Seasonal modification of the association between temperature and adult emergency department visits for asthma: a case-crossover study

Abstract Background The objective of this study is to characterize the effect of temperature on emergency department visits for asthma and modification of this association by season. This association is of interest in its own right, and also important to understand because temperature may be an important confounder in analyses of associations between other environmental exposures and asthma. For example, the case-crossover study design is commonly used to investigate associations between air pollution and respiratory outcomes, such as asthma. This approach controls for confounding by month and season by design, and permits adjustment for potential confounding by temperature through regression modeling. However, such models may fail to adequately control for confounding if temperature effects are seasonal, since case-crossover analyses rarely account for interactions between matching factors (such as calendar month) and temperature. Methods We conducted a case-crossover study to determine whether the association between temperature and emergency department visits for asthma varies by season or month. Asthma emergency department visits among North Carolina adults during 2007–2008 were identified using a statewide surveillance system. Marginal as well as season- and month-specific associations between asthma visits and temperature were estimated with conditional logistic regression. Results The association between temperature and adult emergency department visits for asthma is near null when the overall association is examined [odds ratio (OR) per 5 degrees Celsius = 1.01, 95% confidence interval (CI): 1.00, 1.02]. However, significant variation in temperature-asthma associations was observed by season (chi-square = 18.94, 3 degrees of freedom, p <0.001) and by month of the year (chi-square = 45.46, 11 degrees of freedom, p <0.001). ORs per 5 degrees Celsius were increased in February (OR = 1.06, 95% CI: 1.02, 1.10), July (OR = 1.16, 95% CI: 1.04, 1.29), and December (OR = 1.04, 95% CI: 1.01, 1.07) and decreased in September (OR = 0.92, 95% CI: 0.87, 0.97). Conclusions Our empirical example suggests that there is significant seasonal variation in temperature-asthma associations. Epidemiological studies rarely account for interactions between ambient temperature and temporal matching factors (such as month of year) in the case-crossover design. These findings suggest that greater attention should be given to seasonal modification of associations between temperature and respiratory outcomes in case-crossover analyses of other environmental asthma triggers

SPR Perspectives: scientific opportunities in the Environmental influences on Child Health Outcomes Program

Drawing upon extant data from existing pediatric cohorts and new follow-up of a diverse set of pediatric cohorts from across the United States, the Environmental influences on Child Health Outcomes (ECHO) Program creates the opportunity for novel and innovative investigations of many previously inaccessible scientific questions in the area of child health. We describe how the large sample size, diversity of participants, emphasis on team science, and infrastructure for improving research methodology make the ECHO Program a major research resource for improving our understanding of early life determinants of childhood health and well-being. Pediatric researchers leverage the unique features of the ECHO Program to address research questions with the potential to yield far-reaching and long-term impacts on child health. IMPACT: The ECHO Program unites pediatric cohorts from across the United States, allowing for investigations of compelling research questions that were previously infeasible due to limited sample sizes or lack of participant diversity. The focus of the ECHO Program on team science, solution-oriented research, and methodological innovation propels novel scientific investigations that are responsive to the needs of a wide range of stakeholders. Features of the ECHO program\u27s infrastructure poise its investigators to rapidly launch research endeavors that are responsive to time-sensitive and critical needs within the realm of pediatric research

Emerging exposures of developmental toxicants

PURPOSE OF REVIEW: The purpose of this review is to identify emerging developmental toxicants that are understudied in children's health. Exposures may arise from new products designed to improve utility, to reduce toxicity, or to replace undesirable chemicals. Exposures to less-toxic chemicals may also be significant if they are very commonly used, thereby generating widespread exposure. Sources of exposure include the workplace, personal, home, and office products; food, water, and air. RECENT FINDINGS: We describe eight exposure categories that contain numerous potential developmental toxicants. References are discussed if reported in PubMed during the past decade at least 10 times more frequently than in 1990-2000. Examples included phthalates, phenols, sunscreens, pesticides, halogenated flame retardants, perfluoroalkyl coatings, nanoparticles, e-cigarettes, and dietary polyphenols. Replacements are often close structural homologs of their precursors. We suggest biomonitoring as preferred means of exposure assessment to emerging chemicals. Some existing analytic methods would require minimal modification to measure these exposures, but others require toxicokinetic and analytic investigation. SUMMARY: A deliberate strategy for biomonitoring of emerging replacement chemicals is warranted, especially in view of concerns regarding developmental toxicity. To prevent adverse health effects, it is important to characterize such exposures before they become widely disseminated

