RESUMEN

Causal inference for air pollution mixtures is an increasingly important issue with appreciable challenges. When the exposure is a multivariate mixture, there are many exposure contrasts that may be of nominal interest for causal effect estimation, but the complex joint mixture distribution often renders observed data extremely limited in their ability to inform estimates of many commonly-defined causal effects. We use potential outcomes to 1) define causal effects of air pollution mixtures, 2) formalize the key assumption of mixture positivity required for estimation and 3) offer diagnostic metrics for positivity violations in the mixture setting that allow researchers to assess the extent to which data can actually support estimation of mixture effects of interest. For settings where there is limited empirical support, we redefine causal estimands that apportion causal effects according to whether they can be directly informed by observed data versus rely entirely on model extrapolation, isolating key sources of information on the causal effect of an air pollution mixture. The ideas are deployed to assess the ability of a national United States data set on the chemical components of ambient particulate matter air pollution to support estimation of a variety of causal mixture effects.

RESUMEN

Mobile monitoring is increasingly used to assess exposure to traffic-related air pollutants (TRAPs), including ultrafine particles (UFPs). Due to the rapid spatial decrease in the concentration of UFPs and other TRAPs with distance from roadways, mobile measurements may be non-representative of residential exposures, which are commonly used for epidemiologic studies. Our goal was to develop, apply, and test one possible approach for using mobile measurements in exposure assessment for epidemiology. We used an absolute principal component score model to adjust the contribution of on-road sources in mobile measurements to provide exposure predictions representative of cohort locations. We then compared UFP predictions at residential locations from mobile on-road plume-adjusted versus stationary measurements to understand the contribution of mobile measurements and characterize their differences. We found that predictions from mobile measurements are more representative of cohort locations after down-weighting the contribution of localized on-road plumes. Further, predictions at cohort locations derived from mobile measurements incorporate more spatial variation compared to those from short-term stationary data. Sensitivity analyses suggest that this additional spatial information captures features in the exposure surface not identified from the stationary data alone. We recommend the correction of mobile measurements to create exposure predictions representative of residential exposure for epidemiology.

Asunto(s)

RESUMEN

BACKGROUND: Prescribed agricultural burning is a common land management practice, but little is known about the health effects from the resulting smoke exposure. OBJECTIVE: To examine the association between smoke from prescribed burning and cardiorespiratory outcomes in the U.S. state of Kansas. METHODS: We analyzed a zip code-level, daily time series of primary cardiorespiratory emergency department (ED) visits for February-May (months when prescribed burning is common in Kansas) in the years 2009-2011 (n = 109,220). Given limited monitoring data, we formulated a measure of smoke exposure using non-traditional datasets, including fire radiative power and locational attributes from remote sensing data sources. We then assigned a population-weighted potential smoke impact factor (PSIF) to each zip code, based on fire intensity, smoke transport, and fire proximity. We used Poisson generalized linear models to estimate the association between PSIF on the same day and in the past 3 days and asthma, respiratory including asthma, and cardiovascular ED visits. RESULTS: During the study period, prescribed burning took place on approximately 8 million acres in Kansas. Same-day PSIF was associated with a 7% increase in the rate of asthma ED visits when adjusting for month, year, zip code, meteorology, day of week, holidays, and correlation within zip codes (rate ratio [RR]: 1.07; 95% confidence interval [CI]: 1.01, 1.13). Same-day PSIF was not associated with a combined outcome of respiratory ED visits (RR [95% CI]: 0.99 [0.97, 1.02]), or cardiovascular ED visits (RR [95% CI]: 1.01 [0.98, 1.04]). There was no consistent association between PSIF during the past 3 days and any of the outcomes. SIGNIFICANCE: These results suggest an association between smoke exposure and asthma ED visits on the same day. Elucidating these associations will help guide public health programs that address population-level exposure to smoke from prescribed burning.

