Exposure measurement error in PM2.5 health effects studies: a pooled analysis of eight personal exposure validation studies

Estimating the impacts of nonoptimal temperatures on mortality: A study in British Columbia, Canada, 2001-2021

Background

Studies show that more than 5.1 million deaths annually are attributed to nonoptimal temperatures, including extreme cold and extreme heat. However, those studies mostly report average estimates across large geographical areas. The health risks attributed to nonoptimal temperatures in British Columbia (BC) are reported incompletely or limit the study area to urban centers. In this study, we aim to estimate the attributable deaths linked to nonoptimal temperatures in all five regional health authorities (RHAs) of BC from 2001 to 2021.

Methods

We applied the widely used distributed lag nonlinear modeling approach to estimate temperature-mortality association in the RHAs of BC, using daily all-cause deaths and 1 × 1 km gridded daily mean temperature. We evaluated the model by comparing the model-estimated attributable number of deaths during the 2021 heat dome to the number of heat-related deaths confirmed by the British Columbia Coroners Service.

Results

Overall, between 2001 and 2021, we estimate that 7.17% (95% empirical confidence interval = 3.15, 10.32) of deaths in BC were attributed to nonoptimal temperatures, the majority of which are attributed to cold. On average, the mortality rates attributable to moderate cold, moderate heat, extreme cold, and extreme heat were 47.04 (95% confidence interval [CI] = 45.83, 48.26), 0.94 (95% CI = 0.81, 1.08), 2.88 (95% CI = 2.05, 3.71), and 3.10 (95% CI = 1.79, 4.4) per 100,000 population per year, respectively.

Conclusions

Our results show significant spatial variability in deaths attributable to nonoptimal temperatures across BC. We find that the effect of extreme temperatures is significantly less compared to milder nonoptimal temperatures between 2001 and 2021. However, the increased contribution of extreme heat cannot be ruled out in the near future.

Short term exposure to low level ambient fine particulate matter and natural cause, cardiovascular, and respiratory morbidity among US adults with health insurance: case time series study

OBJECTIVE

To estimate the excess relative and absolute risks of hospital admissions and emergency department visits for natural causes, cardiovascular disease, and respiratory disease associated with daily exposure to fine particulate matter (PM2.5) at concentrations below the new World Health Organization air quality guideline limit among adults with health insurance in the contiguous US.

DESIGN

Case time series study.

SETTING

US national administrative healthcare claims database.

PARTICIPANTS

50.1 million commercial and Medicare Advantage beneficiaries aged ≥18 years between 1 January 2010 and 31 December 2016.

MAIN OUTCOME MEASURES

Daily counts of hospital admissions and emergency department visits for natural causes, cardiovascular disease, and respiratory disease based on the primary diagnosis code.

RESULTS

During the study period, 10.3 million hospital admissions and 24.1 million emergency department visits occurred for natural causes among 50.1 million adult enrollees across 2939 US counties. The daily PM2.5 levels were below the new WHO guideline limit of 15 μg/m3 for 92.6% of county days (7 360 725 out of 7 949 713). On days when daily PM2.5 levels were below the new WHO air quality guideline limit of 15 μg/m3, an increase of 10 μg/m3 in PM2.5 during the current and previous day was associated with higher risk of hospital admissions for natural causes, with an excess relative risk of 0.91% (95% confidence interval 0.55% to 1.26%), or 1.87 (95% confidence interval 1.14 to 2.59) excess hospital admissions per million enrollees per day. The increased risk of hospital admissions for natural causes was observed exclusively among adults aged ≥65 years and was not evident in younger adults. PM2.5 levels were also statistically significantly associated with relative risk of hospital admissions for cardiovascular and respiratory diseases. For emergency department visits, a 10 μg/m3 increase in PM2.5 during the current and previous day was associated with respiratory disease, with an excess relative risk of 1.34% (0.73% to 1.94%), or 0.93 (0.52 to 1.35) excess emergency department visits per million enrollees per day. This association was not found for natural causes or cardiovascular disease. The higher risk of emergency department visits for respiratory disease was strongest among middle aged and young adults.

CONCLUSIONS

Among US adults with health insurance, exposure to ambient PM2.5 at concentrations below the new WHO air quality guideline limit is statistically significantly associated with higher rates of hospital admissions for natural causes, cardiovascular disease, and respiratory disease, and with emergency department visits for respiratory diseases. These findings constitute an important contribution to the debate about the revision of air quality limits, guidelines, and standards.

Effects of in-utero personal exposure to PM2.5 sources and components on birthweight

In-utero exposure to fine particulate matter (PM2.5) and specific sources and components of PM2.5 have been linked with lower birthweight. However, previous results have been mixed, likely due to heterogeneity in sources impacting PM2.5 and due to measurement error from using ambient data. Therefore, we investigated the effect of PM2.5 sources and their high-loading components on birthweight using data from 198 women in the 3rd trimester from the MADRES cohort 48-h personal PM2.5 exposure monitoring sub-study. The mass contributions of six major sources of personal PM2.5 exposure were estimated for 198 pregnant women in the 3rd trimester using the EPA Positive Matrix Factorization v5.0 model, along with their 17 high-loading chemical components using optical carbon and X-ray fluorescence approaches. Single- and multi-pollutant linear regressions evaluated the association between personal PM2.5 sources/components and birthweight, adjusting for gestational age, maternal age, race, infant sex, parity, diabetes status, temperature, maternal education, and smoking history. Participants were predominately Hispanic (81%), with a mean (SD) gestational age of 39.1 (1.5) weeks and age of 28.2 (6.0) years. Mean birthweight was 3295.8 g (484.1) and mean PM2.5 exposure was 21.3 (14.4) µg/m3. A 1 SD increase in the mass contribution of the fresh sea salt source was associated with a 99.2 g decrease in birthweight (95% CI - 197.7, - 0.6), and aged sea salt was associated with a 70.1 g decrease in birthweight (95% CI - 141.7, 1.4). Magnesium, sodium, and chlorine were associated with lower birthweight, which remained after adjusting for PM2.5 mass. This study found evidence that major sources of personal PM2.5 including fresh and aged sea salt were negatively associated with birthweight, with the strongest effect on birthweight from Na and Mg. The effect of crustal and fuel oil sources differed by infant sex with negative associations seen in boys compared to positive associations in girls.

Correcting for bias due to mismeasured exposure history in longitudinal studies with continuous outcomes

Epidemiologists are often interested in estimating the effect of functions of time-varying exposure histories in relation to continuous outcomes, for example, cognitive function. However, the individual exposure measurements that constitute the history upon which an exposure history function is constructed are usually mismeasured. To obtain unbiased estimates of the effects for mismeasured functions in longitudinal studies, a method incorporating main and validation studies was developed. Simulation studies under several realistic assumptions were conducted to assess its performance compared to standard analysis, and we found that the proposed method has good performance in terms of finite sample bias reduction and nominal confidence interval coverage. We applied it to a study of long-term exposure toPM 2.5 $\text{PM}_{2.5}$ , in relation to cognitive decline in the Nurses' Health Study Previously, it was found that the 2-year decline in the standard measure of cognition was 0.018 (95% CI, -0.034 to -0.001) units worse per 10μ g/m 3 $\mu \text{g/m}^3$ increase inPM 2.5 $\text{PM}_{2.5}$ exposure. After correction, the estimated impact ofPM 2.5 $\text{PM}_{2.5}$ on cognitive decline increased to 0.027 (95% CI, -0.059 to 0.005) units lower per 10μ g/m 3 $\mu \text{g/m}^3$ increase. To put this into perspective, effects of this magnitude are about 2/3 of those found in our data associated with each additional year of aging: 0.044 (95% CI, -0.047 to -0.040) units per 1 year older after applying our correction method.

Air Pollution and Temperature: a Systematic Review of Ubiquitous Environmental Exposures and Sudden Cardiac Death

PURPOSE OF REVIEW

Environmental exposures have been associated with increased risk of cardiovascular mortality and acute coronary events, but their relationship with out-of-hospital cardiac arrest (OHCA) and sudden cardiac death (SCD) remains unclear. SCD is an important contributor to the global burden of cardiovascular disease worldwide.

RECENT FINDINGS

Current literature suggests a relationship between environmental exposures and cardiovascular disease, but their relationship with OHCA/SCD remains unclear. A literature search was conducted in PubMed, Embase, Web of Science, and Global Health. Of 5138 studies identified by our literature search, this review included 30 studies on air pollution, 42 studies on temperature, 6 studies on both air pollution and temperature, and 1 study on altitude exposure and OHCA/SCD. Particulate matter air pollution, ozone, and both hot and cold temperatures are associated with increased risk of OHCA/SCD. Pollution and other exposures related to climate change play an important role in OHCA/SCD incidence.

A Long Way from Steubenville: Environmental Epidemiology in a Rapidly Changing World

This commentary focuses on research that has long been at the core of environmental epidemiology: studies of the health effects of air pollution. It highlights publications in the American Journal of Epidemiology going back more than 50 years that have contributed to the debate about the validity of this research and its meaning for public policy. Technological advances have greatly expanded the toolbox of environmental epidemiologists in terms of measuring and analyzing complex exposures in large populations. Yet, discussions about biases in estimating air pollution health effects have always been and remain intense. Epidemiologists have brought new methodologies and concepts to this research, alleviating some but not all concerns. Here, the focus is on seminal epidemiologic work that established valid links between air pollution exposures and health outcomes and generated data for environmental policies and prevention. With this commentary, I hope to inspire epidemiologists to address many more of the burning environmental health questions-wildfires included-with a similar scientific doggedness. The rapidly changing conditions of our planet are challenging us to innovate and offer solutions, albeit perhaps a little bit faster this time around.

Exploring Relevant Time Windows in the Association Between PM2.5 Exposure and Amyotrophic Lateral Sclerosis: A Case-Control Study in Denmark

Studies suggest a link between particulate matter less than or equal to 2.5 μm in diameter (PM2.5) and amyotrophic lateral sclerosis (ALS), but to our knowledge critical exposure windows have not been examined. We performed a case-control study in the Danish population spanning the years 1989-2013. Cases were selected from the Danish National Patient Registry based on International Classification of Diseases codes. Five controls were randomly selected from the Danish Civil Registry and matched to a case on vital status, age, and sex. PM2.5 concentration at residential addresses was assigned using monthly predictions from a dispersion model. We used conditional logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for confounding. We evaluated exposure to averaged PM2.5 concentrations 12-24 months, 2-6 years, and 2-11 years pre-ALS diagnosis; annual lagged exposures up to 11 years prediagnosis; and cumulative associations for exposure in lags 1-5 years and 1-10 years prediagnosis, allowing for varying association estimates by year. We identified 3,983 cases and 19,915 controls. Cumulative exposure to PM2.5 in the period 2-6 years prediagnosis was associated with ALS (OR = 1.06, 95% CI: 0.99, 1.13). Exposures in the second, third, and fourth years prediagnosis were individually associated with higher odds of ALS (e.g., for lag 1, OR = 1.04, 95% CI: 1.00, 1.08). Exposure to PM2.5 within 6 years before diagnosis may represent a critical exposure window for ALS.

