Active MHRN projects

Trauma and PTSD in Medical Records

Grant Details:

Funder: NIMH (MHRN III Feasibility Pilot Program)

Grant Number: U19MH121738

Project Period: 7/1/2022 – 6/30/2023

Narrative:

Background: Exposure to potentially traumatic events such as physical and sexual abuse/assault, serious accidental injury, mass shootings, and terrorism, and associated PTSD are major public health concerns (Magruder, McLaughlin & Elmore Borbon, 2017). It is estimated that over 20 million Americans develop PTSD at some point in their life (Kessler, Berglund et al., 2005). Inadequate treatment of PTSD may lead to chronic impairment and disability and have long-term and widespread familial and societal consequences (e.g., domestic violence, suicide, incarceration).

Incident rates of PTSD appear strikingly low in the health care system compared to estimates derived from representative epidemiological studies of the general public. Conservative estimates suggest that up to 80% of adults will experience a traumatic event during their lifetime. In a large nationally representative epidemiological study, it was estimated that PTSD impacts 3.6% of civilians each year, with a lifetime prevalence rate of 6.8% (Kessler, Berglund et al., 2005; Kessler, Chiu et al., 2005). However, in a recent examination of PTSD in six MHRN-affiliated health care systems we found less than 1% of the patient population had a diagnosis of PTSD when using ICD diagnosis codes only, suggesting patients may be underdiagnosed or inadequately captured using this method. Further, ICD diagnosis codes are limited in their ability to capture trauma exposure type (e.g., combat exposure, motor vehicle crash, sexual abuse, elder abuse, intimate partner violence, natural disaster) and may be underutilized by providers.

This project builds on previously MHRN-funded research conducted by Negriff and colleagues (Lynch, 2022) who examined incidence of child maltreatment comparing rates of those captured by ICD diagnosis codes versus natural language processing (NLP). In their investigation, NLP identified 10 times more children with child maltreatment than just using the diagnosis code. Building on this methodology the proposed pilot project will use NLP to identify patients within one health care system (Kaiser Permanente Hawaii) who experience PTSD compared to those identified using ICD diagnosis codes only. Further, we test the feasibility of using NLP to categorize patients based on exposure type (e.g., combat, motor vehicle crash, sexual abuse, etc.). NLP may help to identify additional trauma-exposed individuals with PTSD that are not documented/captured through ICD codes. This may lead to the identification of care gaps, novel treatment targets, and characteristics (e.g., age, sex, race/ethnicity, trauma exposure type) that may make it more/less likely to have ICD coded PTSD. To date, PTSD has been relatively underexamined within the Mental Health Research Network (MHRN) despite being identified as a priority area in this third funding cycle.

Research Questions:

Does NLP allow us to obtain estimates of the number of adults who experience PTSD that are more comparable to national epidemiologic data?
Are there differences by group (e.g., age, sex, race/ethnicity, trauma type) of those captured through NLP versus ICD diagnosis code?
Can we establish feasibility for systematically identifying trauma exposure using previously collected data within the health care system?

Methods: The project PI will convene a panel of interested MHRN investigators to discuss approach, assist in the identification of terms, interpretation and use of results, and future research. Drs. Frances Lynch, Jordan Braciszewski and Rob Penfold have expressed interest in serving on this panel and a larger invitation will be sent to all MHRN-affiliated investigators, if funded.

We propose to use simple NLP queries at 1 MHRN site to identify incidents of trauma exposure and PTSD and compare the number of cases identified through NLP compared to those identified using ICD codes only. We will Identify a cohort of adults (age 18 and over) at KPHI and develop a Bag of Words (concept unique identifiers), building off those developed by Negriff, Lynch and Penfold, to search chart notes. Following the initial search, the PI, a licensed clinical psychologist, will conduct chart review of up to 150 cases to manually review text for each concept unique identifier and flag confirmed cases (yes/no). This data will be used to retrain NLP and the process will be repeated a second time for quality assurance/validation. We will use standard methods for identifying patients based on ICD-codes only (comparison group). We will then conduct appropriate statistical analyses to examine differences in identification by groups.

Planned Product: The results of this pilot study will serve as the basis for an R01 application to the National Institute of Mental Health under the NOSI Secondary Analysis of Posttraumatic Psychopathology Data. In addition, results from this study will be presented via scientific conference presentation and/or peer-reviewed publication.

Lead Site: KPHI (PI Vanessa Simiola)

Participating Sites: N/A

Current Status:

Summary of Findings:

Publications:

Implementing Predictive Models for Identifying Suicide Risk in Adolescents

Grant Details

Funder: NIMH (MHRN III Feasibility Pilot Program)

Grant Number: U19MH121738

Project Period: 7/1/2022 – 6/30/2023

Narrative:

Background: Adolescent suicide is an urgent public health crisis. Suicide is currently the second leading cause of death among adolescents ages 10-24. Despite decades of research, suicide attempt rates continue to rise across the U.S., particularly among adolescents. Furthermore, new data suggests that adolescents were disparately impacted by the COVID-19 pandemic, with some states reporting increased rates of suicide among youth, galvanizing the urgency for increased prevention. People who die by suicide often see healthcare providers, and specifically primary care providers prior to death, including adolescents. Therefore, identifying suicide risk in healthcare settings among adolescents is an important prevention opportunity.

Mental Health Research Network (MHRN) researchers (led by Greg Simon) have developed suicide risk prediction algorithms that have potential to vastly improve identification of individuals at high risk of suicide, including adolescents. While promising, there is very little evidence to guide routine use of this powerful suicide risk identification method during healthcare encounters with adolescents. A recently completed MHRN project (led by Bobbi Jo Yarborough) explored barriers and facilitators of the use of suicide risk algorithms among adult patients, clinicians, and administrators across three MHRN systems. These stakeholders were generally supportive of implementation, but some patient participants expressed concerns about suicide risk information resulting in coercive treatment, and clinician participants expressed desire for opportunities supporting their role in implementation decision-making.

No studies (to our knowledge) have explored perspectives of adolescents, their parents/guardians or adolescent providers about how suicide risk prediction models should be implemented. Therefore, we plan to build from prior MHRN work and qualitatively elicit adolescent care providers’ perceived barriers and facilitators to implementation of these models in care delivery and their ideologies regarding risk thresholds and risk-concordant care. Simultaneously, we plan to build a qualitative understanding adolescents and family perceptions, ideas, and preferences regarding the use of suicide risk prediction models in their care.

Research questions: (1) What perspectives do primary care providers have on suicide risk prediction algorithms and what suggestions or considerations do they have for clinical practice? (2) How do primary care providers envision risk concordant care delivery to look like in clinical practice? (3) What are adolescent and parent/caregiver perceptions and preferences on the use of suicide risk predications models as a tool for enhanced clinical care? (4) What ideas or suggestions do adolescents and parents/caregivers have for comfortable and effective implementation of risk prediction algorithms in primary care?

Methods: Provider interview guides will be developed based on interview findings by the prior qualitative MHRN study (described above) which used the Consolidated Framework for Implementation Research (CFIR), with additional questions aimed at understanding risk thresholds and associated concordant care. Caregiver and adolescent interviews will explore their thoughts, ideas, and preferences regarding EHR-based suicide risk prediction models as part of patient standard of care. We will aim to interview 10-15 adolescent care providers and 10-15 caregiver-adolescent dyads across the two sites. Care providers will be purposively selected in consultation with KPWA leaders involved in an initiative to improve adolescent access to timely mental health care. The suicide risk prediction algorithm will be used to purposively sample adolescents at high risk of suicide and their parent/guardian caregivers. Identified dyads will be recruited via mailed and telephone invitation materials (developed from a prior project recruiting adolescents & caregivers). Interviews will be audio-recorded, transcribed and double-coded to support thematic content analysis.

Planned products: A synthesis of stakeholder needs/perspectives to support suicide risk prediction model implementation in routine care delivery for adolescents. This key deliverable will be used to support: 1) current predictive analytic implementation efforts across MHRN sites 2) an external grant submission to NIMH focused on application of Human-Centered Design methods to design, build, and test clinical decision support for identifying and engaging adolescents at high-risk of suicide in evidence-based healthcare, 2) a peer-reviewed manuscript submission led by Taylor Ryan, MS (PhD student in Health Systems & Population Health at the University of Washington) & Julie Richards, MPH, PhD (MHRN researcher and faculty advisor at UW).

Lead Site: KPWA (PI Julie Richards)

Participating Sites: N/A

Current Status:

Summary of Findings:

Publications:

Weight loss and perinatal depression

Grant Details

Funder: NIMH (MHRN III Feasibility Pilot Program)

Grant Number: U19MH121738

Project Period: 7/1/2021 – 6/30/2022

Narrative:

Background: Rates of overweight (body mass index (BMI)=25.0-29.9kg/m2) and obesity (BMI>30.0kg/m2) among adult American women have continuously increased for the past 20 years, with 41.9% having obesity in 20181. Obesity is a risk factor for adverse outcomes in the 85% of women who become pregnant by age 442. Most women are advised to lose weight prior to becoming pregnant, to help alleviate several pregnancy and postpartum complications3. One of these complications is the development of prenatal and postpartum mental health disorders, including depression and anxiety4. Around 10-25% of mothers will experience depression during pregnancy5 and 10-15% in the postpartum period6. Between 0.9%−22.7% of mothers will experience generalized anxiety disorder during pregnancy7 and 4.4-8.5% postpartum8. Mothers who were overweight or obese at time of pregnancy appear to have higher risk for the development of postpartum depression and anxiety compared to their normal weight counterparts9.

