Lead (Pb) is a neurotoxicant that particularly harms young children.
Urban environments are often plagued with elevated Pb in soils and
dusts, posing a health exposure risk from inhalation and ingestion of
these contaminated media. Thus, a better understanding of where to
prioritize risk screening and intervention is paramount from a public
health perspective. We have synthesized a large national dataset of Pb
concentrations in household dusts from across the United States (U.S.),
part of a community science initiative called “DustSafe.” Using these
results, we have developed a straightforward logistic regression model
that correctly predicts whether Pb is elevated (> 80 ppm)
or low (< 80 ppm) in household dusts 75% of the time.
Additionally, our model estimated 18% false negatives for elevated Pb,
displaying that there was a low probability of elevated Pb in homes
being misclassified. Our model uses only variables of approximate
housing age and whether there is peeling paint in the interior of the
home, illustrating how a simple and successful Pb predictive model can
be generated if researchers ask the right screening questions. Scanning
electron microscopy supports a common presence of Pb paint in several
dust samples with elevated bulk Pb concentrations, which explains the
predictive power of housing age and peeling paint in the model. This
model was also implemented into an interactive mobile app that aims to
increase community-wide participation with Pb household screening. The
app will hopefully provide greater awareness of Pb risks and a highly
efficient way to begin mitigation.