Showing papers in "Epidemiology in 2014"

Global variation in the effects of ambient temperature on mortality: a systematic evaluation.

Abstract: BACKGROUND: Studies have examined the effects of temperature on mortality in a single city, country, or region. However, less evidence is available on the variation in the associations between temperature and mortality in multiple countries, analyzed simultaneously. METHODS: We obtained daily data on temperature and mortality in 306 communities from 12 countries/regions (Australia, Brazil, Thailand, China, Taiwan, Korea, Japan, Italy, Spain, United Kingdom, United States, and Canada). Two-stage analyses were used to assess the nonlinear and delayed relation between temperature and mortality. In the first stage, a Poisson regression allowing overdispersion with distributed lag nonlinear model was used to estimate the community-specific temperature-mortality relation. In the second stage, a multivariate meta-analysis was used to pool the nonlinear and delayed effects of ambient temperature at the national level, in each country. RESULTS: The temperatures associated with the lowest mortality were around the 75th percentile of temperature in all the countries/regions, ranging from 66th (Taiwan) to 80th (UK) percentiles. The estimated effects of cold and hot temperatures on mortality varied by community and country. Meta-analysis results show that both cold and hot temperatures increased the risk of mortality in all the countries/regions. Cold effects were delayed and lasted for many days, whereas heat effects appeared quickly and did not last long. CONCLUSIONS: People have some ability to adapt to their local climate type, but both cold and hot temperatures are still associated with increased risk of mortality. Public health strategies to alleviate the impact of ambient temperatures are important, in particular in the context of climate change.

On the causal interpretation of race in regressions adjusting for confounding and mediating variables

Abstract: We consider several possible interpretations of the "effect of race" when regressions are run with race as an exposure variable, controlling also for various confounding and mediating variables. When adjustment is made for socioeconomic status early in a person's life, we discuss under what contexts the regression coefficients for race can be interpreted as corresponding to the extent to which a racial inequality would remain if various socioeconomic distributions early in life across racial groups could be equalized. When adjustment is also made for adult socioeconomic status, we note how the overall racial inequality can be decomposed into the portion that would be eliminated by equalizing adult socioeconomic status across racial groups and the portion of the inequality that would remain even if adult socioeconomic status across racial groups were equalized. We also discuss a stronger interpretation of the effect of race (stronger in terms of assumptions) involving the joint effects of race-associated physical phenotype (eg, skin color), parental physical phenotype, genetic background, and cultural context when such variables are thought to be hypothetically manipulable and if adequate control for confounding were possible. We discuss some of the challenges with such an interpretation. Further discussion is given as to how the use of selected populations in examining racial disparities can additionally complicate the interpretation of the effects.

Methodological challenges in Mendelian randomization

Abstract: We give critical attention to the assumptions underlying Mendelian randomization analysis and their biological plausibility. Several scenarios violating the Mendelian randomization assumptions are described, including settings with inadequate phenotype definition, the setting of time-varying exposures, the presence of gene-environment interaction, the existence of measurement error, the possibility of reverse causation, and the presence of linkage disequilibrium. Data analysis examples are given, illustrating that the inappropriate use of instrumental variable techniques when the Mendelian randomization assumptions are violated can lead to biases of enormous magnitude. To help address some of the strong assumptions being made, three possible approaches are suggested. First, the original proposal of Katan (Lancet. 1986;1:507-508) for Mendelian randomization was not to use instrumental variable techniques to obtain estimates but merely to examine genotype-outcome associations to test for the presence of an effect of the exposure on the outcome. We show that this more modest goal and approach can circumvent many, though not all, the potential biases described. Second, we discuss the use of sensitivity analysis in evaluating the consequences of violations in the assumptions and in attempting to correct for those violations. Third, we suggest that a focus on negative, rather than positive, Mendelian randomization results may turn out to be more reliable.

A unification of mediation and interaction: a 4-way decomposition.