Linking Ambient NO2 Pollution Measures with Electronic Health Record Data to Study Asthma Exacerbations

Electronic health record (EHR)-derived data can be linked to geospatially distributed socioeconomic and environmental factors to conduct large-scale epidemiologic studies. Ambient NO2 is a known environmental risk factor for asthma. However, health exposure studies often rely on data from geographically sparse regulatory monitors that may not reflect true individual exposure. We contrasted use of interpolated NO2 regulatory monitor data with raw satellite measurements and satellite-derived ground estimates, building on previous work which has computed improved exposure estimates from remotely sensed data. Raw satellite and satellite-derived ground measurements captured spatial variation missed by interpolated ground monitor measurements. Multivariable analyses comparing these three NO2 measurement approaches (interpolated monitor, raw satellite, and satellite-derived) revealed a positive relationship between exposure and asthma exacerbations for both satellite measurements. Exposure-outcome relationships using the interpolated monitor NO2 were inconsistent with known relationships to asthma, suggesting that interpolated monitor data might yield misleading results in small region studies.

Effects of In-Utero Personal Exposure to PM2.5 Sources and Components on Birthweight

Background

In-utero exposure to fine particulate matter (PM2.5) and specific sources and components of PM2.5 have been linked with lower birthweight. However, previous results have been mixed, likely due to heterogeneity in sources impacting PM2.5 and due to measurement error from using ambient data. Therefore, we investigated the effect of PM2.5 sources and their high-loading components on birthweight using data from 198 women in the 3rd trimester from the MADRES cohort 48-hour personal PM2.5 exposure monitoring sub-study.

Methods

The mass contributions of six major sources of personal PM2.5 exposure were estimated for 198 pregnant women in the 3rd trimester using the EPA Positive Matrix Factorization v5.0 model, along with their 17 high-loading chemical components using optical carbon and X-ray fluorescence approaches. Single- and multi-pollutant linear regressions were used to evaluate the association between personal PM2.5 sources and birthweight. Additionally, high-loading components were evaluated with birthweight individually and in models further adjusted for PM2.5 mass.

Results

Participants were predominately Hispanic (81%), with a mean (SD) gestational age of 39.1 (1.5) weeks and age of 28.2 (6.0) years. Mean birthweight was 3,295.8g (484.1) and mean PM2.5 exposure was 21.3 (14.4) μg/m3. A 1 SD increase in the mass contribution of the fresh sea salt source was associated with a 99.2g decrease in birthweight (95% CI: -197.7, -0.6), while aged sea salt was associated with lower birthweight (β =-70.1; 95% CI: -141.7, 1.4). Magnesium sodium, and chlorine were associated with lower birthweight, which remained after adjusting for PM2.5 mass.

Conclusions

This study found evidence that major sources of personal PM2.5 including fresh and aged sea salt were negatively associated with birthweight, with the strongest effect on birthweight from Na and Mg. The effect of crustal and fuel oil sources differed by infant sex with negative associations seen in boys compared to positive associations in girls.

Maternal exposure to ozone and risk of gestational hypertension and eclampsia in the United States

BACKGROUND

Exposure to ambient ozone during pregnancy may be linked with hypertensive disorders in pregnancy, but evidence is largely unknown. We aimed to estimate the association between maternal exposure to ozone and risk of gestational hypertension and eclampsia in the contiguous United States (US).

METHODS

We included 2,393,346 normotensive mothers aged from 18 to 50 years old who had a live singleton birth documented in the National Vital Statistics system in the US in 2002. We obtained information on gestational hypertension and eclampsia from birth certificates. We estimated daily ozone concentrations from a spatiotemporal ensemble model. We used distributed lag model and logistic regression to estimate the association between monthly ozone exposure and risk of gestational hypertension or eclampsia after adjusting for individual-level covariates and county poverty rate.

RESULTS

Of the 2,393,346 pregnant women, there were 79,174 women with gestational hypertension and 6034 with eclampsia. A 10 parts per billion (ppb) increase in ozone was associated with an increased risk of gestational hypertension over 1-3 months before conception (OR = 1.042, 95 % CI: 1.029, 1.056), 2-3 months after conception (OR = 1.058, 95 % CI: 1.040, 1.077), and 3-5 months after conception (OR = 1.031, 95 % CI: 1.018, 1.044). The corresponding OR for eclampsia was 1.115 (95 % CI: 1.074, 1.158), 1.048 (95 % CI: 1.020, 1.077), and 1.070 (95 % CI: 1.032, 1.110), respectively.

CONCLUSIONS

Exposure to ozone was associated with an increased risk of gestational hypertension or eclampsia, especially during 2 to 4 months after conception.

Mapping the personal PM2.5 exposure of China's population using random forest

Ambient monitoring may cause estimation errors, and wearable monitoring is expensive and labor-intensive when assessing PM_2.5 personal exposure. Estimation errors have limited the development of exposure science and environmental epidemiology. Thus, we developed a scenario-based exposure (SBE) model that covered 8 outdoor exposure scenarios and 1 indoor scenario with corresponding time-activity patterns in Baoding City. The linear regression analysis of the SBE yielded an R² value of 0.913 with satisfactory accuracy and reliability. To apply the SBE model to large-scale studies, we predicted time-activity patterns with the random forest model and atmosphere-to-scenario ratios with the linear regression model to obtain the essential parameters of the SBE model; their R² was 0.65-0.93. The developed model would economize the study expenditure of field sampling for personal PM_2.5 and deepen the understanding of the influences of indoor and outdoor factors on personal PM_2.5. Using this method, we found that the personal PM_2.5 exposure of Chinese residents was 10.50-347.02 μg/m³ in 2020, higher than the atmospheric PM_2.5 concentration. Residents in North and Central China, especially the Beijing-Tianjin-Hebei region and the Fen-Wei Plains, had higher personal PM_2.5 exposure than those in other areas.

Ambient temperature and major structural anomalies: A retrospective study of over 2 million newborns

BACKGROUND

Maternal exposure to ambient heat may be associated with congenital anomalies, but evidence is still limited.

OBJECTIVES

We aimed to estimate the association between maternal exposure to ambient heat during the 3-12 weeks post-conception (critical window of organogenesis) and risk of total and various diagnostic categories of major structural anomalies among live singleton births in the contiguous United States (US).

METHODS

We included data on 2,352,529 births with the first day of critical developmental windows falling within months of May through August from 2000 to 2004 across 525 US counties. We used a validated spatial-temporal model to estimate daily county-level population-weighted temperature. We used logistic regression to estimate the association between ambient temperature and risk of diagnostic categories of anomalies during the critical window after adjusting for individual and county-level factors. We conducted subgroup analysis to identify potential susceptible subpopulations.

RESULTS

A total of 29,188 anomalies (12.4 per 1000 births) were recorded during the study period. Maternal exposure to extreme heat (> 95th percentile) was associated with higher risk of total anomalies, central nervous system anomalies, and other uncategorized anomalies with an odds ratio (OR) of 1.05 (95 % CI: 1.00, 1.11), 1.17 (95 % CI: 1.01, 1.37), and 1.16 (95 % CI: 1.04, 1.29) compared with minimum morbidity temperature, respectively. The associations were homogeneous across subgroups defined by maternal age, maternal race/ethnicity, marital status, educational attainment, and parity, but were more pronounced among mothers residing in more socially vulnerable counties and births with multiple anomalies.

CONCLUSIONS

Among US live singleton births, maternal exposure to ambient heat may be associated with higher risk of total anomalies, central nervous system anomalies, and other uncategorized anomalies. We suggest additional research is carried out to better understand the relations between maternal heat exposure and congenital anomalies in the presence of global warming.

Ambient air pollution and clinical dementia: systematic review and meta-analysis

OBJECTIVE

To investigate the role of air pollutants in risk of dementia, considering differences by study factors that could influence findings.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

EMBASE, PubMed, Web of Science, Psycinfo, and OVID Medline from database inception through July 2022.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Studies that included adults (≥18 years), a longitudinal follow-up, considered US Environmental Protection Agency criteria air pollutants and proxies of traffic pollution, averaged exposure over a year or more, and reported associations between ambient pollutants and clinical dementia. Two authors independently extracted data using a predefined data extraction form and assessed risk of bias using the Risk of Bias In Non-randomised Studies of Exposures (ROBINS-E) tool. A meta-analysis with Knapp-Hartung standard errors was done when at least three studies for a given pollutant used comparable approaches.

RESULTS

2080 records identified 51 studies for inclusion. Most studies were at high risk of bias, although in many cases bias was towards the null. 14 studies could be meta-analysed for particulate matter <2.5 µm in diameter (PM2.5). The overall hazard ratio per 2 μg/m3 PM2.5 was 1.04 (95% confidence interval 0.99 to 1.09). The hazard ratio among seven studies that used active case ascertainment was 1.42 (1.00 to 2.02) and among seven studies that used passive case ascertainment was 1.03 (0.98 to 1.07). The overall hazard ratio per 10 μg/m3 nitrogen dioxide was 1.02 ((0.98 to 1.06); nine studies) and per 10 μg/m3 nitrogen oxide was 1.05 ((0.98 to 1.13); five studies). Ozone had no clear association with dementia (hazard ratio per 5 μg/m3 was 1.00 (0.98 to 1.05); four studies).

CONCLUSION

PM2.5 might be a risk factor for dementia, as well as nitrogen dioxide and nitrogen oxide, although with more limited data. The meta-analysed hazard ratios are subject to limitations that require interpretation with caution. Outcome ascertainment approaches differ across studies and each exposure assessment approach likely is only a proxy for causally relevant exposure in relation to clinical dementia outcomes. Studies that evaluate critical periods of exposure and pollutants other than PM2.5, and studies that actively assess all participants for outcomes are needed. Nonetheless, our results can provide current best estimates for use in burden of disease and regulatory setting efforts.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42021277083.

