Spatial density of vacant properties: A model for estimating the magnitude of background lead (Pb) exposure in children.

Date of Award

May 2018

Degree Type


Degree Name

Master of Science (MS)


Nutrition Science and Dietetics


Brooks Gump

Second Advisor

Lutchmie Narine

Subject Categories

Medicine and Health Sciences


Background. Heightened blood levels of lead (Pb) are associated with cognitive deficiencies and adverse behavioral outcomes. Pb-contaminated house dust is the primary source of exposure in U.S. children, and it disproportionally affects low-income, racial minorities. Evidence suggests that even background (low-level) exposure has negative consequences. Identifying mechanisms of background exposure is of great public health significance because of the larger proportion of children that can be affected.

Methods. Blood-Pb was assessed during 2013-2017 in a bi-racial sample of children aged 9-11 using established biomonitoring methods. Vacant properties were identified from publicly available datasets. Kernel density estimation and regression models were used to measure spatial density of vacant properties within the city, and their impact on children’s blood-Pb levels.

Results. In a sample of 221 children, with a mean blood-Pb of 1.06 µg/dL (SD = 0.68), results showed increases in spatial density of vacant properties predict increases in median blood-PB levels, b = 0.14 (0.06 – 0.21), p < .001. This association held true even after accounting for demographic covariates, and age of individual housing. Further analysis showed spatial autocorrelation of the residuals changed from a clustered pattern to a random pattern once the spatial density variable was introduced to the model.

Conclusion. This study is the first to identify an exposure mechanism using spatial density modelling. Results suggest that the spatial distribution of vacant properties is a significant mechanism of background exposure. High densities serve as a measurement of accumulated levels of soil- and dust-Pb in the surroundings areas, and might explain socio-demographic disparities in exposure.


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