Category: Projects

COVID-19 Vaccine Uptake and Psychiatric Disorders

Grant Details

Funder: NIMH (MHRN III Feasibility Pilot Program)

Grant Number: U19MH121738

Project Period: 7/1/2021 – 6/30/2022

Narrative:

Background: Psychiatric disorders, and especially severe mental illness (SMI), are associated with an increased risk of COVID-19 infection and COVID-19-related morbidity and mortality. Several studies have found an association between an existing psychiatric disorder and increased risk for COVID-19 infection and COVID-19-related hospitalization, morbidity, and mortality. Factors that contribute to worse outcomes include concomitant medications, poorer premorbid general health, physical comorbidity, reduced access to medical care, and environmental and lifestyle factors such as lower socioeconomic status, smoking, or obesity. In light of these vulnerabilities, it is important that people with SMI receive a vaccination. However, people with SMI are less likely to receive preventive or guideline-appropriate health care for concerns such as cardiovascular disease and cancer. This reduced access to preventive care is reflected in the low uptake of immunizations recommended for adults among people with SMI. Of these, influenza may serve as a particularly useful model given the recommendation for an annual vaccination. In contrast with other vulnerable groups in the United States, influenza vaccination rates among people with SMI are as low as 25%. The purpose of this analysis is to examine COVID-19 vaccine uptake among individuals with diagnosed psychiatric disorders compared to individuals without any diagnosed psychiatric disorders and to examine whether there is variation by type of diagnosis, sociodemographic and/or clinical characteristics. There have been no known studies published to date that address this topic.

  • Research Questions:
    • Are individuals with diagnosed psychiatric disorders more or less likely to have received the COVID vaccine compared to those without any diagnosed psychiatric disorders? How does this pattern compare to uptake of the flu vaccine in this population?
    • Among those with diagnosed psychiatric disorders, is there variation in COVID vaccination status by type of psychiatric disorder? By other sociodemographic and clinical characteristics?

Methods: Using electronic medical record data across 2 Mental Health Research Network sites (KPGA and KPSC), individuals with diagnosed psychiatric disorders will be identified and matched on age and sex to controls with no diagnosed psychiatric disorders.

  • Analyses:
    • Compare sample characteristics of persons with and without any psychiatric disorder using χ2 tests for categorical variables and t tests for continuous variables.
    • Calculate the proportion of eligible patients who received the COVID-19 vaccine by psychiatric status (no diagnosis vs. psychiatric diagnosis).
    • Use multivariable methods to examine the relationship between psychiatric disorders status and vaccine uptake, controlling for demographic characteristics, medical comorbidities (Charlson score), and whether individual lives in rural or medically-underserved area.

Planned Product: The results of this study will be published and presented at a conference and will be used as preliminary data to guide (1) qualitative research to better understand any differences between patients with vs. without mental health conditions and/or (2) intervention research to improve vaccine uptake in this population.

  • Lead Site: KPGA (PI Ashli Owen-Smith)
  • Participating Sites:
    • KPSC (Co-I Karen Coleman)
    • KPWA (Lead Analyst Chris Stewart)

Current Status:

Manuscript is in-progress (will be ready to submit for publication by end of the year)

Summary of Findings:

Publications:

Using NLP to Increase Identification of Child Maltreatment in EHR

Grant Details

Funder: NIMH (MHRN III Feasibility Pilot Program)

Grant Number: U19MH121738

Project Period: 7/1/2020 – 6/30/2021

Narrative:

Background: Child maltreatment is a critical public health issue and health care systems play an important role in identifying and treating children who experience maltreatment. To date, few studies of child maltreatment have used data from large health systems to try and understand how these systems identify and manage youth who experience maltreatment. Preliminary analyses of the number of children identified as having experienced child maltreatment in the most recent MHRN quarterly descriptive analyses (2018) indicate that there is likely a significant under-reporting of child maltreatment in the MHRN health systems. Epidemiologic studies suggest that many more youth would have been identified with child maltreatment. One reason for this potential under reporting is that providers may not use the ICD codes to document child maltreatment consistently. Some maltreatment may be discussed in chart notes but not documented using ICD codes. Better identification of maltreatment could aid both research and practice within health care systems. Natural Language Processing may help to identify additional youth with maltreatment. If NLP identifies cases that are not documented through ICD codes, this could indicate the need for health system efforts to develop new ways of consistently document child maltreatment. NLP might also help to identify any groups (e.g., age, gender, race/ethnicity) that may be particularly likely to have insufficient documentation of child maltreatment. 

