Differential respiratory health effects from the 2008 northern California wildfires: A spatiotemporal approach.

We investigated health effects associated with fine particulate matter during a long-lived, large wildfire complex in northern California in the summer of 2008. We estimated exposure to PM2.5 for each day using an exposure prediction model created through data-adaptive machine learning methods from a large set of spatiotemporal data sets. We then used Poisson generalized estimating equations to calculate the effect of exposure to 24-hour average PM2.5 on cardiovascular and respiratory hospitalizations and ED visits. We further assessed effect modification by sex, age, and area-level socioeconomic status (SES). We observed a linear increase in risk for asthma hospitalizations (RR=1.07, 95% CI=(1.05, 1.10) per 5µg/m(3) increase) and asthma ED visits (RR=1.06, 95% CI=(1.05, 1.07) per 5µg/m(3) increase) with increasing PM2.5 during the wildfires. ED visits for chronic obstructive pulmonary disease (COPD) were associated with PM2.5 during the fires (RR=1.02 (95% CI=(1.01, 1.04) per 5µg/m(3) increase) and this effect was significantly different from that found before the fires but not after. We did not find consistent effects of wildfire smoke on other health outcomes. The effect of PM2.5 during the wildfire period was more pronounced in women compared to men and in adults, ages 20-64, compared to children and adults 65 or older. We also found some effect modification by area-level median income for respiratory ED visits during the wildfires, with the highest effects observed in the ZIP codes with the lowest median income. Using a novel spatiotemporal exposure model, we found some evidence of differential susceptibility to exposure to wildfire smoke.

Framing air pollution epidemiology in terms of population interventions, with applications to multipollutant modeling.

Air pollution epidemiology continues moving toward the study of mixtures and multipollutant modeling. Simultaneously, there is a movement in epidemiology to estimate policy-relevant health effects that can be understood in reference to specific interventions. Scaling regression coefficients from a regression model by an interquartile range (IQR) is one common approach to presenting multipollutant health effect estimates. We are unaware of guidance on how to interpret these effect estimates as an intervention. To illustrate the issues of interpretability of IQR-scaled air pollution health effects, we analyzed how daily concentration changes in 2 air pollutants (nitrogen dioxide and particulate matter with aerodynamic diameter ≤ 2.5 µm) related to one another within 2 seasons (summer and winter), within 3 cities with distinct air pollution profiles (Burbank, California; Houston, Texas; and Pittsburgh, Pennsylvania). In each city season, we examined how realistically IQR scaling in multipollutant lag-1 time-series studies reflects a hypothetical intervention that is possible given the observed data. We proposed 2 causal conditions to explicitly link IQR-scaled effects to a clearly defined hypothetical intervention. Condition 1 specified that the index pollutant had to experience a daily concentration change of greater than 1 IQR, reflecting the notion that the IQR is an appropriate measure of variability between consecutive days. Condition 2 specified that the copollutant had to remain relatively constant. We found that in some city seasons, there were very few instances in which these conditions were satisfied (eg, 1 day in Pittsburgh during summer). We discuss the practical implications of IQR scaling and suggest alternative approaches to presenting multipollutant effects that are supported by empirical data.

Air Pollution, Neighbourhood Socioeconomic Factors, and Neural Tube Defects in the San Joaquin Valley of California.

BACKGROUND: Environmental pollutants and neighbourhood socioeconomic factors have been associated with neural tube defects, but the potential impact of interaction between ambient air pollution and neighbourhood socioeconomic factors on the risks of neural tube defects is not well understood. METHODS: We used data from the California Center of the National Birth Defects Study and the Children's Health and Air Pollution Study to investigate whether associations between air pollutant exposure in early gestation and neural tube defects were modified by neighbourhood socioeconomic factors in the San Joaquin Valley of California, 1997-2006. There were 5 pollutant exposures, 3 outcomes, and 9 neighbourhood socioeconomic factors included for a total of 135 investigated associations. Estimates were adjusted for maternal race-ethnicity, education, and multivitamin use. RESULTS: We present below odds ratios (ORs) that exclude 1 and a chi-square test of homogeneity P-value of <0.05. We observed increased odds of spina bifida comparing the highest to lowest quartile of particulate matter <10 µm (PM10 ) among those living in a neighbourhood with: (i) median household income of less than $30 000 per year [OR 5.1, 95% confidence interval (CI) 1.7, 15.3]; (ii) more than 20% living below the federal poverty level (OR 2.6, 95% CI 1.1, 6.0); and (iii) more than 30% with less than or equal to a high school education (OR 3.2, 95% CI 1.4, 7.4). The ORs were not statistically significant among those higher socioeconomic status (SES) neighbourhoods. CONCLUSIONS: Our results demonstrate effect modification by neighbourhood socioeconomic factors in the association of particulate matter and neural tube defects in California.

Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning.

Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires. We estimated PM2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM2.5 concentrations during a major wildfire event.

Exposure to airborne polycyclic aromatic hydrocarbons during pregnancy and risk of preterm birth.

BACKGROUND: Preterm birth is an important marker of health and has a prevalence of 12-13% in the U.S. Polycyclic aromatic hydrocarbons (PAHs) are a group of organic contaminants that form during the incomplete combustion of hydrocarbons, such as coal, diesel and gasoline. Studies suggest that exposure to PAHs during pregnancy is related to adverse birth outcomes. The aim of this study is to evaluate the association between exposure to PAHs during the pregnancy and preterm birth. METHODS: The study population included births from years 2001 to 2006 of women whose maternal residence was within 20km of the primary monitoring site in Fresno, California. Data in the Fresno area were used to form a spatio-temporal model to assign daily exposure to PAHs with 4, 5, or 6 rings at the maternal residence throughout pregnancy of all of the births in the study area. Gestational age at birth and relevant covariates were extracted from the birth certificate. RESULTS: We found an association between PAHs during the last 6 weeks of pregnancy and birth at 20-27 weeks (OR=2.74; 95% CI: 2.24-3.34) comparing the highest quartile to the lower three. The association was consistent when each quartile was compared to the lowest (OR2nd=1.49, 95% CI: 1.08-2.06; OR3rd=2.63, 95% CI:1.93-3.59; OR4th=3.94, 95% CI:3.03-5.12). Inverse associations were also observed for exposure to PAHs during the entire pregnancy and the first trimester and birth at 28-31 weeks and 20-27 weeks. CONCLUSION: An association between PAH exposure during the 6 weeks before delivery and early preterm birth was observed. However, the inverse association with early preterm birth offers an unclear, and potentially complex, inference of these associations.

The association of ambient air pollution and traffic exposures with selected congenital anomalies in the San Joaquin Valley of California.

Congenital anomalies are a leading cause of infant mortality and are important contributors to subsequent morbidity. Studies suggest associations between environmental contaminants and some anomalies, although evidence is limited. We aimed to investigate whether ambient air pollutant and traffic exposures in early gestation contribute to the risk of selected congenital anomalies in the San Joaquin Valley of California, 1997-2006. Seven exposures and 5 outcomes were included for a total of 35 investigated associations. We observed increased odds of neural tube defects when comparing the highest with the lowest quartile of exposure for several pollutants after adjusting for maternal race/ethnicity, education, and multivitamin use. The adjusted odds ratio for neural tube defects among those with the highest carbon monoxide exposure was 1.9 (95% confidence interval: 1.1, 3.2) compared with those with the lowest exposure, and there was a monotonic exposure-response across quartiles. The highest quartile of nitrogen oxide exposure was associated with neural tube defects (adjusted odds ratio = 1.8, 95% confidence interval: 1.1, 2.8). The adjusted odds ratio for the highest quartile of nitrogen dioxide exposure was 1.7 (95% confidence interval: 1.1, 2.7). Ozone was associated with decreased odds of neural tube defects. Our results extend the limited body of evidence regarding air pollution exposure and adverse birth outcomes.

Traffic-related air pollution and selected birth defects in the San Joaquin Valley of California.

