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JOURNAL ARTICLES
"Using exterior housing conditions to predict elevated pediatric blood lead levels" Neal J. Wilson, lead author, with Elizabeth Friedman, Kevin Kennedy, Panayiotis T. Manolakos, Lori Reierson, Amy Roberts, and Steve Simon
Volume 218, 1 February 2023
https://doi.org/10.1016/j.envres.2022.114944
Abstract: Housing-based lead paint dust is the most common source of lead exposure for US-born children. Although year of housing construction is a critical indicator of the lead hazard to US children, not all housing of the same age poses the same risk to children. Additional information about housing condition is required to differentiate the housing-based lead risk at the parcel level. This study aimed to identify and assess a method for gathering and using observations of exterior housing conditions to identify active housing-based lead hazards at the parcel level. We used a dataset of pediatric blood lead observations (sample years 2000–2013, ages 6–72 months, n = 6,589) to assess associations between observations of exterior housing conditions and housing-based lead risk. We used graphical and Lasso regression methods to estimate the likelihood of an elevated blood lead observation (≥3.5 μg/dL). Our methods estimate a monotonic increase in the likelihood of an elevated blood lead observation as housing conditions deteriorate with the largest changes associated with homes in the greatest disrepair. Additionally we estimate that age of home construction works in consort with housing conditions to amplify risks among those houses built before 1952. Our analysis indicates that a survey of external housing conditions can be used in combination with age of housing in the identification process, at the parcel level, of homes that pose a housing-based lead hazard to children.
"Historic racism in Kansas City affects today's pediatric asthma burden" co-authored with Elizabeth Friedman, Brian Lee, and Casey Kalman.
Volume 78, November 2022
https://doi.org/10.1016/j.healthplace.2022.102927
Abstract: Asthma morbidity is unequally distributed across populations throughout the United States, and reasons remain unclear. To assess how historical structural racism correlates with current day asthma disparities, we conducted a retrospective cohort study of 10,736 pediatric patients, ages 3–19 years, with two or more asthma encounters between October 2017–October 2019. Patient addresses were matched with historic Home Owners' Loan Corporation (HOLC) maps – which provide a measure of historic structural racism. Residential proximity to pollution sources served as an additional exposure measure. Healthcare utilization and asthma severity were studied against age, race, SES, geographic proximity to pollution, and HOLC grades. Patients living in historically divested neighborhoods and BIPOC patients were likely to require more acute care for asthma, even when adjusting for present day SES and residential proximity to pollution sources. This supports the assertion that historic structural racism influences present-day health.
"Using GIS to Advance Social Economics Research: Geocoding, aggregation, and spatial thinking" co-authored with Ben Wilson and Sierra Martin.
Forum for Social Economics 50.4 2021
The political, social, and economic conditions which lead to inequality, poverty, and health disparities have distinct spatial footprints. Geographic information systems (GIS) are a collection of tools that can aid the social economist in the investigation of such phenomena. Geocoding is a technical procedure that matches attribute data to spatial features in GIS. This analysis of social economy discusses the possibilities of spatial analysis and the technical process of geocoding. Using 15 years of address level pediatric data from a local children’s hospital, a novel iterative geocoding process is applied for the purpose of investigating the relationship between household environments and health outcomes. This procedure adheres to traditional standards for geocoding (positional accuracy, completeness, and repeatability) while producing multiple spatial data sets that can be associated with a range of environmental and socioeconomic variables related to human health. Describing this technical procedure contributes to a growing methodological toolbox for applying GIS to research in social economics.