This work aligns with NIMH’s strategies to increase research and improve outcomes of mental health services in diverse and vulnerable populations, and to conduct research that helps health systems to base care decisions on the best possible data.   

Research Question: The overarching question is does NLP allow us to obtain estimates the number of children who experience maltreatment more comparable to national epidemiologic data? Does NLP of chart notes identify new cases of child maltreatment that are not already documented with ICD codes? What is the overlap between the two methods? Are there differences by age group or race-ethnicity? Does NLP allow us to differentiate between new/current maltreatment versus history of maltreatment?

Methods: We propose to use simple NLP queries at 1 MHRN site (e.g., terms such as physical abuse, maltreatment) to search chart notes and to compare the number of cases identified through NLP and compare those to cases identified through ICD codes. We will also conduct analyses to see if there is variation in identification by age group, gender, and race/ethnicity.  

Planned Product: We plan to write a paper documenting our findings. We also plan to write a grant related to child maltreatment using this data.

  • Lead Site: KPWA (PI Rob Penfold)
  • Participating Sites:
  • KPSC (Co-I Sonya Negriff)
  • KPNW (Co-I Frances Lynch)

Current Status

  • NLP pipeline created
  • Manual adjudication of NLP “hits” complete
  • Descriptive statistics complete

Summary of Findings

The prevalence of child maltreatment as measured by adjudicated occurrences of terms and phrases discovered by NLP is much higher than when measured via discrete data elements.

Publications

None yet

PHQ9 Differential Item Functioning

Grant Details

Funder: NIMH (MHRN III Feasibility Pilot Program)

Grant Number: U19MH121738

Project Period: 7/1/2020 – 6/30/2021

Narrative:

Background: Depression and suicide screeners like the Patient Health Questionnaire 9 (PHQ-9) are widely employed within healthcare systems in the U.S. as part of measurement-based care. Some research suggests the full or partial cross-cultural equivalence of the PHQ-9 among different racial and ethnic groups, including the standard one-factor model (Harry & Waring, 2019; Keum, Miller, & Kurotsuchi Inkelas, 2018; Merz et al., 2011; Patel et al., 2019), although in some cases two-factor models have presented the best fit (Granillo, 2012; Harry & Waring, 2019; Harry, Coley, Waring, & Simon, under review; Keum et al., 2018). Findings of cross-cultural equivalence allows for meaningful comparisons to be made in scale mean scores between different cultural groups. However, research has also shown the differential item functioning for some PHQ-9 items based on race (Huang et al., 2006). Furthermore, little cross-cultural research is available on the PHQ-9 that includes American Indian/Alaska Native people (AI/AN) (Harry & Waring, 2019; Harry et al., under review). This is even though AI/AN people have a higher rate of suicide than the general population (Curtin & Hedegaard, 2019) and few studies have researched depression prevalence among this group (Garrett et al., 2015). While available evidence suggests elevated depression rates amongst AI/AN people, most research has focused on individual tribal groups, and the little research that has included national samples has primarily only included those who identify solely as AI/AN and not additional racial or ethnic groups (Asdigian et al., 2018). Depression prevalence may differ between sub-populations of AI/AN people (Asdigian et al., 2018). Mental and behavioral health scales may also function differently between separate tribal or cultural AI/AN groups (Walls et al., 2018).

