Perfluoroalkyl substances (PFAS), chemicals used to make products stain and stick resistant, have been linked to health effects in adults and adverse birth outcomes. A growing body of literature also addresses health effects in children exposed to PFAS. This review summarizes the epidemiologic evidence for relationships between prenatal and/or childhood exposure to PFAS and health outcomes in children as well as to provide a risk of bias analysis of the literature. A systematic review was performed by searching PubMed for studies on PFAS and child health outcomes. We identified 64 studies for inclusion and performed risk of bias analysis on those studies. We determined that risk of bias across studies was low to moderate. Six categories of health outcomes emerged. These were: immunity/infection/asthma, cardio-metabolic, neurodevelopmental/attention, thyroid, renal, and puberty onset. While there are a limited number of studies for any one particular health outcome, there is evidence for positive associations between PFAS and dyslipidemia, immunity (including vaccine response and asthma), renal function, and age at menarche. One finding of note is that while PFASs are mixtures of multiple compounds few studies examine them as such, therefore the role of these compounds as complex mixtures remains largely unknown.

Background: Photochemical modeling can predict the level and distribution of pollutant concentrations over time, but is resource-intensive. Partly for this reason, there are few studies exploring the multi-year trajectory of the historical change in fine particle (PM 2.5) levels and associated health impacts in the U.S. Objectives: We used a unique dataset of Community Multi-Scale Air Quality (CMAQ) model simulations performed for a subset of years over a decade-long period fused with observations to estimate the change in ambient levels of PM 2.5 across the contiguous U.S. We also quantified the change in PM 2.5-attributable health risks and characterized the level of risk inequality over this period. Methods: We estimated annual mean PM 2.5 concentrations in 2005, 2011 and 2014. Using loglinear and logistic concentration-response coefficients we estimated changes in the numbers of deaths, hospital admissions and other morbidity outcomes. Calculating the Gini coefficient and Atkinson Index, we characterized the extent to which PM 2.5 attributable risks were shared equally across the population or instead concentrated among certain subgroups. Results: In 2005 the estimated fraction of deaths due to PM 2.5 was 6.1%. This estimated value falls to 4.6% by 2014. Every portion of the contiguous U.S. experiences a decline in the risk of PM-related premature death over the 10-year period. As measured by the Gini coefficient and Atkinson index, the level of PM mortality risk is shared more equally in 2014 than in 2005 among all subgroups. Conclusions: Between 2005 and 2014, the level of PM 2.5 concentrations fall, and the risk of premature death, declined and became more equitably distributed across the U.S. population.

BackgroundMetabolic syndrome (MetS) is the co-occurrence of several conditions that increase risk of chronic disease and mortality. Multivariate models for calculating risk of MetS-related diseases based on combinations of biomarkers are promising for future risk estimation if based on large population samples. Given biomarkers’ nonspecificity and commonality in predicting diseases, we hypothesized that unique combinations of the same clinical diagnostic criteria can be used in different multivariate models to develop more accurate individual and cumulative risk estimates for specific MetS-related diseases.MethodsWe utilized adult biomarker and cardiovascular disease (CVD) data from the National Health and Nutrition Examination Survey as part of a cross-sectional analysis. Serum C-reactive protein (CRP), glycohemoglobin, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, fasting glucose, and apolipoprotein-B were modeled. CVDs included congestive heart failure, coronary heart disease, angina, myocardial infarction, and stroke. Decile analysis for disease prevalence in each biomarker group and multivariate logistic regression for estimation of odds ratios were employed to measure the joint association between multiple biomarkers and CVD diagnoses.ResultsOf the biomarkers considered, glycohemoglobin, triglycerides, and CRP were consistently associated with the CVD outcomes of interest in decile analysis and were selected for the final models. Associations were overestimated when using single-marker models in comparison with full models; individual odds ratios decreased an average of 16.4% from the single-biomarker models to the joint association models for CRP, 6.6% for triglycerides, and 1.4% for glycohemoglobin. However, joint associations were stronger than any single-marker estimate. Additionally, reduced models produced unique combinations of biomarkers for specific CVD outcomes.ConclusionThe reduced joint association modeling results suggest that unique combinations of biomarkers with their related measure of association can be used to produce more accurate cumulative risk estimates for each CVD. Additionally, our results indicate that the use of multiple biomarkers in a single multivariate model may provide increased accuracy of individual biomarker association estimates by controlling for statistical artifacts and spurious relationships due to co-biomarker confounding.Electronic supplementary materialThe online version of this article (doi:10.1186/s12963-015-0041-5) contains supplementary material, which is available to authorized users.