Low level exposure to hydrogen sulfide: a review of emissions, community exposure, health effects, and exposure guidelines

Hydrogen sulfide (H2S) is a toxic gas that is well-known for its acute health risks in occupational settings, but less is known about effects of chronic and low-level exposures. This critical review investigates toxicological and experimental studies, exposure sources, standards, and epidemiological studies pertaining to chronic exposure to H2S from both natural and anthropogenic sources. H2S releases, while poorly documented, appear to have increased in recent years from oil and gas and possibly other facilities. Chronic exposures below 10 ppm have long been associated with odor aversion, ocular, nasal, respiratory and neurological effects. However, exposure to much lower levels, below 0.03 ppm (30 ppb), has been associated with increased prevalence of neurological effects, and increments below 0.001 ppm (1 ppb) in H2S concentrations have been associated with ocular, nasal, and respiratory effects. Many of the studies in the epidemiological literature are limited by exposure measurement error, co-pollutant exposures and potential confounding, small sample size, and concerns of representativeness, and studies have yet to consider vulnerable populations. Long-term community-based studies are needed to confirm the low concentration findings and to refine exposure guidelines. Revised guidelines that incorporate both short- and long-term limits are needed to protect communities, especially sensitive populations living near H2S sources.

Mediation analysis in the presence of continuous exposure measurement error

The difference method is used in mediation analysis to quantify the extent to which a mediator explains the mechanisms underlying the pathway between an exposure and an outcome. In many health science studies, the exposures are almost never measured without error, which can result in biased effect estimates. This article investigates methods for mediation analysis when a continuous exposure is mismeasured. Under a linear exposure measurement error model, we prove that the bias of indirect effect and mediation proportion can go in either direction but the mediation proportion is usually be less biased when the associations between the exposure and its error-prone counterpart are similar with and without adjustment for the mediator. We further propose methods to adjust for exposure measurement error with continuous and binary outcomes. The proposed approaches require a main study/validation study design where in the validation study, data are available for characterizing the relationship between the true exposure and its error-prone counterpart. The proposed approaches are then applied to the Health Professional Follow-up Study, 1986-2016, to investigate the impact of body mass index (BMI) as a mediator for mediating the effect of physical activity on the risk of cardiovascular diseases. Our results reveal that physical activity is significantly associated with a lower risk of cardiovascular disease incidence, and approximately half of the total effect of physical activity is mediated by BMI after accounting for exposure measurement error. Extensive simulation studies are conducted to demonstrate the validity and efficiency of the proposed approaches in finite samples.

Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality

Numerous studies have examined the associations between long-term exposure to fine particulate matter (PM2.5) and adverse health outcomes. Recently, many of these studies have begun to employ high-resolution predicted PM2.5 concentrations, which are subject to measurement error. Previous approaches for exposure measurement error correction have either been applied in non-causal settings or have only considered a categorical exposure. Moreover, most procedures have failed to account for uncertainty induced by error correction when fitting an exposure-response function (ERF). To remedy these deficiencies, we develop a multiple imputation framework that combines regression calibration and Bayesian techniques to estimate a causal ERF. We demonstrate how the output of the measurement error correction steps can be seamlessly integrated into a Bayesian additive regression trees (BART) estimator of the causal ERF. We also demonstrate how locally-weighted smoothing of the posterior samples from BART can be used to create a more accurate ERF estimate. Our proposed approach also properly propagates the exposure measurement error uncertainty to yield accurate standard error estimates. We assess the robustness of our proposed approach in an extensive simulation study. We then apply our methodology to estimate the effects of PM2.5 on all-cause mortality among Medicare enrollees in New England from 2000-2012.

Joint Associations of Short-Term Exposure to Ambient Air Pollutants with Hospital Admission of Ischemic Stroke

BACKGROUND

Studies have estimated the associations of short-term exposure to ambient air pollution with ischemic stroke. However, the joint associations of ischemic stroke with air pollution as a mixture remain unknown.

METHODS

We employed a time-stratified case-crossover study to investigate 824,808 ischemic stroke patients across China. We calculated daily mean concentrations of particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5), maximum 8-h average for O3 (MDA8 O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) across all monitoring stations in the city where the IS patients resided. We conducted conditional logistic regression models to estimate the exposure-response associations.

RESULTS

Results from single-pollutant models showed positive associations of hospital admission for ischemic stroke with PM2.5 (excess risk [ER] = 0.38%, 95% confidence interval [CI]: 0.29% to 0.47%, for 10 μg/m3), MDA8 O3 (ER = 0.29%, 95% CI: 0.18% to 0.40%, for 10 μg/m3), NO2 (ER = 1.15%, 95% CI: 0.92% to 1.39%, for 10 μg/m3), SO2 (ER = 0.82%, 95% CI: 0.53% to 1.11%, for 10 μg/m3) and CO (ER = 3.47%, 95% CI: 2.70% to 4.26%, for 1 mg/m3). The joint associations (ER) with all air pollutants (for interquartile range width increases in each pollutant) estimated by the single-pollutant model was 8.73% and was 4.27% by the multipollutant model. The joint attributable fraction of ischemic stroke attributable to air pollutants based on the multipollutant model was 7%.

CONCLUSIONS

Short-term exposures to PM2.5, MDA8 O3, NO2, SO2, and CO were positively associated with increased risks of hospital admission for ischemic stroke. The joint associations of air pollutants with ischemic stroke might be overestimated using single-pollutant models. See video abstract at, http://links.lww.com/EDE/C8.

Measurement error correction for ambient PM2.5 exposure using stratified regression calibration: Effects on all-cause mortality

BACKGROUND

Previous studies on the impact of measurement error for PM2.5 were mostly simulation studies, did not control for other pollutants, or used a single regression calibration model to correct for measurement error. However, the relationship between actual and error-prone PM2.5 concentration may vary by time and region. We aim to correct the measurement error of PM2.5 predictions using stratified regression calibration and investigate how the measurement error biases the association between PM2.5 and mortality in the Medicare Cohort.

METHODS

The "gold-standard" measurements of PM2.5 were defined as daily monitoring data. We regressed daily monitoring PM2.5 on modeled PM2.5 using the simple linear regression by strata of season, elevation, census division and time period. Calibrated PM2.5 was calculated with stratum-specific calibration parameters β0 (intercept) and β1 (slope) for each strata and aggregated to annual level. Associations between calibrated and error-prone annual PM2.5 and all-cause mortality among Medicare beneficiaries were estimated with Quasi-Poisson regression models.

RESULTS

Across 208 strata, the median of β0 and β1 were 0.62 (25% 0.0.20, 75% 1.06) and 0.93 (25% 0.87, 75% 0.99). From calibrated and error-prone PM2.5 data, we estimated that each 10 μg/m3 increase in PM2.5 was respectively associated with 4.9% (95%CI 4.6-5.2) and 4.6% (95%CI 4.4-4.9) increases in the mortality rate among Medicare beneficiaries, conditional on confounders.

CONCLUSIONS

Regression calibration parameters of PM2.5 varied by time and region. Using error-prone measures of PM2.5 underestimated the association between PM2.5 and all-cause mortality. Modern exposure models produce relatively small bias.

Time-lagged relationships between a decade of air pollution exposure and first hospitalization with Alzheimer's disease and related dementias

Alzheimer's disease and related dementias (ADRD) poses substantial health challenges among an aging population. One of the primary challenges in studying ADRD is that biological processes underlying these ailments begin decades prior to diagnosis. Previous studies indicate a relationship between ADRD and air pollution exposure to both fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) but are limited in their interpretation because they consider exposure measurements at a single time point. Our retrospective cohort study considered 27 + million Medicare enrollees in the United States followed up to 17 years and matched with highly accurate annual air pollution exposure measurements for PM_2.5, NO₂, and summer ozone. We applied distributed lag models and estimated the lagged associations between air pollution and odds of first hospitalization with ADRD. We found significantly increased odds due to overall PM_2.5 and NO₂ exposure and time-lagged exposure 10 and 8 years prior to admission, respectively. Furthermore, we found the connection between air pollution exposure and increased odds of first hospitalization with ADRD exists at air pollution levels below current National Ambient Air Quality Standards set by the US Environmental Protection Agency, with the steepest increase in odds occurring at low concentrations of PM_2.5. Our findings are the first to show that air pollution exposures from as many as 10 years prior to the admission are related to increased odds of hospitalizations with ADRD. As there are no clear treatments available for ADRD, identifying modifiable risk factors such as air pollution exposure may make significant contributions towards prevention or delayed disease progression.

A nationwide study of maternal exposure to ambient ozone and term birth weight in the United States

BACKGROUND

Maternal exposure to ozone (O₃) may cause systemic inflammation and oxidative stress and contribute to fetal growth restriction. We sought to estimate the association between maternal exposure to O₃ and term birth weight and term small for gestational age (SGA) in the United States (US).

METHODS

We conducted a nationwide study including 2,179,040 live term singleton births that occurred across 453 populous counties in the contiguous US in 2002. Daily county-level concentrations of O₃ data were estimated using a Bayesian fusion model. We used linear regression to estimate the association between O₃ exposure and term birth weight and logistic regression to estimate the association between O₃ exposure and term SGA during each trimester of the pregnancy and the entire pregnancy after adjusting for maternal characteristics, infant sex, season of conception, ambient temperature, county poverty rate, and census region. We additionally used distributed lag models to identify the critical exposure windows by estimating the monthly and weekly associations.

RESULTS

A 10 parts per billion (ppb) increase in O₃ over the entire pregnancy was associated with a lower term birth weight (-7.6 g; 95 % CI: -8.8 g, -6.4 g) and increased risk of SGA (odds ratio = 1.030; 95 % CI: 1.020, 1.040). The identified critical exposure windows were the 13th- 25th and 32nd -37th gestational weeks for term birth weight and 13th- 25th for term SGA. We found the association was more pronounced among mothers who were non-Hispanic Black, unmarried, or had lower education level.

CONCLUSIONS

Among US singleton term births, maternal exposure to O₃ was associated with lower rates of fetal growth, and the 13th- 25th gestational weeks were the identified critical exposure windows.

Long-term air pollution exposure and incident stroke in American older adults: A national cohort study

Aims

Stroke is a leading cause of death and disability for Americans, and growing evidence suggests that air pollution may play an important role. To facilitate pollution control efforts, the National Academy of Sciences and the World Health Organization have prioritized determining which air pollutants are most toxic. However, evidence is limited for the simultaneous effects of multiple air pollutants on stroke.

Methods and results

We constructed a nationwide population-based cohort study, using the Medicare Chronic Conditions Warehouse (2000-2017) and high-resolution air pollution data, to investigate the impact of long-term exposure to ambient PM2.5, NO2, and ground-level O3 on incident stroke. Hazard ratios (HR) for stroke incidence were estimated using single-, bi-, and tri-pollutant Cox proportional hazards models. We identified ~2.2 million incident stroke cases among 17,443,900 fee-for-service Medicare beneficiaries. Per interquartile range (IQR) increase in the annual average PM2.5 (3.7 μg/m3), NO2 (12.4 ppb), and warm-season O3 (6.5 ppb) one-year prior to diagnosis, the HRs were 1.022 (95% CI: 1.017-1.028), 1.060 (95% CI: 1.054-1.065), and 1.021 (95% CI: 1.017-1.024), respectively, from the tri-pollutant model. There was strong evidence of linearity in concentration-response relationships for all three air pollutants in single-pollutant models. This linear relationship remained robust for NO2 and O3 in tri-pollutant models while the effect of PM2.5 attenuated at the lower end of concentrations.

