Innovative Data Science
Approaches to Identify
Individuals, Populations,
and Communities at High
Risk for Suicide

_____

Francis K. Amankwah, Robert Pool, and
Sharyl J. Nass, Rapporteurs

Forum on Mental Health and Substance
Use Disorders

Board on Health Care Services

Health and Medicine Division

Proceedings of a Workshop

THE NATIONAL ACADEMIES PRESS 500 Fifth Street, NW Washington, DC 20001

This activity was funded by the Office of the Assistant Secretary for Health [Contract No. HHSP233201400020B (Task Order No. 75P00121F37111)], with support from the Centers for Disease Control and Prevention and the National Institutes of Health. Any opinions, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect the views of any organization or agency that provided support for the project.

International Standard Book Number-13: 978-0-309-69506-0
International Standard Book Number-10: 0-309-69506-6
Digital Object Identifier: https://doi.org/10.17226/26752

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Suggested citation: National Academies of Sciences, Engineering, and Medicine. 2022. Innovative data science approaches to identify individuals, populations, and communities at high risk for suicide. Washington, DC: The National Academies Press. https://doi.org/10.17226/26752.

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WORKSHOP PLANNING COMMITTEE¹

BENJAMIN MILLER (Co-Chair), President, Well Being Trust

SEAN JOE (Co-Chair), Benjamin E. Youngdahl Professor of Social Development; Fellow, American Academy of Social Welfare and Social Work; Principal Director, Race and Opportunity Lab, George Warren Brown School of Social Work, Washington University in Saint Louis

PATRICIA AREÁN, Professor, Psychiatry and Behavioral Sciences; Co-Director, UW/NIMH ALACRITY Center; Director, UW CREATIV Lab, University of Washington

COLLEEN CARR, Director, National Action Alliance for Suicide Prevention, Education Development Center

GLEN COPPERSMITH, Chief Data Officer, SonderMind; Advisory Board Member and Adjunct Research Scientist, Johns Hopkins University Human Language Technology Center of Excellence; Adjunct Staff, Institute for Defense Analyses

JOHN MCCARTHY, Office of Mental Health and Suicide Prevention, Department of Veterans Affairs; Director, Data and Surveillance, Suicide Prevention Program; Director, Serious Mental Illness Treatment Resource and Evaluation Center; Research Associate Professor, Department of Psychiatry, University of Michigan

GREGORY SIMON, Investigator, Kaiser Permanente Washington Health Research Institute

AYAH ZIRIKLY, Assistant Research Scientist, Center for Language and Speech Processing, Johns Hopkins University

Project Staff

FRANCIS AMANKWAH, Program Officer (starting May 2022)

ADRIENNE FORMENTOS, Research Associate

ANESIA WILKS, Senior Program Assistant

ARZOO TAYYEB, Finance Business Partner

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¹ The National Academies of Sciences, Engineering, and Medicine’s planning committees are solely responsible for organizing the workshop, identifying topics, and choosing speakers. The responsibility for the published Proceedings of a Workshop rests with the workshop rapporteurs and the institution.

ALEXANDRA ANDRADA, Director, Forum on Mental Health and Substance Use Disorders

SHARYL NASS, Senior Director, Board on Health Care Services

FORUM ON MENTAL HEALTH AND SUBSTANCE USE DISORDERS¹

MARGARITA ALEGRÍA (Co-Chair), Harvard Medical School

HOWARD H. GOLDMAN (Co-Chair), University of Maryland School of Medicine

ERIN BAGALMAN, Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services

CARLOS BLANCO, National Institute on Drug Abuse, National Institutes of Health

CHRIS M. CROWE, U.S. Department of Veterans Affairs

W. PERRY DICKINSON, University of Colorado

ANTHONY T. ESTREET, Morgan State University

SYLVIA K. FISHER, Health Resources and Services Administration

RICHARD G. FRANK, Harvard Medical School

ROBERT HEINSSEN, National Institute of Mental Health

KRISTIN KROEGER, American Psychiatric Association

LAUREN B. LOWENSTEIN, Centers for Medicare & Medicaid Services

HUSSEINI K. MANJII, Janssen Research & Development, LLC

R. KATHRYN MCHUGH, Harvard Medical School

BEN MILLER, Well Being Trust

ANNIE PETERS, National Association of Addiction Treatment Providers

KATHY PHAM, American College of Clinical Pharmacy

JOE PYLE, Scattergood Foundation

DEIDRA ROACH, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health

MARY ROACH, Substance Abuse and Mental Health Services Administration

GLORINDA SEGAY, Indian Health Service

RUTH SHIM, University of California, Davis

MATTHEW TIERNEY, University of California, San Francisco, School of Nursing and Office of Population Health

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¹ The National Academies of Sciences, Engineering, and Medicine’s forums and roundtables do not issue, review, or approve individual documents. The responsibility for the published Proceedings of a Workshop rests with the workshop rapporteurs and the institution.

