datasets to provide daily wildfire-specific PM 2.5 at a fine spatial scale for a 15-year period, thus providing a relevant spatiotemporal resolution and timely contribution for epidemiological studies. [ABSTRACT FROM AUTHOR]

• Air Quality Alerts (AQA) may not have systematic benefits for population health. • Difference-in-differences approach coupled with propensity-score matching. • No evidence of an effect on mortality of the PM 10 AQA with initial thresholds. • Reduction in cardiovascular mortality when thresholds were revised. • Still no change in overall respiratory mortality with revised threshold levels. Daily exposure to air pollution has been shown to increase cardiovascular and respiratory mortality. While increases in short-term exposure to air pollutants at any daily concentrations has been shown to be associated to adverse health outcomes, days with extreme levels, also known as air pollution peaks based on specific thresholds, have been used to implement air quality alerts in various cities across the globe. We aimed at evaluating the potential effects of the Air Quality Alerts (AQA) system on different causes of premature mortality in Paris, France. Air quality alerts (AQA) based on particulate matter (PM 10) levels and related interventions were implemented in the region of Paris in 2008 and were revised to be more stringent in 2011. In this study, we applied a difference-in-differences (DID) approach coupled with propensity-score matching (PSM) to daily mortality data for the period 2000 to 2015 to evaluate the effects of the Paris AQA program on different causes of premature mortality for the entire population and for adults > 75 years old. Overall, results did not show evidence of a reduction in mortality of the PM 10 AQA program when first implemented in 2008 with initial thresholds (80 µg/m3); DID estimates were slightly above 1 for cardiovascular and respiratory mortality. However, when evaluating the drastic reduction in revised thresholds in 2011 (50 µg/m3) to trigger interventions, we identified a reduction in cardiovascular (DID = 0.84, 95% CI: 0.755 to 0.930) mortality, but no change in respiratory mortality was detected (DID = 0.97, 95% CI: 0.796, 1.191). Our study suggests that AQA may not have health benefits for the population when thresholds are set at high daily PM 10 levels. Given that such policies are implemented in many other metropolitan areas across the globe, evaluating the effectiveness of AQA is important to provide public authorities and researchers a rationale for defining specific thresholds and extending the scope of these policies to lower air pollution levels. [ABSTRACT FROM AUTHOR]