Conclusion

Using a large nationwide cohort, our study suggests that long-term exposure to PM2.5, NO2, and O3 may independently increase the risk of stroke among the US elderly, among which traffic-related air pollution plays a particularly crucial role.

In-utero personal exposure to PM2.5 impacted by indoor and outdoor sources and birthweight in the MADRES cohort

BACKGROUND

In-utero exposure to outdoor particulate matter with aerodynamic diameter less than 2.5 μm (PM_2.5) is linked with low birthweight. However, previous results are mixed, likely due to measurement error introduced by estimating personal exposure from ambient data. This study investigated the effect of total personal PM_2.5 exposure on birthweight and whether it differed when it was more heavily impacted by sources of indoor vs outdoor origin in the MADRES cohort study.

METHODS

Personal PM_2.5 exposure was measured in 205 pregnant women in the 3rd trimester using 48 h integrated, filter-based sampling. Linear regression was used to test the association between personal PM_2.5 exposure and birthweight, adjusting for key covariates. Interactions of PM_2.5 with variables representing indoor sources of PM_2.5, home ventilation, or time spent indoors tested whether the effect of total PM_2.5 on birthweight varied when it was more impacted by sources of indoor vs outdoor origin.

RESULTS

In a sample of largely Hispanic (81%) pregnant women, total personal PM_2.5 was not significantly associated with birthweight (β = 38.6 per 1SD increase in PM_2.5; 95% CI:-21.1, 98.2). This association however, differed by home type (single family home: 156.9 (26.9, 287.0), 2-4 attached units:-16.6 (-111.9, 78.7), 5+ units:-62.6 (-184.9, 59.6), missing: 145.4 (-4.1, 294.9), interaction p = 0.028) and by household air conditioner use (none of the time: -27.6 (-101.5, 46.3) vs. some of the time: 139.9 (42.9, 237.0), interaction p = 0.008) Additionally, the effect of personal PM_2.5 on birthweight varied by time spent indoors (none or little of the time: - 45.1 (-208.3, 118.1) vs. most or all of the time: 57.1 (-7.3, 121.6), interaction p = 0.255).

CONCLUSIONS

While no significant association between total personal PM_2.5 exposure and birthweight was found, there was evidence that multi-unit housing (vs. single-family homes), candle and/or incense smoke, and greater outdoor source contributions to personal PM_2.5 were more strongly associated with lower birthweight.

Long-Term Fine Particulate Matter Concentrations and Prevalence of Severe Acute Respiratory Syndrome Coronavirus 2: Differential Relationships by Socioeconomic Status Among Pregnant Individuals in New York City

We aimed to determine whether long-term ambient concentrations of fine particulate matter (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5)) were associated with increased risk of testing positive for coronavirus disease 2019 (COVID-19) among pregnant individuals who were universally screened at delivery and whether socioeconomic status (SES) modified this relationship. We used obstetrical data collected from New-York Presbyterian Hospital/Columbia University Irving Medical Center in New York, New York, between March and December 2020, including data on Medicaid use (a proxy for low SES) and COVID-19 test results. We linked estimated 2018-2019 PM2.5 concentrations (300-m resolution) with census-tract-level population density, household size, income, and mobility (as measured by mobile-device use) on the basis of residential address. Analyses included 3,318 individuals; 5% tested positive for COVID-19 at delivery, 8% tested positive during pregnancy, and 48% used Medicaid. Average long-term PM2.5 concentrations were 7.4 (standard deviation, 0.8) μg/m3. In adjusted multilevel logistic regression models, we saw no association between PM2.5 and ever testing positive for COVID-19; however, odds were elevated among those using Medicaid (per 1-μg/m3 increase, odds ratio = 1.6, 95% confidence interval: 1.0, 2.5). Further, while only 22% of those testing positive showed symptoms, 69% of symptomatic individuals used Medicaid. SES, including unmeasured occupational exposures or increased susceptibility to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) due to concurrent social and environmental exposures, may explain the increased odds of testing positive for COVID-19 being confined to vulnerable pregnant individuals using Medicaid.

The Impact of Exposure Measurement Error on the Estimated Concentration-Response Relationship between Long-Term Exposure to PM2.5 and Mortality

BACKGROUND

Exposure measurement error is a central concern in air pollution epidemiology. Given that studies have been using ambient air pollution predictions as proxy exposure measures, the potential impact of exposure error on health effect estimates needs to be comprehensively assessed.

OBJECTIVES

We aimed to generate wide-ranging scenarios to assess direction and magnitude of bias caused by exposure errors under plausible concentration-response relationships between annual exposure to fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] and all-cause mortality.

METHODS

In this simulation study, we use daily PM2.5 predictions at 1-km2 spatial resolution to estimate annual PM2.5 exposures and their uncertainties for ZIP Codes of residence across the contiguous United States between 2000 and 2016. We consider scenarios in which we vary the error type (classical or Berkson) and the true concentration-response relationship between PM2.5 exposure and mortality (linear, quadratic, or soft-threshold-i.e., a smooth approximation to the hard-threshold model). In each scenario, we generate numbers of deaths using error-free exposures and confounders of concurrent air pollutants and neighborhood-level covariates and perform epidemiological analyses using error-prone exposures under correct specification or misspecification of the concentration-response relationship between PM2.5 exposure and mortality, adjusting for the confounders.

RESULTS

We simulate 1,000 replicates of each of 162 scenarios investigated. In general, both classical and Berkson errors can bias the concentration-response curve toward the null. The biases remain small even when using three times the predicted uncertainty to generate errors and are relatively larger at higher exposure levels.

DISCUSSION

Our findings suggest that the causal determination for long-term PM2.5 exposure and mortality is unlikely to be undermined when using high-resolution ambient predictions given that the estimated effect is generally smaller than the truth. The small magnitude of bias suggests that epidemiological findings are relatively robust against the exposure error. In practice, the use of ambient predictions with a finer spatial resolution will result in smaller bias. https://doi.org/10.1289/EHP10389.

Isotopic signatures and source apportionment of Pb in ambient PM2.5

Particulate lead (Pb) is a primary air pollutant that affects society because of its health impacts. This study investigates the source sectors of Pb associated with ambient fine particulate matter (PM_2.5) over central-western Taiwan (CWT) with new constraints on the Pb-isotopic composition. We demonstrate that the contribution of coal-fired facilities is overwhelming, which is estimated to reach 35 ± 16% in the summertime and is enhanced to 57 ± 24% during the winter monsoon seasons. Moreover, fossil-fuel vehicles remain a major source of atmospheric Pb, which accounts for 12 ± 5%, despite the current absence of a leaded gasoline supply. Significant seasonal and geographical variations in the Pb-isotopic composition are revealed, which suggest that the impact of East Asian (EA) pollution outflows is important in north CWT and drastically declines toward the south. We estimate the average contribution of EA outflows as accounting for 35 ± 15% (3.6 ± 1.5 ng/m³) of the atmospheric Pb loading in CWT during the winter monsoon seasons.

Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: Implementation of Causal Inference Methods

This report provides a final summary of the principal findings and key conclusions of a study supported by an HEI grant aimed at "Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution." It is the second and final report on this topic. The study was designed to advance four critical areas of inquiry and methods development. First, it focused on predicting short- and long-term exposures to ambient fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) at high spatial resolution (1 km × 1 km) for the continental United States over the period 2000-2016 and linking these predictions to health data. Second, it developed new causal inference methods for estimating exposure-response (ER) curves (ERCs) and adjusting for measured confounders. Third, it applied these methods to claims data from Medicare and Medicaid beneficiaries to estimate health effects associated with short- and long-term exposure to low levels of ambient air pollution. Finally, it developed pipelines for reproducible research, including approaches for data sharing, record linkage, and statistical software. Our HEI-funded work has supported an extensive portfolio of analyses and the development of statistical methods that can be used to robustly understand the health effects of short- and long-term exposure to low levels of ambient air pollution. Our Phase 1 report (Dominici et al. 2019) provided a high-level overview of our statistical methods, data analysis, and key findings, grouped into the following five areas: (1) exposure prediction, (2) epidemiological studies of ambient exposures to air pollution at low levels, (3) sensitivity analysis, (4) methodological contributions in causal inference, and (5) an open access research data platform. The current, final report includes a comprehensive overview of the entire research project.

Considering our (1) massive study population, (2) numerous sensitivity analyses, and (3) transparent assessment of covariate balance indicating the quality of causal inference for simulating randomized experiments, we conclude that conditionally on the required assumptions for causal inference, our results collectively indicate that long-term PM2.5 exposure is likely to be causally related to mortality. This conclusion assumes that the causal inference assumptions hold and, more specifically, that we accounted adequately for confounding bias. We explored various modeling approaches, conducted extensive sensitivity analyses, and found that our results were robust across approaches and models. This work relied on publicly available data, and we have provided code that allows for reproducibility of our analyses.

Our work provides comprehensive evidence of associations between exposures to PM2.5, NO2, and O3 and various health outcomes. In the current report, we report more specific results on the causal link between long-term exposure to PM2.5 and mortality, even at PM2.5 levels below or equal to 12 μg/m3, and mortality among Medicare beneficiaries (ages 65 and older). This work relies on newly developed causal inference methods for continuous exposure.

For the period 2000-2016, we found that all statistical approaches led to consistent results: a 10-μg/m3 decrease in PM2.5 led to a statistically significant decrease in mortality rate ranging between 6% and 7% (= 1 - 1/hazard ratio [HR]) (HR estimates 1.06 [95% CI, 1.05 to 1.08] to 1.08 [95% CI, 1.07 to 1.09]). The estimated HRs were larger when studying the cohort of Medicare beneficiaries that were always exposed to PM2.5 levels lower than 12 μg/m3 (1.23 [95% CI, 1.18 to 1.28] to 1.37 [95% CI, 1.34 to 1.40]).

Comparing the results from multiple and single pollutant models, we found that adjusting for the other two pollutants slightly attenuated the causal effects of PM2.5 and slightly elevated the causal effects of NO2 exposure on all-cause mortality. The results for O3 remained almost unchanged.

We found evidence of a harmful causal relationship between mortality and long-term PM2.5 exposures adjusted for NO2 and O3 across the range of annual averages between 2.77 and 17.16 μg/m3 (included >98% of observations) in the entire cohort of Medicare beneficiaries across the continental United States from 2000 to 2016. Our results are consistent with recent epidemiological studies reporting a strong association between long-term exposure to PM2.5 and adverse health outcomes at low exposure levels. Importantly, the curve was almost linear at exposure levels lower than the current national standards, indicating aggravated harmful effects at exposure levels even below these standards.

