Understanding Structural Social Determinants of Suicidal Trajectories

Grant Details

Title: Understanding Structural Social Determinants of Suicidal Trajectories

Funder: American Foundation for Suicide Prevention, Inc.

Grant Number: YIG-2-133-22

Grant Period: 11/01/2023 – 10/31/2025

Narrative: 

Suicide is the second leading cause of death among U.S. adolescents and young adults aged 10 to 34. Racial/ethnic disparities in suicidal behaviors among youth and young adults in the US have emerged in recent years. Structural social determinants of health (SSDoH) are critical to understanding the impact of structural racism on suicide risks as they reflect systematic stressors that burden minorities. Yet, the longitudinal effects (and mechanisms) of the multidimensional structural social determinants on the disparities in suicidal trajectories (i.e., changes in suicidal ideation/attempts over the life course) are less clear. Few studies have examined disparity-related mediation pathways through stress (e.g., depression) and networks (e.g., parental closeness) to suicidal trajectories by race/ethnicity.  

We propose to leverage a cohort study of a nationally representative sample of 9421 respondents from Waves I-V National Longitudinal Study of Adolescent to Adult Health (1994-2018), with substantial diversity in race, and neighborhood and socioeconomic status, recruited in 7-12th grade across the U.S. and followed up five times through young adulthood (ages ~42) with over 80% response rates per wave, to innovatively study where, why, and to whom intra-neighborhood differences in SSDoH influence the incidence and persistence of suicidal ideation and suicide attempts. We will link SSDoH data of neighborhood physical, healthcare, social, education, and economic contexts to each participant’s residential and school neighborhoods. We will also innovatively assess confounding via individual-level factors known or hypothesized to influence suicidal behaviors, including social networks, mental illness, and access/quality of healthcare, and then construct SSDoH typologies that will directly inform policy. Our central hypothesis is that living in high-risk SSDoH increases suicidal behaviors concurrently and longitudinally, particularly in racial/ethnic minorities. We will produce findings of direct relevance to public health, city planning, and design decisions around temperature-reducing, healthcare-producing, and social cohesion-promoting interventions, greening, tree planting, and building construction. Our aims are:

 Aim 1: Intensively characterize SSDoH and their associations with suicidal trajectories of >9,000 longitudinally followed adolescents till their young adulthood.

Aim 2: Apply novel latent variable techniques to physical and social environment data to identify SSDoH typologies related to suicidal trajectories that are relevant to city planners.

Aim 3: Identify mediators (e.g., social networks, depressive symptoms) and moderators (e.g., health care access) of the impact of SSDoH and suicidal trajectories to advance future research and inform policies.

The emerging concerns on social risks underscores that investment in research on the multidimensional SSDoH on suicide risks is critical, timely, and can produce real-world changes with population-level benefits. We will produce innovative findings with direct health policy implications and clear and downstream interventions that improve health. We have previously conducted large-scale longitudinal studies and have collaborated with policymakers to leverage our science in improving health for local communities, making us well-situated to complete the proposed project.

Findings of our study will support an NIMH R01 application to further identify place-based suicide prevention. Focusing on SSDoH over the life course provides a unique and timely opportunity to unravel the impact of structural racism that systematically burdens racial/ethnic minorities.

  • Lead Site:
    • Overall PI: Cornell (Project lead/site PI Ridout)
  • Participating Sites/Subcontractors:
  • Funder Contacts
    • Program Official:
    • Grants Management Official:
  • Awarded Budget
    • $

Current Status

Summary of Findings

Publications

Computational Strategies to Tailor Existing Interventions for First Major Depressive Episodes to Inform and Test Personalized Interventions

Grant Details

Title: Computational Strategies to Tailor Existing Interventions for First Major Depressive Episodes to Inform and Test Personalized Interventions

Funder: NIMH

Grant Number: 1R01MH132973-01

Grant Period: 07/19/2023 – 03/31/2028

Narrative:

  • Lead Site:
    • Overall PI: KPNC (Project lead/site PI Kathyrn Ridout)
  • Participating Sites/Subcontractors:
  • Funder Contacts
    • Program Official: Matthew Rudorfer
    • Grants Management Official:
  • Awarded B
    • $812,514

Current Status

Summary of Findings

Publications

Pilot Testing Implementation of Suicide Risk Prediction Algorithms to Support Suicide Prevention in Primary Care

Grant Details

Title: Pilot Testing Implementation of Suicide Risk Prediction Algorithms to Support Suicide Prevention in Primary Care

Funder: NIMH

Grant Number: 1R34MH132829-01 

Grant Period: 07/07/2023 – 04/30/2024

Narrative: Suicide is one of the main drivers of increased mortality attributed to “diseases of despair” in the U.S. Suicide risk prediction algorithms have potential to vastly improve identification of individuals at high risk of suicide, but there is very little evidence to guide routine use of this powerful technology in primary care. Project deliverables will include clinical decision support tools designed to support use of suicide risk predictive analytics in primary care, that can be potentially scaled nationwide, and lay a foundation for future evaluation of the effectiveness of implementation for preventing suicide attempts and deaths.

  • Lead Site:
    • Overall PI: KPWA (Project lead/site PI Julie Richards)
  • Participating Sites/Subcontractors:
    • KPCO (PI Jenn Boggs)
    • University of Washington (PI Katherine Comtois)
  • Funder Contacts
    • Program Official: Victor Lushin
    • Grants Management Official: Christine Clarkson
  • Awarded Budget (Total Cost)
    • $256,413

Current Status

Summary of Findings

Publications

None

Employing a Stepped-Wedge Design to Implement an Evidence-Based Psychotherapy for PTSD in Six Large, Diverse Health Care Systems

Grant Details

Funder: PCORI

Grant Number:

Project Period: 2022 – 2025

  • Lead Sites:
    • Yale (co-PI Joan Cook) and KPHI (co-PI Vanessa Simiola)
  • Participating Sites:
    • Henry Ford Health System (co-I Lisa Matero)
    • Kaiser Permanente Northwest (co-I Frances Lynch)
    • Kaiser Permanente Georgia (co-Is Ashli Owen-Smith, Kanetha Wilson, Courtney McCracken)
    • Essentia Health (co-I Melissa Harry)
    • Baylor Scott & White Health (co-I Katherine Sanchez)

Brief Narrative: Written Exposure Therapy (WET) is a five-session exposure-based EBP for PTSD that was efficacious in randomized controlled trials for treating PTSD from different types of traumas. In addition to PCORI’s recognition, WET is recommended as a first-line treatment by the Department of Veteran Affairs (VA) and the Department of Defense (DoD). In two recent trials, WET was non-inferior to the more time-intensive, gold-standard EBP, Cognitive Processing Therapy. Thus, WET seems to meet the need for alternative PTSD treatments that are brief, with little dropout, and require less clinical training. Indeed, WET’s brevity and tolerability make it an ideal first-level intervention, appealing to patients who have opted not to seek out more time- and therapist-intensive EBPs. WET addresses significant barriers to other EBPs for PTSD at the patient, provider, and system levels.

The project will employ a stepped wedge design to implement WET in six, large, diverse, integrated, civilian health care systems across the United States— Kaiser Permanente (KP) Hawaii, Henry Ford Health System, Kaiser Permanente Northwest, Kaiser Permanente Georgia, Essentia Health, and Baylor Scott & White Health — with all sites receiving the intervention during the project period. The healthcare systems are members of the Mental Health Research Network (MHRN), a consortium of 14 research centers. Sites will be assigned to one of two implementation groups. Every site will receive WET training, consultation, and multi-component implementation strategies, promoting equity and advancing the field of implementation science.

The specific aims of this project are to:

  1. Employ multi-component implementation strategies to help mental health providers implement WET for their PTSD patients in mental health settings in six health care systems.
  2. Use Consolidated Framework for Implementation Research (CFIR) to understand the determinants and process of implementation.
  3. Utilize RE-AIM framework to evaluate implementation outcomes for mental health providers and patients.