In the general population, losing weight, defined as losing at least 5-10% of one’s body weight10, has produced mixed results in terms of changes in mental health symptoms. Some evidence indicates weight loss is associated with improved depressive11 and anxiety symptoms12, while others have found that weight loss was associated with increased depression symptoms13 and no association with anxiety14. However, no studies have examined how the process of losing weight prior to pregnancy interacts with the development of prenatal and postpartum mental health disorders. There is also evidence that the burden of obesity15 and postpartum depression and anxiety17 is greater in African-Americans and Latina mothers compared to White mothers, suggesting racial identity may moderate the relationship between weight loss and prenatal and postpartum mental health outcomes.

This project is responsive to the NIMH strategic goal “Strengthen the Public Health Impact of NIMH-Supported Research” by identifying specific groups of individuals who may have an elevated risk for developing depression and anxiety, and specific time points (prenatal or postpartum) that may be most vulnerable to psychopathology in a large, population level dataset. By identifying these individuals and timepoints, empirically-supported interventions can be implemented and tested for efficacy in a targeted manner.

Research Question: In a cohort of women 20 to 44 years of age who have obesity and are free of a diagnosis of depression or anxiety for a year prior to pregnancy, this study aims to:

1) determine if patients who experience successful weight loss (losing at least 10% of one’s body weight) vs. those who do not, in the year prior to pregnancy, have a lower risk for new onset prenatal and postpartum depression and anxiety.

2) Determine if the magnitude of association between pre-pregnancy weight loss and prenatal and postpartum depression and anxiety is greater in African-American and Latina women compared to White women.

Methods: The study will pull data from the electronic health record system of a large Midwestern hospital system and create a sample by identifying women of reproductive age (20- 44 years old) who experienced a live birth, and have a weight recorded sometime in the year prior to pregnancy. Case-matched samples will be created based on important demographics, such as insurance status and age, and clinical factors, including BMI at time of pregnancy. These samples will be divided into two groups: those who successfully lost weight prior to pregnancy and those who did not. The research questions will be analyzed using modified Poisson models.

Planned Product: The results of this study will be published and presented at a conference. Findings will provide preliminary evidence to support an R01 submission that will involve multi- HCSRN sites. Aims of the R01 submission will be expanded to examine dose response relationships in baseline BMI and pre-natal and post-partum depression and anxiety disorder and will determine if weight loss thresholds (moving from obese to overweight vs. obese to normal weight) are associated with greater reduction in risk for prenatal and postpartum depression.

Lead Site: St. Louis University (Co-PIs Megan Ferber and Kara Christopher)

Participating Sites: N/A

Current Status:

Summary of Findings:

Publications:

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

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

MHRN III Pilot Project 1: Stakeholder Views on Implementation of Suicide Risk Prediction Models

Grant Details

Funder: NIMH

Grant Number: U19MH121738

Grant Period: 09/24/2019 – 6/30/2021

Narrative: Age-adjusted suicide rates have been increasing in the U.S. over the past two decades. In 2017, more than 47,000 Americans died of suicide. Health care visits represent opportunities for suicide prevention because most individuals make an outpatient health care visit within a year of their suicide death and almost half have a visit within a month of their death. However, suicide risk is not always easily recognizable to clinicians—traditional clinical prediction is hardly better than chance. Predictive modeling that identifies patterns in “big data” from administrative and electronic health records has proven superior to clinical suicide risk prediction and routinely used suicide screening instruments. While predictive modeling holds promise for suicide prevention, how models should be implemented in routine clinical practice and the contextual factors that influence their use are understudied. The potential benefits of any risk prediction model, including those designed to identify suicide risks, are dependent on making sure that the models are deployed in a manner that does not harm patients, supports clinical care management, and is sustainable for health care delivery systems. We propose a pre-implementation pilot study in three settings, using one-on-one, in-depth interviews to explore health system administrators’, clinicians’, and patients’ expectations, experiences with, concerns, and suggestions for the early use of suicide risk prediction models. In the first setting, health system administrators are still considering what might be the best implementation approach. Interviews will help us understand how various stakeholder expectations match what is actually occurring in the two other settings where small pilot studies will be in process. One of these settings is planning outreach to high-risk patients independent of health care visits while the other is planning delivery of risk scores at the point of care. By studying different implementation strategies, we can compare relative advantages and disadvantages. We are particularly interested in effects on clinical workflows, clinician-patient relationships, and patient experiences. While there is an emerging literature supporting the promise of predictive models in health care, implementation factors and patient impacts have been largely ignored. Yet decisions regarding design and modeling methods and implementation processes should be driven by stakeholder requirements. Results of this pilot study will have important clinical implications and will not only inform large-scale implementation of suicide risk prediction models in health systems across the country but will also inform development of future risk prediction models and associated care processes tailored to stakeholders needs more generally (not limited to suicide risk). The long-term goals of this pilot project are to inform ongoing health system-level efforts to reduce suicide prevalence and prevent suicides by optimizing the use of suicide risk prediction tools.

