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 5 large health systems serving a diverse and representative population of over 7.5 million people.
Specific aims of this program include: 1) Use electronic records data from large integrated health care systems to validate and refine a generalizable algorithm for identifying first presentations of psychosis. 2) 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. 3) Examine patterns of treatment following first diagnosis of psychosis in order to identify the gaps in care leading to prolonged duration of untreated psychosis. 4) 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. 5) 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. 6) Understand patient, family, and clinician perspectives regarding population-based research outreach following a new diagnosis of psychosis - to inform future research and care delivery.

Public Health Relevance

Early intervention for psychotic disorders could substantially improve long-term prognosis and reduce disability. Current models for early intervention research and clinical care delivery have limited reach and scalability. This application proposes research to address immediate questions about early intervention for psychosis and to establish a nationwide population-based laboratory to accelerate future research.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Chambers, David A
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Group Health Cooperative
United States
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Simon, Gregory E; Stewart, Christine; Yarborough, Bobbi Jo et al. (2018) Mortality Rates After the First Diagnosis of Psychotic Disorder in Adolescents and Young Adults. JAMA Psychiatry 75:254-260
Simon, Gregory E; Stewart, Christine; Hunkeler, Enid M et al. (2018) Care Pathways Before First Diagnosis of a Psychotic Disorder in Adolescents and Young Adults. Am J Psychiatry 175:434-442
Simon, Gregory E; Coleman, Karen J; Yarborough, Bobbi Jo H et al. (2017) First Presentation With Psychotic Symptoms in a Population-Based Sample. Psychiatr Serv 68:456-461