Because humans are invariably exposed to complex chemical mixtures, estimating the health effects of multi-pollutant exposures is of critical concern in environmental epidemiology, and to regulatory ...agencies such as the U.S. Environmental Protection Agency. However, most health effects studies focus on single agents or consider simple two-way interaction models, in part because we lack the statistical methodology to more realistically capture the complexity of mixed exposures. We introduce Bayesian kernel machine regression (BKMR) as a new approach to study mixtures, in which the health outcome is regressed on a flexible function of the mixture (e.g. air pollution or toxic waste) components that is specified using a kernel function. In high-dimensional settings, a novel hierarchical variable selection approach is incorporated to identify important mixture components and account for the correlated structure of the mixture. Simulation studies demonstrate the success of BKMR in estimating the exposure-response function and in identifying the individual components of the mixture responsible for health effects. We demonstrate the features of the method through epidemiology and toxicology applications.
The influence of particulate air pollution on respiratory health starts in utero. Fetal lung growth and structural development occurs in stages; thus, effects on postnatal respiratory disorders may ...differ based on timing of exposure.
We implemented an innovative method to identify sensitive windows for effects of prenatal exposure to particulate matter with a diameter less than or equal to 2.5 μm (PM2.5) on children's asthma development in an urban pregnancy cohort.
Analyses included 736 full-term (≥37 wk) children. Each mother's daily PM2.5 exposure was estimated over gestation using a validated satellite-based spatiotemporal resolved model. Using distributed lag models, we examined associations between weekly averaged PM2.5 levels over pregnancy and physician-diagnosed asthma in children by age 6 years. Effect modification by sex was also examined.
Most mothers were ethnic minorities (54% Hispanic, 30% black), had 12 or fewer years of education (66%), and did not smoke in pregnancy (80%). In the sample as a whole, distributed lag models adjusting for child age, sex, and maternal factors (education, race and ethnicity, smoking, stress, atopy, prepregnancy obesity) showed that increased PM2.5 exposure levels at 16-25 weeks gestation were significantly associated with early childhood asthma development. An interaction between PM2.5 and sex was significant (P = 0.01) with sex-stratified analyses showing that the association exists only for boys.
Higher prenatal PM2.5 exposure at midgestation was associated with asthma development by age 6 years in boys. Methods to better characterize vulnerable windows may provide insight into underlying mechanisms.
Studies looking at air temperature (Ta) and birth outcomes are rare.
We investigated the association between birth outcomes and daily Ta during various prenatal exposure periods in Massachusetts ...(USA) using both traditional Ta stations and modeled addresses.
We evaluated birth outcomes and average daily Ta during various prenatal exposure periods in Massachusetts (USA) using both traditional Ta stations and modeled address Ta. We used linear and logistic mixed models and accelerated failure time models to estimate associations between Ta and the following outcomes among live births > 22 weeks: term birth weight (≥ 37 weeks), low birth weight (LBW; < 2,500 g at term), gestational age, and preterm delivery (PT; < 37 weeks). Models were adjusted for individual-level socioeconomic status, traffic density, particulate matter ≤ 2.5 μm (PM2.5), random intercept for census tract, and mother's health.
Predicted Ta during multiple time windows before birth was negatively associated with birth weight: Average birth weight was 16.7 g lower (95% CI: -29.7, -3.7) in association with an interquartile range increase (8.4 °C) in Ta during the last trimester. Ta over the entire pregnancy was positively associated with PT odds ratio (OR) = 1.02; 95% CI: 1.00, 1.05 and LBW (OR = 1.04; 95% CI: 0.96, 1.13).
Ta during pregnancy was associated with lower birth weight and shorter gestational age in our study population.
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Dostopno za:
CEKLJ, DOBA, IZUM, KILJ, NUK, OILJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
Knowing the geographical and temporal variation in radon concentrations is essential for assessing residential exposure to radon, the leading cause of lung cancer in never-smokers in the United ...States. Tens of millions of short-term radon measurements, which normally last 2 to 4 days, have been conducted during the past decades. However, these massive short-term measurements have not been commonly used in exposure assessment because of the conflicting evidence regarding their correlation with long-term measurements, the gold standard of assessing long-term radon exposure.