Recent studies have begun to fill the gap on the cross-cultural equivalence of the PHQ-9 with AI/AN people. Current findings have been mixed, including the study by Harry and Waring (2019) with a general patient population and another study by Harry et al. (under review) that included only those with mental health or substance abuse disorder diagnoses, suggesting that more research is needed. It is unknown if any individual PHQ-9 items function differently between AI/AN people and other racial and ethnic groups (Harry et al., under review). Both researchers and clinicians would benefit from understanding how individual PHQ-9 items function for different groups of AI/AN people and in comparison to other diverse racial and ethnic groups. This is a timely opportunity to extend our work by leveraging the findings from our prior research in this area, focus more closely on the functioning of item 9 between racial and ethnic groups, as well as develop additional preliminary results for a future R01 grant application.

This project supports the NIMH strategic goal of striving for prevention and cures. It does so by focusing on the cultural context component of developing strategies for tailoring existing interventions to optimize outcomes.

Research Question: In a patient population with mental health or substance abuse disorder diagnoses, how do individual PHQ-9 items function for AI/AN adults and other diverse racial and ethnic groups?

Methods: The differential item functioning of PHQ-9 items would be assessed using item response theory, or how different groups with differing levels of depression endorse PHQ-9 items. We would compare two geographically and culturally distinct groups of AI/AN adults (ages 18 to 64), as well as groups of Hispanic, non-Hispanic Native Hawaiian/Pacific Islander, non-Hispanic White, non-Hispanic Black, and non-Hispanic Asian adults. This study would be conducted using existing data from prior research and therefore would not require additional analyst support. The project has already been approved by the Essentia Health Institutional Review Board.

Planned Product: The primary product would be a paper presenting our results. Those results would also provide additional preliminary data for a series of broader, multi-MHRN site NIH grant applications on the cross-cultural assessment of depression and suicide risk and culturally competent interventions for AI/AN people and other indigenous groups, like Native Hawaiians. Collaboration with local tribal communities and researchers would be emphasized.

Lead Site: Essential Rural Health Institute (PI Melissa Harry)

Participating Sites: N/A

Current Status

Paper is under review with Psychological Assessment as of 10/12/2022.

Summary of Findings

Publications

Improved tailoring of depression care using customized clinical decision support

Grant Details

Funder: NIMH

Grant number: R01MH114873

Grant period: 07/01/2018 – 04/30/2023

Narrative: Treatments for mental health conditions such as unipolar depression provide modest average benefit but have wide variation between individuals and within individuals over time. Evidence-based customized treatment protocols would improve the mental health care of many people by providing treatment recommendations for individuals that take into account potential variation because of personal characteristics such as current health status, symptoms, and response to earlier treatment. Generating customized treatment protocols requires large amounts of data, such as from networks of health systems that can link electronic health records from millions of individuals. Current statistical approaches for discovering customized treatment protocols are limited in three important ways. First, current approaches rely on scientists to select the patient characteristics to use to customize treatments instead of using data to find the patient characteristics that will lead to improved, customized care. Second, customized treatment protocols discovered with current statistical methods assume no unobserved differences between individuals who receive various treatment options. Third, investigators do not have ways to know if the available data contain enough information to discover and compare customized treatment protocols precisely enough to make clinical decisions. We will address these three limitations by developing new statistical tools for discovering customized treatment protocols using electronic health records data. Our research team has expertise and experience in statistics, epidemiology, and mental health care. We will integrate methods that have been successfully used in other settings to improve statistical approaches for discovering customized treatment protocols and address these three important limitations. We will extend machine learning tools for selecting important pieces of information to the time-varying data structure required for discovering customized treatment protocols. We will build approaches that use available knowledge about the size of unobserved differences between groups of people who received different treatments to assess how those differences change study results. By building on the math used to estimate the sample sizes needed for precision in randomized trials with complex designs, we will develop new formulas for determining how many people with a particular condition and who took a particular drug are needed in a health system to provide enough accurate information to discover customized treatment protocols. Using data from the electronic health records of more than 15,000 patients, we will discover customized treatment protocols for depression. By improving statistical tools and addressing current limitations, our customized treatment protocols will have immediate impact for people living with unipolar depression. The statistical tools we develop will also be useful for discovering customized treatment protocols for people with a wide variety of mental health conditions.

