Tag: Schizophrenia and psychotic disorders

Optimizing Care to Prevent Diabetes and Promote Cardiovascular Health Among Younger Adults with Severe Mental Illness

Grant Details

Funder: NIMH

Grant number: 1K23MH126078

Grant period: 4/1/2022 – 3/31/2027

Brief narrative: People with severe mental illness (SMI) face double the risk for type 2 diabetes compared to the general population, contributing to higher rates of cardiovascular disease and premature death. Common use of antipsychotic medications contributes to these health risks due to prevalent metabolic side effects. Many younger adults with SMI do not receive targeted, evidence-based cardiometabolic disease prevention care. Underused strategies include: prescribing alternative, less obesogenic psychotropic medications; lifestyle change supports; additional risk-reducing medications; and smoking cessation therapies. Our preliminary qualitative data with patients and clinicians identified a need for tools to match prevention care to individuals’ risk level and preferences, and tools suited to population-based care strategies. Clinical Decision Support (CDS) tools are computer algorithms that use patients’ data, predictive analytics, and clinical guidelines to promote evidence-based care by helping patients and clinicians navigate complex treatment decisions. Through this mentored K23 career development award, Esti Iturralde, PhD will build upon her background as a clinical psychologist and behavioral diabetes researcher. Through planned mentoring, coursework, and career development activities, Dr. Iturralde will gain a strong understanding of psychopharmacology and cardiometabolic health, advanced predictive analytics, and implementation science, including methods for stakeholder-engaged intervention design and pragmatic clinical trials. As a researcher in the Kaiser Permanente Northern California (KPNC) Division of Research (DOR), she will leverage robust, longitudinal electronic health record (EHR) data (> 50,000 adults from diverse racial/ethnic groups) and stakeholder insights (patients, clinicians, and health system decision-makers) within health systems including KPNC and 2 others belonging to the NIMH-funded Mental Health Research Network (HealthPartners Institute and Henry Ford Health System). The proposed research will support the training goals while contributing to the development of a novel CDS tool seeking to increase targeted, evidence-based diabetes and cardiovascular disease prevention care for adults under age 45 who are starting antipsychotic medications. Specific research aims are to: (1) inform predictive analytics of the CDS tool by developing and validating diabetes risk prediction models for the target population; (2) engage stakeholders in the design of CDS tool messaging and implementation pathways; and field-test CDS tool messaging through a pragmatic clinical trial conducted within an existing KPNC telehealth-based population management program serving this population. A future R01 application will build on the results from this project to further refine and test the CDS tool within multiple health systems. The linked research and training aims will directly prepare Dr. Iturralde for success as an embedded health system researcher and prepare her to lead a programmatic line of studies developing and implementing data-driven, feasible, scalable interventions improving the cardiometabolic health of people with SMI.

Lead site: KPNC (PI Esti Iturralde)

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:

