Category: Projects

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.

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:

  1. Does NLP allow us to obtain estimates of the number of adults who experience PTSD that are more comparable to national epidemiologic data?
  2. Are there differences by group (e.g., age, sex, race/ethnicity, trauma type) of those captured through NLP versus ICD diagnosis code?
  3. 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

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: