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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.

Communities with lower socioeconomic status and higher prevalence of racial/ethnic minority populations are often more exposed to environmental pollutants. Although studies have shown associations between aircraft noise and property values and various health outcomes, little is known about how aircraft noise exposures are sociodemographically patterned.Our aim was to describe characteristics of populations exposed to aviation noise by race/ethnicity, education, and income in the United States.Aircraft noise contours characterized as day-night average sound level (DNL) were developed for 90 U.S. airports in 2010 for DNLAggregated across multiple airports, block groups with a higher Hispanic population had higher odds of being exposed to aircraft noise. For example, the multinomial analysis showed that a 10-percentage point increase in a block group's Hispanic population was associated with an increased odds ratio of 39% (95% CI: 25%, 54%) of being exposed toThese results suggest that across U.S. airports, there is indication of sociodemographic disparities in noise exposures. https://doi.org/10.1289/EHP9307.

Asthma disparities have complex, neighborhood-level drivers that are not well understood. Consequently, identifying particular contextual factors that contribute to disparities is a public health goal. We study pediatric asthma emergency department (ED) visit disparities and neighborhood factors associated with them in South Carolina (SC) census tracts from 1999 to 2015. Leveraging a Bayesian framework, we identify risk clusters, spatially-varying relationships, and risk percentile-specific associations. Clusters of high risk occur in both rural and urban census tracts with high probability, with neighborhood-specific associations suggesting unique risk factors for each locale. Bayesian methods can help clarify the neighborhood drivers of health disparities.

Introduction The human papillomavirus (HPV) increases the risk for cancers of the cervix, oropharynx, vulva, vagina, penis, and anus. HPV vaccination rates are low in many states having large medically underserved areas. In such areas, school nurses are a potential partner for improving population health, but their perceptions about HPV, HPV vaccination, and their role in promoting HPV vaccination have not been well documented. Methods We administered a cross-sectional survey to 61 of 74 lead school nurses at their 2019 annual training session in South Carolina. Survey questions assessed lead school nurses’ HPV vaccination beliefs, barriers, and HPV vaccination role in schools. We tabulated descriptive data and created heat maps to visualize correlations between responses. Results Despite 95.1% of nurses envisioning a role in supporting HPV vaccination at their schools, only 41.0% envisioned an active role in promoting HPV vaccine among students. Lead nurses consistently believed in vaccinating both male and female students; in vaccine safety, effectiveness, and health benefits; and in recommending HPV vaccination. The nurses agreed that lack of time and competing priorities were barriers to HPV vaccination. Few other barriers were consistently identified. Conclusion Partnering with school nurses may be a feasible strategy to overcome barriers to increasing HPV vaccination rates in medically underserved areas. However, to increase nurses’ confidence and time allotment to assume an active role in HPV vaccine promotion in their schools, coordinated and sustained partnerships between public health agencies, school districts, and school nurses are needed.

Background Ecologic health studies often rely on outcomes from health service utilization data that are limited by relatively coarse spatial resolutions and missing geographic information, particularly neighborhood level identifiers. When fine-scale geographic data are missing, the ramifications and strategies for addressing them are not well researched or developed. This study illustrates a novel spatio-temporal framework that combines a geographic identifier assignment (i.e., geographic imputation) algorithm with predictive Bayesian variable selection to identify neighborhood factors associated with disparities in emergency department (ED) visits for asthma. Methods ED visit records with missing fine-scale spatial identifiers (~ 20%) were geocoded using information from known, coarser, misaligned spatial units using an innovative geographic identifier assignment algorithm. We then employed systematic variable selection in a spatio-temporal Bayesian hierarchical model (BHM) predictive framework within the NIMBLE package in R. Our novel methodology is illustrated in an ecologic case study aimed at identifying neighborhood-level predictors of asthma ED visits in South Carolina, United States, from 1999 to 2015. The health outcome was annual ED visit counts in small areas (i.e., census tracts) with primary diagnoses of asthma (ICD9 codes 493.XX) among children ages 5 to 19 years. Results We maintained 96% of ED visit records for this analysis. When the algorithm used areal proportions as probabilities for assignment, which addressed differential missingness of census tract identifiers in rural areas, variable selection consistently identified significant neighborhood-level predictors of asthma ED visit risk including pharmacy proximity, average household size, and carbon monoxide interactions. Contrasted with common solutions of removing geographically incomplete records or scaling up analyses, our methodology identified critical differences in parameters estimated, predictors selected, and inferences. We posit that the differences were attributable to improved data resolution, resulting in greater power and less bias. Importantly, without this methodology, we would have inaccurately identified predictors of risk for asthma ED visits, particularly in rural areas. Conclusions Our approach innovatively addressed several issues in ecologic health studies, including missing small-area geographic information, multiple correlated neighborhood covariates, and multiscale unmeasured confounding factors. Our methodology could be widely applied to other small-area studies, useful to a range of researchers throughout the world.

