Mapping chronic stress: Can we track the silent epidemic?

Elizabethtown, Kentucky
MedicinePsychology
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About This Project

Chronic stress is a silent driver of health disparities across U.S. communities. We hypothesize that the CDC’s Social Vulnerability Index (SVI) can serve as a population-level proxy for chronic stress. Using machine learning and public health data, we will identify high-stress regions and predict downstream health outcomes. Our goal is to build a stress-aware AI model that helps target interventions and advance long-term health equity.

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What is the context of this research?

Chronic stress contributes to many leading causes of death in the U.S., yet it’s rarely included in predictive health models. Direct stress measures are scarce in public datasets, but the CDC’s Social Vulnerability Index (SVI) may serve as a scalable proxy—capturing stressors like poverty, housing instability, and healthcare inaccessibility. While widely used in emergency planning, SVI hasn’t been validated as a proxy for stress in health prediction. Advances in AI now allow us to test this relationship using population-level, geospatial data. Our hypothesis is that SVI can help create stress-aware AI models that more accurately predict disease burden in vulnerable areas. This approach is promising because it fills a critical data gap with an established index. Prior barriers were conceptual (no consensus on stress measurement) and technical (lack of tools for large-scale modeling). This project addresses both, with implications for more equitable health interventions.

What is the significance of this project?

This project introduces stress as a missing variable in health equity modeling by validating the CDC’s Social Vulnerability Index (SVI) as a proxy for chronic stress. SVI includes structural risk factors—like poverty, disability, and housing insecurity—that align closely with long-term stress exposure. Yet SVI has not been tested systematically as an input to AI health models. We will combine SVI with county-level health outcomes to build “stress-aware” models that detect underrecognized risk patterns. This approach offers a scalable way to guide more precise interventions for overdose, chronic illness, and maternal health disparities. By integrating public datasets that are often siloed, we aim to provide backers with an open-source framework that can be used by public health leaders, AI developers, and policymakers to make more equitable, data-driven decisions.

What are the goals of the project?

This project aims to validate the CDC’s Social Vulnerability Index (SVI) as a proxy for chronic stress in predictive health models. We will build machine learning models predicting health outcomes such as overdose, maternal complications, and chronic disease incidence using county-level data. To test the predictive value of SVI as a stress proxy, we will compare model performance with and without SVI variables included. Positive controls will include known stress-related outcomes (e.g., ER visits for anxiety or hypertension), while negative controls will include outcomes unrelated to stress (e.g., appendicitis rates). Model predictions will be validated using out-of-sample testing, AUC scores, and comparison to published benchmarks. The goal is to determine whether incorporating SVI significantly improves predictive power for stress-linked conditions—laying the groundwork for stress-aware, equity-focused public health tools.


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Data visualizations—especially geospatial maps—are central to this project’s goal of validating SVI as a proxy for stress. Chronic stress is spatially patterned and often invisible in raw datasets. Visualizing predicted stress “hotspots” alongside health outcomes like overdose or maternal mortality will help reveal actionable geographic correlations. These visuals will support both technical validation and clear communication with stakeholders, public health officials, and the public. Budget funds will support data cleaning, mapping software, and design expertise to create interpretable, equity-focused outputs.

Endorsed by

Really excited to see where this goes! Stress has long been recognized as a major detriment to health, but we don't measure it. This study will be the first to measure and predict stress like a 'weather forecast'.

Project Timeline

The 6-month project begins with data collection and harmonization (months 1–2), followed by model development and validation (months 3–4). In month 5, we’ll create a public-facing dashboard. Month 6 is dedicated to preparing a peer-reviewed manuscript and sharing all data, models, and code openly.


Jun 07, 2025

Project Launched

Aug 31, 2025

Milestone 1: Data Collection and Harmonization

Oct 31, 2025

Milestone 2: Model Development and Validation

Dec 31, 2025

Milestone 3: Visualization and Public Dashboard

Feb 28, 2026

Milestone 4: Manuscript and Open Data Release

Meet the Team

Craig Stillwell
Craig Stillwell

Craig Stillwell

Dr. R. Craig Stillwell is a data scientist and health equity researcher with over 20 peer-reviewed publications and a Ph.D. in Entomology, specializing in genetics, biostatistics, and computational biology. He served as lead data scientist for the White House COVID-19 Home Test Kit Mission, where he translated large-scale public health data into national decision-making tools used by HHS and the CDC. His expertise spans machine learning, epidemiology, and federal data integration systems, including platforms like Tiberius and Palantir.

Dr. Stillwell is a first-generation college graduate from rural Kentucky. His personal journey through poverty, addiction recovery, and structural barriers directly informs his mission to build better health systems for the underserved. He has worked in academic, government, and private sectors, including the CDC, NIH-funded institutions, and international research teams. Across all roles, he has remained committed to amplifying the needs of structurally marginalized populations—especially those impacted by substance use disorders, rural health inequities, and stress-related diseases.

With extensive experience in public datasets, policy translation, and stakeholder communication, Dr. Stillwell designs science for public good. His current focus is on developing interpretable, stress-aware AI models to improve health outcomes in high-risk communities—an effort that bridges technical innovation with lived empathy and public health impact.

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