Assessing the Longevity of Changes from a 10 Year Healthy Homes Intervention Program
SERI has been working in low income and marginalized communities since 1994 utilizing the Promotora method to educate and promote environmental justice. During 2011-2014 SERI conducted a healthy homes program that consisted of a 29 hazards evaluation where over 2,500 homes were visited and changes were conducted such as providing education; installing items such as smoke alarms, CO2 alarms, or grab bars; or major interventions such as mold removal or roof repair. We are now going back to these homes to re-evaluate the homes, to compare today’s findings with the conditions of the homes from after our interventions over 10 years ago, and to measure if the changes made are sustainable over time. There have been many barriers and challenges faced especially due to the pandemic, but we have learned how to overcome those barriers and successfully conduct the re-evaluation of these homes.
Session Presenter: Flor Sandoval, Program Director, Sonora Environmental Research Institute
PREDICT Healthy Homes Study: Targeting Homes with High Pb Exposure Risk by Leveraging Big Data and Advanced Machine-Learning Algorithms
Childhood lead (Pb) exposure is a persistent public health problem in the U.S., disproportionately impacting low-income and Black children. Currently, state, and local agencies use blood tests and house age to prioritize homes for Pb hazard interventions. However, this approach has critical limitations. First, a proactive, preventive approach that does not require using children as sentinels would be more cost-effective in avoiding the cognitive damage associated with early-life Pb exposure. Second, a variety of risk factors, not captured by house age alone, may contribute to Pb exposure. Our project addresses the need for more cost-effective methods to prevent the damage from childhood Pb exposure by seeking to shift the current hazard control paradigm toward an approach analogous that used in precision medicine. In this approach, machine-learning techniques, and data on a child’s total residential Pb exposome are used to match interventions to household context. We will discuss our approach and lessons learned on integrating “big” data, and training and validating prediction models with community partners and citizen scientists. If successful, this project will equip state and local agencies with a proactive, preventive, and more cost-effective approach and actionable tools for prioritizing neighborhoods and households for potential participation in federal and/or state Pb hazard remediation programs.
Session Presenter/Panelist: Michelle Del Rio, Ph.D., MPH; Department of Environmental and Occupational Health, School of Public Health, Indiana University
