Publications

Pediatric lead poisoning remains a persistent public health problem. Children in the US spend the preponderance of their time at home; thus, housing is an important social determinant of health. Improving health outcomes derived from housing-based sources involves differentiating the risks posed by the existing housing stock. In this paper, we developed a parcel-level lead risk index (LRI) based on external housing conditions and the year of home construction. The purpose of this study was to introduce a housing-based lead risk index (LRI), developed using retrospective data, to estimate parcel-by-parcel variation in housing-based lead risk. We described how the LRI is constructed, relate it to the likelihood of a pediatric occupant’s blood lead level (BLL) > 3.5 µg/dL using Lasso regression (n = 6589), visualized this relationship graphically, and mapped the outcome. We found that mapping the LRI provided more information at a more precise geographic level than was possible using other public health surveillance methods.

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.

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.

This article details an iterative process for the address geocoding of a large collection of health encounters (n = 242,804) gathered over a 13 year period to a parcel geography which varies by year. This procedure supports an investigation of the relationship between basic housing conditions and the corresponding health of occupants. Successful investigation of this relationship necessitated matching individuals, their health outcomes and their home environments. This match process may be useful to researchers in a variety of fields with particular emphasis on predictive modeling and up-stream medicine.