There is, in general, a harmful causal impact of long-term NO2 exposures to mortality adjusted for PM2.5 and O3 across the range of annual averages between 3.4 and 80 ppb (included >98% of observations). Yet within low levels (annual mean ≤53 ppb) below the current national standards, the causal impacts of NO2 exposures on all-cause mortality are nonlinear with statistical uncertainty.

The ERCs of long-term O3 exposures on all-cause mortality adjusted for PM2.5 and NO2 are almost flat below 45 ppb, which shows no statistically significant effect. Yet we observed an increased hazard when the O3 exposures were higher than 45 ppb, and the HR was approximately 1.10 when comparing Medicare beneficiaries with annual mean O3 exposures of 50 ppb versus those with 30 ppb.

institutions, including those that support the Health Effects Institute; therefore, it may not reflect the views or policies of these parties, and no endorsement by them should be inferred.

A list of abbreviations and other terms appears at the end of this volume.

A national cohort study (2000-2018) of long-term air pollution exposure and incident dementia in older adults in the United States

Air pollution may increase risk of Alzheimer’s disease and related dementias (ADRD) in the U.S., but the extent of this relationship is unclear. Here, we constructed two national U.S. population-based cohorts of those aged ≥65 from the Medicare Chronic Conditions Warehouse (2000–2018), combined with high-resolution air pollution datasets, to investigate the association of long-term exposure to ambient fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and ozone (O₃) with dementia and AD incidence, respectively. We identified ~2.0 million incident dementia cases (N = 12,233,371; dementia cohort) and ~0.8 million incident AD cases (N = 12,456,447; AD cohort). Per interquartile range (IQR) increase in the 5-year average PM_2.5 (3.2 µg/m³), NO₂ (11.6 ppb), and warm-season O₃ (5.3 ppb) over the past 5 years prior to diagnosis, the hazard ratios (HRs) were 1.060 (95% confidence interval [CI]: 1.054, 1.066), 1.019 (95% CI: 1.012, 1.026), and 0.990 (95% CI: 0.987, 0.993) for incident dementias, and 1.078 (95% CI: 1.070, 1.086), 1.031 (95% CI: 1.023, 1.039), and 0.982 (95%CI: 0.977, 0.986) for incident AD, respectively, for the three pollutants. For both outcomes, concentration-response relationships for PM_2.5 and NO₂ were approximately linear. Our study suggests that exposures to PM_2.5 and NO₂ are associated with incidence of dementia and AD.

Air pollution has been linked to neurodegenerative disease. Here the authors carried out a population-based cohort study to investigate the association between long-term exposure to PM_2.5, NO₂, and warm-season O₃ on dementia and Alzheimer’s disease incidence in the United States.

Risk for preeclampsia following exposure to PM2.5 during pregnancy

BACKGROUND

Previous findings concerning the risk for preeclampsia following exposure to particulate matter are inconclusive.

METHODS

We used data from all singleton pregnancies of women insured by the "Clalit health services" (CHS) maintenance organization in southern Israel that resulted in delivery or perinatal mortality at Soroka Medical Center (SMC). Daily PM_2.5 concentrations were estimated by a hybrid satellite-based model at one-squared kilometer spatial resolution. We used Cox proportional hazard models coupled with distributed lag models to examine the association between the mean exposure to PM_2.5 in every gestational week and the diagnosis of preeclampsia, adjusting for maternal age, parity, year of birth, season of birth and socio-economic status. Hazard Ratios (HR) and 95% Confidence Intervals (CI) were calculated for individual gestational weeks and for cumulative exposure until the 25th gestational week.

RESULTS

A total of 133,197 pregnancies ended at SMC during the study period, of which 68,126 (51.1%) were Jewish and 65,071 (48.9%) were Bedouin. For pregnancies of Jewish women, exposure to PM_2.5 from the 7th until the 14st gestational week was significantly associated with preeclampsia (maximal HR = 1.06; 95%CI: 1.01 - 1.11 during the 10th gestational week per 10 μg/m³ increase in PM_2.5). Cumulative exposure to PM_2.5 during the first 25th gestational weeks was also significantly associated with preeclampsia (HR = 2.08; 95%CI: 1.10 - 3.94 per 10 μg/m³ increase in PM_2.5). We observed no association for pregnancies of Bedouin women.

CONCLUSIONS

Exposure to PM_2.5 between the 7th and the 14st gestational weeks was associated with preeclampsia among Jewish women but not among Bedouin women.

Metabolomic signatures of the short-term exposure to air pollution and temperature

BACKGROUND

Short-term exposures to air pollution and temperature have been reported to be associated with inflammation and oxidative stress. However, mechanistic understanding of the affected metabolic pathways is still lacking and literature on the short-term exposure of air-pollution on the metabolome is limited.

OBJECTIVES

We aimed to determine changes in the plasma metabolome and associated metabolic pathways related to short-term exposure to outdoor air pollution and temperature.

METHODS

We performed mass-spectrometry based untargeted metabolomic profiling of plasma samples from a large and well-characterized cohort of men (Normative Aging Study) to identify metabolic pathways associated with short-term exposure to PM2.5, NO2, O3, and temperature (one, seven-, and thirty-day average of address-specific predicted estimates). We used multivariable linear mixed-effect regression and independent component analysis (ICA) while simultaneously adjusting for all exposures and correcting for multiple testing.

RESULTS

Overall, 456 white men provided 648 blood samples, in which 1158 metabolites were quantified, between 2000 and 2016. Average age and body mass index were 75.0 years and 27.7 kg/m2, respectively. Only 3% were current smokers. In the adjusted models, NO2, and temperature showed statistically significant associations with several metabolites (19 metabolites for NO2 and 5 metabolites for temperature). We identified six metabolic pathways (sphingolipid, butanoate, pyrimidine, glycolysis/gluconeogenesis, propanoate, and pyruvate metabolisms) perturbed with short-term exposure to air pollution and temperature. These pathways were involved in inflammation and oxidative stress, immunity, and nucleic acid damage and repair.

CONCLUSIONS

This is the first study to report an untargeted metabolomic signature of temperature exposure, the largest to report an untargeted metabolomic signature of air pollution, and the first to use ICA. We identified several significant plasma metabolites and metabolic pathways associated with short-term exposure to air pollution and temperature; using an untargeted approach. Those pathways were involved in inflammation and oxidative stress, immunity, and nucleic acid damage and repair. These results need to be confirmed by future research.

Parkinson's disease aggravation in association with fine particle components in New York State

BACKGROUND

Long-term exposure to fine particulate matter (PM2.5) has been associated with neurodegenerative diseases, including disease aggravation in Parkinson's disease (PD), but associations with specific PM2.5 components have not been evaluated.

OBJECTIVE

To characterize the association between specific PM2.5 components and PD first hospitalization, a surrogate for disease aggravation.

METHODS

We obtained data on hospitalizations from the New York Department of Health Statewide Planning and Research Cooperative System (2000-2014) to calculate annual first PD hospitalization counts in New York State per county. We used well-validated prediction models at 1 km2 resolution to estimate county level population-weighted annual black carbon (BC), organic matter (OM), nitrate, sulfate, sea salt (SS), and soil particle concentrations. We then used a multi-pollutant mixed quasi-Poisson model with county-specific random intercepts to estimate rate ratios (RR) of one-year exposure to each PM2.5 component and PD disease aggravation. We evaluated potential nonlinear exposure-outcome relationships using penalized splines and accounted for potential confounders.

RESULTS

We observed a total of 197,545 PD first hospitalizations in NYS from 2000 to 2014. The annual average count per county was 212 first hospitalizations. The RR (95% confidence interval) for PD aggravation was 1.06 (1.03, 1.10) per one standard deviation (SD) increase in nitrate concentrations and 1.06 (1.04, 1.09) for the corresponding increase in OM concentrations. We also found a nonlinear inverse association between PD aggravation and BC at concentrations above the 96th percentile. We found a marginal association with SS and no association with sulfate or soil exposure.

CONCLUSION

In this study, we detected associations between the PM2.5 components OM and nitrate with PD disease aggravation. Our findings support that PM2.5 adverse effects on PD may vary by particle composition.

Air pollution and the pandemic: Long-term PM2.5 exposure and disease severity in COVID-19 patients

Background and objective

Ecological studies have suggested an association between exposure to particulate matter ≤2.5 μm (PM_2.5) and coronavirus disease 2019 (COVID‐19) severity. However, these findings are yet to be validated in individual‐level studies. We aimed to determine the association of long‐term PM_2.5 exposure with hospitalization among individual patients infected with severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2).

Methods

We estimated the 10‐year (2009–2018) PM_2.5 exposure at the residential zip code of COVID‐19 patients diagnosed at the University of Cincinnati healthcare system between 13 March 2020 and 30 September 2020. Logistic regression was used to determine the odds ratio (OR) and 95% CI for COVID‐19 hospitalizations associated with PM_2.5, adjusting for socioeconomic characteristics and comorbidities.

Results

Among the 14,783 COVID‐19 patients included in our study, 13.6% were hospitalized; the geometric mean (SD) PM_2.5 was 10.48 (1.12) μg/m³. In adjusted analysis, 1 μg/m³ increase in 10‐year annual average PM_2.5 was associated with 18% higher hospitalization (OR: 1.18, 95% CI: 1.11–1.26). Likewise, 1 μg/m³ increase in PM_2.5 estimated for the year 2018 was associated with 14% higher hospitalization (OR: 1.14, 95% CI: 1.08–1.21).

Conclusion

Long‐term PM_2.5 exposure is associated with increased hospitalization in COVID‐19. Therefore, more stringent COVID‐19 prevention measures may be needed in areas with higher PM_2.5 exposure to reduce the disease morbidity and healthcare burden.

Ambient PM2.5 species and ultrafine particle exposure and their differential metabolomic signatures

BACKGROUND

The metabolomic signatures of short- and long-term exposure to PM_2.5 have been reported and linked to inflammation and oxidative stress. However, little is known about the relative contribution of the specific PM_2.5 species (hence sources) that drive these metabolomic signatures.

OBJECTIVES

We aimed to determine the relative contribution of the different species of PM_2.5 exposure to the perturbed metabolic pathways related to changes in the plasma metabolome.

METHODS

We performed mass-spectrometry based metabolomic profiling of plasma samples among men from the Normative Aging Study to identify metabolic pathways associated with PM_2.5 species. The exposure windows included short-term (one, seven-, and thirty-day moving average) and long-term (one year moving average). We used linear mixed-effect regression with subject-specific intercepts while simultaneously adjusting for PM_2.5, NO₂, O₃, temperature, relative humidity, and covariates and correcting for multiple testing. We also used independent component analysis (ICA) to examine the relative contribution of patterns of PM_2.5 species.

