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Preventing Childhood Lead Poisoning

Introduction

Lead poisoning is a major public health problem that affects hundreds of thousands of children in the United States every year. A common approach to identifying lead hazards is to test all children for elevated blood lead levels and then investigate and remediate the homes of children with elevated tests. This can prevent exposure to lead of future residents, but only after a child has been irreversibly poisoned. In parternship with the Chicago Department of Public Health (CDPH), we have built a model that predicts the risk of a child being poisoned. Our model's risk scores facillitates an intervention before lead posioning occurs. Using two decades of blood lead level tests, home lead inspections, property value assessments, and census data, our model allows inspectors to prioritize houses on an intractably long list of potential hazards and identify children who are at the highest risk. This work has been described by CDPH as pioneering in the use of machine learning and predictive analytics in public health and has the potential to have a significant impact on both health and economic outcomes for communities across the US. For a longer overview of the project, see our preliminary results which were written up and published in the 21st ACM SIGKDD Proceedings. This project is closely based on previous work of Joe Brew, Alex Loewi, Subho Majumdar, and Andrew Reece as part of the 2014 Data Science for Social Good Summer Fellowship.

Implementation

The code for each phase is located in the corresponding subdirectory and is executed using a drakefile. The output of each phase is contained in a database schema of the same name. Each folder also has a corresponding README documenting the steps.

features: Generate model features by aggregating the datasets at various spatial and temporal resolutions.

model: Use our drain pipeline to run models in parallel and serialize the results.

pilot: SQL script for generating a contact list for the model pilot.

explore: Miscellanous scripts for one-off exploration of the data.

Deployment

1. Load and transform the data

Follow the instructions in the lead-etl repository to load and transform the data.

2. Configure variables:

Copy ./lead/example_profile to ./lead/default_profile and set the indicated variables.

Include this repository in your Python path, e.g. by adding this line to your .bashrc:

export PYTHONPATH=$PYTHONPATH:~/project/lead-model

3. Install requirements

Install python requirements:

pip install -r requirements.txt

3. Run models using drain.

To fit a current model and make predictions change to ./lead and run:

drain execute lead.model.workflows::bll6_forest_today ...

Here lead.model.workflows.bll6_forest_today is a drain workflow, i.e. a function taking no arguments that returns collection of drain steps.

For temporal cross validation use the bll6_forest workflow.

License

See LICENSE

Contributors

- Eric Potash (epotash@uchicago.edu)

References

  1. Potash, Eric, Joe Brew, Alexander Loewi, Subhabrata Majumdar, Andrew Reece, Joe Walsh, Eric Rozier, Emile Jorgenson, Raed Mansour, and Rayid Ghani. "Predictive modeling for public health: Preventing childhood lead poisoning." In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2039-2047. ACM, 2015.

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  • Python 93.3%
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