Lead site: KPWA (PI Susan Shortreed)

Participating site: McGill University (co-I Erica Moodie)

  • Funder contacts:
    • Program Official: Michael Freed

Current Status

We have published papers proposing approaches to sample size estimation, unmeasured confounding sensitivity analyses, and selecting tailoring variables. We are continuing to work on alternative methods for tailoring variable selection.

Summary of Findings

  • Shrinkage regression based methods can identify important tailoring variables
  • Distributed regression methods can optimize individual treatment rules while protecting individual privacy
  • Dynamic weighted survival modeling can identify more effective individualized antidepressant treatment strategies using health records data

Publications

  1. Coulombe J, Moodie EEM, Shortreed SM, Renoux C. Can the Risk of Severe Depression-Related Outcomes Be Reduced by Tailoring the Antidepressant Therapy to Patient Characteristics? Am J Epidemiol. 2021 Jul 1;190(7):1210-1219. doi: 10.1093/aje/kwaa260. PMID: 33295950; PMCID: PMC8245894.
  2. Bian Z, Moodie EEM, Shortreed SM, Bhatnagar S. Variable selection in regression-based estimation of dynamic treatment regimes. Biometrics. 2021 Nov 27. doi: 10.1111/biom.13608. Epub ahead of print. PMID: 34837380.
  3. Moodie EEM, Coulombe J, Danieli C, Renoux C, Shortreed SM. Privacy-preserving estimation of an optimal individualized treatment rule: a case study in maximizing time to severe depression-related outcomes. Lifetime Data Anal. 2022 Jul;28(3):512-542. doi: 10.1007/s10985-022-09554-8. Epub 2022 May 2. PMID: 35499604.

Current MHRN Projects

The MHRN cooperative agreement includes the following cores and research projects:

Active projects affiliated with MHRN:

Syncing Screening and Services for Suicide Prevention across Health and Justice Systems

Grant Details

Title: Project 1: Syncing Screening and Services for Suicide Prevention across Health and Justice Systems

Funder: NIMH

Parent project number: 1P50MH127512

Sub-project ID: 8576

Project period: 08/22/2022 – 07/31/2027

Brief Narrative: This is a 5-year Signature Project within the NIMH-funded P50 Suicide Prevention Center, titled The National Center for Health and Justice Integration for Suicide Prevention. As suicide rates in the United States continue to rise, with nearly 50,000 suicide deaths and over 1 million suicide attempts annually per most recent data, increased attention has been paid to how to best integrate and coordinate suicide risk identification and prevention across multiple sectors, where some of our most vulnerable community members “fall through the cracks” in the continuum of care. Perhaps nowhere is this need for coordination and integration more pronounced than at the intersection of the US jail system, with over 10 million admissions per year, and the community healthcare system; an intercept known to impact individuals at disproportionately high risk for suicide. Given that roughly 10% of all suicides in the US with known circumstances occur following a recent criminal legal stressor (often arrest and jail detention), reducing suicide risk in the year after jail detention could have a noticeable impact on national suicide rates. There is thus a vital need to develop suicide risk care pathways between jails and healthcare systems to offer immediate access to care. Yet this process has been stymied by major fissures in the integration of data and clinical information between jails and health systems, preventing effective coordination of care between these community sectors. To address these needs, the proposed Signature Project is a Hybrid Type I effectiveness-implementation trial that harmonizes local jail booking and release data with healthcare records at two large healthcare systems in Minnesota and Michigan, to identify health system patients who are released from jail, and to pair the data linkage with randomization into usual care or a multi-level health system suicide prevention care pathway (consisting of care coordination, Safety Planning, Caring Contacts, and a telehealth delivered Coping Long- Term with Active Suicide Program). In so doing, this project leverages the study team’s experience in health system data linkage in the NIMH-funded Mental Health Research Network, from which the participating healthcare systems were chosen, as well as in suicide prevention around the period of jail detention and release (i.e., in the SPIRIT Trial), and in telephone-based suicide prevention intervention (i.e., in ED-SAFE). The proposed project will randomize 1050 individuals into the 5S intervention at both sites (comparing to more than 60,000 people in a usual care no contact comparison arm). Findings on suicide attempt and death outcomes, healthcare utilization mechanisms, cost- effectiveness, and implementation factors will provide data for a future fully scaled implementation trial and widespread adoption in community settings. Notably, the proposed Signature Project will be the first trial of a comprehensive health system intervention to prevent suicide in response to patients’ justice involvement.