Improving Suicide Risk Prediction with Social Determinants Data

Grant Details

Funder: NIMH

Grant Number: R56MH125794-01A1

Grant Period: 1/1/2022 – 12/31/2022

Brief Narrative: 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.

Lead Site: KPWA (PI Rob Penfold)

Participating Sites: N/A

Current Status

We fielded the discrete choice experiment in mid-October 2022. Planned recruitment is 720.

Summary of Findings

Publications

Trans-America Consortium of the Health Care Systems Research Network for the All of Us Research Program

Grant Details

Funder: NIH Office of the Director

Grant Number: OT2OD026550

Grant Period: 1/4/2018 – 3/31/2023

Narrative:

Lead Site: HFHS (co-PIs Christine Johnson and Brian Ahmedani)

Participating Sites:

Current Status:

Ongoing recruitment, enrollment and retention of 100,000 participants and members.

Summary of Findings:

Publications:

Cronin, R.M., Jerome, R.N., Mapes, B.M., Andrade, R., Johnston, R., Ayala, J., Schlundt, D., Bonnet, K.R., Kripalani, S., Goggins, K., Wallston, K.A., Couper, M.P., Elliott, M.R., Harris, P.A., Begale, M.A., Munoz, F.A., Lopez-Class, M., Cella, D., Condon, D.M., AuYoung, M., Mazor, K.M., Mikita, S., Manganiello, M., Borselli, N., Fowler, S.L., Rutter, J.L., Denny, J.C., Karlson, E.W., Ahmedani, B.K., O’Donnell, C.J. Vanderbilt University Medical Center Pilot Team, and the Participant Provided Information Committee. (2019). Development of the Initial Surveys for the All of Us Research Program. Epidemiology, 30(4), 597-608.. doi: 10.1097/EDE.0000000000001028. PMID: 31045611. 

Ramirez AH, Sulieman L, Schlueter DJ, Halvorson A, Qian J, Ratsimbazafy F, Loperena R, Mayo K, Basford M, Deflaux N, Muthuraman KN, Natarajan K, Kho A, Xu H, Wilkins C, Anton-Culver H, Boerwinkle E, Cicek M, Clark CR, Cohn E, Ohno-Machado L, Schully SD, Ahmedani BK, Argos M, Cronin RM, O’Donnell C, Fouad M, Goldstein DB, Greenland P, Hebbring SJ, Karlson EW, Khatri P, Korf B, Smoller JW, Sodeke S, Wilbanks J, Hentges J, Mockrin S, Lunt C, Devaney SA, Gebo K, Denny JC, Carroll RJ, Glazer D, Harris PA, Hripcsak G, Philippakis A, Roden DM; All of Us Research Program. (2022). The All of Us Research Program: Data quality, utility, and diversity. Patterns (N Y); 3(8), 100570. doi: 10.1016/j.patter.2022.100570. PMID: 36033590.

Cronin, R.M., Halvorson, A.E., Springer, C., Feng, X., Sulieman, L., Loperena-Cortes, R., Mayo, K., Carroll, R.J., Chen, Q., Ahmedani, B.K., Karnes, J., Korf, B., O’Donnell, C.J., Qian, J., Ramirez, A.H., All of Us Research Program Investigators.  (2021). Comparison of Family Health History in Surveys versus Electronic Health Records in the All of Us Research Program. Journal of the American Medical Informatics Association, 28(4):695-703. doi: 10.1093/jamia/ocaa315. PMID: 33404595. 