We aim to evaluate the extent to which a long-term radon measurement can be predicted by a collocated short-term radon measurement under different conditions.
We compiled a national dataset of 2245 pairs of collocated short- and long-term measurements, analyzed the predictability of long-term measurements with stratified linear regression and bootstrapping resampling.
We found that the extent to which a long-term measurement can be predicted by the collocated short-term measurement was a joint function of two factors: the temporal difference in starting dates between two measurements and the length of the long-term measurement. Short-term measurements, jointly with other factors, could explain up to 79% (0.95 Confidence Interval CI: 0.73-0.84) of the variance in seasonal radon concentrations and could explain up to 67% (0.95 CI: 0.52-0.81) of the variance in annual radon concentrations. The large proportions of variance explained suggest that short-term measurement can be used as convenient proxy for seasonal radon concentrations. Accurate annual radon estimation entails averaging multiple short-term measurements in different seasons.
Our findings will facilitate the usage of abundant short-term radon measurements, which have been obtained but was previously underutilized in assessing residential radon exposure.
Tens of millions of short-term radon measurements have been conducted but underutilized in assessing residential exposure to radon, the greatest cause of lung cancer in non-smokers. We investigate the correlations between collocated short- and long-term measurements in 2245 U.S. buildings and find that short-term measurements can explain ~75% of the variance in subsequent long-term measurements in the same buildings. Our results can facilitate the usage of massive short-term radon measurements that have been conducted to estimate the spatial and longitudinal distribution of radon concentrations, which can be used in epidemiological studies to quantify the health effects of radon.
Air temperature (Ta) stations have limited spatial coverage, particularly in rural areas. Since temperature can vary greatly both spatially and temporally, Ta stations are often inadequate for ...studies on the health effects of extreme temperature and climate change. Satellites can provide us with daily physical surface temperature (Ts) measurements, enabling us to estimate daily Ta. In this study, we aimed to extend our previous work on predicting Ta from Ts in Massachusetts by predicting 24h Ta means on a 1km grid across the Northeast and Mid-Atlantic states, extending both the temporal and spatial coverage, improving upon the methodology and validating our model in other geographical regions across the Northeastern part of the USA. We used mixed model regressions to first calibrate Ts and Ta measurements, regressing Ta measurements against day-specific random intercepts, and fixed and random Ts slopes. Then to capture the ability of neighboring cells to fill in the cells with missing Ts values, we regress the Ta predicted from the first mixed effects model against the mean of the Ta measurements on that day, separately for each grid cell. Out-of-sample tenfold cross-validation was used to quantify the accuracy of our predictions. Our model performance was excellent for both days with available Ts and days without Ts observations (mean out-of-sample R2=0.95 and R2=0.94 respectively). We demonstrate how Ts can be used reliably to predict daily Ta at high resolution in large geographical areas even in non-retrieval days while reducing exposure measurement error.
•We predict daily air temperature at high resolution in large geographical areas.•Excellent model performance (R2=0.947 and R2=0.940 respectively).•Our model reduces exposure measurement error.
Many studies have reported the associations between long-term exposure to PM2.5 and increased risk of death. However, to our knowledge, none has used a causal modeling approach or controlled for ...long-term temperature exposure, and few have used a general population sample.
We estimated the causal effects of long-term PM2.5 exposure on mortality and tested the effect modifications by seasonal temperatures, census tract-level socioeconomic variables, and county-level health conditions.
We applied a variant of the difference-in-differences approach, which serves to approximate random assignment of exposure across the population and hence estimate a causal effect. Specifically, we estimated the association between long-term exposure to PM2.5 and mortality while controlling for geographical differences using dummy variables for each census tract in New Jersey, a state-wide time trend using dummy variables for each year from 2004 to 2009, and mean summer and winter temperatures for each tract in each year. This approach assumed that no variable changing differentially over time across space other than seasonal temperatures confounded the association.