RESULTS

Between 2000 and 2016, 456 men provided 648 blood samples, in which 1158 metabolites were quantified. We chose 305 metabolites for the short-term and 288 metabolites for the long-term exposure in this analysis that were significantly associated (p-value < 0.01) with PM_2.5 to include in our PM_2.5 species analysis. On average, men were 75.0 years old and their body mass index was 27.7 kg/m². Only 3% were current smokers. In the adjusted models, ultrafine particles (UFPs) were the most significant species of short-term PM_2.5 exposure followed by nickel, vanadium, potassium, silicon, and aluminum. Black carbon, vanadium, zinc, nickel, iron, copper, and selenium were the significant species of long-term PM_2.5 exposure. We identified several metabolic pathways perturbed with PM_2.5 species including glycerophospholipid, sphingolipid, and glutathione. These pathways are involved in inflammation, oxidative stress, immunity, and nucleic acid damage and repair. Results were overlapped with the ICA.

CONCLUSIONS

We identified several significant perturbed plasma metabolites and metabolic pathways associated with exposure to PM_2.5 species. These species are associated with traffic, fuel oil, and wood smoke. This is the largest study to report a metabolomic signature of PM_2.5 species' exposure and the first to use ICA.

Air Pollution and Risk of Placental Abruption: A Study of Births in New York City, 2008-2014

We evaluated the associations of exposure to fine particulate matter (particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) at concentrations of <12 μg/m3, 12-14 μg/m3, and ≥15 μg/m3) and nitrogen dioxide (at concentrations of <26 parts per billion (ppb), 26-29 ppb, and ≥30 ppb) with placental abruption in a prospective cohort study of 685,908 pregnancies in New York, New York (2008-2014). In copollutant analyses, these associations were examined using distributed-lag nonlinear models based on Cox models. The prevalence of abruption was 0.9% (n = 6,025). Compared with a PM2.5 concentration less than 12 μg/m3, women exposed to PM2.5 levels of ≥15 μg/m3 in the third trimester had a higher rate of abruption (hazard ratio (HR) = 1.68, 95% confidence interval (CI): 1.41, 2.00). Compared with a nitrogen dioxide concentration less than 26 ppb, women exposed to nitrogen dioxide levels of 26-29 ppb (HR = 1.11, 95% CI: 1.02, 1.20) and ≥30 ppb (HR = 1.06, 95% CI: 0.96, 1.24) in the first trimester had higher rates of abruption. Compared with both PM2.5 and nitrogen dioxide levels less than the 95th percentile in the third trimester, rates of abruption were increased with both PM2.5 and nitrogen dioxide ≥95th percentile (HR = 1.44, 95% CI: 1.15, 1.80) and PM2.5 ≥95th percentile and nitrogen dioxide <95th percentile (HR = 1.43 95% CI: 1.23, 1.66). Increased levels of PM2.5 exposure in the third trimester and nitrogen dioxide exposure in the first trimester are associated with elevated rates of placental abruption, suggesting that these exposures may be important triggers of premature placental separation through different pathways.

Exposure misclassification in propensity score-based time-to-event data analysis

In epidemiology, identifying the effect of exposure variables in relation to a time-to-event outcome is a classical research area of practical importance. Incorporating propensity score in the Cox regression model, as a measure to control for confounding, has certain advantages when outcome is rare. However, in situations involving exposure measured with moderate to substantial error, identifying the exposure effect using propensity score in Cox models remains a challenging yet unresolved problem. In this paper, we propose an estimating equation method to correct for the exposure misclassification-caused bias in the estimation of exposure-outcome associations. We also discuss the asymptotic properties and derive the asymptotic variances of the proposed estimators. We conduct a simulation study to evaluate the performance of the proposed estimators in various settings. As an illustration, we apply our method to correct for the misclassification-caused bias in estimating the association of PM2.5 level with lung cancer mortality using a nationwide prospective cohort, the Nurses' Health Study. The proposed methodology can be applied using our user-friendly R program published online.

The Role of Ambient Particle Radioactivity in Inflammation and Endothelial Function in an Elderly Cohort

BACKGROUND

The mechanisms by which exposure to particulate matter might increase risk of cardiovascular morbidity and mortality are not fully known. However, few existing studies have investigated the potential role of particle radioactivity. Naturally occurring radionuclides attach to particulate matter and continue to release ionizing radiation after inhalation and deposition in the lungs. We hypothesize that exposure to particle radioactivity increases biomarkers of inflammation.

METHODS

Our repeated-measures study included 752 men in the greater Boston area. We estimated regional particle radioactivity as a daily spatial average of gross beta concentrations from five monitors in the study area. We used linear mixed-effects regression models to estimate short- and medium-term associations between particle radioactivity and biomarkers of inflammation and endothelial dysfunction, with and without adjustment for additional particulate air pollutants.

RESULTS

We observed associations between particle radioactivity on C-reactive protein (CRP), intercellular adhesion molecule-1 (ICAM-1), and vascular cell adhesion molecule-1 (VCAM-1), but no associations with fibrinogen. An interquartile range width increase in mean 7-day particle radioactivity (1.2 × 10 Bq/m) was associated with a 4.9% increase in CRP (95% CI = 0.077, 9.9), a 2.8% increase in ICAM-1 (95% CI = 1.4, 4.2), and a 4.3% increase in VCAM-1 (95% CI = 2.5, 6.1). The main effects of particle radioactivity remained similar after adjustment in most cases. We also obtained similar effect estimates in a sensitivity analysis applying a robust causal model.

CONCLUSION

Regional particle radioactivity is positively associated with inflammatory biomarkers, indicating a potential pathway for radiation-induced cardiovascular effects.

Long-term exposure to fine particulate matter and hospitalization in COVID-19 patients

BACKGROUND

Ecological evidence suggests that exposure to air pollution affects coronavirus disease 2019 (COVID-19) outcomes. However, no individual-level study has confirmed the association to date.

METHODS

We identified COVID-19 patients diagnosed at the University of Cincinnati hospitals and clinics and estimated particulate matter ≤2.5 μm (PM_2.5) exposure over a 10-year period (2008-2017) at their residential zip codes. We used logistic regression to evaluate the association between PM_2.5 exposure and hospitalizations for COVID-19, adjusting for socioeconomic characteristics and comorbidities.

RESULTS

Among the 1128 patients included in our study, the mean (standard deviation) PM_2.5 was 11.34 (0.70) μg/m³ for the 10-year average exposure and 13.83 (1.03) μg/m³ for the 10-year maximal exposures. The association between long-term PM_2.5 exposure and hospitalization for COVID-19 was contingent upon having pre-existing asthma or chronic obstructive pulmonary (COPD) (P_interaction = 0.030 for average PM_2.5 and P_interaction = 0.001 for maximal PM_2.5). In COVID-19 patients with asthma or COPD, the odds of hospitalization were 62% higher with 1 μg/m³ increment in 10-year average PM_2.5 (odds ratio [OR]: 1.62, 95% confidence interval [CI]: 1.00-2.64) and 65% higher with 1 μg/m³ increase in 10-year maximal PM_2.5 levels (OR: 1.65, 95% CI: 1.16-2.35). However, among COVID-19 patients without asthma or COPD, PM_2.5 exposure was not associated with higher hospitalizations (OR: 0.84, 95% CI: 0.65-1.09 for average PM_2.5 and OR: 0.78, 95% CI: 0.65-0.95 for maximal PM_2.5).

CONCLUSIONS

Long-term exposure to PM_2.5 is associated with higher odds of hospitalization in COVID-19 patients with pre-existing asthma or COPD.

Fine Particle Exposure and Clinical Aggravation in Neurodegenerative Diseases in New York State

BACKGROUND

Adult-onset neurodegenerative diseases affect millions and negatively impact health care systems worldwide. Evidence suggests that air pollution may contribute to aggravation of neurodegeneration, but studies have been limited.

OBJECTIVE

We examined the potential association between long-term exposure to particulate matter ≤ 2.5 μ m in aerodynamic diameter [fine particulate matter (PM 2.5 )] and disease aggravation in Alzheimer's (AD) and Parkinson's (PD) diseases and amyotrophic lateral sclerosis (ALS), using first hospitalization as a surrogate of clinical aggravation.

METHODS

We used data from the New York Department of Health Statewide Planning and Research Cooperative System (SPARCS 2000-2014) to construct annual county counts of first hospitalizations with a diagnosis of AD, PD, or ALS (total, urbanicity-, sex-, and age-stratified). We used annual PM 2.5 concentrations estimated by a prediction model at a 1 -km 2 resolution, which we aggregated to population-weighted county averages to assign exposure to cases based on county of residence. We used outcome-specific mixed quasi-Poisson models with county-specific random intercepts to estimate rate ratios (RRs) for a 1-y PM 2.5 exposure. We allowed for nonlinear exposure-outcome relationships using penalized splines and accounted for potential confounders.

RESULTS

We found a positive nonlinear PM 2.5 - PD association that plateaued above 11   μ g / m 3 (RR = 1.09 , 95% CI: 1.04, 1.14 for a PM 2.5 increase from 8.1 to 10.4   μ g / m 3 ). We also found a linear PM 2.5 - ALS positive association (RR = 1.05 , 95% CI: 1.01, 1.09 per 1 - μ g / m 3 PM 2.5 increase), and suggestive evidence of an association with AD. We found effect modification by age for PD and ALS with a stronger positive association in patients < 70  years of age but found insufficient evidence of effect modification by sex or urbanization level for any of the outcomes.

CONCLUSION

Our findings suggest that annual increase in county-level PM 2.5 concentrations may contribute to clinical aggravation of PD and ALS. Importantly, the average annual PM 2.5 concentration in our study was 8.1   μ g / m 3 , below the current American national standards, suggesting the standards may not adequately protect the aging population. https://doi.org/10.1289/EHP7425.

Long-term exposure to particulate matter and roadway proximity with age at natural menopause in the Nurses' Health Study II Cohort

Evidence has shown associations between air pollution and traffic-related exposure with accelerated aging, but no study to date has linked the exposure with age at natural menopause, an important indicator of reproductive aging. In this study, we sought to examine the associations of residential exposure to ambient particulate matter (PM) and distance to major roadways with age at natural menopause in the Nurses' Health Study II (NHS II), a large, prospective female cohort in US. A total of 105,996 premenopausal participants in NHS II were included at age 40 and followed through 2015. Time-varying residential exposures to PM10, PM2.5-10, and PM2.5 and distance to roads was estimated. We calculated hazard ratios (HR) and 95% confidence intervals (CIs) for natural menopause using Cox proportional hazard models adjusting for potential confounders and predictors of age at menopause. We also examined effect modification by region, smoking, body mass, physical activity, menstrual cycle length, and population density. There were 64,340 reports of natural menopause throughout 1,059,229 person-years of follow-up. In fully adjusted models, a 10 μg/m3 increase in the cumulative average exposure to PM10 (HR: 1.02, 95% CI: 1.00, 1.04), PM2.5-10 (HR: 1.03, 95% CI: 1.00, 1.05), and PM2.5 (HR: 1.03, 95% CI: 1.00, 1.06) and living within 50 m to a major road at age 40 (HR: 1.03, 95%CI: 1.00, 1.06) were associated with slightly earlier menopause. No statistically significant effect modification was found, although the associations of PM were slightly stronger for women who lived in the West and for never smokers. To conclude, we found exposure to ambient PM and traffic in midlife was associated with slightly earlier onset of natural menopause. Our results support previous evidence that exposure to air pollution and traffic may accelerate reproductive aging.