Many areas of the United States have air pollution levels typically below Environmental Protection Agency (EPA) regulatory limits. Most health effects studies of air pollution use meteorological (e.g., warm/cool) or astronomical (e.g., solstice/equinox) definitions of seasons despite evidence suggesting temporally-misaligned intra-annual periods of relative asthma burden (i.e., “asthma seasons”). We introduce asthma seasons to elucidate whether air pollutants are associated with seasonal differences in asthma emergency department (ED) visits in a low air pollution environment. Within a Bayesian time-stratified case-crossover framework, we quantify seasonal associations between highly resolved estimates of six criteria air pollutants, two weather variables, and asthma ED visits among 66,092 children ages 5–19 living in South Carolina (SC) census tracts from 2005 to 2014. Results show that coarse particulates (particulate matter 2.5 μm: PM10-2.5) and nitrogen oxides (NOx) may contribute to asthma ED visits across years, but are particularly implicated in the highest-burden fall asthma season. Fine particulate matter (2.5) is only associated in the lowest-burden summer asthma season. Relatively cool and dry conditions in the summer asthma season and increased temperatures in the spring and fall asthma seasons are associated with increased ED visit odds. Few significant associations in the medium-burden winter and medium-high-burden spring asthma seasons suggest other ED visit drivers (e.g., viral infections) for each, respectively. Across rural and urban areas characterized by generally low air pollution levels, there are acute health effects associated with particulate matter, but only in the summer and fall asthma seasons and differing by PM size.

In this study, we aim to illustrate how novel technologies and methodologies can be used to enhance neighborhood level studies of ambient particulate matter (PM). This is achieved by characterizing temporal and spatial features of PM levels and by assessing patterns in particle size composition using simultaneous measures across multiple size fraction ranges in Charleston, SC, USA. The study is conducted in three stages: (1) we monitor real-time PM concentrations for the following: PM ≤ 15 μm, PM ≤ 10 μm, PM ≤ 4 μm, PM ≤ 2.5 μm, and PM ≤ 1 μm at five locations during February–July, 2016; (2) we apply a generalized additive model (GAM) to assess temporal and spatial trends in PM2.5 after controlling for meteorology, instrument, and temporal confounders; and (3) we employ a self-organizing map (SOM) to identify hourly profiles that characterize the types of size fraction distribution compositions measured at our sites. Monitoring results found that average PM2.5 levels during our ‘snapshot’ monitoring were 6–8 μg/m3 at our sites, with 95th percentiles ranging from 9 to 13 μg/m3. GAM results identified that temporal peaks for PM2.5 occurred during the early morning hours (6–8 am) across all sites and that the marginal means for four of our inland sites were significantly different (higher) than a waterfront site. SOM results identified six hourly profiles, ranging from hours when all size fractions were relatively low, to hours dominated by single size fractions (e.g., PM1), and to hours when multiple size fractions were relatively high (e.g., PM15–10 and PM10-PM2.5). Frequency and duration distributions show variability in the occurrence and persistence of each hourly type. Collectively, our findings reveal the complexity of PM behavior across a relatively small geographic region and illustrate the potential usefulness of using size fraction composition to better understand air quality. However, it is important to note that this study only presents a snapshot of air quality and that longer monitoring periods are recommended for more definitive characterizations.

BACKGROUND: Health studies of air pollution are increasingly aiming to study associations between air pollutant mixtures and health. OBJECTIVE: Estimate associations between observed combinations of ambient air pollutants and select cardiorespiratory outcomes in Columbia, SC during 2002 to 2013. METHODS: We estimate associations using a two-stage approach. First, we identified a collection of observed pollutant combinations, which we define as multipollutant day types (MDTs), by applying a self-organizing map (SOM) to daily measures of nitrogen dioxide (NO(2)), sulfur dioxide (SO(2)), ozone (O(3)), and particulate matter ≤ 2.5 microns (PM(2.5)). Then, overdispersed Poisson time-series models were used to estimate associations between MDTs and each outcome using a ‘clean’ MDT referent and controlling for long-term, seasonal, and day-of-the-week trends and meteorology. Outcomes included daily emergency department visits for asthma and upper respiratory infection (URI), and hospital admissions for congestive heart failure (CHF) and ischemic heart disease (IHD). RESULTS: We found that a number of MDTs were significantly and positively associated (point estimates ranged from~2–5%) with cardiorespiratory outcomes in Columbia when compared to days with low pollution. Estimated associations revealed that outcomes for asthma, URIs, and IHD increased 2–4% on warm, dry days experiencing elevated levels of O(3) and PM(2.5). We also found that cooler days with higher NO(2) pollution associated with increased asthma, CHF, and IHD outcomes (2–5%). CONCLUSION: Our analysis continues support for using self-organizing maps to develop multipollutant exposure metrics and further illustrates how such metrics can be applied to explore associations between pertinent pollutant combinations and health.

Globally, the extraction of minerals and fossil fuels is increasingly penetrating into isolated regions inhabited by indigenous peoples, potentially undermining their livelihoods and well-being. To provide new insight to this issue, we draw on a unique longitudinal dataset collected in the Ecuadorian Amazon over an 11-year period from 484 indigenous households with varying degrees of exposure to oil extraction. Fixed and random effects regression models of the consequences of oil activities for livelihood outcomes reveal mixed and multidimensional effects. These results challenge common assumptions about these processes and are only partly consistent with hypotheses drawn from the Dutch disease literature.