Precursors of first-episode psychosis in a population-based sample

Project Name:
Precursors of first-episode psychosis in a population-based sample
Principal Investigator:
Gregory Simon, MD MPH
Principal Investigator Contact Information:
simon.g@ghc.org
Principal Investigator institution:
Kaiser Permanente Washington
Funding Period:
07/2013 – 06/2018
Abstract: Schizophrenia is estimated to be the 8th-ranked cause of life years lost to disability and premature death among people aged 15 to 44. Reducing this disease burden is a public health priority. Among people experiencing first onset of psychotic symptoms, delay in receipt of effective treatment contributes significantly to poor long-term outcomes. Furthermore, warning signs or prodromal symptoms may be identifiable prior to onset of actual psychotic symptoms. Accumulating evidence suggests that preventive interventions (prior to onset of actual psychotic symptoms) or early clinical interventions (to reduce the interval between onset of symptoms and receipt of effective care) can both improve long-term prognosis. Existing models for early detection and early intervention – either for research or care delivery – have limited reach and scalability. Our preliminary studies suggest that a generalizable algorithm retrospectively applied to electronic medical records data can accurately identify first episodes of psychosis with a positive predictive value of 80 to 90% and a sensitivity of over 80% – when compared to structured chart review. If electronic records in large health systems could be used to efficiently identify large and representative samples of people experiencing first-episode psychosis, this method could dramatically accelerate research and transform care delivery. We propose a population-based research program to address immediate questions regarding early intervention programs and to develop methods to support the next generation of early intervention research. This research will draw from five large health systems serving a diverse and representative population of over 7.5 million people. Specific aims of this program include: Use electronic records data from large integrated health care systems to validate and refine a generalizable algorithm for identifying first presentations of psychosis. Examine patterns of health care contact prior to first diagnosis of psychosis to identify the optimal care settings and target populations for early detection programs and preventive interventions. Examine patterns of treatment following first diagnosis of psychosis in order to identify the gaps in care leading to prolonged duration of untreated psychosis. Examine sources of health insurance coverage at first diagnosis of psychotic disorder and subsequent lapses in coverage in order to inform the design of future intervention programs. Explore the use of text mining methods to identify potential indicators of prodromal symptoms in notes of outpatient visits prior to first diagnosis of psychosis in order to develop innovative strategies for accurate real-time identification of prodromal symptoms. Understand patient, family, and clinician perspectives regarding population-based research outreach following a new diagnosis of psychosis to inform future research and care delivery. Examine rate and causes of mortality after first diagnosis of psychotic disorder
Grant Number:
5R01MH099666
Funder:
NIMH
Participating Sites:
Group Health Cooperative
Kaiser Permanente Colorado
Kaiser Permanente Northern California
Kaiser Permanente Northwest
Kaiser Permanente Southern California
Investigators:
Gregory Simon, MD MPH
David Carrell, PhD
Frances Lynch, PhD
Bobbi Jo Yarborough, PhD
Carla Green, PhD
Arne Beck, PhD
Enid Hunkeler, MA
Stacy Sterling, PhD
Karen Coleman, PhD
Major Goals:
Goals include Aim 1: Describe initial presentation with psychotic symptoms in a population-based sample. Aim 2: Examine patterns of care prior to diagnosis. Aim 3: Examine treatment adherence/continuity after diagnosis. Aim 4: Examine impact of health insurance coverage. Aim 5: Mine clinical text to identify possible prodromal “signals”. Aim 6: Explore acceptability of outreach interventions. Aim 7: Examine mortality after first diagnosis of psychotic disorder
Description of study sample:
Cases: First diagnosis of schizophrenia spectrum psychosis, mood disorder with psychosis or other psychotic disorder between 1/1/2007 and 12/31/2012. Continuously enrolled in the health plan for >=12 months prior to the initial diagnosis. Age 15 and above. Controls: No prior record of psychosis or mood disorder. Continuously enrolled in the health plan for >=24 months. Age 15 and above. Meet one of these criteria for one of three control groups. First diagnosis of depression (matched to cases by age, sex, and year)First diagnosis of inflammatory bowel disease (matched to cases by age, sex, and year)Any outpatient visit (matched to cases by age, sex, and year).
Current Status:
Analyses for all aims are complete
Study Registration:
N/A
Publications:
Simon GE, Coleman KJ, Yarborough BJ, Operskalski B, Stewart C, Hunkeler EM, Lynch F, Carrell D, Beck A.  First presentation with psychotic symptoms in a population-based sample.  Psychiatr Serv. 2017; 68: 457-461.Simon GE, Stewart C, Yarborough BJ, Lynch F, Coleman KJ, Beck A, Operskalski BH, Penfold RS, Hunkeler EM.  Mortality after first diagnosis of psychotic disorder in adolescents and young adults.  JAMA Psychiatry 2018; 75: 254-260.Simon GE, Stewart C, Hunkeler EM, Yarborough BJ, Lynch F, Coleman KJ, Beck A, Operskalski BH, Penfold RS, Carrell DS.  Care pathways prior to first diagnosis of psychotic disorder in adolescents and young adults.  Am J Psychiatry 2018 (Jan 24 epub ahead of print).
Resources:
None available yet
Lessons Learned:
DetectionIncidence rate suggests reasonable capture of all new casesHalf have some prior mental health contact. Patterns of utilization prior to first diagnosis differ by race/ethnicityInitiation>90% without patient specialty MH follow-up within 1 month>60% with filled prescription for antipsychotic medication within 1 month. Engagement/ContinuationOnly 50% still engaged in outpatient specialty MH care by 1 yea. rOf those who disengage:  outcome is poor in 1/3 and unknown in 1/3