Lead Site:
- Overall PI: KPNW (Bobbi Jo Yarborough)

Participating Sites/Subcontractors:
- HPI (site project lead Rebecca Rossom)
- KPWA (site project lead Julie Richards; site PI Greg Simon)

Funder Contacts
- Science Officer: Susan Azrin
- Program Official: Michael Freed
- Grants Management Official: Julie Bergerud

Documents

Funding Announcement

Notice of Award

Personnel Contact List

Current Status

We have completed and analyzed interviews with 10 health care administrators, 30 clinicians in behavioral health departments, and 62 patients across three health systems.

Summary of Findings

Administrators and clinicians

Use of a suicide risk prediction model and two differing implementation approaches were acceptable.
Clinicians desired opportunities for input on implementation decision-making.
They wanted to know how this manner of risk identification enhanced existing suicide prevention efforts.
They wanted additional training on how the models determined risk and why some patients appeared at risk while others do not.
Clinicians were concerned about lack of suicide prevention resources for newly identified patients.
They wanted clear procedures for situations when they could not reach patients or when patients remained at-risk over a sustained period.
They would like consolidated suicide risk information in a dedicated module in the EHR to increase efficiency.

Patients

Patients were generally supportive of suicide risk prediction models derived from EHR data.
Concerns included: 1) apprehension about inducing anxiety and suicidal thoughts, or 2) triggering coercive treatment, particularly among those who reported prior negative experiences seeking mental health care.
Participants engaged in mental health care or case management expected to be asked about suicide risk and largely appreciated suicide risk conversations
Patients preferred conversations to come from clinicians comfortable discussing suicidality.

Publications

Yarborough BJH, Stumbo SP. Patient perspectives on acceptability of, and implementation preferences for, use of electronic health records and machine learning to identify suicide risk. Gen Hosp Psychiatry. 2021 May-Jun;70:31-37. doi: 10.1016/j.genhosppsych.2021.02.008.

Yarborough BJH, Stumbo SP, Schneider JL, Richards JE, Hooker SA, Rossom RC . Patient expectations of and experiences with a suicide risk identification algorithm in clinical practice. BMC Psychiatry. 2022 Jul 23;22(1):494. doi: 10.1186/s12888-022-04129-1 .

MHRN III Signature Project 1: Mindfulness-Based Cognitive Therapy to Prevent Perinatal Depression

Grant Details

Funder: NIMH

Grant Number: U19MH121738

Grant Period: 9/23/2019 – 6/30/2024

Narrative: An increasing number of digital mental health technologies are being developed to expand access to mental health treatments and deliver them in a cost-effective manner. Although efficacy trials of these technologies demonstrate improved patient outcomes, especially when combined with coaching support, there is little evidence that such digital tools can be widely implemented and sustained in routine care settings.

Perinatal depression is one area of significant public health concern where the role of digital mental health technology is especially relevant. Approximately 30-40% of women with histories of depression experience relapse during the perinatal period, a majority show poor adherence to antidepressants (ADs), the most common prevention treatment, and a majority express a preference for non-pharmacologic treatments. However, effective and easily accessible non-pharmacologic treatments are not widely available. Inadequate treatment for perinatal depression poses unique risks, including potential obstetrical and neonatal complications associated with perinatal depression itself and with fetal exposure to ADs. It is therefore imperative to test the implementation of effective and scalable non-pharmacological treatments to reduce the risk of depression relapse in the perinatal period.