Abstract: The overall effect of an exposure on an outcome, in the presence of a mediator with which the exposure may interact, can be decomposed into 4 components: (1) the effect of the exposure in the absence of the mediator, (2) the interactive effect when the mediator is left to what it would be in the absence of exposure, (3) a mediated interaction, and (4) a pure mediated effect. These 4 components, respectively, correspond to the portion of the effect that is due to neither mediation nor interaction, to just interaction (but not mediation), to both mediation and interaction, and to just mediation (but not interaction). This 4-way decomposition unites methods that attribute effects to interactions and methods that assess mediation. Certain combinations of these 4 components correspond to measures for mediation, whereas other combinations correspond to measures of interaction previously proposed in the literature. Prior decompositions in the literature are in essence special cases of this 4-way decomposition. The 4-way decomposition can be carried out using standard statistical models, and software is provided to estimate each of the 4 components. The 4-way decomposition provides maximum insight into how much of an effect is mediated, how much is due to interaction, how much is due to both mediation and interaction together, and how much is due to neither.

Net Reclassification Indices for Evaluating Risk Prediction Instruments: A Critical Review

Abstract: Net reclassification indices have recently become popular statistics for measuring the prediction increment of new biomarkers. We review the various types of net reclassification indices and their correct interpretations. We evaluate the advantages and disadvantages of quantifying the prediction inc

Mortality related to air pollution with the moscow heat wave and wildfire of 2010.

Abstract: Background: Prolonged high temperatures and air pollution from wildfires often occur together, and the two may interact in their effects on mortality. However, there are few data on such possible interactions.

Effect decomposition in the presence of an exposure-induced mediator-outcome confounder

Abstract: Methods from causal mediation analysis have generalized the traditional approach to direct and indirect effects in the epidemiologic and social science literature by allowing for interaction and nonlinearities. However, the methods from the causal inference literature have themselves been subject to a major limitation, in that the so-called natural direct and indirect effects that are used are not identified from data whenever there is a mediator-outcome confounder that is also affected by the exposure. In this article, we describe three alternative approaches to effect decomposition that give quantities that can be interpreted as direct and indirect effects and that can be identified from data even in the presence of an exposure-induced mediator-outcome confounder. We describe a simple weighting-based estimation method for each of these three approaches, illustrated with data from perinatal epidemiology. The methods described here can shed insight into pathways and questions of mediation even when an exposure-induced mediator-outcome confounder is present.

Long-term exposure to air pollution and cardiovascular mortality: an analysis of 22 European cohorts.

Abstract: Background: Air pollution has been associated with cardiovascular mortality, but it remains unclear as to whether specific pollutants are related to specific cardiovascular causes of death. Within ...

Regression discontinuity designs in epidemiology: causal inference without randomized trials.

Abstract: When patients receive an intervention based on whether they score below or above some threshold value on a continuously measured random variable, the intervention will be randomly assigned for patients close to the threshold. The regression discontinuity design exploits this fact to estimate causal treatment effects. In spite of its recent proliferation in economics, the regression discontinuity design has not been widely adopted in epidemiology. We describe regression discontinuity, its implementation, and the assumptions required for causal inference. We show that regression discontinuity is generalizable to the survival and nonlinear models that are mainstays of epidemiologic analysis. We then present an application of regression discontinuity to the much-debated epidemiologic question of when to start HIV patients on antiretroviral therapy. Using data from a large South African cohort (2007–2011), we estimate the causal effect of early versus deferred treatment eligibility on mortality. Patients whose first CD4 count was just below the 200 cells/μL CD4 count threshold had a 35% lower hazard of death (hazard ratio = 0.65 [95% confidence interval = 0.45–0.94]) than patients presenting with CD4 counts just above the threshold. We close by discussing the strengths and limitations of regression discontinuity designs for epidemiology.

Association among socioeconomic status, health behaviors, and all-cause mortality in the United States.

Abstract: Background Health behaviors may contribute to socioeconomic inequalities in mortality, although the extent of such contribution remains unclear. We assessed the extent to which smoking, alcohol consumption, and physical inactivity have mediated the association between socioeconomic status (SES) and all-cause mortality in a representative sample of US adults. Methods Initiated in 1992, the Health and Retirement Study is a longitudinal, biennial survey of a national sample of US adults born between 1931 and 1941. Our analyses are based on a sample of 8037 participants enrolled in 1992 and followed for all-cause mortality from 1998 through 2008. We used exploratory and confirmatory factor analysis to derive a measure of adult SES based on respondents' education, occupation, labor force status, household income, and household wealth. Potential mediators (smoking, alcohol consumption, and physical inactivity) were assessed biennially. We used inverse probability-weighted mediation models to account for time-varying covariates. Results During the 10-year mortality follow-up, 859 (10%) participants died. After accounting for age, sex, and baseline confounders, being in the most-disadvantaged quartile of SES compared with the least disadvantaged was associated with a mortality risk ratio of 2.84 (95% confidence interval = 2.25-3.60). Together, smoking, alcohol consumption, and physical inactivity explained 68% (35-104%) of this association, leaving a risk ratio of 1.59 (1.03-2.45) for low SES. Conclusions The distribution of health-damaging behaviors may explain a substantial proportion of excess mortality associated with low SES in the United States, suggesting the importance of social inequalities in unhealthy behaviors.

