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

2021 ◽  
pp. 1-12
Author(s):  
James Altunkaya ◽  
Jung-Seok Lee ◽  
Apostolos Tsiachristas ◽  
Felicity Waite ◽  
Daniel Freeman ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Mental Illness ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Severe Mental Illness ◽  
Economic Models ◽  
Model Verification ◽  
Major Depressive ◽  
Patient Level

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.

Mortality and rehospitalization rates among patients with severe mental illness (SMI) at different levels of hospital spending

2020 ◽  
Author(s):  
Shih-Chi Lin ◽  
Kuan-Yi Tsai ◽  
Hung-Yu Wang ◽  
Shih-Pei Shen ◽  
Frank Chou
Keyword(s):  
Bipolar Disorder ◽  
Mental Illness ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Severe Mental Illness ◽  
Treatment Outcomes ◽  
Psychiatric Hospitalization ◽  
Major Depressive ◽  
National Health Research Institute ◽  
Patient Level

Abstract Background Evidence has shown that the relationships between hospital spending and treatment outcomes for physical conditions have been inconclusive. So to investigate the association between hospital spending and both risk-adjusted mortality and rehospitalization rates among patients with severe mental illness (SMI). Method This was a retrospective cohort study that used the Taiwan National Health Research Institute Database (NHRID) from 1999 to 2010. Hospital end-of-life (EOL) spending was used to quantify hospital spending and was determined by the total medical costs of the last year of life of patients with at least one previous psychiatric hospitalization. Patients with schizophrenia (n=13,229), bipolar disorder (n=4,476) and major depressive disorder (n=5,177) were followed for mortality and rehospitalization to psychiatric wards from 2009 to 2010 after they had been discharged from the study hospitals. Results Patients with schizophrenia had lower rehospitalization and mortality rates when treated at higher-spending hospitals than when treated at the lowest-spending hospitals. However, these associations became weak, even nonsignificant, when adjusted for patient-level variables. There were no significant findings for patients with bipolar disorder and major depressive disorder when patient-level variables were adjusted for. Patient-level variables showed more determinant roles than hospital-level variables in the relationships between hospital spending and treatment outcomes. Conclusion Hospitals that spend more at the EOL had lower mortality and rehospitalization rates for patients with schizophrenia but higher rates for bipolar disorder and major depressive disorder. Most of these associations could be explained by patients’ characteristics more than hospitals’ characteristics.

scholarly journals The overlap of genetic susceptibility to schizophrenia and cardiometabolic disease can be used to identify metabolically different groups of individuals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rona J. Strawbridge ◽  
Keira J. A. Johnston ◽  
Mark E. S. Bailey ◽  
Damiano Baldassarre ◽  
Breda Cullen ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
European Ancestry ◽  
Cardiometabolic Disease ◽  
Metabolic Profiles ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Major Depressive ◽  
Increased Risk

AbstractUnderstanding why individuals with severe mental illness (Schizophrenia, Bipolar Disorder and Major Depressive Disorder) have increased risk of cardiometabolic disease (including obesity, type 2 diabetes and cardiovascular disease), and identifying those at highest risk of cardiometabolic disease are important priority areas for researchers. For individuals with European ancestry we explored whether genetic variation could identify sub-groups with different metabolic profiles. Loci associated with schizophrenia, bipolar disorder and major depressive disorder from previous genome-wide association studies and loci that were also implicated in cardiometabolic processes and diseases were selected. In the IMPROVE study (a high cardiovascular risk sample) and UK Biobank (general population sample) multidimensional scaling was applied to genetic variants implicated in both psychiatric and cardiometabolic disorders. Visual inspection of the resulting plots used to identify distinct clusters. Differences between these clusters were assessed using chi-squared and Kruskall-Wallis tests. In IMPROVE, genetic loci associated with both schizophrenia and cardiometabolic disease (but not bipolar disorder or major depressive disorder) identified three groups of individuals with distinct metabolic profiles. This grouping was replicated within UK Biobank, with somewhat less distinction between metabolic profiles. This work focused on individuals of European ancestry and is unlikely to apply to more genetically diverse populations. Overall, this study provides proof of concept that common biology underlying mental and physical illness may help to stratify subsets of individuals with different cardiometabolic profiles.

Predictors of conversion of major depressive disorder to bipolar disorder

2021 ◽  
pp. 113939
Author(s):  
Satish Suhas ◽  
Abha Thakurdesai ◽  
Amal Jolly Joseph ◽  
Chittaranjan Andrade
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Major Depressive

scholarly journals A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jakub Tomasik ◽  
Sung Yeon Sarah Han ◽  
Giles Barton-Owen ◽  
Dan-Mircea Mirea ◽  
Nayra A. Martin-Key ◽  
...  
Keyword(s):  
Mental Health ◽  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Diagnostic Algorithm ◽  
Health Questionnaire ◽  
Major Depressive ◽  
Blood Biomarker ◽  
Mental Health Questionnaire ◽  
Biomarker Data

AbstractThe vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18–45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86–0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86–0.91) and 0.90 (0.87–0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57–0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.

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