Mindfulness-Based Cognitive Therapy (MBCT) is a promising preventive intervention for pregnant women with recurrent depression (as well as for adults in general), demonstrating significant reductions in rates of depressive relapse and residual depressive symptoms. MBCT is an eight-session in-person group intervention targeting risk factors for depressive relapse through a combination of mindfulness meditation and cognitive-behavioral strategies. Because of challenges in delivering in-person MBCT (difficulty for health systems to scale up the intervention, barriers to access for pregnant women), we developed a mobile-first digital adaptation of MBCT for pregnant women, Mindful Mood Balance for Moms (MMBFM).

The critical next phase of our work is to evaluate the potential of MMBFM as an effective intervention that can be more widely adopted, implemented, and sustained across heterogeneous patient populations and health care systems. We propose a large pragmatic hybrid type II effectiveness–implementation trial comparing MMBFM to usual care (UC) among pregnant women at risk for recurrent depression at four MHRN sites: KP Colorado, KP Southern California, HealthPartners, and KP Georgia to address the following aims:

AIM 1: Test the effectiveness of MMBFM in reducing depression symptoms, reducing risk of relapse or significant worsening, and improving perinatal outcomes when implemented in real-world health systems.

AIM 2: Evaluate the incremental cost-effectiveness of MMBFM compared to UC.

AIM 3: Evaluate healthcare system’s implementation of MMBFM using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) model.

Lead Site:
- Overall PI: KPCO (Project lead Arne Beck)

Participating Sites/Subcontractors:
- HPI (Site PI Kristen Palmsten)
- KPGA (Site PI Courtney McCracken)
- GSU (Site PI and site project lead for KPGA Ashli Owen-Smith)
- KPNW (Site PI Frances Lynch)
- KPSC (Site PI Karen Coleman)
- UCB (Co-I Sona Dimidjian)

Funder Contacts
- Science Officer: Susan Azrin
- Program Official: Michael Freed
- Grants Management Official: Julie Bergerud

Documents

Funding Announcement

Notice of Award

Personnel Contact List

Current status

Enrollment is approximately 80% complete for the randomized trial comparing depression outcomes for participants in the Mindful Mood Balance for Moms (MMBFM) online program who receive professional or peer telephonic coaching. All four sites have engaged their OB leaders and stakeholders and are starting the cluster randomized trial to assess the impact of implementation strategies on participants’ initial engagement in the MMBFM program. Coaching trial enrollment will be complete by end of 2022, and implementation trial enrollment will be complete by second quarter of 2023. Follow-up data collection through three months postpartum and data analysis for both trials and for the cost-effectiveness analysis will be conducted from third quarter 2023 through third quarter of 2024.

Summary of findings

Not yet available

Publications

None

MHRN III Infrastructure: Methods Core

Grant Details

Funder: NIMH

Grant Number: U19MH121738

Grant Period: 09/23/2019 – 06/30/2020

Narrative: The Methods Core will include an Informatics Unit, led by Drs. Gregory Simon and Christine Stewart, and a Scientific Analysis Unit, led by Drs. Susan Shortreed and Patrick Heagerty. The Informatics Unit will continue highly successful work over the past 8 years, supporting routine data quality assessment and descriptive analyses of diagnosis and treatment patterns across all participating health systems. New work will include development of tools and resources to assess and minimize privacy risks when sharing sensitive health data for research and development of specific new data areas (perinatal mental health and prenatal exposures, expanded list of patient-reported outcomes, and assessments of social determinants of health). The Informatics Unit will provide consultation to all MHRN core and affiliated projects and share all resources with other researchers and health systems via MHRN’s public repository of specifications, code lists, and analytic code. The Scientific Analysis Unit will support to all MHRN core and affiliated projects via project-specific consultation and development of a learning community of analysts and biostatisticians across MHRN research centers. This Unit will also focus on development and dissemination of analytic methods in two areas directly relevant to MHRN research. Work on evaluating adaptive treatment strategies will build on Dr. Shortreed’s recently funded methods grant to evaluate and disseminate methods for using health system data to tailor treatments for individuals with more chronic or severe mental health conditions, focusing on assessing treatment effects when treatments are adjusted or switched according to previous treatment failures or adverse effects. Work on stakeholder-driven predictive analytics will build on MHRN’s development of accurate suicide risk prediction models, focusing on matching specific study designs and model development methods with stakeholder priorities and implementation constraints.

Lead Site: KPWA (PI Greg Simon)

Participating Sites: University of Washington (Site PI Patrick Heagerty)

Funder Contacts
- Science Officer: Susan Azrin
- Program Official: Michael Freed
- Grants Management Official: Jackie Chia

Documents & Reports

Submitted Proposal

Specific Aims

Research Plan

Notice of Award