For each interquartile range (2 μg/m3) increase in annual PM2.5, there was a 3.0% 95% confidence interval (CI): 0.2, 5.9% increase in all natural-cause mortality for the whole population, with similar results for people > 65 years old 3.5% (95% CI: 0.1, 6.9%) and people ≤ 65 years old 3.1% (95% CI: -1.8, 8.2%). The mean summer temperature and the mean winter temperature in a census tract significantly modified the effects of long-term exposure to PM2.5 on mortality. We observed a higher percentage increase in mortality associated with PM2.5 in census tracts with more blacks, lower home value, or lower median income.
Under the assumption of the difference-in-differences approach, we identified a causal effect of long-term PM2.5 exposure on mortality that was modified by seasonal temperatures and ecological socioeconomic status.
Wang Y, Kloog I, Coull BA, Kosheleva A, Zanobetti A, Schwartz JD. 2016. Estimating causal effects of long-term PM2.5 exposure on mortality in New Jersey. Environ Health Perspect 124:1182-1188; http://dx.doi.org/10.1289/ehp.1409671.
Celotno besedilo
Dostopno za:
CEKLJ, DOBA, IZUM, KILJ, NUK, OILJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
Many studies report significant associations between PM(2.5) (particulate matter <2.5 micrometers) and hospital admissions. These studies mostly rely on a limited number of monitors which introduces ...exposure error, and excludes rural and suburban populations from locations where monitors are not available, reducing generalizability and potentially creating selection bias.
Using prediction models developed by our group, daily PM(2.5) exposure was estimated across the Mid-Atlantic (Washington D.C., and the states of Delaware, Maryland, New Jersey, Pennsylvania, Virginia, New York and West Virginia). We then investigated the short-term effects of PM(2.5)exposures on emergency hospital admissions of the elderly in the Mid-Atlantic region.We performed case-crossover analysis for each admission type, matching on day of the week, month and year and defined the hazard period as lag01 (a moving average of day of admission exposure and previous day exposure).
We observed associations between short-term exposure to PM(2.5) and hospitalization for all outcomes examined. For example, for every 10-µg/m(3) increase in short-term PM(2.5) there was a 2.2% increase in respiratory diseases admissions (95% CI = 1.9 to 2.6), and a 0.78% increase in cardiovascular disease (CVD) admission rate (95% CI = 0.5 to 1.0). We found differences in risk for CVD admissions between people living in rural and urban areas. For every10-µg/m(3) increase in PM(2.5) exposure in the 'rural' group there was a 1.0% increase (95% CI = 0.6 to 1.5), while for the 'urban' group the increase was 0.7% (95% CI = 0.4 to 1.0).
Our findings showed that PM(2.5) exposure was associated with hospital admissions for all respiratory, cardio vascular disease, stroke, ischemic heart disease and chronic obstructive pulmonary disease admissions. In addition, we demonstrate that our AOD (Aerosol Optical Depth) based exposure models can be successfully applied to epidemiological studies investigating the health effects of short-term exposures to PM(2.5).
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Adverse birth outcomes such as low birth weight and premature birth have been previously linked with exposure to ambient air pollution. Most studies relied on a limited number of monitors in the ...region of interest, which can introduce exposure error or restrict the analysis to persons living near a monitor, which reduces sample size and generalizability and may create selection bias.
We evaluated the relationship between premature birth and birth weight with exposure to ambient particulate matter (PM₂.₅) levels during pregnancy in Massachusetts for a 9-year period (2000-2008). Building on a novel method we developed for predicting daily PM₂.₅ at the spatial resolution of a 10x10 km grid across New-England, we estimated the average exposure during 30 and 90 days prior to birth as well as the full pregnancy period for each mother. We used linear and logistic mixed models to estimate the association between PM₂.₅ exposure and birth weight (among full term births) and PM₂.₅ exposure and preterm birth adjusting for infant sex, maternal age, maternal race, mean income, maternal education level, prenatal care, gestational age, maternal smoking, percent of open space near mothers residence, average traffic density and mothers health.
Birth weight was negatively associated with PM₂.₅ across all tested periods. For example, a 10 μg/m³ increase of PM₂.₅ exposure during the entire pregnancy was significantly associated with a decrease of 13.80 g 95% confidence interval (CI) = -21.10, -6.05 in birth weight after controlling for other factors, including traffic exposure. The odds ratio for a premature birth was 1.06 (95% confidence interval (CI) = 1.01-1.13) for each 10 μg/m3 increase of PM₂.₅ exposure during the entire pregnancy period.