Physical activity and the risk of preeclampsia: a systematic review and meta-analysis.

Abstract: Background:Physical activity has been hypothesized to reduce the risk of preeclampsia, but epidemiologic studies have not shown consistent results. Therefore, we conducted a systematic review and dose–response meta-analysis of epidemiologic studies.Methods:PubMed, Embase, and Ovid databases were sea

Air pollution during pregnancy and childhood cognitive and psychomotor development: six European birth cohorts.

Abstract: Background: Accumulating evidence from laboratory animal and human studies suggests that air pollution exposure during pregnancy affects cognitive and psychomotor development in childhood. Methods: We analyzed data from 6 European population-based birth cohorts-GENERATI ON R (The Netherlands), DUISBURG (Germany), EDEN (France), GASPII (Italy), RHEA (Greece), and INMA (Spain)-that recruited mother-infant pairs from 1997 to 2008. Air pollution levels-nitrogen oxides (NO2, NOx) in all regions and particulate matter (PM) with diameters of Results: A total of 9482 children were included. Air pollution exposure during pregnancy, particularly NO2, was associated with reduced psychomotor development (global psychomotor development score decreased by 0.68 points [95% confidence interval = -1.25 to -0.11] per increase of 10 mu g/m(3) in NO2). Similar trends were observed in most regions. No associations were found between any air pollutant and cognitive development. Conclusions: Air pollution exposure during pregnancy, particularly NO2 (for which motorized traffic is a major source), was associated with delayed psychomotor development during childhood. Due to the widespread nature of air pollution exposure, the public health impact of the small changes observed at an individual level could be considerable.

Prenatal exposure to phenols and growth in boys.

Abstract: Background:Phenols interact with nuclear receptors implicated in growth and adipogenesis regulation. Only a few studies have explored their effects on growth in humans.Objectives:We studied the associations of maternal exposure to phenols during pregnancy with prenatal and postnatal growth of male n

Causal Models and Learning from Data: Integrating Causal Modeling and Statistical Estimation

Abstract: The practice of epidemiology requires asking causal questions. Formal frameworks for causal inference developed over the past decades have the potential to improve the rigor of this process. However, the appropriate role for formal causal thinking in applied epidemiology remains a matter of debate. We argue that a formal causal framework can help in designing a statistical analysis that comes as close as possible to answering the motivating causal question, while making clear what assumptions are required to endow the resulting estimates with a causal interpretation. A systematic approach for the integration of causal modeling with statistical estimation is presented. We highlight some common points of confusion that occur when causal modeling techniques are applied in practice and provide a broad overview on the types of questions that a causal framework can help to address. Our aims are to argue for the utility of formal causal thinking, to clarify what causal models can and cannot do, and to provide an accessible introduction to the flexible and powerful tools provided by causal models.

Traffic-related Air Pollution in Relation to Cognitive Function in Older Adults

Abstract: all sources and from traffic exhaust) were modeled at resolution of 20 × 20 m for 2003–2009. We investigated the relationship between exposure to particles and a cognitive battery composed of tests of reasoning, memory, and phonemic and semantic fluency. We also investigated exposure in relation to decline in these tests over 5 years. Results: Mean age of participants was 66 (standard deviation = 6) years. all particle metrics were associated with lower scores in reasoning and memory measured in the 2007–2009 wave but not with lower verbal fluency. Higher PM 2.5 of 1.1 μg/m 3 (lag 4) was associated with a 0.03 (95% confidence interval = −0.06 to 0.002) 5-year decline in standardized memory score and a 0.04 (−0.07 to −0.01) decline when restricted to participants remaining in london between study waves. Conclusions: this study provides support for an association between particulate air pollution and some measures of cognitive function, as well as decline over time in cognition; however, it does not support

Heat and Mortality in New York City Since the Beginning of the 20th Century.

