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The diagnostic concept of unipolar mania (UM), i.e. the lifetime occurrence of mania without major depressive episodes, remains a topic of debate despite the evidence accumulated in the last few years. We carried out a systematic review and meta-analysis of observational studies testing factors associated with UM as compared to bipolar disorder with a manic-depressive course (md-BD).
Studies indexed up to July 2022 in main electronic databases were searched. Random-effects meta-analyses of the association between UM and relevant correlates yielded odds ratio (OR) or standardized mean difference (SMD), with 95% confidence intervals (CIs).
Based on data from 21 studies, factors positively or negatively associated with UM, as compared to md-BD, were: male gender (OR 1.47; 95% CI 1.11–1.94); age at onset (SMD −0.25; 95% CI −0.46 to −0.04); number of hospitalizations (SMD 0.53; 95% CI 0.21–0.84); family history of depression (OR 0.55; 95% CI 0.36–0.85); suicide attempts (OR 0.25; 95% CI 0.19–0.34); comorbid anxiety disorders (OR 0.35; 95% CI 0.26–0.49); psychotic features (OR 2.16; 95% CI 1.55–3.00); hyperthymic temperament (OR 1.99; 95% CI 1.17–3.40). The quality of evidence for the association with previous suicide attempts was high, moderate for anxiety disorders and psychotic features, and low or very low for other correlates.
Despite the heterogeneous quality of evidence, this work supports the hypothesis that UM might represent a distinctive diagnostic construct, with peculiar clinical correlates. Additional research is needed to better differentiate UM in the context of affective disorders, favouring personalized care approaches.
Aims – To obtain a new, well-balanced mental health funding system, through the creation of i) a list of psychiatric interventions provided by Italian Community-based Psychiatric Services (CPS), and associated costs; ii) a new prospective funding system for patients with a high use of resources, based on packages of care. Methods – Five Italian Community-based Psychiatric Services collected data from 1250 patients during October 2002. Socio-demographical and clinical characteristics and GAF scores were collected at baseline. All psychiatric contacts during the following six months were registered and categorised into 24 service contact types. Using elasticity equation and contact characteristics, we estimate the costs of care. Cluster analysis techniques identified packages of care. Logistic regression defined predictive variables of high use patients. Multinomial Logistic Model assigned each patient to a package of care. Results – The sample's socio-demographic characteristics are similar, but variations exist between the different CPS. Patients were then divided into two groups, and the group with the highest use of resources was divided into three smaller groups, based on number and type of services provided. Conclusions – Our findings show how is possible to develop a cost predictive model to assign patients with a high use of resources to a group that can provide the right level of care. For these patients it might be possible to apply a prospective per-capita funding system based on packages of care.
Declaration of Interest: None
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