Asunto(s)

RESUMEN

Unmeasured, spatially-structured factors can confound associations between spatial environmental exposures and health outcomes. Adding flexible splines to a regression model is a simple approach for spatial confounding adjustment, but the spline degrees of freedom do not provide an easily interpretable spatial scale. We describe a method for quantifying the extent of spatial confounding adjustment in terms of the Euclidean distance at which variation is removed. We develop this approach for confounding adjustment with splines and using Fourier and wavelet filtering. We demonstrate differences in the spatial scales these bases can represent and provide a comparison of methods for selecting the amount of confounding adjustment. We find the best performance for selecting the amount of adjustment using an information criterion evaluated on an outcome model without exposure. We apply this method to spatial adjustment in an analysis of fine particulate matter and blood pressure in a cohort of United States women.

RESUMEN

BACKGROUND: Indoor air pollution is an important risk factor for health in low- and middle-income countries. METHODS: We measured indoor fine particulate matter (PM2.5) and carbon monoxide (CO) concentrations in 617 houses across four settings with varying urbanisation, altitude, and biomass cookstove use in Peru, between 2010 and 2016. We assessed the associations between indoor pollutant concentrations and blood pressure (BP), exhaled carbon monoxide (eCO), C-reactive protein (CRP), and haemoglobin A1c (HbA1c) using multivariable linear regression among all participants and stratifying by use of biomass cookstoves. RESULTS: We found high concentrations of indoor PM2.5 across all four settings (geometric mean ± geometric standard deviation of PM2.5 daily average in µg/m3): Lima 41.1 ± 1.3, Tumbes 35.8 ± 1.4, urban Puno 14.1 ± 1.7, and rural Puno 58.8 ± 3.1. High indoor CO concentrations were common in rural households (geometric mean ± geometric standard deviation of CO daily average in ppm): rural Puno 4.9 ± 4.3. Higher indoor PM2.5 was associated with having a higher systolic BP (1.51 mmHg per interquartile range (IQR) increase, 95% CI 0.16 to 2.86), a higher diastolic BP (1.39 mmHg higher DBP per IQR increase, 95% CI 0.52 to 2.25), and a higher eCO (2.05 ppm higher per IQR increase, 95% CI 0.52 to 3.57). When stratifying by biomass cookstove use, our results were consistent with effect measure modification in the association between PM2.5 and eCO: among biomass users eCO was 0.20 ppm higher per IQR increase in PM2.5 (95% CI - 2.05 to 2.46), and among non-biomass users eCO was 5.00 ppm higher per IQR increase in PM2.5 (95% CI 1.58 to 8.41). We did not find associations between indoor air concentrations and CRP or HbA1c outcomes. CONCLUSIONS: Excessive indoor concentrations of PM2.5 are widespread in homes across varying levels of urbanisation, altitude, and biomass cookstove use in Peru and are associated with worse BP and higher eCO.

Asunto(s)

RESUMEN

Epidemiologic studies of the short-term effects of ambient particulate matter (PM) on the risk of acute cardiovascular or cerebrovascular events often use data from administrative databases in which only the date of hospitalization is known. A common study design for analyzing such data is the case-crossover design, in which exposure at a time when a patient experiences an event is compared to exposure at times when the patient did not experience an event within a case-control paradigm. However, the time of true event onset may precede hospitalization by hours or days, which can yield attenuated effect estimates. In this article, we consider a marginal likelihood estimator, a regression calibration estimator, and a conditional score estimator, as well as parametric bootstrap versions of each, to correct for this bias. All considered approaches require validation data on the distribution of the delay times. We compare the performance of the approaches in realistic scenarios via simulation, and apply the methods to analyze data from a Boston-area study of the association between ambient air pollution and acute stroke onset. Based on both simulation and the case study, we conclude that a two-stage regression calibration estimator with a parametric bootstrap bias correction is an effective method for correcting bias in health effect estimates arising from delayed onset in a case-crossover study.

Asunto(s)