Abstract: Background:Heat is recognized as one of the deadliest weather-related phenomena. Although the impact of high temperatures on mortality has been a subject of extensive research, few previous studies have assessed the impact of population adaptation to heat.Methods:We examined adaptation patterns by a

Instrumental variable analysis with a nonlinear exposure-outcome relationship.

Abstract: Most methods for estimating causal effects using instrumental variables (IVs) make the assumption that the relation between the exposure and outcome is linear.1 Although this may be approximately true in many cases, especially after transforming the exposure or outcome, in some situations, the exposure–outcome relation will be nonlinear. In this case, the shape of the relation may be a target for investigation. For example, the observed relation between body mass index (BMI) and mortality is highly nonlinear, with mortality increasing sharply as BMI increases. However, an increased risk of mortality has also been observed for individuals with low BMI.2 It is unclear whether this merely reflects reverse causation (sick people lose weight) or confounding (underweight individuals differ in other risk factors from those of average weight) or whether there is a causal effect of low BMI on increased mortality.3 In a randomized trial where the exposure is the treatment received and the IV is treatment assignment, an IV analysis estimates a local average treatment effect.4,5 This is the average change in the outcome resulting from a change in the exposure among those patients for whom treatment assignment influences the treatment received. In a trial context, such patients are known as compliers, and the local average treatment effect is also known as a complier-averaged causal effect.6 Consistency of the IV estimator is subject to the assumption that any effect of the IV on the exposure is in the same direction for all persons (known as the monotonicity assumption). In an observational study, the IV and the exposure may be continuous rather than dichotomous. Here, the monotonicity assumption is that the exposure is a nondecreasing function of the IV for all persons (or, equivalently, a nonincreasing function for all persons). This is plausible in the context of Mendelian randomization—the use of genetic variants as IVs—because the biological effects of genetic variants are likely to be in the same direction in each person.7 The IV estimate can then be viewed as a weighted average of partial derivatives of the relation of the outcome with the exposure.8 In the discrete case, these derivatives can be interpreted as local average treatment effects at different values of the exposure and the IV. In this study, we explore the implications of nonlinear exposure–outcome relations for IV analyses, particularly in the context of Mendelian randomization. We initially consider the consequences of using linear IV models to estimate the effect of an exposure on an outcome when the true causal relation is nonlinear. We then introduce a novel approach for estimating localized average causal effects, which are IV estimates (local average treatment effects) estimated for strata of the population defined by the value of the exposure. These can provide evidence of a nonlinear effect of the exposure on the outcome. We discuss the findings and implications of our results and compare the approach introduced in this study with other parametric and nonparametric approaches to nonlinear IV analysis. We assume that the exposure and outcome are continuous; issues relating to binary outcomes are reserved for the discussion. This study is illustrated using data on 8090 subcohort participants from the multicenter case-cohort study European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct, the diabetes-focused component of the EPIC.9 We use data on BMI (kg/m2) and a range of cardiovascular risk factors: systolic blood pressure (mmHg), C-reactive protein (mg/L, log-transformed), uric acid (μmol/L), glycated hemoglobin (HbA1c, %), total cholesterol (mmol/L), and triglycerides (mmol/L, log-transformed). Increases in BMI have been shown to have causal effects on each of these factors in previous Mendelian randomization studies.10–12 The observational association of each of the risk factors with BMI in a linear regression model, and with BMI and BMI-squared in a quadratic regression model, is given in Table ​Table1.1. (BMI is centered before analysis, adjustment is made for age, sex, and center.) The mean levels and 95% confidence intervals (CIs) of the risk factors for each quintile of BMI are shown in Figure ​Figure1.1. The observational relations of BMI with several of the risk factors are nonlinear, although this does not necessarily imply that the causal relations will be nonlinear. TABLE 1. Coefficients from Observational Analysis of the Association of Body Mass Index (BMI) with a Range of Cardiovascular Risk Factors in Linear and Quadratic Regression Models FIGURE 1. Mean level of cardiovascular risk factors stratified by quintile of body mass index against mean value of body mass index in quintile (lines are ±1.96 standard errors).