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Reviewers

This Proceedings of Innovative Data Science Approaches to Identify Individuals, Populations, and Communities at High Risk for Suicide: A Workshop was reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise. The purpose of this independent review is to provide candid and critical comments that will assist the National Academies of Sciences, Engineering, and Medicine in making each published proceedings as sound as possible and to ensure that it meets the institutional standards for quality, objectivity, evidence, and responsiveness to the charge. The review comments and draft manuscript remain confidential to protect the integrity of the process.

We thank the following individuals for their review of this proceedings:

Although the reviewers listed above provided many constructive comments and suggestions, they were not asked to endorse the content of the proceedings nor did they see the final draft before its release. The review of this proceedings was overseen by JOHN ZAVEN AYANIAN, Institute for Healthcare Policy and Innovation, University of Michigan. He was responsible for making certain that an independent examination of this proceedings was carried out in accordance with standards of the National Academies and that

all review comments were carefully considered. Responsibility for the final content rests entirely with the rapporteurs and the National Academies.

We also thank staff member Daniel Talmage for reading and providing helpful comments on this manuscript.

Acknowledgments

The Forum on Mental Health and Substance Use Disorders wishes to express its gratitude to the expert speakers whose presentations and discussions helped inform efforts to advance innovative data science techniques to help inform upstream suicide prevention efforts at the individual, community, and population levels. The Forum also wishes to thank the members of the planning committee who collaborated to ensure a workshop with informative presentations and rich discussions.

The Forum is grateful for the support of the Office of the Assistant Secretary for Health, the Centers for Disease Control and Prevention, and the National Institutes of Health, for this workshop.

The Forum is sponsored by the American College of Clinical Pharmacy, American Psychiatric Nurses Association, American Psychological Association, Association for Behavioral Health and Wellness, Council on Social Work Education, Janssen Research & Development, National Academy of Medicine, National Institute on Alcohol Abuse and Alcoholism, National Institute on Drug Abuse, National Institute of Mental Health, Office of the Assistant Secretary for Planning and Evaluation, Optum Behavioral Health, Think Bigger Do Good Policy Series (a partnership of the Scattergood Foundation, Peg’s Foundation, Patrick P. Lee Foundation, and Peter and Elizabeth Tower Foundation), U.S. Department of Veterans Affairs, and Well Being Trust.

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Contents

ACRONYMS AND ABBREVIATIONS

WORKSHOP OVERVIEW

Suicide Detection and Prediction

Artificial Intelligence–Based Suicide Prediction

The Promise of Social Media Data

Social Media and Population-Level Digital Mental Health

Working with Social Media Data to Predict Suicide Risk at the Individual and Population Levels

Ethical and Pragmatic Suggestions to Improve the Ability to Identify Individuals and Populations at Risk for Suicide

Learning from Search Engine Data

CURRENT OFFLINE APPROACHES TO PREDICTING AND PREVENTING SUICIDE

Best Practices from the Department of Veterans Affairs

Suicide Prediction and Prevention in the Military Health System

Alternative Crisis Response

Linking Datasets to Predict Suicide Risk

Identifying Suicidal Individuals through High-Performance Computing

Leveraging Digital Health Applications to Assist in Mental Health Treatment

Current Online Approaches to Predicting and Preventing Suicide

Google’s Efforts to Help Users in Crisis

Koko: Offering Mental Health Help Online

Peer-to-Peer Mental Health Support

Considerations for Moving Forward

Moving from Individual Predictions to Population Priorities in Suicide Prevention

Youth-Centric Technology Approaches for Mental Health

Ethical Issues Consideration

Designing Systems for Equity

Intervening throughout the Entire Crisis Care Continuum

Opportunities for Research on Risk Assessment

Opportunities for Policies to Improve Mental Health Care in the Digital Environment

Opportunities to Improve Data and Enhance Effective Detection and Response

Reflections and Suggestions

REFERENCES

APPENDIX A STATEMENT OF TASK

APPENDIX B WORKSHOP AGENDA

Boxes and Figures

BOXES

1 Goals: 2012 National Strategy for Suicide Prevention

2 Suggestions from Individual Workshop Participants to Advance Innovative Data Science Techniques to Help Inform Upstream Suicide Prevention Efforts at the Individual, Community, and Population Levels

3 Collecting Mental Health Data from an Online Screener

4 Perspectives from Those with Lived Experiences

FIGURES

1 Age-adjusted suicide rates, by sex: United States, 1999–2017

2 Preferred method of communication among 13- to 17-year-olds, 2012–2018

3 Percentage of U.S. adults, by age bracket, using at least one social media site, 2005–2021

4 Data from social media sources can fill in the gaps of data from the health care system

5 A framework for considering potential fairness metrics

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Acronyms and Abbreviations