BACKGROUND: Birth defects are a leading cause of infant morbidity and mortality. Studies suggest associations between environmental contaminants and some structural anomalies, although evidence is limited and several anomalies have not been investigated previously. METHODS: We used data from the California Center of the National Birth Defects Prevention Study and the Children's Health and Air Pollution Study to estimate the odds of 26 congenital birth defect phenotypes with respect to quartiles of seven ambient air pollutant and traffic exposures in California during the first 2 months of pregnancy, 1997 to 2006 (874 cases and 849 controls). We calculated odds ratios (adjusted for maternal race/ethnicity, education, and vitamin use; aOR) for 11 phenotypes that had at least 40 cases. RESULTS: Few odds ratios had confidence intervals that did not include 1.0. Odds of esophageal atresia were increased for the highest versus lowest of traffic density (aOR = 2.8, 95% confidence interval [CI], 1.1-7.4) and PM10 exposure (aOR 4.9; 95% CI, 1.4-17.2). PM10 was associated with a decreased risk of hydrocephaly (aOR= 0.3; 95% CI, 0.1-0.9) and CO with decreased risk of anotia/microtia (aOR = 0.4; 95% CI, 0.2-0.8) and transverse limb deficiency (aOR = 0.4; 95% CI, 0.2-0.9), again reflecting highest versus lowest quartile comparisons. CONCLUSION: Most analyses showed no substantive association between air pollution and the selected birth defects with few exceptions of mixed results.

Ambient air pollution and traffic exposures and congenital heart defects in the San Joaquin Valley of California.

BACKGROUND: Congenital anomalies are a leading cause of infant morbidity and mortality. Studies suggest associations between environmental contaminants and some anomalies, although evidence is limited. METHODS: We used data from the California Center of the National Birth Defects Prevention Study and the Children's Health and Air Pollution Study to estimate the odds of 27 congenital heart defects with respect to quartiles of seven ambient air pollutant and traffic exposures in California during the first 2 months of pregnancy, 1997-2006 (n = 822 cases and n = 849 controls). RESULTS: Particulate matter < 10 microns (PM10 ) was associated with pulmonary valve stenosis [adjusted odds ratio (aOR)Fourth Quartile = 2.6] [95% confidence intervals (CI) 1.2, 5.7] and perimembranous ventricular septal defects (aORThird Quartile = 2.1) [95% CI 1.1, 3.9] after adjusting for maternal race/ethnicity, education and multivitamin use. PM2.5 was associated with transposition of the great arteries (aORThird Quartile = 2.6) [95% CI 1.1, 6.5] and inversely associated with perimembranous ventricular septal defects (aORFourth Quartile = 0.5) [95% CI 0.2, 0.9]. Secundum atrial septal defects were inversely associated with carbon monoxide (aORFourth Quartile = 0.4) [95% CI 0.2, 0.8] and PM2.5 (aORFourth Quartile = 0.5) [95% CI 0.3, 0.8]. Traffic density was associated with muscular ventricular septal defects (aORFourth Quartile = 3.0) [95% CI 1.2, 7.8] and perimembranous ventricular septal defects (aORThird Quartile = 2.4) [95% CI 1.3, 4.6], and inversely associated with transposition of the great arteries (aORFourth Quartile = 0.3) [95% CI 0.1, 0.8]. CONCLUSIONS: PM10 and traffic density may contribute to the occurrence of pulmonary valve stenosis and ventricular septal defects, respectively. The results were mixed for other pollutants and had little consistency with previous studies.

Mapping and modeling airborne urban phenanthrene distribution using vegetation biomonitoring.

