Targeting Homes with High Lead Exposure Risks by Leveraging Big Data and Advanced Machine-Learning Algorithms

Wednesday, April 9, 2025

1:30 PM – 2:30 PM

Location: Celestin FG&H (Level 3)

CE: 1 CEU credits

CEU Type(s): BPI, BSI, EA, HHE, INTERNACHI, NARI, QCI, RESNET

Alyson Alde

Research Associate
Indiana University Bloomington
Bloomington, Indiana
Michelle Del Rio (she/her/hers)

Assistant Professor
Indiana University Bloomington
Bloomington, Indiana
Kelly Hoffman

Research Environmental Scientist
RTI International
Research Triangle Park, North Carolina
Hana C. Long

Research Assistant Professor
North Carolina State University
Raleigh, North Carolina

Childhood lead exposure is a persistent public health issue linked to a range of developmental and neurological problems. Low-income and Black children are particularly vulnerable, as they are more likely to live in older homes (built before 1978) with Pb-based paint or near industrial sites that released Pb into the environment.
Currently, state and local agencies rely on children’s blood lead levels to indicate whether neighborhoods and homes are lead-free. This approach is reactive, slow, and potentially has grave impacts on the life course of impacted children. Simply put, by the time a child is tested, they may have already suffered irreversible harm.
The PREDICT Healthy Homes Project is seeking to shift the current approach towards a proactive, preventive model that relies on risks factors rather than children to target neighborhoods and homes for lead exposure prevention.

Learning Objectives:

By attending this session, attendees will:

Describe new insights into risk factors and prediction models associated with lead (Pb) exposure in homes
Discuss experiences related to engaging families with voluntary sampling for home Pb testing
Describe mapping tools that identify high-risk areas and match interventions to household context