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Background: Evaluating environmental health risks in communities requires models characterizing geographic and demographic patterns of exposure to multiple stressors. These exposure models can be constructed from multivariable regression analyses using individual-level predictors (microdata), but these microdata are not typically available with sufficient geographic resolution for community risk analyses given privacy concerns., Methods: We developed synthetic geographically-resolved microdata for a low-income community (New Bedford, Massachusetts) facing multiple environmental stressors. We first applied probabilistic reweighting using simulated annealing to data from the 2006-2010 American Community Survey, combining 9,135 microdata samples from the New Bedford area with census tract-level constraints for individual and household characteristics. We then evaluated the synthetic microdata using goodness-of-fit tests and by examining spatial patterns of microdata fields not used as constraints. As a demonstration, we developed a multivariable regression model predicting smoking behavior as a function of individual-level microdata fields using New Bedford-specific data from the 2006-2010 Behavioral Risk Factor Surveillance System, linking this model with the synthetic microdata to predict demographic and geographic smoking patterns in New Bedford., Results: Our simulation produced microdata representing all 94,944 individuals living in New Bedford in 2006-2010. Variables in the synthetic population matched the constraints well at the census tract level (e.g., ancestry, gender, age, education, household income) and reproduced the census-derived spatial patterns of non-constraint microdata. Smoking in New Bedford was significantly associated with numerous demographic variables found in the microdata, with estimated tract-level smoking rates varying from 20% (95% CI: 17%, 22%) to 37% (95% CI: 30%, 45%)., Conclusions: We used simulation methods to create geographically-resolved individual-level microdata that can be used in community-wide exposure and risk assessment studies. This approach provides insights regarding community-scale exposure and vulnerability patterns, valuable in settings where policy can be informed by characterization of multi-stressor exposures and health risks at high resolution.

Background: Although indoor environmental conditions can affect pediatric asthmatic patients, few studies have characterized the effect of building interventions on asthma-related outcomes. Simulation models can evaluate such complex systems but have not been applied in this context., Objective: We sought to evaluate the impact of building interventions on indoor environmental quality and pediatric asthma health care use, and to conduct cost comparisons between intervention and health care costs and energy savings., Methods: We applied our previously developed discrete event simulation model (DEM) to simulate the effect of environmental factors, medication compliance, seasonality, and medical history on (1) pollutant concentrations indoors and (2) asthma outcomes in low-income multifamily housing. We estimated health care use and costs at baseline and subsequent to interventions, and then compared health care costs with energy savings and intervention costs., Results: Interventions, such as integrated pest management and repairing kitchen exhaust fans, led to 7% to 12% reductions in serious asthma events with 1- to 3-year payback periods. Weatherization efforts targeted solely toward tightening a building envelope led to 20% more serious asthma events, but bundling with repairing kitchen exhaust fans and eliminating indoor sources (eg, gas stoves or smokers) mitigated this effect., Conclusion: Our pediatric asthma model provides a tool to prioritize individual and bundled building interventions based on their effects on health and costs, highlighting the tradeoffs between weatherization, indoor air quality, and health. Our work bridges the gap between clinical and environmental health sciences by increasing physicians' understanding of the effect that home environmental changes can have on their patients' asthma., (Copyright © 2013 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.)

The CDC recommends that healthcare settings provide influenza patients with facemasks as a means of reducing transmission to staff and other patients, and a recent report suggested that surgical masks can capture influenza virus in large droplet spray. However, there is minimal data on influenza virus aerosol shedding, the infectiousness of exhaled aerosols, and none on the impact of facemasks on viral aerosol shedding from patients with seasonal influenza. We collected samples of exhaled particles (one with and one without a facemask) in two size fractions ("coarse">5 µm, "fine"≤5 µm) from 37 volunteers within 5 days of seasonal influenza onset, measured viral copy number using quantitative RT-PCR, and tested the fine-particle fraction for culturable virus. Fine particles contained 8.8 (95% CI 4.1 to 19) fold more viral copies than did coarse particles. Surgical masks reduced viral copy numbers in the fine fraction by 2.8 fold (95% CI 1.5 to 5.2) and in the coarse fraction by 25 fold (95% CI 3.5 to 180). Overall, masks produced a 3.4 fold (95% CI 1.8 to 6.3) reduction in viral aerosol shedding. Correlations between nasopharyngeal swab and the aerosol fraction copy numbers were weak (r = 0.17, coarse; r = 0.29, fine fraction). Copy numbers in exhaled breath declined rapidly with day after onset of illness. Two subjects with the highest copy numbers gave culture positive fine particle samples. Surgical masks worn by patients reduce aerosols shedding of virus. The abundance of viral copies in fine particle aerosols and evidence for their infectiousness suggests an important role in seasonal influenza transmission. Monitoring exhaled virus aerosols will be important for validation of experimental transmission studies in humans.

