Targeting Proactive Inspections for Lead Hazards

Lead poisoning is irreversible and has devastating ramifications on health and educational attainment outcomes. DSaPP has worked with CDPH over the last year to develop a prototype predictive model that identifies children’s homes that are most at risk of having life-threatening lead hazards. The goal is to create and scale a model and technological interface to be used by public health officials – first in Illinois and later nationwide – to predictively inform the deployment of scarce public resources to address lead hazards with great accuracy rather than remediating after lead-exposed children have been identified.

The Lead project grew out of our 2014 DSSG project Targeting Proactive Public Health Inspections. DSaPP presented our research and model at KDD2015 and at the American Public Health Association’s 2015 Annual Meeting.

You can learn more about this project here, including what data and organizational characteristics are necessary.

Targeting HIV Interventions: Diagnosis and Treatment

Many HIV infected persons fail to get diagnosed or, once diagnosed, to remain in a treatment program due to factors unrelated to their medical condition. This result of this project will be a series of models that can predict risk of infection, risk of not getting diagnosed and risk of dropping out of treatment.

Targeted Preventive Healthcare

DSaPP is committed to improving outcomes for communities that are underserved and vulnerable. Particular to Public Health, we are determined to design innovations that target the nexus where social and physical environmental factors meet with health access.

We are currently working with one of the largest health systems in the country to address this issue and explore the potential of targeted preventive health interventions.

In the past we have worked with a variety of partners across the private, nonprofit, and government sectors to improve health outcomes in the most at-risk segments of the population. We have extensive experience working in the areas of maternal, prenatal, and early-childhood health (Predicting and Reducing Adverse Birth Outcomes, Predicting Success in Mother-Child Interventions, Reducing Maternal Mortality Rates in Mexico, Tracking the Impact of Early Childhood Health Programs) and client interactions with insurance and social services (Targeting the Uninsured for Health Insurance Enrollment, Improving Social Service Interactions, Using Electronic Medical Record Data to Predict Better Health).