Effects of heat waves on mortality: effect modification and confounding by air pollutants.

Abstract: Background:Heat waves and air pollution are both associated with increased mortality. Their joint effects are less well understood.Methods:We explored the role of air pollution in modifying the effects of heat waves on mortality, within the EuroHEAT project. Daily mortality, meteorologic, and air po

Maternal vitamin D status and the risk of mild and severe preeclampsia.

Abstract: Preeclampsia is a multisystem disorder diagnosed by new-onset hypertension and proteinuria. In developed countries, the perinatal mortality rate among preeclamptic pregnancies is five times as great as non-preeclamptic pregnancies, 1 and indicated preterm deliveries for preeclampsia account for 15% of preterm births.2 Mothers who develop preeclampsia are at elevated risk of abruptio placentae, acute renal failure, and neurologic and cardiovascular complications. 1 Moreover, preeclampsia contributes to 18% of maternal deaths in the U.S. and 20–80% of maternal deaths in developing countries. 1,3 Delivery is the only known cure for preeclampsia, and few interventions have been effective in preventing the disorder. There is a growing interest in the role of maternal vitamin D status in the development of preeclampsia. Vitamin D is a prohormone that is either made in the skin through ultraviolet B radiation exposure or ingested orally. 4 Vitamin D deficiency is widespread in U.S. pregnant women 5–8 due to inadequate sunlight exposure, limited vitamin D-rich food sources, and use of prenatal vitamins with low doses of vitamin D. 4 Vitamin D has diverse and protean functions that may be relevant in the pathophysiology of preeclampsia, including abnormal placental implantation and angiogenesis, excessive inflammation, hypertension, and immune dysfunction. 4,9–12 Unfortunately, most vitamin D-preeclampsia research has been conducted in predominantly Caucasian populations with small numbers of preeclampsia cases, and results have been inconsistent. 13–21 We sought to determine the association between maternal vitamin D status at ≤26 weeks and the risk of preeclampsia in a large, geographically-diverse U.S. multicenter cohort of pregnant women.

Obesity paradox: conditioning on disease enhances biases in estimating the mortality risks of obesity.

Abstract: Background:Many studies have documented an obesity paradox—a survival advantage of being obese—in populations diagnosed with a medical condition. Whether obesity is causally associated with improved mortality in these conditions is unresolved.Methods:We develop the logic of collider bias as it perta

PM2.5 exposure and birth outcomes: use of satellite- and monitor-based data.

Abstract: Background:Air pollution may be related to adverse birth outcomes. Exposure information from land-based monitoring stations often suffers from limited spatial coverage. Satellite data offer an alternative data source for exposure assessment.Methods:We used birth certificate data for births in Connec

Impact of noise and air pollution on pregnancy outcomes.

Abstract: Background:Motorized traffic is an important source of both air pollution and community noise. While there is growing evidence for an adverse effect of ambient air pollution on reproductive health, little is known about the association between traffic noise and pregnancy outcomes.Methods:We evaluate

Residential proximity to major roads and term low birth weight: the roles of air pollution, heat, noise, and road-adjacent trees.

Abstract: Background: Maternal residential proximity to roads has been associated with adverse pregnancy outcomes. However, there is no study investigating mediators or buffering effects of road-adjacent trees on this association. We investigated the association between mothers' residential proximity to major roads and term low birth weight (LBW), while exploring possible mediating roles of air pollution (PM2.5, PM2.5-10, PM10, PM2.5 absorbance, nitrogen dioxide, and nitrogen oxides), heat, and noise and buffering effect of road-adjacent trees on this association. Methods: This cohort study was based on 6438 singleton term births in Barcelona, Spain (2001-2005). Road proximity was measured as both continuous distance to and living within 200 m from a major road. We assessed individual exposures to air pollution, noise, and heat using, respectively, temporally adjusted land-use regression models, annual averages of 24-hour noise levels across 50 m and 250 m, and average of satellite-derived land-surface temperature in a 50-m buffer around each residential address. We used vegetation continuous fields to abstract tree coverage in a 200-m buffer around major roads. Results: Living within 200 m of major roads was associated with a 46% increase in term LBW risk; an interquartile range increase in heat exposure with an 18% increase; and third-trimester exposure to PM2.5, PM2.5-10, and PM10 with 24%, 25%, and 26% increases, respectively. Air pollution and heat exposures together explained about one-third of the association between residential proximity to major roads and term LBW. Our observations on the buffering of this association by road-adjacent trees were not consistent between our 2 measures of proximity to major roads. Conclusion: An increased risk of term LBW associated with proximity to major roads was partly mediated by air pollution and heat exposures.