To capture the spatial distribution of phenanthrene in an urban setting we used vegetation biomonitoring with Jeffrey pine trees (Pinus jeffreyi). The major challenge in characterizing spatial variation in polycyclic aromatic hydrocarbon (PAH) concentrations within a metropolitan area has been sampling at a fine enough resolution to observe the underlying spatial pattern. However, field and chamber studies show that the primary pathway through which PAHs enter plants is from air into leaves, making vegetation biomonitoring a feasible way to examine the spatial distribution of these compounds. Previous research has shown that phenanthrene has adverse health effects and that it is one of the most abundant PAHs in urban air. We collected 99 pine needle samples from 91 locations in Fresno in the morning on a winter day, and analyzed them for PAHs in the inner needle. All 99 pine needle samples had detectable levels of phenanthrene, with mean concentration of 41.0 ng g-1, median 36.9 ng g-1, and standard deviation of 28.5 ng g-1 fresh weight. The ratio of the 90th:10th percentile concentrations by location was 3.3. The phenanthrene distribution had a statistically significant Moran's I of 0.035, indicating a high degree of spatial clustering. We implemented land use regression to fit a model to our data. Our model was able to explain a moderate amount of the variability in the data (R 2 = 0.56), likely reflecting the major sources of phenanthrene in Fresno. The spatial distribution of modeled airborne phenanthrene shows the influences of highways, railroads, and industrial and commercial zones.

Exposure to traffic-related air pollution during pregnancy and term low birth weight: estimation of causal associations in a semiparametric model.

Traffic-related air pollution is recognized as an important contributor to health problems. Epidemiologic analyses suggest that prenatal exposure to traffic-related air pollutants may be associated with adverse birth outcomes; however, there is insufficient evidence to conclude that the relation is causal. The Study of Air Pollution, Genetics and Early Life Events comprises all births to women living in 4 counties in California's San Joaquin Valley during the years 2000-2006. The probability of low birth weight among full-term infants in the population was estimated using machine learning and targeted maximum likelihood estimation for each quartile of traffic exposure during pregnancy. If everyone lived near high-volume freeways (approximated as the fourth quartile of traffic density), the estimated probability of term low birth weight would be 2.27% (95% confidence interval: 2.16, 2.38) as compared with 2.02% (95% confidence interval: 1.90, 2.12) if everyone lived near smaller local roads (first quartile of traffic density). Assessment of potentially causal associations, in the absence of arbitrary model assumptions applied to the data, should result in relatively unbiased estimates. The current results support findings from previous studies that prenatal exposure to traffic-related air pollution may adversely affect birth weight among full-term infants.

Causal inference in epidemiological studies with strong confounding.

One of the identifiability assumptions of causal effects defined by marginal structural model (MSM) parameters is the experimental treatment assignment (ETA) assumption. Practical violations of this assumption frequently occur in data analysis when certain exposures are rarely observed within some strata of the population. The inverse probability of treatment weighted (IPTW) estimator is particularly sensitive to violations of this assumption; however, we demonstrate that this is a problem for all estimators of causal effects. This is due to the fact that the ETA assumption is about information (or lack thereof) in the data. A new class of causal models, causal models for realistic individualized exposure rules (CMRIER), is based on dynamic interventions. CMRIER generalize MSM, and their parameters remain fully identifiable from the observed data, even when the ETA assumption is violated, if the dynamic interventions are set to be realistic. Examples of such realistic interventions are provided. We argue that causal effects defined by CMRIER may be more appropriate in many situations, particularly those with policy considerations. Through simulation studies, we examine the performance of the IPTW estimator of the CMRIER parameters in contrast to that of the MSM parameters. We also apply the methodology to a real data analysis in air pollution epidemiology to illustrate the interpretation of the causal effects defined by CMRIER.

Usual physical activity and hip fracture in older men: an application of semiparametric methods to observational data.

Few studies have examined the relation between usual physical activity level and rate of hip fracture in older men or applied semiparametric methods from the causal inference literature that estimate associations without assuming a particular parametric model. Using the Physical Activity Scale for the Elderly, the authors measured usual physical activity level at baseline (2000-2002) in 5,682 US men ≥65 years of age who were enrolled in the Osteoporotic Fractures in Men Study. Physical activity levels were classified as low (bottom quartile of Physical Activity Scale for the Elderly score), moderate (middle quartiles), or high (top quartile). Hip fractures were confirmed by central review. Marginal associations between physical activity and hip fracture were estimated with 3 estimation methods: inverse probability-of-treatment weighting, G-computation, and doubly robust targeted maximum likelihood estimation. During 6.5 years of follow-up, 95 men (1.7%) experienced a hip fracture. The unadjusted risk of hip fracture was lower in men with a high physical activity level versus those with a low physical activity level (relative risk = 0.51, 95% confidence interval: 0.28, 0.92). In semiparametric analyses that controlled confounding, hip fracture risk was not lower with moderate (e.g., targeted maximum likelihood estimation relative risk = 0.92, 95% confidence interval: 0.62, 1.44) or high (e.g., targeted maximum likelihood estimation relative risk = 0.88, 95% confidence interval: 0.53, 2.03) physical activity relative to low. This study does not support a protective effect of usual physical activity on hip fracture in older men.