  • Lead MHRN site: HFHS (PI: Brian Ahmedani)
  • Participating site: HPI (co-I: Rebecca Rossom)

Evaluating Effectiveness and Implementation of a Risk Model for Suicide Prevention Across Health Systems

Grant Details

Title: Evaluating Effectiveness and Implementation of a Risk Model for Suicide Prevention Across Health Systems

Funder: NIMH

Grant number: 1R01MH130548

Project period: 08/23/2022 – 05/31/2026

Brief Narrative: Suicide is a major public health concern in the United States; nearly 50,000 individuals die by suicide annually and almost 1.5 million attempt suicide. To date, identification of individuals at risk for suicide has relied on suicide risk screening practices, including using a variety of self- report instruments. However, sensitivity of these measures are only moderate; more precise tools for identifying patients at risk for suicide are needed. Suicide risk models, developed by our team, incorporate health records data and historical self-report screening questionnaire responses to improve accuracy of risk prediction. Our models have outperformed traditional clinical screening and similar risk models for adults receiving care in outpatient mental health specialty settings. However, while accurate, they have not been evaluated in real world care; whether the models actually increase identification or result in patients receiving more suicide prevention services, fewer crisis services, or making fewer suicide attempts is unknown. There is substantial clinical interest in implementing suicide risk models but little scientific evidence about the effectiveness of these models in real world settings compared to standard screening practices alone. Additionally, there is almost no guidance for their implementation in healthcare. The proposed project leverages the NIMH-funded Mental Health Research Network (MHRN), a collaboration of large health systems with established clinical data infrastructure to support multi-site studies. MHRN members Henry Ford Health System, Kaiser Permanente Northwest, and HealthPartners will participate in this project and collectively serve >170,000 behavioral health patients per year. The patient populations are diverse, including thousands of individuals with Medicaid and Medicare. Each of these systems has implemented a suicide prevention care model in their behavioral health departments, including robust suicide risk screening and assessment processes. However, none of these systems has implemented a suicide risk model. The proposed project includes a pragmatic trial approach with randomization of behavioral health clinics across the three participating health systems. It is innovative because it seeks to implement an MHRN suicide risk model (intervention) into each system’s existing suicide prevention care model (usual care) to increase the reach and effectiveness of the suicide prevention care models. Sites will receive implementation planning support based on stakeholder feedback from preliminary studies and deliverables include an implementation planning tool kit to facilitate spread. This high-impact study has important clinical implications as health systems consider whether it makes sense to enhance their existing suicide prevention care models with a suicide risk model. It is timely because many health systems are advancing toward suicide risk model implementation without evidence to support this innovation.

  • Lead site:
    • KPNW (PI Bobbi Jo Yarborough)
  • Participating sites:
    • HFHS (co-I Brian Ahmedani)
    • HPI (co-I Rebecca Rossom)

Reduce Racial/Ethnic Disparities in Suicide Risk Prediction (RED)

Grant Details

Title: Innovative methods to reduce racial and ethnic disparities in suicide risk prediction

Funder: NIMH

Grant number: 1R01MH125821

Grant period: 1/1/2022 – 12/31/2025

Brief Narrative: Suicide risk prediction models are being used by health care systems to guide delivery of suicide prevention interventions, but these prediction models may not accurately identify high-risk patients in racial and ethnic subgroups that are less prevalent or have lower rates of suicide attempt and death. This project will reduce racial and ethnic disparities in suicide risk models by developing methods for prediction model estimation that optimize performance within subgroups, rather than across the whole population, and adjust for misclassification of suicide outcomes. We will also design sample size calculations that evaluate the ability of a prediction study to accurately identify high-risk individuals within racial and ethnic subgroups.