RESUMEN

Near-road monitoring creates opportunities to provide direct measurement on traffic-related air pollutants and to better understand the changing near-road environment. However, how such observations represent traffic-related air pollution exposures for estimating adverse health effect in epidemiologic studies remains unknown. A better understanding of potential exposure measurement error when utilizing near-road measurement is needed for the design and interpretation of the many observational studies linking traffic pollution and adverse health. The Dorm Room Inhalation to Vehicle Emission (DRIVE) study conducted near-road measurements of several single traffic indicators at six indoor and outdoor sites ranging from 0.01 to 2.3 km away from a heavily-trafficked (average annual daily traffic over 350,000) highway artery between September 2014 to January 2015. We examined spatiotemporal variability trends and assessed the potential for bias and errors when using a roadside monitor as a primary traffic pollution exposure surrogate, in lieu of more spatially-refined, proximal exposure indicators. Pollutant levels measured during DRIVE showed a low impact of this highway hotspot source. Primary pollutant species, including NO, CO, and BC declined to near background levels by 20-30 m from the highway source. Patterns of correlation among the sites also varied by pollutant and time of day. NO2, specifically, exhibited spatial trends that differed from other single-pollutant primary traffic indicators. This finding provides some indication of limitations in the use of NO2 as a primary traffic exposure indicator in panel-based health effect studies. Interestingly, roadside monitoring of NO, CO, and BC tended to be more strongly correlated with sites, both near and far from the road, during morning rush hour periods, and more weakly correlated during other periods of the day. We found pronounced attenuation of observed changes in health effects when using measured pollutant from the near-road monitor as a surrogate for true exposure, and the magnitude varied substantially over the course of the day. Caution should be taken when using near-road monitoring network observations, alone, to investigate health effects of traffic pollutants.

Asunto(s)

RESUMEN

Often spatiotemporal resolution/scale of environmental and health data do not align. Therefore, researchers compute exposure by interpolation or by aggregating data to coarse spatiotemporal scales. The latter is often preferred because of sparse geographic coverage of environmental monitoring, as interpolation method cannot reliably compute exposure using the small sample of sparse data points. This paper presents a methodology of diagnosing the levels of uncertainty in exposure at a given distance and time interval, and examines the effects of particulate matter (PM) ≤2.5 µm and ≤10 µm in diameter (PM2.5 and PM10, respectively) on birth weight (BW) and low birth weight (LBW), i.e., birth weight <2500 g in Chicago (IL, USA), accounting for exposure uncertainty. Two important findings emerge from this paper. First, uncertainty in PM exposure increases significantly with the increase in distance from the monitoring stations, e.g., 50.6% and 38.5% uncertainty in PM10 and PM2.5 exposure respectively for 0.058° (~6.4 km) distance from the monitoring stations. Second, BW was inversely associated with PM2.5 exposure, and PM2.5 exposure during the first trimester and entire gestation period showed a stronger association with BW than the exposure during the second and third trimesters. But PM10 did not show any significant association with BW and LBW. These findings suggest that distance and time intervals need to be chosen with care to compute exposure, and account for the uncertainty to reliably assess the adverse health risks of exposure.

Asunto(s)

RESUMEN

In environmental epidemiology, exposures are not always available at subject locations and must be predicted using monitoring data. The monitor locations are often outside the control of researchers, and previous studies have shown that "preferential sampling" of monitoring locations can adversely affect exposure prediction and subsequent health effect estimation. We adopt a slightly different definition of preferential sampling than is typically seen in the literature, which we call population-based preferential sampling. Population-based preferential sampling occurs when the location of the monitors is dependent on the subject locations. We show the impact that population-based preferential sampling has on exposure prediction and health effect estimation using analytic results and a simulation study. A simple, one-parameter model is proposed to measure the degree to which monitors are preferentially sampled with respect to population density. We then discuss these concepts in the context of PM2.5 and the EPA Air Quality System monitoring sites, which are generally placed in areas of higher population density to capture the population's exposure.

Asunto(s)

RESUMEN

RATIONALE: Growing evidence suggests that long-term exposure to fine particulate matter (PM2.5) air pollution contributes to risk of cardiovascular disease (CVD) morbidity and mortality. There is uncertainty about who are most susceptible. Individuals with underlying cardiometabolic disorders, including hypertension, diabetes mellitus, and obesity, may be at greater risk. PM2.5 pollution may also contribute to cardiometabolic disorders, augmenting CVD risk. OBJECTIVE: This analysis evaluates relationships between long-term PM2.5 exposure and cardiometabolic disease on risk of death from CVD and cardiometabolic conditions. METHODS AND RESULTS: Data on 669 046 participants from the American Cancer Society Cancer Prevention Study II cohort were linked to modeled PM2.5 concentrations at geocoded home addresses. Cox proportional hazards regression models were used to estimate adjusted hazards ratios for death from CVD and cardiometabolic diseases based on death-certificate information. Effect modification by pre-existing cardiometabolic risk factors on the PM2.5-CVD mortality association was examined. PM2.5 exposure was associated with CVD mortality, with the hazards ratios (95% confidence interval) per 10 µg/m(3) increase in PM2.5 equal to 1.12 (1.10-1.15). Deaths linked to hypertension and diabetes mellitus (mentioned on death certificate as either primary or contributing cause of death) were also associated with PM2.5. There was no consistent evidence of effect modification by cardiometabolic disease risk factors on the PM2.5-CVD mortality association. CONCLUSIONS: Pollution-induced CVD mortality risk is observed for those with and without existing cardiometabolic disorders. Long-term exposure may also contribute to the development or exacerbation of cardiometabolic disorders, increasing risk of CVD, and cardiometabolic disease mortality.