Autism spectrum disorder: interaction of air pollution with the MET receptor tyrosine kinase gene.

Abstract: Autism and autism spectrum disorders are complex neurodevelopmental disorders characterized by deficits in social interaction, communication, and behavioral flexibility. The complex phenotypic presentation of these disorders suggests that multiple genetic and environmental factors contribute to risk, and gene-environment interactions are widely believed to underlie autism spectrum disorders. Few studies have addressed joint risk from specific genetic susceptibility in combination with a specific environmental exposure or class of exposures.1 In previous independent studies, we have identified (1) increased autism spectrum disorder risk among children exposed to high levels of local near-roadway traffic-related air pollution and regional particulate matter near the time of birth2,3; (2) increased autism spectrum disorder risk among children with the C allele of the MET gene promoter variant rs1858830,4,5 which is associated with decreased expression of MET protein in brain6 and immune system7; and (3) decreased MET protein expression in brain and altered behavior in offspring of mouse dams exposed during pregnancy to the polycyclic aromatic hydrocarbon benzo(a)pyrene (a component of traffic-related air pollution and particulate matter).8 Based on these independent autism spectrum disorder associations and the biological link between benzo(a)pyrene and MET, we hypothesized that a gene-environment interaction contributes to autism spectrum disorder risk. In children, as in animals, prenatal polycyclic aromatic hydrocarbon exposure has been associated with intelligence (IQ) deficits at age 5 years as well as with increased anxiety, depression, and inattention at age 6–7.8–10 In this study we investigated the relationship of air pollution exposure and genotype at the MET rs1858830 locus with autism spectrum disorder.

The parametric g-formula for time-to-event data: intuition and a worked example.

Abstract: Imagine an oncologist knocks on your door with the following problem: she wants to know how much she could reduce mortality among her bone marrow transplant patients by prescribing a new drug that prevents graft-versus-host disease, a side effect of allogeneic marrow transplantation.1 While graft-versus-host disease is associated in observational studies with an increased risk of mortality, it also reduces the risk of leukemia relapse – thus, any drug that prevents graft-versus-host disease may have the very undesirable side effect of increasing the rate of relapse.2 She wants to compare the mortality in her cohort with what mortality would be in that same cohort if they had taken this new drug. We cannot answer this question with a regression model because leukemia relapse is a risk factor for mortality and subsequent graft-versus-host disease and it will also decrease the incidence of subsequent relapse (i.e. relapse is a confounder affected by exposure).3, 4 However, we can answer this question using the g-formula. The g-formula is an analytic tool for estimating standardized outcome distributions using covariate (exposure and confounders) specific estimates of the outcome distribution.5The g-formula can be used to estimate familiar measures of association, such as the hazard ratio. In the current paper, we address the oncologist’s question: we compare observed mortality in our cohort with the expected mortality in that cohort under the new treatment. Epidemiologists often use regression models (for example, the Cox proportional hazards model) to adjust for confounding; this is equivalent to estimating stratum-specific hazard ratios and then averaging the information-weighted hazard ratios. When some of those confounders are also causal intermediates, this amounts to adjusting away some of the effect of exposure.6, 7 The g-formula works differently: first, one finds weighted averages of the stratum-specific hazards, and then those averaged (standardized) hazards are combined in a summary hazard ratio. Thus, bias resulting from time-varying covariates that can be both confounders and causal intermediates is a shortcoming of using regression models to control for confounding, rather than a general principle of observational data analysis.8, 9 The g-formula is a tool that overcomes this shortcoming, but its use in the literature has been sparse – we could find only 9 examples using observational data.8, 10-17 We hypothesize that the dearth of software packages and lack of useful, yet simple, examples of the g-formula have been the main barriers to broader use. We show how the g-formula can be used with standard software tools that many epidemiologists already employ, and we illustrate it using publicly-available data from a small cohort study with accompanying SAS code in an eAppendix. We illustrate how we can estimate the net (total) effect of a hypothetical treatment to prevent graft-versus-host disease on mortality and compare the g-formula approach with a regression approach. The g-formula (as with any statistical method) relies on making assumptions in order to make sense of the complex processes underlying the data. We discuss possible ways to assess how well we meet the assumptions as well as the robustness of the g-formula to violations of these assumptions.

