MHRN III feasibility pilots
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1
PHQ9 differential item functioning
2
Using NLP to increase identification of child maltreatment in EHR
3
COVID-19 vaccine uptake and psychiatric disorders feasibility pilot
4
Weight loss and perinatal depression
5
Implementing predictive models for identifying suicide risk in adolescents
6
Trauma and PTSD in Medical Records
7
INSPIRED: INtegrating Social determinants and Policy In REducing Disparities in youth suicide
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NIMH
U19MH121738
7/1/2020
6/30/2021
$7,663

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, includ

EIRH
Melissa Harry
Melissa.Harry@EssentiaHealth.org
KPWA
Stephen Waring
Gregory Simon
Yates Coley
Completed


Assessing the differential item functioning of PHQ-9 items for diverse racial and ethnic adults with mental health and/or substance use disorder diagnoses: A retrospective cohort study.

Harry ML, Sanchez K, Ahmedani BK, Beck AL, Coleman KJ, Coley RY, Daida YG, Lynch FL, Rossom RC, Waring SC, Simon GE.

J Affect Disord. 2023 Oct 1;338:402-413. doi: 10.1016/j.jad.2023.04.091. Epub 2023 Apr 29.

PMID: 37127116

NIMH
U19MH121738
7/1/2020
6/30/2021
$11,092

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 tha

KPWA
Rob Penfold
robert.b.penfold@kp.org
KPSC
KPNW
Sonya Negriff
Frances Lynch
Completed
  • NLP pipeline created
  • Manual adjudication of NLP "hits" complete
  • Descriptive statistics complete

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.


Using natural language processing to identify child maltreatment in health systems. Negriff S, Lynch FL, Cronkite DJ, Pardee RE, Penfold RB. Child Abuse Negl. 2023 Apr;138:106090. doi: 10.1016/j.chiabu.2023.106090. Epub 2023 Feb 8. PMID: 36758373

NIMH
U19MH121738
7/1/2021
6/30/2022
$25,421

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?

KPGA
Ashli Owen-Smith
aowensmith@gsu.edu
KPSC
Karen J. Coleman
Chris Stewart
Completed

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

NIMH
U19MH121738
7/1/2021
6/30/2022
$14,124

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.

 

SLU
Megan Ferber
Kara Christopher
megan.ferber@health.slu.edu, kara.christopher@health.slu.edu
Completed

Depression & Weight Loss Abstract

Introduction: The objective of the study was to determine if women with pre-pregnancy weight loss (≥10%) vs. those who do not, in the two years prior to pregnancy, have a lower risk for new onset prenatal and postpartum mental health conditions

Methods: This retrospective cohort study used data from the Virtual Data Warehouse of a large Midwestern hospital system. Univariate analysis between weight loss and outcome variables (anxiety, pre-natal depression, and post-partum depression) and multivariate analysis using logistic regression was conducted for variable significant on univariate analysis. All analyses were conducted using SAS 9.4.

Results: On univariate analysis, women with pre-pregnancy weight loss had increased odds of post-partum depression (OR=1.47, 95% CI 1.03-2.10), though decreased odds of anxiety (OR=0.59, 95% CI 0.33-0.90). On multivariate analysis, there was not a significant difference in the odds of post-partum depression; women who lost weight had approximately half the odds of having prenatal anxiety than those who did not lose weight (OR=0.54, 95% CI = 0.33-0.90).

A manuscript has been submitted; acceptance is pending.

NIMH
U19MH121738
7/1/2022
6/30/2023
$24,600

Background: Adolescent suicide is an urgent public health crisis. Suicide is currently the second leading cause of death among adolescents ages 10-24. [1 ]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. [2-4] People who die by suicide often see healthcare providers, and specifically primary care providers prior to death, including adolescents. [5-8] 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. [9,10] 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. [11,12]

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 facil

KPWA
Julie Richards
Julie.E.Richards@kp.org
In progress

Qualitative interviews with adolescent care providers (N=9) are complete and preliminary analyses completed. Qualitative interviews with adolescents (N=10) and parents/guardians (N=10) are underway.

NIMH
U19MH121738
7/1/2022
6/30/2023
$23,046

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

KPHI
Vanessa Simiola
Vanessa.L.Simiola@kp.org
In progress

ICD codes for potentially traumatic events were categorized into 22 domains. A total of 4,005 potentially traumatic events were identified in the EHR using ICD codes between 2018-2023. Using ICD codes-only 6,827 new PTSD diagnoses of PTSD were identified. A sample of 507 random behavioral health progress notes from these patients were manually chart reviewed to identify trauma exposure relevant terms and phrases. Using data gathered from the chart review, a “bag of words” was created to identify trauma exposure in free text using Natural Language Processing (NLP), which may not be documented in structured diagnosis fields. The most significant words and phrases were grouped into the most salient 8 unique trauma exposure categories (i.e., military/combat, natural disaster, assault, verbal or emotional abuse, sexual abuse/assault, physical abuse, intimate partner violence, motor vehicle related crash). These words and phrases are currently being run through the NLP system using Python to identify unique cases. Next, quality checks will be conducted on a sample of 100 notes to identify true and false positives, to further train the NLP system. 

NIMH
U19MH121738
7/1/2023
6/30/2024
$25,000

Background: US children experienced up to 200% increases in stress, depression, 130% in suicidal ideation and suicide attempts (SI/SA); and the highest pediatric suicide rates, compared to the pre-COVID period. Black and Hispanic youth, females, and low-income families are especially at risk. Our research, along with others’ found that food insecurity, as a vital social determinant of health (SDoH), can worsen child mental health. In response, the federal, state, and local governments have expanded and added flexibility to ongoing public policies (e.g., food assistance, unemployment) and initiated new pandemic-related policies. Addressing SDoH, especially by leveraging policies, is key to reducing mental health disparities. However, most research focused on individual SDoH (e.g., negative life events) and cross-sectional design, ignoring structural SDoH at the population level and in the long term. We propose INSPIRED: INtegrating Social determinants and Policy In REducing Disparities in youth suicide, to fill the gap and bring a richly linked external database on multilevel SDoH and policies (by local, county, and state) to electronic health records (EHR) in MHRN.

Research Questions:

1.    What are the relationships between population-level SDoH and youth suicide (SI/SA)?

2.    How do pandemic-era policies (containment, health, economy, food, housing) affect youth suicide?

Cornell
Yunyu Xiao
yux4008@med.cornell.edu
7 records

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