MortalityMortality is markedly increased soon after first diagnosis of psychotic disorderExcess mortality is largely due to injuries and poisonings, especially self-inflicted injuries and poisonings
What’s next? Submit manuscripts for Aims 3 to 7

Reducing Excess Cardiovascular Risk in People with Serious Mental Illness

Project Name:
Reducing Excess Cardiovascular Risk in People with Serious Mental Illness
Principal Investigator:
Rebecca Rossom, MD, MS
Principal Investigator Contact Information:
Rebecca.C.Rossom@HealthPartners.com
Principal Investigator institution:
HealthPartners, Minneapolis, MN
Funder
NIMH
Funding Period:
08/2014 – 06/2019 
Abstract:
People with serious mental illness (SMI) (schizophrenia, schizoaffective disorder, bipolar disorder) die, on average, 20 years earlier than their peers. Cardiovascular (CV) disease is the predominant cause.  Primary care clinicians are often unaware of increased risk in patients with SMI and, even when they do identify elevated CV risk factors, often do not take appropriate clinical actions. Electronic health record-based clinical decision support can identify at-risk patients with SMI and systematically prompt more effective treatment of their CV risk factors, but its potential has been largely untapped. 
Grant Number:
U19MH092201
Participating Sites:
HealthPartners, Minneapolis, MN (Lead Site)
Essentia Health, Duluth, MN
Park Nicollet, Minneapolis, MN
Investigators:
Rebecca Rossom, MD, MS     
Steve Waring, PhD
Patrick O’Connor, MD, MS, MA
JoAnn Sperl-Hillen, MD
Lauren Crain, PhD
Kris Kopski, MD
Stephanie Hooker, PhD
Goals:
The objectives of this project were to improve CV risk factor care in patients with SMI through a pragmatic trial of a point-of-care electronic health record-based clinical decision support system (referred to as “CV Wizard”).  The trial was conducted in over 80 primary care clinics in three large healthcare systems.
Description of study sample:
Patients enrolled in the study were ages 18-75 with diagnoses of schizophrenia, schizoaffective disorder or bipolar disorder (i.e. serious mental illness (SMI)) and were not at goal for at least one of the following reversible cardiovascular risk factors: BMI, tobacco use, LDL, blood pressure, A1c or aspirin use.
Current Status:
The project was implemented in all 3 sites and completed patient enrollment in September 2018.   A total of 11,046 patients with SMI made at least one primary care visit during the study period, and 8937 patients made at least 2 primary care visits.
Study Registration:
ClinicalTrials.gov # NCT02451670
Publications:
Rossom RC, O’Connor PJ, Crain AL, Waring S, Ohnsorg K, Taran A, Kopski K, Sperl-Hillen JM. Pragmatic trial design of an intervention to reduce cardiovascular risk in people with serious mental illness. Contemp Clin Trials. 2020 Feb 20;91:105964. doi: 10.1016/j.cct.2020.105964. PubMed PMID: 32087336. Sperl-Hillen JM, Rossom RC, Kharbanda EO, Gold R, Geissal ED, Elliott TE, Desai JR, Rindal DB, Saman DM, Waring SC, Margolis KL, O’Connor PJ. Priorities Wizard: Multisite Web-Based Primary Care Clinical Decision Support Improved Chronic Care Outcomes with High Use Rates and High Clinician Satisfaction Rates. EGEMS (Wash DC). 2019 Apr 3;7(1):9. doi: 10.5334/egems.284. Review. PubMed PMID: 30972358; PubMed Central PMCID: PMC6450247.
Resources:
N/A
Lessons Learned:
N/A
What’s next?
Analyses and manuscript development are in progress.