  • Lead site:
    • KPWA (PI Yates Coley)
  • Participating sites:
    • University of Washington (Co-I Noah Simon)
    • KPSC (Co-I Karen Coleman)

Awarded budget (total cost): $1,622,626

Human Subjects: Reviewed by KPWA IRB, IRBNet# 1870253

Current status

Statistical methods research is underway. IRB and data use approvals are in place for all planned analyses. Current activities are focused on methods for accounting for outcome misclassification; evaluating variable importance in suicide prediction models; and designing estimation methods to optimize performance in racial/ethnic subgroups.

Summary of findings

Publications

MHRN III Pilot Project 2: Outreach to Reduce Depression Treatment Disparities

Funder: NIMH

Grant Number: U19MH121738

Project Period: 07/01/2021 – 06/30/2024

Brief Narrative:

Failure to initiate treatment is a major gap in care for depression – A recent Mental Health Research Network (MHRN) study involving more than 240,000 patients in 5 health systems with a new diagnosis of depression in primary care found that only about a third (36%) had completed a psychotherapy visit or filled a prescription for antidepressant medication within 90 days of a new depression diagnosis.
Large racial and ethnic disparities in depression treatment initiation exist – In that MHRN study the odds of Asians, Blacks and Hispanics initiating treatment were 30% lower than for Non-Hispanic Whites.
Previous research has focused on care after treatment initiation – Collaborative care and care management programs can reduce disparities, improving outcomes among traditionally under-served racial and ethnic groups. This work, however, has usually focused on those who have already initiated treatment.
Interventions to improve treatment initiation must accommodate diversity of patient experience and preferences –Underserved racial and ethnic groups may prefer psychotherapy over medication and may also prefer alternative treatments or alternative care providers. One size of depression treatment does not fit all.
eHealth technologies have the potential to address failures in treatment initiation – Previous research by MHRN investigators and others demonstrates that online messaging and other telehealth technologies can effectively and efficiently improve depression treatment adherence. These interventions, however, have focused on adherence after treatment initiation and have been tested primarily in non-Hispanic white patients.
Proposed trial: This pilot study will refine, adapt and test an outreach intervention to improve depression treatment initiation among patients recently receiving a new diagnosis of depression in primary care. Focusing on African American, Asian, Native Hawaiian/Pacific Islander and Hispanic patients, the study will leverage existing MHRN work to implement an automated outreach program with follow-up care facilitation by mental health clinicians. The intervention will utilize analytic and technological expertise developed by the MHRN to rapidly identify patients, send outreach messages, conduct assessments and facilitate care for patients with depression who fail to initiate treatment in a timely manner. The intervention will be developed with the input of patients in the target racial and ethnic minority populations and providers. Approximately 400 eligible patients in two MHRN health systems will be randomized to the intervention group or usual care. Outcomes (treatment initiation and rates recorded depression remission and response) will be ascertained from health system records. Analyses will examine intervention participation and compare the primary outcome (treatment initiation) and secondary outcomes (recorded depression remission and response) between groups. Results will inform a subsequent full-scale pragmatic trial to assess reduction in population-level disparities.

  • Lead Site:
    • KPHI (PI Vanessa Simiola)
  • Participating Sites:
    • HFHS (Site PI Lisa Matero)
    • KPWA (Co-I Greg Simon)
  • Awarded Budget (total costs):
    • Year 1: $112,382

Current Status

Over the reporting period Institutional Board Approval has been granted and focus group materials have been finalized as part of the formative research. Eligible participants were identified within the health care systems via distributed SAS code. Participant recruitment is currently underway within one (KPHI) of the two health care systems, with online focus groups scheduled in the beginning of May. The second health care system (HFHS) is awaiting local IRB approval and will begin recruitment immediately following. Provider surveys are scheduled for the end of the reporting period.

Summary of findings

Not yet available

Publications

None

Documents

Funding Announcement

Notice of Award

Personnel Contact List

Human Subjects: YES

IRB Review: KPSC is single IRB reviewing for KPHI, HFHS, and KPWA. File #12874.