The association between persistent fetal occiput posterior position and perinatal outcomes: an example of propensity score and covariate distance matching.

In a retrospective cohort study of 18,880 full-term, cephalic singletons born in San Francisco, California, during 1976-2001, the authors used multivariable logistic regression (MVLR) and propensity score analysis (PSA) to examine the association between persistent fetal occiput posterior (OP) position and perinatal outcomes. The principles and applications of these techniques are compared and discussed. Pregnancies with OP positions at delivery were compared with those with occiput anterior positions. Perinatal outcomes were examined as adjusted odds ratios determined by MVLR and PSA and as risk differences determined by propensity score matched bootstrapping based on covariate distance. Persistent OP position was associated with operative delivery and maternal morbidity. The odds ratio estimates based on PSA were somewhat larger than those obtained with standard MVLR, and the confidence intervals were narrower. When statistical inference was evaluated with the permutation test, the results were more consistent with the PSA. These analyses demonstrate that PSA is likely to provide more precise estimates of exposure associations and more reliable statistical inferences than MVLR. The authors show that PSA can be extended with Mahalanobis distance matching to obtain estimates of risk difference between exposed and unexposed subjects that avoid violations of the experimental treatment assignment (positivity) assumption that is required for valid causal inference.

Secondhand smoke, vascular disease, and dementia incidence: findings from the cardiovascular health cognition study.

Recent studies have found that smoking is associated with an increased risk of dementia, but the effects of secondhand smoke (SHS) on dementia risk are not known to have previously been studied. The authors used Cox proportional hazards marginal structural models to examine the association between self-reported lifetime household SHS exposure and risk of incident dementia over 6 years among 970 US participants in the Cardiovascular Health Cognition Study (performed from 1991 to 1999) who were never smokers and were free of clinical cardiovascular disease (CVD), dementia, and mild cognitive impairment at baseline. In addition, because prior studies have found that SHS is associated with increased risk of CVD and that CVD is associated with increased risk of dementia, the authors tested for interactions between SHS and measures of clinical and subclinical CVD on dementia risk. Moderate (16-25 years) and high (>25 years) SHS exposure levels were not independently associated with dementia risk; however, subjects with >25 years of SHS exposure and >25% carotid artery stenosis had a 3-fold increase (hazard ratio = 3.00, 95% confidence interval: 1.03, 9.72) in dementia risk compared with subjects with no/low (0-15 years) SHS exposure and < or =25% carotid artery stenosis. High lifetime SHS exposure may increase the risk of dementia in elderly with undiagnosed CVD.

Age and cystatin C in healthy adults: a collaborative study.

BACKGROUND: Kidney function declines with age, but a substantial portion of this decline has been attributed to the higher prevalence of risk factors for kidney disease at older ages. The effect of age on kidney function has not been well described in a healthy population across a wide age spectrum. METHODS: The authors pooled individual-level cross-sectional data from 18 253 persons aged 28-100 years in four studies: the Cardiovascular Health Study; the Health, Aging and Body Composition Study; the Multi-Ethnic Study of Atherosclerosis and the Prevention of Renal and Vascular End-Stage Disease cohort. Kidney function was measured by cystatin C. Clinical risk factors for kidney disease included diabetes, hypertension, obesity, smoking, coronary heart disease, cerebrovascular disease, peripheral arterial disease and heart failure. RESULTS: Across the age range, there was a strong, non-linear association of age with cystatin C concentration. This association was substantial, even among participants free of clinical risk factors for kidney disease; mean cystatin C levels were 46% higher in participants 80 and older compared with those <40 years (1.06 versus 0.72 mg/L, P < 0.001). Participants with one or more risk factors had higher cystatin C concentrations for a given age, and the age association was slightly stronger (P < 0.001 for age and risk factor interaction). CONCLUSIONS: There is a strong, non-linear association of age with kidney function, even in healthy individuals. An important area for research will be to investigate the mechanisms that lead to deterioration of kidney function in apparently healthy persons.