The importance of the aerosol mode for transmission of influenza is unknown. Understanding the role of aerosols is essential to developing public health interventions such as the use of surgical masks as a source control to prevent the release of infectious aerosols. Little information is available on the number and size of particles generated by infected persons, which is partly due to the limitations of conventional air samplers, which do not efficiently capture fine particles or maintain microorganism viability. We designed and built a new sampler, called the G-II, that collects exhaled breath particles that can be used in infectivity analyses. The G-II allows test subjects to perform various respiratory maneuvers (i.e. tidal breathing, coughing, and talking) and allows subjects to wear a mask or respirator during testing. A conventional slit impactor collects particles > 5.0 μm. Condensation of water vapor is used to grow remaining particles, including fine particles, to a size large enough to be efficiently collected by a 1.0 μm slit impactor and be deposited into a buffer-containing collector. We evaluated the G-II for fine particle collection efficiency with inert particle aerosols and evaluated infective virus collection using influenza A virus aerosols. Testing results showed greater than 85% collection efficiency for particles greater than 50nm and influenza virus collection comparable with a reference SKC BioSampler®. The new design will enable determination of exhaled infectious virus generation rate and evaluate control strategies such as wearing a surgical type mask to prevent the release of viruses from infected persons.

Background: In the United States, asthma is the most common chronic disease of childhood across all socioeconomic classes and is the most frequent cause of hospitalization among children. Asthma exacerbations have been associated with exposure to residential indoor environmental stressors such as allergens and air pollutants as well as numerous additional factors. Simulation modeling is a valuable tool that can be used to evaluate interventions for complex multifactorial diseases such as asthma but in spite of its flexibility and applicability, modeling applications in either environmental exposures or asthma have been limited to date., Methods: We designed a discrete event simulation model to study the effect of environmental factors on asthma exacerbations in school-age children living in low-income multi-family housing. Model outcomes include asthma symptoms, medication use, hospitalizations, and emergency room visits. Environmental factors were linked to percent predicted forced expiratory volume in 1 second (FEV1%), which in turn was linked to risk equations for each outcome. Exposures affecting FEV1% included indoor and outdoor sources of NO2 and PM2.5, cockroach allergen, and dampness as a proxy for mold., Results: Model design parameters and equations are described in detail. We evaluated the model by simulating 50,000 children over 10 years and showed that pollutant concentrations and health outcome rates are comparable to values reported in the literature. In an application example, we simulated what would happen if the kitchen and bathroom exhaust fans were improved for the entire cohort, and showed reductions in pollutant concentrations and healthcare utilization rates., Conclusions: We describe the design and evaluation of a discrete event simulation model of pediatric asthma for children living in low-income multi-family housing. Our model simulates the effect of environmental factors (combustion pollutants and allergens), medication compliance, seasonality, and medical history on asthma outcomes (symptom-days, medication use, hospitalizations, and emergency room visits). The model can be used to evaluate building interventions and green building construction practices on pollutant concentrations, energy savings, and asthma healthcare utilization costs, and demonstrates the value of a simulation approach for studying complex diseases such as asthma.

Background: Collection of exhaled breath samples for the analysis of inflammatory biomarkers is an important area of research aimed at improving our ability to diagnose, treat and understand the mechanisms of chronic pulmonary disease. Current collection methods based on condensation of water vapor from exhaled breath yield biomarker levels at or near the detection limits of immunoassays contributing to problems with reproducibility and validity of biomarker measurements. In this study, we compare the collection efficiency of two aerosol-to-liquid sampling devices to a filter-based collection method for recovery of dilute laboratory generated aerosols of human cytokines so as to identify potential alternatives to exhaled breath condensate collection., Methodology/principal Findings: Two aerosol-to-liquid sampling devices, the SKC® Biosampler and Omni 3000™, as well as Teflon® filters were used to collect aerosols of human cytokines generated using a HEART nebulizer and single-pass aerosol chamber setup in order to compare the collection efficiencies of these sampling methods. Additionally, methods for the use of Teflon® filters to collect and measure cytokines recovered from aerosols were developed and evaluated through use of a high-sensitivity multiplex immunoassay. Our results show successful collection of cytokines from pg/m(3) aerosol concentrations using Teflon® filters and measurement of cytokine levels in the sub-picogram/mL concentration range using a multiplex immunoassay with sampling times less than 30 minutes. Significant degradation of cytokines was observed due to storage of cytokines in concentrated filter extract solutions as compared to storage of dry filters., Conclusions: Use of filter collection methods resulted in significantly higher efficiency of collection than the two aerosol-to-liquid samplers evaluated in our study. The results of this study provide the foundation for a potential new technique to evaluate biomarkers of inflammation in exhaled breath samples.

Objectives: The indoor environment has not been fully incorporated into the environmental justice dialogue. To inform strategies to reduce disparities, we developed a framework to identify the individual and place-based drivers of indoor environment quality., Methods: We reviewed empirical evidence of socioeconomic disparities in indoor exposures and key determinants of these exposures for air pollutants, lead, allergens, and semivolatile organic compounds. We also used an indoor air quality model applied to multifamily housing to illustrate how nitrogen dioxide (NO(2)) and fine particulate matter (PM(2.5)) vary as a function of factors known to be influenced by socioeconomic status., Results: Indoor concentrations of multiple pollutants are elevated in low-socioeconomic status households. Differences in these exposures are driven by the combined influences of indoor sources, outdoor sources, physical structures, and residential activity patterns. Simulation models confirmed indoor sources' importance in determining indoor NO(2) and PM(2.5) exposures and showed the influence of household-specific determinants., Conclusions: Both theoretical models and empirical evidence emphasized that disparities in indoor environmental exposure can be significant. Understanding key determinants of multiple indoor exposures can aid in developing policies to reduce these disparities.