Metabolomic signatures of the long-term exposure to air pollution and temperature

BACKGROUND

Long-term exposures to air pollution has been reported to be associated with inflammation and oxidative stress. However, the underlying metabolic mechanisms remain poorly understood.

OBJECTIVES

We aimed to determine the changes in the blood metabolome and thus the metabolic pathways associated with long-term exposure to outdoor air pollution and ambient temperature.

METHODS

We quantified metabolites using mass-spectrometry based global untargeted metabolomic profiling of plasma samples among men from the Normative Aging Study (NAS). We estimated the association between long-term exposure to PM2.5, NO2, O3, and temperature (annual average of central site monitors) with metabolites and their associated metabolic pathways. We used multivariable linear mixed-effect regression models (LMEM) while simultaneously adjusting for the four exposures and potential confounding and correcting for multiple testing. As a reduction method for the intercorrelated metabolites (outcome), we further used an independent component analysis (ICA) and conducted LMEM with the same exposures.

RESULTS

Men (N = 456) provided 648 blood samples between 2000 and 2016 in which 1158 metabolites were quantified. On average, men were 75.0 years and had an average body mass index of 27.7 kg/m2. Almost all men (97%) were not current smokers. The adjusted analysis showed statistically significant associations with several metabolites (58 metabolites with PM2.5, 15 metabolites with NO2, and 6 metabolites with temperature) while no metabolites were associated with O3. One out of five ICA factors (factor 2) was significantly associated with PM2.5. We identified eight perturbed metabolic pathways with long-term exposure to PM2.5 and temperature: glycerophospholipid, sphingolipid, glutathione, beta-alanine, propanoate, and purine metabolism, biosynthesis of unsaturated fatty acids, and taurine and hypotaurine metabolism. These pathways are related to inflammation, oxidative stress, immunity, and nucleic acid damage and repair.

CONCLUSIONS

Using a global untargeted metabolomic approach, we identified several significant metabolites and metabolic pathways associated with long-term exposure to PM2.5, NO2 and temperature. This study is the largest metabolomics study of long-term air pollution, to date, the first study to report a metabolomic signature of long-term temperature exposure, and the first to use ICA in the analysis of both.

Nationwide Study of Short-term Exposure to Fine Particulate Matter and Cardiovascular Hospitalizations Among Medicaid Enrollees

BACKGROUND

Fine particulate matter (PM2.5) has been consistently linked to cardiovascular disease (CVD). Although studies have reported modification by income, to our knowledge, no study to date has examined this relationship among adults in Medicaid, which provides health coverage to low-income and/or disabled Americans.

METHODS

We estimated the association between short-term PM2.5 exposure (average of PM2.5 on the day of hospitalization and the preceding day) and CVD admissions rates among adult Medicaid enrollees in the continental United States (2000-2012) using a time-stratified case-crossover design. We repeated this analysis at PM2.5 concentrations below the World Health Organization daily guideline of 25 μg/m. We compared the PM2.5-CVD association in the Medicaid ≥65 years old versus non-Medicaid-eligible Medicare enrollees (≥65 years old).

RESULTS

Using information on 3,666,657 CVD hospitalizations among Medicaid adults, we observed a 0.9% (95% CI = 0.6%, 1.1%) increase in CVD admission rates per 10 μg/m PM2.5 increase. The association was stronger at low PM2.5 levels (1.3%; 95% CI = 0.9%, 1.6%). Among Medicaid enrollees ≥65 years old, the association was 0.9% (95% CI = 0.6%, 1.3%) vs. 0.8% (95% CI = 0.6%, 0.9%) among non-Medicaid-eligible Medicare enrollees ≥65 years old.

CONCLUSION

We found robust evidence of an association between short-term PM2.5 and CVD hospitalizations among the vulnerable subpopulation of adult Medicaid enrollees. Importantly, this association persisted even at PM2.5 levels below the current national standards.

Long-term effects of PM2·5 on neurological disorders in the American Medicare population: a longitudinal cohort study

BACKGROUND

Accumulating evidence links fine particulate matter (PM_2·5) to premature mortality, cardiovascular disease, and respiratory disease. However, less is known about the influence of PM_2·5 on neurological disorders. We aimed to investigate the effect of long-term PM_2·5 exposure on development of Parkinson's disease or Alzheimer's disease and related dementias.

METHODS

We did a longitudinal cohort study in which we constructed a population-based nationwide open cohort including all fee-for-service Medicare beneficiaries (aged ≥65 years) in the contiguous United States (2000-16) with no exclusions. We assigned PM_2·5 postal code (ie, ZIP code) concentrations based on mean annual predictions from a high-resolution model. To accommodate our very large dataset, we applied Cox-equivalent Poisson models with parallel computing to estimate hazard ratios (HRs) for first hospital admission for Parkinson's disease or Alzheimer's disease and related dementias, adjusting for potential confounders in the health models.

FINDINGS

Between Jan 1, 2000, and Dec 31, 2016, of 63 038 019 individuals who were aged 65 years or older during the study period, we identified 1·0 million cases of Parkinson's disease and 3·4 million cases of Alzheimer's disease and related dementias based on primary and secondary diagnosis billing codes. For each 5 μg/m³ increase in annual PM_2·5 concentrations, the HR was 1·13 (95% CI 1·12-1·14) for first hospital admission for Parkinson's disease and 1·13 (1·12-1·14) for first hospital admission for Alzheimer's disease and related dementias. For both outcomes, there was strong evidence of linearity at PM_2·5 concentrations less than 16 μg/m³ (95th percentile of the PM_2·5 distribution), followed by a plateaued association with increasingly larger confidence bands.

INTERPRETATION

We provide evidence that exposure to annual mean PM_2·5 in the USA is significantly associated with an increased hazard of first hospital admission with Parkinson's disease and Alzheimer's disease and related dementias. For the ageing American population, improving air quality to reduce PM_2·5 concentrations to less than current national standards could yield substantial health benefits by reducing the burden of neurological disorders.

FUNDING

The Health Effects Institute, The National Institute of Environmental Health Sciences, The National Institute on Aging, and the HERCULES Center.

Hot under the collar: A 14-year association between temperature and violent behavior across 436 U.S. counties

OBJECTIVES

Violent behavior is influenced by individual and societal characteristics, but the role of environmental factors is less understood. Our aims were to use national-level data to identify the association between criminal behavior and short-term temperature conditions, including the departure of daily temperatures from normal conditions.

METHODS

We conducted a multi-stage hierarchical time-series model across 436 U.S. counties and 14-years representing 100.4 million people to investigate the association between daily mean temperature and daily mean temperatures departing from normal conditions with violent and non-violent crime counts. First-stage comparisons were made within counties to control for population and geographic heterogeneities, while a second stage combined estimates. We evaluated differences in risk based on county sociodemographic characteristics and estimated non-linear exposure-response relationships.

RESULTS

We observed a total of 9.0 million violent crimes and 20.9 million non-violent property crimes between 2000 through 2013. We estimated that each 10 °C increase in daily temperature or daily departure from long-term normal temperatures were associated with 11.92% (95% PI: 11.57, 12.27) and 10.37% (95% PI: 10.05, 10.69) increase in the risk of violent crime, respectively. Similar, but lower in magnitude trends, were observed for property crime risks. We found that crime risk plateaus and decreases at high daily temperatures, but for temperatures departing from normal, the association with crime increased linearly. Seasonal variations showed that anomalously warm temperatures days during cool months had the greatest risk.

CONCLUSIONS

Our study revealed an association between higher temperatures and high departure from normal temperatures with both violent and non-violent crime risk, regardless of community-type. However, our findings on seasonal and daily trends suggest that daily mean temperature may impact crime by affecting routine activities and behavior, as opposed to a temperature-aggression relationship. These results may advance public response and planning to prevent violent behavior.

Association of ambient PM2·5 exposure with maternal bone strength in pregnant women from Mexico City: a longitudinal cohort study

BACKGROUND

Pregnancy is associated with deteriorations in maternal bone strength and heightened susceptibility to bone fractures. We aimed to investigate whether ambient particulate matter (PM)_2·5 concentrations were associated with bone strength during pregnancy.

METHODS

In this longitudinal cohort study, we analysed longitudinal data from women participating in the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) cohort in Mexico City, Mexico. Eligible women were aged 18 years or older, at less than 20 weeks' gestation at the time of recruitment, planning to stay in Mexico City for the next 3 years, without heart or kidney disease, did not use steroids or anti-epileptic drugs, were not daily consumers of alcohol, and had access to a telephone. Daily ambient PM_2·5 concentrations were estimated from a spatio-temporal model that was based on the individual's address. Trabecular bone strength was measured using quantitative ultrasound from the radius of the middle finger and cortical bone strength from the proximal phalanx of the middle finger, during the second trimester, third trimester, and 1 and 6 months post partum. Bone strength T scores were modelled with PM_2·5 concentrations using linear mixed models and distributed lag models.

FINDINGS

Adjusting for multiple exposure windows, each 10 ug/m³ increase in PM_2·5 exposure concentrations in the first trimester was associated with a 0·18 SD decrease (95% CI -0·35 to -0·01; p=0·033) in ultrasound speed-of-sound (SOS) T score of trabecular bone strength from the second trimester until 6 months post partum. Similarly, each 10 μg/m³ increase in third trimester PM_2·5 exposure was associated with a 0·18 SD decrease (-0·36 to -0·01; p=0·044) in the SOS T score of trabecular bone strength from the third trimester until 6 months post partum. PM_2·5 exposure in the first month post partum was associated with a 0·20 SD decline (-0·39 to -0·01; p=0·043) in cortical bone strength until 6 months post partum.

INTERPRETATION

Ambient PM_2·5 exposure during and after pregnancy was associated with diminished trabecular and cortical bone strength. Early pregnancy PM_2·5 exposure was associated with a greater decline in bone strength later during pregnancy. Late pregnancy and early post-partum exposures adversely affected the post-partum bone strength recovery. Technological and policy solutions to reduce PM_2·5 pollution could improve public health by reducing bone fracture risk.

