# Improving Suicide Risk Prediction with Social Determinants Data

> **NIH NIH R56** · KAISER FOUNDATION RESEARCH INSTITUTE · 2022 · $468,384

## Abstract

ABSTRACT
Suicide accounted for 47,511 deaths in the United States in 2019 and the suicide rate has increased by 39% since 1999.
Suicide prevention is an NIMH research priority. Recent research in estimating machine learning algorithms to predict
suicide risk has been tremendously successful. The models have been implemented as part of routine prevention
programs in health systems such as Kaiser Permanente Washington, HealthPartners, and the Veterans Health
Administration. Despite these successes, existing models have important shortcomings. A significant proportion of
suicides followed healthcare visits where the predicted risk was low (and where an intervention might have taken place
otherwise). The models do not currently include any information about social determinants of suicide (e.g., living alone,
financial stress) or negative life events (NLE), such as divorce, bankruptcy, and criminal arrest. Adding social
determinants data and NLE data to models may improve predictive accuracy. The specific aims of this study are: (1)
expand and enhance the risk prediction dataset with over 1500 date-stamped variables describing social determinants
of suicide risk and NLE; (2) construct and evaluate suicide risk prediction models using social determinants and NLE data
alone; (3) construct and evaluate suicide risk prediction models using social determinants, NLE and healthcare data
together and estimate interaction terms between social determinants, NLE, and healthcare predictors. An example
would be “depression diagnosis” interacted with “divorce filing in last 30 days”. This will be the first large scale study to
incorporate individual-level, date-stamped measures of social determinants and NLE into machine learning suicide risk
prediction models. Upon successful completion of this study we expect to know how much incorporating these new data
contributes to the accuracy of suicide risk prediction models. This will be an important next step towards implementing
better suicide prevention programs and reducing overall suicide rates.

## Key facts

- **NIH application ID:** 10528534
- **Project number:** 1R56MH125794-01A1
- **Recipient organization:** KAISER FOUNDATION RESEARCH INSTITUTE
- **Principal Investigator:** Robert B. Penfold
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $468,384
- **Award type:** 1
- **Project period:** 2022-01-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10528534

## Citation

> US National Institutes of Health, RePORTER application 10528534, Improving Suicide Risk Prediction with Social Determinants Data (1R56MH125794-01A1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10528534. Licensed CC0.

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