Prenatal exposure to environmental phenols and childhood fat mass in the Mount Sinai Children's Environmental Health Study

Early life exposure to endocrine disrupting chemicals may alter adipogenesis and energy balance leading to changes in obesity risk. Several studies have evaluated the association of prenatal bisphenol A exposure with childhood body size but only one study of male infants has examined other environmental phenols. Therefore, we assessed associations between prenatal exposure to environmental phenols and fat mass in a prospective birth cohort. We quantified four phenol biomarkers in third trimester maternal spot urine samples in a cohort of women enrolled in New York City between 1998 and 2002 and evaluated fat mass in their children using a Tanita scale between ages 4 and 9 years (173 children with 351 total observations). We estimated associations of standard deviation differences in natural log creatinine-standardized phenol biomarker concentrations with percent fat mass using linear mixed effects regression models. We did not observe associations of bisphenol A or triclosan with childhood percent fat mass. In unadjusted models, maternal urinary concentrations of 2,5-dichlorophenol were associated with greater percent fat mass and benzophenone-3 was associated with lower percent fat mass among children. After adjustment, phenol biomarkers were not associated with percent fat mass. However, the association between benzophenone-3 and percent fat mass was modified by child’s sex: benzophenone-3 concentrations were inversely associated with percent fat mass in girls (beta = −1.51, 95% CI = −3.06, 0.01) but not boys (beta = −0.20, 95% CI = −1.69, 1.26). Although we did not observe strong evidence that prenatal environmental phenols exposures influence the development of childhood adiposity, the potential antiadipogenic effect of benzophenone-3 in girls may warrant further investigation

A quantile-based g-computation approach to addressing the effects of exposure mixtures

Exposure mixtures frequently occur in data across many domains, particularly in the fields of environmental and nutritional epidemiology. Various strategies have arisen to answer questions about mixtures, including methods such as weighted quantile sum (WQS) regression that estimate a joint effect of the mixture components.We demonstrate a new approach to estimating the joint effects of a mixture: quantile g-computation. This approach combines the inferential simplicity of WQS regression with the flexibility of g-computation, a method of causal effect estimation. We use simulations to examine whether quantile g-computation and WQS regression can accurately and precisely estimate effects of mixtures in common scenarios. We examine the bias, confidence interval coverage, and bias-variance tradeoff of quantile g-computation and WQS regression, and how these quantities are impacted by the presence of non-causal exposures, exposure correlation, unmeasured confounding, and non-linear effects. Quantile g-computation, unlike WQS regression allows inference on mixture effects that is unbiased with appropriate confidence interval coverage at sample sizes typically encountered in epidemiologic studies and when the assumptions of WQS regression are not met. Further, WQS regression can magnify bias from unmeasured confounding that might occur if important components of the mixture are omitted. Unlike inferential approaches that examine effects of individual exposures, methods like quantile g-computation that can estimate the effect of a mixture are essential for understanding effects of potential public health actions that act on exposure sources. Our approach may serve to help bridge gaps between epidemiologic analysis and interventions such as regulations on industrial emissions or mining processes, dietary changes, or consumer behavioral changes that act on multiple exposures simultaneously.Comment: Main manuscript (3 figures, 4 tables, 7000 words) + appendi

Statistical Approaches for Estimating Sex-Specific Effects in Endocrine Disruptors Research