Treatment Initiation for New Episodes of Depression in Pregnant Women

Grant Details

Funder: NICHHD

Grant Number: R01HD100579

Grant Period: 5/6/2021 – 3/31/2026

Narrative: Up to 12% of pregnant women have a new episode of depression, ie, an incident or recurrent depressive episode with symptom onset during pregnancy. Effects of untreated antenatal depression include unhealthy maternal behaviors (eg, diminished self-care, smoking, substance use, self-harm) and emotional and behavioral problems in offspring. Antenatal depression or elevated depression scores, identified by screening instruments, increase the risk of preterm birth (PTB), low birth weight (LBW), and small for gestational age (SGA) birth, and are associated with breastfeeding discontinuation before 3 months postpartum. In-person psychotherapy and antidepressant medication improve depression symptoms in many with depression, yet <50% of pregnant women with new episodes of depression initiate these treatments. Although some barriers to initiating antidepressants and psychotherapy are known, other factors have not been well described, especially after accounting for depression severity. Furthermore, the impact of antidepressants and psychotherapy on perinatal outcomes, including PTB, LBW, SGA, and breastfeeding continuation among pregnant women with new episodes of depression after accounting for confounding by depression severity is unknown. Given the importance of factors influencing the decision to initiate antidepressant or psychotherapy treatment during pregnancy and the need for further evidence on the perinatal risks and benefits associated with antidepressant use and psychotherapy in pregnant women, the goal of this study is to identify predictors and perinatal effects of psychotherapy and antidepressant use for new episodes of depression during pregnancy while accounting for depression severity. We will conduct this study in a racially and ethnically diverse multi- site population using electronic health data, enriched with survey data from a subset of women. Among pregnant women with new episodes of depression, we will evaluate factors that influence the propensity to initiate psychotherapy or antidepressants; accounting for these is crucial when studying treatment effects. We will describe patterns of use of alternative depression management approaches (eg, Internet- based psychotherapy, peer support groups, and complementary and alternative medicine) and will evaluate whether initiation of psychotherapy or antidepressants is associated with these practices while accounting for depression severity. We will quantify the impact of psychotherapy and antidepressants (including dose, timing, and duration of use) on PTB, LBW, SGA, and breastfeeding continuation accounting for the propensity to initiate psychotherapy or antidepressants and depression severity. We are uniquely positioned to overcome limitations of confounding and small size in prior studies given our data on depression severity and maternal comorbidity for more than 8,000 pregnant women. Our study will be informative for understanding the mental health interventions utilized by pregnant women with depression and will inform decision making on optimal depression management during pregnancy.

  • Lead site:
    • HPI (PI Kristin Palmsten)
  • Participating Sites:
    • HFHS
    • KPHI
    • KPNC
    • KPSC

Current Status:

We are currently conducting the first aim of the study, which is a survey among people with new episodes of depression during pregnancy. We aim to learn about the treatments and strategies participants used to manage new episodes of depression during pregnancy, how they are supported by others, and how they feed their new babies. The survey also asks about childhood and life experiences.  We completed a pilot survey at HealthPartners this spring and we are launching the survey across all sites this fall.

Summary of Findings:

None yet

Publications:

None yet

Assisted Identification and Navigation of Early Mental Health Symptoms in Youth

Grant Details

Funder: NIMH

Grant Number: R01MH124652

Grant Period: 1/18/2021 – 11/30/2024

Narrative: About 55% of children with significant mental health difficulties receive treatment and up to 80% of children with sub-clinical symptoms receive no treatment. Treatments are often not initiated until issues are significantly impacting the child and family. This study aims to conduct a pragmatic randomized trial in two non-academic health care systems to test a mental health family navigator model to promote early access to, engagement in, and coordination of needed mental health services for children. The first task of the study will focus on the implementation of a predictive model to identify symptomatic children with no diagnosed mental health disorder(s) or treatments initiated. The tool identifies patients with documentation of mental health symptoms or complaints in the free text of a progress note from a recent primary care or urgent care visit. Using this predictive algorithm, we will conduct a pragmatic randomized trial comparing intervention and usual care arm patients enrolled from Kaiser Permanente (KP) Washington and KP Northern California. The trial will enroll 200 patients per arm (n=400). Children with (1) a new mental health diagnosis but no treatment initiated; (2) a new mental health medication ordered with no mental health diagnosis; and (3) symptoms identified by the predictive model with no new mental health diagnosis or treatment initiated will be recruited. The study intervention will offer 6 months of support to the family by a mental health navigator (social worker). The navigator will perform an initial needs and barriers assessment with the family around mental health services, conduct ongoing motivational interviewing around mental health care, provide up to 4 psychotherapy sessions (when appropriate) via clinic-to-home video visits, help the family find and schedule with appropriate mental health providers in the community, and reach out ad hoc if mental health appointments or medication refills are missed. The primary outcome is the percentage of youth initiating psychotherapy. The secondary outcome is the percentage of youth with at least 4 mental health visits. We hypothesize that the intervention arm will have higher rates of psychotherapy use compared to the control arm. We will also assess initiation of psychotropic medications. All primary analyses will follow an intent-to-treat approach. A waiver of consent will be obtained to include data for all individuals offered the intervention in the analysis, regardless of the amount of intervention (“dose” of navigation) received.