The presented study suggests that exposure to PM₂.₅ during the last month of pregnancy contributes to risks for lower birth weight and preterm birth in infants.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Brain growth and structural organization occurs in stages beginning prenatally. Toxicants may impact neurodevelopment differently dependent upon exposure timing and fetal sex.
We implemented ...innovative methodology to identify sensitive windows for the associations between prenatal particulate matter with diameter≤2.5μm (PM2.5) and children's neurodevelopment.
We assessed 267 full-term urban children's prenatal daily PM2.5 exposure using a validated satellite-based spatio-temporally resolved prediction model. Outcomes included IQ (WISC-IV), attention (omission errors OEs, commission errors CEs, hit reaction time HRT, and HRT standard error HRT-SE on the Conners' CPT-II), and memory (general memory GM index and its components — verbal VEM and visual VIM memory, and attention-concentration AC indices on the WRAML-2) assessed at age 6.5±0.98years. To identify the role of exposure timing, we used distributed lag models to examine associations between weekly prenatal PM2.5 exposure and neurodevelopment. Sex-specific associations were also examined.
Mothers were primarily minorities (60% Hispanic, 25% black); 69% had ≤12years of education. Adjusting for maternal age, education, race, and smoking, we found associations between higher PM2.5 levels at 31–38weeks with lower IQ, at 20–26weeks gestation with increased OEs, at 32–36weeks with slower HRT, and at 22–40weeks with increased HRT-SE among boys, while significant associations were found in memory domains in girls (higher PM2.5 exposure at 18–26weeks with reduced VIM, at 12–20weeks with reduced GM).
Increased PM2.5 exposure in specific prenatal windows may be associated with poorer function across memory and attention domains with variable associations based on sex. Refined determination of time window- and sex-specific associations may enhance insight into underlying mechanisms and identification of vulnerable subgroups.
•We used data-driven methods to objectively examine sensitive windows of prenatal PM2.5 on childhood neurodevelopment.•Our findings suggested sex-specific, time-dependent associations that may vary dependent on different cognitive domains.•A more definitive understanding of such sex-specific temporal associations may provide insights into underlying mechanisms.
BACKGROUND:Many studies have reported associations between ambient particulate matter (PM) and adverse health effects, focused on either short-term (acute) or long-term (chronic) PM exposures. For ...chronic effects, the studied cohorts have rarely been representative of the population. We present a novel exposure model combining satellite aerosol optical depth and land-use data to investigate both the long- and short-term effects of PM2.5 exposures on population mortality in Massachusetts, United States, for the years 2000–2008.
METHODS:All deaths were geocoded. We performed two separate analysesa time-series analysis (for short-term exposure) where counts in each geographic grid cell were regressed against cell-specific short-term PM2.5 exposure, temperature, socioeconomic data, lung cancer rates (as a surrogate for smoking), and a spline of time (to control for season and trends). In addition, for long-term exposure, we performed a relative incidence analysis using two long-term exposure metricsregional 10 × 10 km PM2.5 predictions and local deviations from the cell average based on land use within 50 m of the residence. We tested whether these predicted the proportion of deaths from PM-related causes (cardiovascular and respiratory diseases).
RESULTS:For short-term exposure, we found that for every 10-µg/m increase in PM 2.5 exposure there was a 2.8% increase in PM-related mortality (95% confidence interval CI = 2.0–3.5). For the long-term exposure at the grid cell level, we found an odds ratio (OR) for every 10-µg/m increase in long-term PM2.5 exposure of 1.6 (CI = 1.5–1.8) for particle-related diseases. Local PM2.5 had an OR of 1.4 (CI = 1.3–1.5), which was independent of and additive to the grid cell effect.
CONCLUSIONS:We have developed a novel PM2.5 exposure model based on remote sensing data to assess both short- and long-term human exposures. Our approach allows us to gain spatial resolution in acute effects and an assessment of long-term effects in the entire population rather than a selective sample from urban locations.
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