Search

Children may be exposed to numerous in-home environmental exposures (IHEE) that trigger asthma exacerbations. Spatially linking social and environmental exposures to electronic health records (EHR) can aid exposure assessment, epidemiology, and clinical treatment, but EHR data on exposures are missing for many children with asthma. To address the issue, we predicted presence of indoor asthma trigger allergens, and estimated effects of their key geospatial predictors.Our study samples were comprised of children with asthma who provided self-reported IHEE data in EHR at a safety-net hospital in New England during 2004-2015. We used an ensemble machine learning algorithm and 86 multilevel features (e.g., individual, housing, neighborhood) to predict presence of cockroaches, rodents (mice or rats), mold, and bedroom carpeting/rugs in homes. We reduced dimensionality via elastic net regression and estimated effects by the G-computation causal inference method.Our models reasonably predicted presence of cockroaches (area under receiver operating curves [AUC] = 0.65), rodents (AUC = 0.64), and bedroom carpeting/rugs (AUC = 0.64), but not mold (AUC = 0.54). In models adjusted for confounders, higher average household sizes in census tracts were associated with more reports of pests (cockroaches and rodents). Tax-exempt parcels were associated with more reports of cockroaches in homes. Living in a White-segregated neighborhood was linked with lower reported rodent presence, and mixed residential/commercial housing and newer buildings were associated with more reports of bedroom carpeting/rugs in bedrooms.We innovatively applied a machine learning and causal inference mixture methodology to detail IHEE among children with asthma using EHR and geospatial data, which could have wide applicability and utility.

Many techniques for estimating exposure to airborne contaminants do not account for building characteristics that can magnify contaminant contributions from indoor and outdoor sources. Building characteristics that influence exposure can be challenging to obtain at scale, but some may be incorporated into exposure assessments using public datasets. We present a methodology for using public datasets to generate housing models for a test cohort, and examined sensitivity of predicted fine particulate matter (PM

BackgroundPediatric asthma is currently the most prevalent chronic disease in the United States, with children in lower income families disproportionately affected. This increased health burden is partly due to lower-quality and insufficient maintenance of affordable housing. A movement towards ‘green’ retrofits that improve energy efficiency and increase ventilation in existing affordable housing offers an opportunity to provide cost-effective interventions that can address these health disparities.MethodsWe combine indoor air quality modeling with a previously developed discrete event model for pediatric asthma exacerbation to simulate the effects of different types of energy retrofits implemented at an affordable housing site in Boston, MA.ResultsSimulation results show that retrofits lead to overall better health outcomes and healthcare cost savings if reduced air exchange due to energy-saving air tightening is compensated by mechanical ventilation. Especially when exposed to indoor tobacco smoke and intensive gas-stove cooking such retrofit would lead to an average annual cost saving of over USD 200, while without mechanical ventilation the same children would have experienced an increase of almost USD 200/year in health care utilization cost.ConclusionThe combination of indoor air quality modeling and discrete event modeling applied in this paper can allow for the inclusion of health impacts in cost-benefit analyses of proposed affordable housing energy retrofits.

Residential energy efficiency and ventilation retrofits (e.g., building weatherization, local exhaust ventilation, HVAC filtration) can influence indoor air quality (IAQ) and occupant health, but these measures’ impact varies by occupant activity. In this study, we used the multi-zone airflow and IAQ analysis program CONTAM to simulate the impacts of energy retrofits on indoor concentrations of PM(2.5) and NO(2) in a low-income multifamily housing complex in Boston, Massachusetts (USA). We evaluated the differential impact of residential activities, such as low- and high-emission cooking, cigarette smoking, and window-opening, on IAQ across two seasons. We found that a comprehensive package of energy and ventilation retrofits was resilient to a range of occupant activities, while less holistic approaches without ventilation improvements led to increases in indoor PM(2.5) or NO(2) for some populations. In general, homes with simulated concentration increases included those with heavy cooking and no local exhaust ventilation, and smoking homes without HVAC filtration. Our analytical framework can be used to identify energy efficient home interventions with indoor retrofit resiliency (i.e., those that provide IAQ benefits regardless of occupant activity), as well as less resilient retrofits that can be coupled with behavioral interventions (e.g., smoking cessation) to provide cost-effective, widespread benefits.

Secondhand exposure to environmental tobacco smoke (ETS) in multifamily housing remains a health concern despite strong recommendations to implement non-smoking policies. Multiple studies have documented exposure to ETS in non-smoking units located in buildings with smoking units. However, characterizing the magnitude of ETS infiltration or measuring the impact of building interventions or resident behavior on ETS is challenging due to the complexities of multifamily buildings, which include variable resident behaviors and complex airflows between numerous shared compartments (e.g., adjacent apartments, common hallways, elevators, heating, ventilating and air conditioning (HVAC) systems, stack effect). In this study, building simulation models were used to characterize changes in ETS infiltration in a low income, multifamily apartment building in Boston which underwent extensive building renovations targeting energy savings. Results suggest that exterior wall air sealing can lead to increases in ETS infiltration across apartments, while compartmentalization can reduce infiltration. The magnitude and direction of ETS infiltration depends on apartment characteristics, including construction (i.e., level and number of exterior walls), resident behavior (e.g., window opening, operation of localized exhaust fans), and seasonality. Although overall ETS concentrations and infiltration were reduced post energy-related building retrofits, these trends were not generalizable to all building units. Whole building smoke-free policies are the best approach to eliminate exposure to ETS in multifamily housing.