Lead Site: KPWA (PI Rob Penfold)

Participating Site: KPNC

Current Status:

Recruitment is active at both KPWA and KPNC. N = 44 as of 10/25/2022.

Summary of Findings:

Publications:

STAR Caregivers – Virtual Training and Follow-up

Grant Details

Funder: NIMH

Grant Number: R01AG061926

Grant Period: 9/30/2018 – 5/31/2023

Narrative: Alzheimer’s Disease and related Dementias (ADRD) are debilitating conditions affecting more than 5 million Americans in 2014. It is projected that 8.4 million people with be diagnosed with ADRD over the next 15 years and health care costs attributable to ADRD are projected to be more than $1.2 trillion by 2050.  Behavioral interventions such as STAR-Caregivers are efficacious first-line treatments for managing BPSD endorsed by the Administration on Aging. However, the programs have not been implemented widely – partly due to the intensity/cost of the programs and difficulty conducting outreach. No study has investigated CG willingness to reduce or discontinue antipsychotic use. We propose a Stage III clinical trial to ascertain the feasibility and acceptability of STAR Virtual Training and Follow-up (STAR- VTF) in which (a) training materials are delivered electronically and learning is self-directed, (b) caregivers have two in-home visits with a social worker and (c) where caregivers receive support from a social worker via secure messaging (email) within a web-based portal. We will compare outcomes in the STAR-VTF group to an attention control group (mailed material plus generic secure messages). Our specific aims are: (1) Assess the feasibility and acceptability of STAR-VTF to caregivers; (2) Assess the feasibility and acceptability of the program from the payer perspective; and (3) Test the hypotheses that (H1) caregiver participants in STAR-VTF will have lower levels of caregiver burden at 8 weeks and 6 months compared to an attention control group; and (H2) PWD participants in STAR-VTF will have lower rates of daily antipsychotic medication use at 6 months compared to attention control. We propose to recruit 100 CG-PWD dyads (50 in each arm). This will be the first study to test a low intensity, self-directed caregiver training program with secure message support from social workers. It will also be the first study to measure changes in antipsychotic medication use by PWD after CG training. The study is also innovative because it brings together leading experts in caregiver training, health information management, and care management. Third, this will be the first study to use automated data and natural language processing to identify potential caregivers in need of education/support at a time when antipsychotic medication use begins. Results of this study will inform a future multi-site trial in the Mental Health Research Network.

Lead Site: KPWA (PI Rob Penfold)

Participating Sites: N/A

Current Status

Currently enrolling person-living-with-dementia – Caregiver dyads. Recruitment will end December 2022.

Summary of Findings

none yet

Publications

Ramirez M, Duran MC, Pabiniak CJ, Hansen KE, Kelley A, Ralston JD, McCurry SM, Teri L, Penfold RB. Family Caregiver Needs and Preferences for Virtual Training to Manage Behavioral and Psychological Symptoms of Dementia: Interview Study. JMIR Aging. 2021 Feb 10;4(1):e24965. doi: 10.2196/24965. PMID: 33565984; PMCID: PMC8081155.

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.