Asthma morbidity and ambient air pollution: effect modification by residential traffic-related air pollution.

Abstract: BACKGROUND:: Ambient air pollution has been associated with asthma-related hospital admissions and emergency department visits (hospital encounters). We hypothesized that higher individual exposure to residential traffic-related air pollutants would enhance these associations. METHODS:: We studied 11,390 asthma-related hospital encounters among 7492 subjects 0-18 years of age living in Orange County, California. Ambient exposures were measured at regional air monitoring stations. Seasonal average traffic-related exposures (PM2.5, ultrafine particles, NOx, and CO) were estimated near subjects' geocoded residences for 6-month warm and cool seasonal periods, using dispersion models based on local traffic within 500 m radii. Associations were tested in case-crossover conditional logistic regression models adjusted for temperature and humidity. We assessed effect modification by seasonal residential traffic-related air pollution exposures above and below median dispersion-modeled exposures. Secondary analyses considered effect modification by traffic exposures within race/ethnicity and insurance group strata. RESULTS:: Asthma morbidity was positively associated with daily ambient O3 and PM2.5 in warm seasons and with CO, NOx, and PM2.5 in cool seasons. Associations with CO, NOx, and PM2.5 were stronger among subjects living at residences with above-median traffic-related exposures, especially in cool seasons. Secondary analyses showed no consistent differences in association, and 95% confidence intervals were wide, indicating a lack of precision for estimating these highly stratified associations. CONCLUSIONS:: Associations of asthma with ambient air pollution were enhanced among subjects living in homes with high traffic-related air pollution. This may be because of increased susceptibility (greater asthma severity) or increased vulnerability (meteorologic amplification of local vs. correlated ambient exposures). Copyright © 2013 by Lippincott Williams & Wilkins.

Constructing inverse probability weights for continuous exposures: a comparison of methods.

Abstract: Inverse probability-weighted marginal structural models with binary exposures are common in epidemiology. Constructing inverse probability weights for a continuous exposure can be complicated by the presence of outliers, and the need to identify a parametric form for the exposure and account for nonconstant exposure variance. We explored the performance of various methods to construct inverse probability weights for continuous exposures using Monte Carlo simulation. We generated two continuous exposures and binary outcomes using data sampled from a large empirical cohort. The first exposure followed a normal distribution with homoscedastic variance. The second exposure followed a contaminated Poisson distribution, with heteroscedastic variance equal to the conditional mean. We assessed six methods to construct inverse probability weights using: a normal distribution, a normal distribution with heteroscedastic variance, a truncated normal distribution with heteroscedastic variance, a gamma distribution, a t distribution (1, 3, and 5 degrees of freedom), and a quantile binning approach (based on 10, 15, and 20 exposure categories). We estimated the marginal odds ratio for a single-unit increase in each simulated exposure in a regression model weighted by the inverse probability weights constructed using each approach, and then computed the bias and mean squared error for each method. For the homoscedastic exposure, the standard normal, gamma, and quantile binning approaches performed best. For the heteroscedastic exposure, the quantile binning, gamma, and heteroscedastic normal approaches performed best. Our results suggest that the quantile binning approach is a simple and versatile way to construct inverse probability weights for continuous exposures.

Fine particle air pollution and mortality: importance of specific sources and chemical species.

Abstract: Background:While exposure to ambient fine particles <2.5 μm in aerodynamic diameter (PM2.5) has well-established health effects, there is limited quantitative evidence that links specific sources of PM2.5 with those effects. This study was designed to examine the risks of exposure to chemical specie

Ambient particulate air pollution and microRNAs in elderly men.

Abstract: Background Ambient particulate matter (PM) has been associated with mortality and morbidity for cardiovascular disease (CVD) MicroRNAs control gene expression at a post-transcriptional level Altered microRNA expression has been reported in processes related to CVD and PM exposure, eg systemic inflammation, endothelial dysfunction and atherosclerosis Polymorphisms in microRNA-related genes could influence response to PM