BACKGROUND: When a biologic mechanism of interest is anticipated to operate differentially according to sex, as is often the case in endocrine disruptors research, investigators routinely estimate sex-specific associations. Less attention has been given to potential sexual heterogeneity of confounder associations with outcomes. When relationships of covariates with outcomes differ according to sex, commonly applied statistical approaches for estimating sex-specific endocrine disruptor effects may produce divergent estimates. OBJECTIVES: We discuss underlying assumptions and evaluate the performance of two traditional approaches for estimating sex-specific effects, stratification and product terms, and introduce a simple modeling alternative: an augmented product term approach. METHODS: We describe the impact of assumptions regarding sexual heterogeneity of confounder relationships on estimates of sex-specific effects of the exposure of interest for three approaches: stratification, traditional product terms, and augmented product terms. Using simulated and applied examples, we demonstrate properties of each approach under a range of scenarios. RESULTS: In simulations, sex-specific exposure effects estimated using the traditional product term approach were biased when confounders had sex-dependent associations with the outcome. Sex-specific estimates from stratification and the augmented product term approach were unbiased but less precise. In the applied example, the three approaches yielded similar estimates, but resulted in some meaningful differences in conclusions based on statistical significance. CONCLUSIONS: Investigators should consider sexual heterogeneity of confounder associations when choosing an analytic approach to estimate sex-specific effects of endocrine disruptors on health. In the presence of sex-dependent confounding, our augmented product term approach may be advantageous over stratification when there is prior knowledge available to fit reduced models or when investigators seek an automated test for effect measure modification. https://doi.org/10.1289/EHP334

Identifying and Prioritizing Chemicals with Uncertain Burden of Exposure: Opportunities for Biomonitoring and Health-Related Research.

BackgroundThe National Institutes of Health's Environmental influences on Child Health Outcomes (ECHO) initiative aims to understand the impact of environmental factors on childhood disease. Over 40,000 chemicals are approved for commercial use. The challenge is to prioritize chemicals for biomonitoring that may present health risk concerns.ObjectivesOur aim was to prioritize chemicals that may elicit child health effects of interest to ECHO but that have not been biomonitored nationwide and to identify gaps needing additional research.MethodsWe searched databases and the literature for chemicals in environmental media and in consumer products that were potentially toxic. We selected chemicals that were not measured in the National Health and Nutrition Examination Survey. From over 700 chemicals, we chose 155 chemicals and created eight chemical panels. For each chemical, we compiled biomonitoring and toxicity data, U.S. Environmental Protection Agency exposure predictions, and annual production usage. We also applied predictive modeling to estimate toxicity. Using these data, we recommended chemicals either for biomonitoring, to be deferred pending additional data, or as low priority for biomonitoring.ResultsFor the 155 chemicals, 97 were measured in food or water, 67 in air or house dust, and 52 in biospecimens. We found in vivo endocrine, developmental, reproductive, and neurotoxic effects for 61, 74, 47, and 32 chemicals, respectively. Eighty-six had data from high-throughput in vitro assays. Positive results for endocrine, developmental, neurotoxicity, and obesity were observed for 32, 11, 35, and 60 chemicals, respectively. Predictive modeling results suggested 90% are toxicants. Biomarkers were reported for 76 chemicals. Thirty-six were recommended for biomonitoring, 108 deferred pending additional research, and 11 as low priority for biomonitoring.DiscussionThe 108 deferred chemicals included those lacking biomonitoring methods or toxicity data, representing an opportunity for future research. Our evaluation was, in general, limited by the large number of unmeasured or untested chemicals. https://doi.org/10.1289/EHP5133

Prenatal phthalate biomarker concentrations and performance on the Bayley Scales of Infant Development-II in a population of young urban children

Evidence suggests prenatal phthalate exposures may have neurodevelopmental consequences. Our objective was to investigate prenatal exposure to phthalates and cognitive development in a cohort of young urban children

Occupational Radon Exposure and Lung Cancer Mortality: Estimating Intervention Effects Using the Parametric g-Formula

Traditional regression analysis techniques used to estimate associations between occupational radon exposure and lung cancer focus on estimating the effect of cumulative radon exposure on lung cancer, while public health interventions are typically based on regulating radon concentration rather than workers’ cumulative exposure. Moreover, estimating the direct effect of cumulative occupational exposure on lung cancer may be difficult in situations vulnerable to the healthy worker survivor bias