A cross-validation deletion-substitution-addition model selection algorithm: Application to marginal structural models.

The cross-validation deletion-substitution-addition (cvDSA) algorithm is based on data-adaptive estimation methodology to select and estimate marginal structural models (MSMs) for point treatment studies as well as models for conditional means where the outcome is continuous or binary. The algorithm builds and selects models based on user-defined criteria for model selection, and utilizes a loss function-based estimation procedure to distinguish between different model fits. In addition, the algorithm selects models based on cross-validation methodology to avoid "over-fitting" data. The cvDSA routine is an R software package available for download. An alternative R-package (DSA) based on the same principles as the cvDSA routine (i.e., cross-validation, loss function), but one that is faster and with additional refinements for selection and estimation of conditional means, is also available for download. Analyses of real and simulated data were conducted to demonstrate the use of these algorithms, and to compare MSMs where the causal effects were assumed (i.e., investigator-defined), with MSMs selected by the cvDSA. The package was used also to select models for the nuisance parameter (treatment) model to estimate the MSM parameters with inverse-probability of treatment weight (IPTW) estimation. Other estimation procedures (i.e., G-computation and double robust IPTW) are available also with the package.

Altered pulmonary function in children with asthma associated with highway traffic near residence.

Cross-sectional analyses were conducted to evaluate the effects of exposure to highway traffic on pulmonary function in Fresno, California. Traffic and spirometry data were available for 214 children (enrollment ages six to 11 years). Multiple linear regression was used to evaluate the relations between pulmonary function and traffic parameters. Heavy-duty vehicle count was used as a surrogate measure for diesel-related exposures. Pulmonary function was non-significantly associated with longer distance-to-road and non-significantly associated with higher traffic intensity. Evaluation of effect modification by FEF(25-75)/FVC (a measure of intrinsic airway size) showed that all pulmonary function measures of flow were significantly inversely related to a traffic metric that incorporates traffic intensity and roadway proximity. The results indicate that residence proximity to highway traffic is associated with lower pulmonary function among children with asthma, and smaller airway size is an important modifier of the effect of traffic exposure on pulmonary function and a marker of increased susceptibility.

The impact of generic-only drug benefits on patients' use of inhaled corticosteroids in a Medicare population with asthma.

BACKGROUND: Patients face increasing insurance restrictions on prescription drugs, including generic-only coverage. There are no generic inhaled corticosteroids (ICS), which are a mainstay of asthma therapy, and patients pay the full price for these drugs under generic-only policies. We examined changes in ICS use following the introduction of generic-only coverage in a Medicare Advantage population from 2003-2004. METHODS: Subjects were age 65+, with asthma, prior ICS use, and no chronic obstructive pulmonary disorder (n = 1,802). In 2004, 74.0% switched from having a $30 brand-copayment plan to a generic-only coverage plan (restricted coverage); 26% had $15-25 brand copayments in 2003-2004 (unrestricted coverage). Using linear difference-in-difference models, we examined annual changes in ICS use (measured by days-of-supply dispensed). There was a lower-cost ICS available within the study setting and we also examined changes in drug choice (higher- vs. lower-cost ICS). In multivariable models we adjusted for socio-demographic, clinical, and asthma characteristics. RESULTS: In 2003 subjects had an average of 188 days of ICS supply. Restricted compared with unrestricted coverage was associated with reductions in ICS use from 2003-2004 (-15.5 days-of-supply, 95% confidence interval (CI): -25.0 to -6.0). Among patients using higher-cost ICS drugs in 2003 (n = 662), more restricted versus unrestricted coverage subjects switched to the lower-cost ICS in 2004 (39.8% vs. 10.3%). Restricted coverage was not associated with decreased ICS use (2003-2004) among patients who switched to the lower-cost ICS (18.7 days-of-supply, CI: -27.5 to 65.0), but was among patients who did not switch (-38.6 days-of-supply, CI: -57.0 to -20.3). In addition, restricted coverage was associated with decreases in ICS use among patients with both higher- and lower-risk asthma (-15.0 days-of-supply, CI: -41.4 to 11.44; and -15.6 days-of-supply, CI: -25.8 to -5.3, respectively). CONCLUSION: In this elderly population, patients reduced their already low ICS use in response to losing drug coverage. Switching to the lower-cost ICS mitigated reductions in use among patients who previously used higher-cost drugs. Additional work is needed to assess barriers to switching ICS drugs and the clinical effects of these drug use changes.

