SCHIP Children: How Long Do They Stay and Where Do They Go?

Presents findings on the length of children's enrollment in State Children's Health Insurance Programs and their coverage after they leave the program in seven states. Explores variations across states and how state policies may affect retention

Exposure to Household Air Pollution from Biomass Cookstoves and Levels of Fractional Exhaled Nitric Oxide (FeNO) among Honduran Women

Household air pollution is estimated to be responsible for nearly three million premature deaths annually. Measuring fractional exhaled nitric oxide (FeNO) may improve the limited understanding of the association of household air pollution and airway inflammation. We evaluated the cross-sectional association of FeNO with exposure to household air pollution (24-h average kitchen and personal fine particulate matter and black carbon; stove type) among 139 women in rural Honduras using traditional stoves or cleaner-burning Justastoves. We additionally evaluated interaction by age. Results were generally consistent with a null association; we did not observe a consistent pattern for interaction by age. Evidence from ambient and household air pollution regarding FeNO is inconsistent, and may be attributable to differing study populations, exposures, and FeNO measurement procedures (e.g., the flow rate used to measure FeNO)

Penalized Distributed Lag Interaction Model: Air Pollution, Birth Weight and Neighborhood Vulnerability

Maternal exposure to air pollution during pregnancy has a substantial public health impact. Epidemiological evidence supports an association between maternal exposure to air pollution and low birth weight. A popular method to estimate this association while identifying windows of susceptibility is a distributed lag model (DLM), which regresses an outcome onto exposure history observed at multiple time points. However, the standard DLM framework does not allow for modification of the association between repeated measures of exposure and the outcome. We propose a distributed lag interaction model that allows modification of the exposure-time-response associations across individuals by including an interaction between a continuous modifying variable and the exposure history. Our model framework is an extension of a standard DLM that uses a cross-basis, or bi-dimensional function space, to simultaneously describe both the modification of the exposure-response relationship and the temporal structure of the exposure data. Through simulations, we showed that our model with penalization out-performs a standard DLM when the true exposure-time-response associations vary by a continuous variable. Using a Colorado, USA birth cohort, we estimated the association between birth weight and ambient fine particulate matter air pollution modified by an area-level metric of health and social adversities from Colorado EnviroScreen.Comment: 41 pages, 4 figures, 2 table

Shape of the concentration–response association between fine particulate matter pollution and human mortality in Beijing, China, and its implications for health impact assessment, The

Includes bibliographical references (pages 107009-12-107009-14).Publisher version: https://doi.org/10.1289/EHP4464.Background: Studies found approximately linear short-term associations between particulate matter (PM) and mortality in Western communities. However, in China, where the urban PM levels are typically considerably higher than in Western communities, some studies suggest nonlinearity in this association. Health impact assessments (HIA) of PM in China have generally not incorporated nonlinearity in the concentration–response (C-R) association, which could result in large discrepancies in estimates of excess deaths if the true association is nonlinear. Objectives: We investigated nonlinearity in the C-R associations between with PM with aerodynamic diameter ≤2.5μm (PM2.5) and mortality in Beijing, China, and the sensitivity of HIA to linearity assumptions. Methods: We modeled the C-R association between PM2.5 and cause-specific mortality in Beijing, China (2009–2012), using generalized linear models (GLM). PM2.5 was included through either linear, piecewise-linear, or spline functions to investigate evidence of nonlinearity. To determine the sensitivity of HIA to linearity assumptions, we estimated PM2.5-attributable deaths using both linear- and nonlinear-based C-R associations between PM2.5 and mortality. Results: We found some evidence that, for nonaccidental and circulatory mortality, the shape of the C-R association was relatively flat at lower concentrations of PM2.5, but then had a positive slope at higher concentrations, indicating nonlinearity. Conversely, the shape for respiratory mortality was positive and linear at lower concentrations of PM2.5, but then leveled off at the higher concentrations. Estimates of excess deaths attributable to short-term PM2.5 exposure were, in some cases, very sensitive to the linearity assumption in the association, but in other cases robust to this assumption. Conclusions: Our results demonstrate some evidence of nonlinearity in PM2.5–mortality associations and that an assumption of linearity in this association can influence HIAs, highlighting the importance of understanding potential nonlinearity in the PM2.5–mortality association at the high concentrations of PM2.5 in developing megacities like Beijing. https://doi.org/10.1289/EHP446

A hierarchical Bayesian model for estimating age-specific COVID-19 infection fatality rates in developing countries

The COVID-19 infection fatality rate (IFR) is the proportion of individuals infected with SARS-CoV-2 who subsequently die. As COVID-19 disproportionately affects older individuals, age-specific IFR estimates are imperative to facilitate comparisons of the impact of COVID-19 between locations and prioritize distribution of scare resources. However, there lacks a coherent method to synthesize available data to create estimates of IFR and seroprevalence that vary continuously with age and adequately reflect uncertainties inherent in the underlying data. In this paper we introduce a novel Bayesian hierarchical model to estimate IFR as a continuous function of age that acknowledges heterogeneity in population age structure across locations and accounts for uncertainty in the estimates due to seroprevalence sampling variability and the imperfect serology test assays. Our approach simultaneously models test assay characteristic, serology, and death data, where the serology and death data are often available only for binned age groups. Information is shared across locations through hierarchical modeling to improve estimation of the parameters with limited data. Modeling data from 26 developing country locations during the first year of the COVID-19 pandemic, we found seroprevalence did not change dramatically with age, and the IFR at age 60 was above the high-income country benchmark for most locations