Asunto(s)

RESUMEN

Preferential sampling has been defined in the context of geostatistical modeling as the dependence between the sampling locations and the process that describes the spatial structure of the data. It can occur when networks are designed to find high values. For example, in networks based on the U.S. Clean Air Act monitors are sited to determine whether air quality standards are exceeded. We study the impact of the design of monitor networks in the context of air pollution epidemiology studies. The effect of preferential sampling has been illustrated in the literature by highlighting its impact on spatial predictions. In this paper, we use these predictions as input in a second stage analysis, and we assess how they affect health effect inference. Our work is motivated by data from two United States regulatory networks and health data from the Multi-Ethnic Study of Atherosclerosis and Air Pollution. The two networks were designed to monitor air pollution in urban and rural areas respectively, and we found that the health analysis results based on the two networks can lead to different scientific conclusions. We use preferential sampling to gain insight into these differences. We designed a simulation study, and found that the validity and reliability of the health effect estimate can be greatly affected by how we sample the monitor locations. To better understand its effect on second stage inference, we identify two components of preferential sampling that shed light on how preferential sampling alters the properties of the health effect estimate.

RESUMEN

Air pollution epidemiologic studies are intrinsically difficult because the expected effect size at general environmental levels is small, exposure and misclassification of exposure are common, and exposure is not selective to a specific pollutant. In this review paper, epidemiologic study designs and analytic methods are described, and two nationwide projects on air pollution epidemiology are introduced. This paper also demonstrates that possible confounding issues in time-series analysis can be resolved and the impact on the use of data from ambient monitoring stations may not be critical. In this paper we provide a basic understanding of the types of air pollution epidemiologic study designs that be subdivided by the mode of air pollution effects on human health (acute or chronic). With the improvements in the area of air pollution epidemiologic studies, we should emphasize that elaborate models and statistical techniques cannot compensate for inadequate study design or poor data collection.

Asunto(s)

RESUMEN

OBJECTIVES: To reexamine the association between air pollution and daily mortality in Seoul, Korea using a method of meta-analysis with the data filed for 1991 through 1995. METHODS: A separate Poisson regression analysis on each district within the metropolitan area of Seoul was conducted to regress daily death counts on levels of each ambient air pollutant, such as total suspended particulates (TSP), sulfur dioxide (SO2), and ozone (O3), controlling for variability in the weather condition. We calculated a weighted mean as a meta-analysis summary of the estimates and its standard error. RESULTS: We found that the p value from each pollutant model to test the homogeneity assumption was small (p<0.01) because of the large disparity among district-specific estimates. Therefore, all results reported here were estimated from the random effect model. Using the weighted mean that we calculated, the mortality at a 100 microgram/m3 increment in a 3-day moving average of TSP levels was 1.034 (95% CI 1.009-1.059). The mortality was estimated to increase 6% (95% CI 3-10%) and 3% (95% CI 0-6%) with each 50 ppb increase for 3-day moving average of SO2 and 1-hr maximum O3, respectively. CONCLUSIONS: Like most of air pollution epidemiologic studies, this meta-analysis cannot avoid fleeing from measurement misclassification since no personal measurement was taken. However, we can expect that a measurement bias be reduced in a district-specific estimate since a monitoring station is better representative of air quality of the matched district. The similar results to those from the previous studies indicated existence of health effect of air pollution at current levels in many industrialized countries, including Korea.

Asunto(s)