Secondhand smoke exposure, pulmonary function, and cardiovascular mortality.

PURPOSE: Cardiovascular disease and obstructive lung disease are leading global causes of death. Despite this, the impact of secondhand smoke (SHS) exposure on pulmonary function and cardiovascular disease remains uncertain. Our goal was to elucidate the association between baseline SHS exposure and the risk of lung function decline and cardiovascular mortality over a period of nearly a decade. METHODS: We used data from a longitudinal cohort study of 1,057 older adults to study the association between baseline SHS exposure and the risk of lung function decline and cardiovascular mortality. The effect of SHS exposure on cardiovascular mortality may be mediated by its influence on FEV1 and biological processes captured by measurement of FEV1. Alternatively, the effect of SHS may be mediated by baseline cardiovascular disease status, which reflects the combined effects of traditional cardiovascular risk factors. To correctly estimate the effect of SHS and FEV1 on cardiovascular mortality, we used marginal structural models (MSMs) that took into account the mediating effects of FEV1 and baseline cardiovascular disease in the causal pathway. RESULTS: In longitudinal multivariate analyses, lifetime cumulative home and work SHS exposure were associated with a greater decline of FEV1 (-15 mL/s; 95% CI, -29 to -1.3 mL/s and -41 mL/s; 95% CI, -55 to -28 mL/s per 10-year cumulative exposure, respectively). Lifetime home SHS exposure was associated with a greater risk of cardiovascular mortality in both conventional multivariate analysis (HR, 1.10 per 10 years of exposure; 95% CI, 0.99 to 1.24) and the MSM for FEV1 (HR, 1.06; 95% CI, 0.95 to 1.19) and baseline cardiovascular disease (HR for subjects with no baseline cardiovascular disease, 1.39; 95% CI, 1.17 to 1.66). CONCLUSIONS: Lifetime SHS exposure appears to result in a greater decline in lung function and risk of cardiovascular mortality, taking into account confounders and the mediating effect of FEV1 and baseline cardiovascular disease.

Cytokines and other immunological biomarkers in children's environmental health studies.

Environmental exposures (e.g. pesticides, air pollution, and environmental tobacco smoke) during prenatal and early postnatal development have been linked to a growing number of childhood diseases including allergic disorders and leukemia. Because the immune response plays a critical role in each of these diseases, it is important to study the effects of toxicants on the developing immune system. Children's unique susceptibility to environmental toxicants has become an important focus of the field of immunotoxicology and the use of immune biomarkers in molecular epidemiology of children's environmental health is a rapidly expanding field of research. In this review, we discuss how markers of immune status and immunotoxicity are being applied to pediatric studies, with a specific focus on the various methods used to analyze T-helper-1/2 (Th1/Th2) cytokine profiles. Furthermore, we review recent data on the effects of children's environmental exposures to volatile organic compounds, metals, and pesticides on Th1/Th2 cytokine profiles and the associations of Th1/Th2 profiles with adverse health outcomes such as pediatric respiratory diseases, allergies, cancer and diabetes. Although cytokine profiles are increasingly used in children's studies, there is still a need to acquire distribution data for different ages and ethnic groups of healthy children. These data will contribute to the validation and standardization of cytokine biomarkers for future studies. Application of immunological markers in epidemiological studies will improve the understanding of mechanisms that underlie associations between environmental exposures and immune-mediated disorders.