Measuring The Lifetime Costs Of Serious Mental Illness And The Mitigating Effects Of Educational Attainment

Seabury, Seth A.; Axeen, Sarah; Pauley, Gwyn; Tysinger, Bryan; Schlosser, Danielle; Hernandez, John; Heun-Johnson, Hanke; Zhao, Henu; Goldman, Dana P.

doi:10.1377/hlthaff.2018.05246

Cited by 45 publications

(42 citation statements)

References 23 publications

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“…[16][17][18][19] From an economic perspective, each of these mental disorders also carries substantial lifetime costs, borne by both individuals and health systems. 20 Although major depressive disorder is not always classified as a severe mental illness, 15 we include it in this review to capture severe depression that leads to psychiatric hospital admission.…”

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confidence: 99%

Appraisal of patient-level health economic models of severe mental illness: systematic review

Altunkaya

Tsiachristas

et al. 2021

Br J Psychiatry

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Background Healthcare decision makers require accurate long-term economic models to evaluate the cost-effectiveness of new mental health interventions. Aims To assess the suitability of current patient-level economic models to estimate long-term economic outcomes in severe mental illness. Method We undertook pre-specified systematic searches in MEDLINE, Embase and PsycINFO to identify reviews and stand-alone publications of economic models of interventions for schizophrenia, bipolar disorder and major depressive disorder (PROSPERO: CRD42020158243). We screened paper titles and abstracts to identify unique patient-level economic models. We conducted a structured extraction of identified models, recording the presence of key predefined model features. Model quality and validation were appraised using the 2014 ISPOR and 2016 AdViSHE model checklists. Results We identified 15 unique patient-level models for psychosis and major depressive disorder from 1481 non-duplicate records. Models addressed schizophrenia (n = 6), bipolar disorder (n = 2) and major depressive disorder (n = 7). The predominant model type was discrete event simulation (n = 9). Model complexity and incorporation of patient heterogeneity varied considerably, and only five models extrapolated costs and outcomes over a lifetime horizon. Key model parameters were often based on low-quality evidence, and checklist quality assessment revealed weak model verification procedures. Conclusions Existing patient-level economic models of interventions for severe mental illness have considerable limitations. New modelling efforts must be supplemented by the generation of good-quality, contemporary evidence suitable for model building. Combined effort across the research community is required to build and validate economic extrapolation models suitable for accurately assessing the long-term value of new interventions from short-term clinical trial data.

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confidence: 99%

Appraisal of patient-level health economic models of severe mental illness: systematic review

Altunkaya

Tsiachristas

et al. 2021

Br J Psychiatry

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“…Mental illness has been shown to affect life span, educational attainment, and employment. 25 The leading cause of disability in the United States, is in fact, 27 Further contributing to the cost and disability burden is the frequent overlap between mental and physical health conditions, particularly chronic diseases. The relationship between physical and mental health is bidirectional and is affected by both direct and indirect effects.…”

Section: The Cost Of Poor Mental Healthmentioning

confidence: 99%

“…Mental illness has been shown to affect life span, educational attainment, and employment. 25 The leading cause of disability in the United States, is in fact, psychiatric conditions, which account for almost 20% of years lost attributable to disability and early mortality. 26 On a global scale, mental illness accounts for 32.4% of years lived with disability and 13.0% of disability-adjusted years.…”

Section: The Cost Of Poor Mental Healthmentioning

confidence: 99%

Mental Health in Lifestyle Medicine: A Call to Action

Merlo

Vela

2021

American Journal of Lifestyle Medicine

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Mental health symptoms are pervasive, with 1 in 5 American adults experiencing a mental disorder. Poor mental health is associated with a significant global cost burden, from disability to economic impacts. The field of lifestyle medicine, which emphasizes the role of lifestyle factors in the onset and treatment of disease and well-being, is well suited to address mental health. More recently, there has been attention to the need to incorporate mental health into the field of lifestyle medicine and to attend to the bidirectional role of mental health and lifestyle. Thus, there is a critical opportunity for the field of lifestyle medicine to incorporate mental health into each of the foundational pillars (diet, exercise, substance use, psychological well-being/stress, relationships, sleep) while also specifically targeting lifestyle interventions for populations with mental disorders. The current article provides a framework for the role of mental health within lifestyle medicine by addressing the scope of the problem, clarification regarding mental health, and areas of practice (ie, psychiatry), and providing an overview of the relevant mental health literature for each pillar. This article serves as a call to action to explicitly address and include mental health within all aspects of lifestyle medicine research and practice.

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“…Around 13 million US adults live with a serious mental illness (SMI) and 65.5% received mental health treatment during 2019 2. People with SMI have a markedly reduced life expectancy3 and the lifetime costs associated with SMI are estimated to be $1.85 million USD per affected individual 4. Substance use disorders are associated with considerable disease burden in the USA (1460.3 Disability Adjusted Life Years per 100 000 people) 5.…”

Section: Introductionmentioning

confidence: 99%

NeuroBlu, an electronic health record (EHR) trusted research environment (TRE) to support mental healthcare analytics with real-world data

Patel

Wee²,

Ramaswamy³

et al. 2022

BMJ Open

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PurposeNeuroBlu is a real-world data (RWD) repository that contains deidentified electronic health record (EHR) data from US mental healthcare providers operating the MindLinc EHR system. NeuroBlu enables users to perform statistical analysis through a secure web-based interface. Structured data are available for sociodemographic characteristics, mental health service contacts, hospital admissions, International Classification of Diseases ICD-9/ICD-10 diagnosis, prescribed medications, family history of mental disorders, Clinical Global Impression—Severity and Improvement (CGI-S/CGI-I) and Global Assessment of Functioning (GAF). To further enhance the data set, natural language processing (NLP) tools have been applied to obtain mental state examination (MSE) and social/environmental data. This paper describes the development and implementation of NeuroBlu, the procedures to safeguard data integrity and security and how the data set supports the generation of real-world evidence (RWE) in mental health.ParticipantsAs of 31 July 2021, 562 940 individuals (48.9% men) were present in the data set with a mean age of 33.4 years (SD: 18.4 years). The most frequently recorded diagnoses were substance use disorders (1 52 790 patients), major depressive disorder (1 29 120 patients) and anxiety disorders (1 03 923 patients). The median duration of follow-up was 7 months (IQR: 1.3 to 24.4 months).Findings to dateThe data set has supported epidemiological studies demonstrating increased risk of psychiatric hospitalisation and reduced antidepressant treatment effectiveness among people with comorbid substance use disorders. It has also been used to develop data visualisation tools to support clinical decision-making, evaluate comparative effectiveness of medications, derive models to predict treatment response and develop NLP applications to obtain clinical information from unstructured EHR data.Future plansThe NeuroBlu data set will be further analysed to better understand factors related to poor clinical outcome, treatment responsiveness and the development of predictive analytic tools that may be incorporated into the source EHR system to support real-time clinical decision-making in the delivery of mental healthcare services.

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Measuring The Lifetime Costs Of Serious Mental Illness And The Mitigating Effects Of Educational Attainment

Cited by 45 publications

References 23 publications

Appraisal of patient-level health economic models of severe mental illness: systematic review

Appraisal of patient-level health economic models of severe mental illness: systematic review

Mental Health in Lifestyle Medicine: A Call to Action

NeuroBlu, an electronic health record (EHR) trusted research environment (TRE) to support mental healthcare analytics with real-world data

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