Clinical Trial: YES

Pragmatic Trial of Stepped Care for Adolescent Suicide Prevention (Youth SPOT)

Grant Details

Funder: Patient-Centered Outcomes Research Institute (PCORI)

Contract Number: PLACER-2020C3-20902

Project Period: 12/01/2021 – 11/30/2027

Brief narrative:

Adolescent suicide is the second leading cause of death in teenagers. Preventing suicide in teens would keep them safe, allow them to get the mental health help that they need, and also protect families, friends, and communities from grief and loss. There are several programs that have been shown to work for preventing suicide, including an approach called dialectical behavior therapy (DBT). However, the studies done so far are so small that it is still unknown whether DBT works for all groups of teens—teens at medium risk versus those at very high risk, boys versus girls, younger versus older teens—or whether different approaches may work better for some groups. This is important information, because it would help teens and their families to make the best choices from several suicide prevention program options. Hospitals, clinics, doctors, and therapists also need information about what suicide prevention services work best and should be made more available. The goal of this study is to answer these questions.

The first aim, which will be completed in the first 18 months of the project, will be to plan the main study comparing two approaches to suicide prevention in collaboration with young people who have lived with suicidal behavior, their parents, doctors, and therapists. The second aim will be to compare how well these two approaches work to prevent suicide attempts in a group of 9,800 teens. The third aim is to see whether the two approaches lead to differences in the mental health care each teen receives—like being hospitalized, taking medications, seeing therapists, and so on—and to see which program works best for different groups, such as young men versus young women, or Hispanic teens and those who are not Hispanic, as well as what works best for teens who are at medium, medium-high, and high risk for suicide.

The first suicide prevention approach is called “stepped care,” and offers three levels of services to teens, depending on their level of risk. Medium-risk teens will be offered monthly phone check-ins; medium-high risk teens will also be offered a chance to work with a therapist to create and use safety plans that spell out how teens can keep themselves safe and what they will do if they feel suicidal. Teens at the highest level of risk will also be offered DBT group therapy for six months. The second suicide prevention approach is called Zero Suicide (ZS) care. This program is used by many healthcare clinics, hospitals, and therapy centers across the United States. It encourages therapists and doctors to ask about suicide frequently, and to make sure that teens who are at risk of suicide are connected to the best health care available, which might be regular therapy, medications, or a combination of the two.

To determine who to include in this study, the team will use a computer program to predict the chance that a teen will make a suicide attempt in the next six months. This program uses data collected by the healthcare system and is about 85 percent accurate. Teens who are at medium or high risk of suicide based on the computer program will be assigned by chance, like the flip of a coin, to one of two suicide prevention approaches. The team will use healthcare and government databases to see what happens for teens over 12 months so the team can compare rates of suicide attempts, self-harm, and healthcare use.

The goal is to help teens to be treated in a way that allows them the most personal freedom. The results from this study will help health insurers and clinics decide what kinds of suicide prevention care to offer and to cover. They will also help doctors and therapists decide what approaches to recommend to patients, and help individual teens and their families decide what kind of care to receive. The team will share its results with researchers, healthcare organizations, and national groups that advocate for youth suicide prevention to make sure that they will have the information they need to make choices about the best suicide prevention options for all types of teens.

  • Lead Sites:
    • KPNW (Clinical Coordinating Center, co-PI Greg Clarke)
    • KPGA (Data Coordinating Center, co-PI Courtney McCracken)
  • Participating Sites:
    • KPWA (site PI Rob Penfold)
    • HealthPartners (site PI Rebecca Rossom)
    • Georgia State University (site PI Ashli Owen-Smith)
    • UCLA (site PI Joan Asarnow)
    • California State Lutheran University (Site PI Jamie Bedics)

Awarded Budget (total cost): $21,324,820

Funding Announcement

Personnel Contact List

Human Subjects: YES

Current status

Pilot testing of outreach and intervention delivery will begin in October 2022.

Summary of findings

Publications