Behavioral Health Vulnerability Map

See the communities most vulnerable to adverse behavioral health outcomes and the social determinants driving their risk.

Scroll or double click to zoom in. Data is analyzed down to the census block group level. Using the legend in the lower left corner of the map, you can choose to view vulnerability at the County Relative level (compares block groups within the county selected) or State Relative level (compares all block groups across the state). The legend to the right of the map provides specific Census location IDs, Vulnerability Ranking (High, Medium, Low) and the top five risk factors for the selected area. Note: Due to limited data sets, Alaska and Hawaii are not included.

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Take Action on the Social Determinants Driving Behavioral Health Vulnerability

The U.S. is experiencing a mental health crisis compounded by the ongoing consequences of the COVID-19 pandemic. According to the CDC, 41.5% of Americans reported symptoms of anxiety or depression since the pandemic started — up from about 11% in 2019.

For clinicians, payers and other stakeholders, social determinants of health (SDOH) lie at the root of this mammoth challenge. These include:

  • Interpersonal dynamics
  • Family dynamics
  • Community dynamics
  • Housing quality
  • Social support
  • Employment opportunities
  • Work conditions
  • School conditions

Jvion analyzed publicly available data using its proprietary AI CORE™ to identify the top social determinants of health influencing behavioral health vulnerability across the U.S. The map allows you to drill down to the Census block level. Communities are ranked as high, medium, or low in terms of their vulnerability to behavioral health-related hospitalizations or self-harm events.

Actionable Insights to Change Behavioral Health Outcomes. That’s CORE™.

Jvion focuses on vulnerability — as opposed to risk — because vulnerability is the part of risk that can be changed. Vulnerabilities come in many forms. They can be emotional or cognitive vulnerabilities, for instance if a patient suffers from depression and struggles to find the motivation to eat healthy, exercise, or take their medication. Or, they can be social and economic vulnerabilities — for example if patients can’t afford to eat healthy foods or pay for their prescriptions. Traditional predictive analytics stop at identifying patients at risk. The Jvion CORE goes further, identifying the most influential vulnerabilities driving their risk, and recommending evidence-based interventions to address their vulnerabilities and change their trajectory.

Learn more about the CORE


1 Vahratian A, Blumberg SJ, Terlizzi EP, Schiller JS. Symptoms of Anxiety or Depressive Disorder and Use of Mental Health Care Among Adults During the COVID-19 Pandemic — United States, August 2020–February 2021. MMWR Morb Mortal Wkly Rep 2021;70:490–494. DOI: http://dx.doi.org/10.15585/mmwr.mm7013e2external icon
2 Lehman B, David D, Gruber J. Rethinking the biophysical model of health: Understanding health as a dynamic system. Soc Personal Psychol Compass. 2017;11(8):1-17. doi: https://doi.org/10.1111/spc3.12328.

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