FUNDING

US National Institute of Environmental Health Sciences.

Determinants of personal exposure to fine particulate matter in the retired adults - Results of a panel study in two megacities, China

This study aimed to investigate the relationship between outdoor, indoor, and personal PM_2.5 exposure in the retired adults and explore the effects of potential determinants in two Chinese megacities. A longitudinal panel study was conducted in Nanjing (NJ) and Beijing (BJ), China, and thirty-three retired non-smoking adults aged 43-86 years were recruited in each city. Repeated measurements of outdoor-indoor-personal PM_2.5 concentrations were measured for five consecutive 24-h periods during both heating and non-heating seasons using real-time and gravimetric methods. Time-activity and household characteristics were recorded. Mixed-effects models were applied to analyze the determinants of personal PM_2.5 exposure. In total, 558 complete sets of collocated 24-h outdoor-indoor-personal PM_2.5 concentrations were collected. The median 24-h personal PM_2.5 exposure concentrations ranged from 43 to 79 μg/m³ across cities and seasons, which were significantly greater than their corresponding indoor levels (ranging from 36 to 68 μg/m³, p < 0.001), but significantly lower than outdoor levels (ranging from 43 to 95 μg/m³, p < 0.001). Indoor and outdoor PM_2.5 concentrations were the strongest determinants of personal exposures in both cities and seasons, with R_M² ranging from 0.814 to 0.915 for indoor and from 0.698 to 0.844 for outdoor PM_2.5 concentrations, respectively. The personal-outdoor regression slopes varied widely among seasons, with a pronounced effect in BJ (NHS: 0.618 ± 0.042; HS: 0.834 ± 0.023). Ventilation status, indoor PM_2.5 sources, personal characteristics, and meteorological factors, were also found to influence personal exposure levels. The city and season-specific models developed here are able to account for 89%-93% of the variance in personal PM_2.5 exposure. A LOOCV analysis showed an R² (RMSE) of 0.80-0.90 (0.21-0.36), while a 10-fold CV analysis demonstrated a R² (RMSE) of 0.83-0.90 (0.20-0.35). By incorporating potentially significant determinants of personal exposure, this modeling approach can improve the accuracy of personal PM_2.5 exposure assessment in epidemiologic studies.

Acute Air Pollution Exposure and the Risk of Violent Behavior in the United States

BACKGROUND

Violence is a leading cause of death and an important public health threat, particularly among adolescents and young adults. However, the environmental causes of violent behavior are not well understood. Emerging evidence suggests exposure to air pollution may be associated with aggressive or impulsive reactions in people.

METHODS

We applied a two-stage hierarchical time-series model to estimate change in risk of violent and nonviolent criminal behavior associated with short-term air pollution in U.S. counties (2000-2013). We used daily monitoring data for ozone and fine particulate matter (PM2.5) from the Environmental Protection Agency and daily crime counts from the Federal Bureau of Investigation. We evaluated the exposure-response relation and assessed differences in risk by community characteristics of poverty, urbanicity, race, and age.

RESULTS

Our analysis spans 301 counties in 34 states, representing 86.1 million people and 721,674 days. Each 10 µg/m change in daily PM2.5 was associated with a 1.17% (95% confidence interval [CI] = 0.90, 1.43) and a 10 ppb change in ozone with a 0.59% (95% CI = 0.41, 0.78) relative risk increase (RRI) for violent crime. However, we observed no risk increase for nonviolent property crime due to PM2.5 (RRI: 0.11%; 95% CI = -0.09, 0.31) or ozone (RRI: -0.05%; 95% CI = -0.22, 0.12). Our results were robust across all community types, except rural regions. Exposure-response curves indicated increased violent crime risk at concentrations below regulatory standards.

CONCLUSIONS

Our results suggest that short-term changes in ambient air pollution may be associated with a greater risk of violent behavior, regardless of community type.

Time-weighted average of fine particulate matter exposure and cause-specific mortality in China: a nationwide analysis

BACKGROUND

Most previous assessments of the hazardous effects attributable to fine particulate matter (PM_2·5) exposure have used ambient PM_2·5 as an exposure metric, resulting in substantial bias in effect estimates. We did a study to examine the association between cause-specific mortality and the time-weighted average of PM_2·5 exposure after accounting for indoor exposure in 267 cities in China.

METHODS

We did a nationwide study, using Laser Egg air quality monitors in 36 cities to obtain data for indoor PM_2·5 concentrations from 18 484 anonymised households between Nov 1, 2015 and July 2, 2018. We developed and validated a nationwide indoor PM_2·5 prediction model for a further 302 cities by retrieving raw records of hourly concentrations from residents' air sensors; the model was used to predict indoor PM_2·5 during 2013 to 2018. Daily ambient PM_2·5 concentration data were estimated by averaging hourly ambient PM_2·5 concentrations obtained from China's National Urban Air Quality Real-time Publishing Platform. Daily numbers of deaths from all non-accidental causes were obtained from 324 cities from the Disease Surveillance Point System of China between Jan 1, 2013, to Dec 31, 2017, and calculated for 267 cities that had an average daily mortality above three, and data for PM_2·5 concentrations and meteorological information for at least 1 year between 2013 and 2017. We used distributed lag non-linear models to estimate city-specific associations between cause-specific mortality and reconstructed PM_2·5 exposure by considering indoor PM_2·5 exposure. We combined the city-specific effect estimates at the national level using a random effects meta-analysis.

FINDINGS

13 972 records of daily indoor PM_2·5 concentrations for 36 cities, extracted from 47 459 183 raw records from the sensors were included for modelling indoor PM_2·5 levels. The nationwide indoor PM_2·5 concentration was 40 μg/m³ (SD 21) between 2013 and 2017, which was approximately 20% lower than the ambient PM_2·5 concentration of 50 μg/m³ (42). An increase of 10 μg/m³ in time-averaged PM_2·5 exposure concentrations was associated with increased daily mortality estimates of 0·44% (95% CI 0·33-0·54) for total non-accidental causes, 0·50% (0·37-0·63) for cardiovascular diseases, 0·46% (0·28-0·63) for coronary heart disease, 0·49% (0·32-0·66) for stroke, 0·59% (0·39-0·79) for respiratory diseases, and 0·69% (0·45-0·92) for chronic obstructive pulmonary disease, respectively. Compared with previous estimations based on ambient PM_2·5, our estimates approximately doubled the size of the effects related to PM_2·5.

INTERPRETATION

This nationwide study revealed a higher mortality risk attributed to time-averaged indoor and ambient PM_2·5 exposure compared with the risk associated with ambient PM_2·5 exposure alone, which indicates that caution should be exercised when using ambient PM_2·5 as a surrogate for PM_2·5 exposure.

FUNDING

National Natural Science Foundation of China (Youth Program) and the Fundamental Research Project of Beihang University.

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1-Basic theory and simple methods of adjustment

Measurement error and misclassification of variables frequently occur in epidemiology and involve variables important to public health. Their presence can impact strongly on results of statistical analyses involving such variables. However, investigators commonly fail to pay attention to biases resulting from such mismeasurement. We provide, in two parts, an overview of the types of error that occur, their impacts on analytic results, and statistical methods to mitigate the biases that they cause. In this first part, we review different types of measurement error and misclassification, emphasizing the classical, linear, and Berkson models, and on the concepts of nondifferential and differential error. We describe the impacts of these types of error in covariates and in outcome variables on various analyses, including estimation and testing in regression models and estimating distributions. We outline types of ancillary studies required to provide information about such errors and discuss the implications of covariate measurement error for study design. Methods for ascertaining sample size requirements are outlined, both for ancillary studies designed to provide information about measurement error and for main studies where the exposure of interest is measured with error. We describe two of the simpler methods, regression calibration and simulation extrapolation (SIMEX), that adjust for bias in regression coefficients caused by measurement error in continuous covariates, and illustrate their use through examples drawn from the Observing Protein and Energy (OPEN) dietary validation study. Finally, we review software available for implementing these methods. The second part of the article deals with more advanced topics.

Evaluating the impact of long-term exposure to fine particulate matter on mortality among the elderly

Many studies link long-term fine particle (PM2.5) exposure to mortality, even at levels below current U.S. air quality standards (12 micrograms per cubic meter). These findings have been disputed with claims that the use of traditional statistical approaches does not guarantee causality. Leveraging 16 years of data-68.5 million Medicare enrollees-we provide strong evidence of the causal link between long-term PM2.5 exposure and mortality under a set of causal inference assumptions. Using five distinct approaches, we found that a decrease in PM2.5 (by 10 micrograms per cubic meter) leads to a statistically significant 6 to 7% decrease in mortality risk. Based on these models, lowering the air quality standard to 10 micrograms per cubic meter would save 143,257 lives (95% confidence interval, 115,581 to 170,645) in one decade. Our study provides the most comprehensive evidence to date of the link between long-term PM2.5 exposure and mortality, even at levels below current standards.

Involvement of fine particulate matter exposure with gene expression pathways in breast tumor and adjacent-normal breast tissue

BACKGROUND

Fine particulate matter (PM_2.5) has been associated with breast cancer specific mortality, particularly for women with Stage I cancer. We examined the biological pathways that are perturbed by PM_2.5 exposures by analyzing gene expression measurements from breast tissue specimens.

METHODS

The Nurses' Health Studies (NHS and NHSII) are prospective cohorts with archival breast tissue specimens from breast cancer cases. Global gene expression data were ascertained with the Affymetrix Glue Human Transcriptome Array 3.0. PM_2.5 was estimated using spatio-temporal models linked to participants' home addresses. All analyses were performed separately in tumor (n = 591) and adjacent-normal (n = 497) samples, and stratified by estrogen receptor (ER) status and stage. We used multivariable linear regression, gene-set enrichment analyses (GSEA), and the least squares kernel machine (LSKM) to assess whether 3-year cumulative average pre-diagnosis PM_2.5 exposure was associated with breast-tissue gene expression pathways among predominately Stage I and II women (90.7%) and postmenopausal (81.2%) women. Replication samples (tumor, n = 245; adjacent-normal, n = 165) were measured on Affymetrix Human Transcriptome Array (HTA 2.0).

RESULTS

Overall, no pathways in the tumor area were significantly associated with PM_2.5 exposure. Among 272 adjacent-normal samples from Stage I ER-positive women, PM_2.5 was associated with perturbations in the oxidative phosphorylation, protein secretion, and mTORC1 signaling pathways (GSEA and LSKM p-values <0.05); however, results were not replicated in a small set of replication samples (n = 80).

CONCLUSIONS

PM_2.5 was generally not associated with breast tissue gene expression though was suggested to perturb oxidative phosphorylation and regulation of proteins and cellular signaling in adjacent-normal breast tissue. More research is needed on the biological role of PM_2.5 that influences breast tumor progression.