Socio-economic functioning in patients with bipolar disorder and their unaffected siblings – results from a nation-wide population-based longitudinal study

Sletved, Kimie Stefanie Ormstrup; Ziersen, Simon Christoffer; Andersen, Per Kragh; Vinberg, Maj; Kessing, Lars Vedel

doi:10.1017/s0033291721002026

Cited by 28 publications

(23 citation statements)

References 33 publications

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“…Another variable associated with SUD risk is the lack of a stable relationship, which is in line with previous evidence [ 59 ]. Similarly, socio-economic functioning is substantially decreased in patients with BD, with lower odds of being in a stable relationship compared with the general population (Sletved et al, 2021 [ 60 ]), while social or family support improves patients’ global functioning [ 61 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder

Oliva

Prisco

Pons-Cabrera

et al. 2022

JCM

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Substance use disorder (SUD) is a common comorbidity in individuals with bipolar disorder (BD), and it is associated with a severe course of illness, making early identification of the risk factors for SUD in BD warranted. We aimed to identify, through machine-learning models, the factors associated with different types of SUD in BD. We recruited 508 individuals with BD from a specialized unit. Lifetime SUDs were defined according to the DSM criteria. Random forest (RF) models were trained to identify the presence of (i) any (SUD) in the total sample, (ii) alcohol use disorder (AUD) in the total sample, (iii) AUD co-occurrence with at least another SUD in the total sample (AUD+SUD), and (iv) any other SUD among BD patients with AUD. Relevant variables selected by the RFs were considered as independent variables in multiple logistic regressions to predict SUDs, adjusting for relevant covariates. AUD+SUD could be predicted in BD at an individual level with a sensitivity of 75% and a specificity of 75%. The presence of AUD+SUD was positively associated with having hypomania as the first affective episode (OR = 4.34 95% CI = 1.42–13.31), and the presence of hetero-aggressive behavior (OR = 3.15 95% CI = 1.48–6.74). Machine-learning models might be useful instruments to predict the risk of SUD in BD, but their efficacy is limited when considering socio-demographic or clinical factors alone.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder

Oliva

Prisco

Pons-Cabrera

et al. 2022

JCM

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“…Compared to individuals from the general population, persons with a diagnosis of bipolar disorder had lower odds of having achieved the highest educational level (45% vs. 54%, odds ratio, OR=0.75); were less often employed (58% vs. 88%, OR=0.16); less often achieved the highest category of personal income (55% vs. 71%, OR=0.33); less often resided with others (36% vs. 54%, OR=0.44); and less often were married (37% vs. 49%, OR=0.54). Bipolar patients demonstrated a substantially decreased ability to enhance their socio‐economic status during the 23‐year follow‐up period when compared to controls 191 .…”

Section: Social Functioningmentioning

confidence: 92%

“…Social functioning is often impaired in bipolar patients during and between episodes. In a recent Danish nation-wide population-based longitudinal register study, social functioning and interpersonal relationships were systematically investigated in 19,955 bipolar patients, their siblings, and gender, age and calendar matched control individuals from the general population 191 . Compared to individuals from the general population, persons with a diagnosis of bipolar disorder had lower odds of having achieved the highest educational level (45% vs. 54%, odds ratio, OR=0.75); were less often employed (58% vs. 88%, OR=0.16); less often achieved the highest category of personal income (55% vs. 71%, OR=0.33); less often resided with others (36% vs. 54%, OR=0.44); and less often were married (37% vs. 49%, OR=0.54).…”

Section: Social Functioningmentioning

confidence: 99%

The clinical characterization of the adult patient with bipolar disorder aimed at personalization of management

et al. 2022

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Bipolar disorder is heterogeneous in phenomenology, illness trajectory, and response to treatment. Despite evidence for the efficacy of multimodal ity interventions, the majority of persons affected by this disorder do not achieve and sustain full syndromal recovery. It is eagerly anticipated that combining datasets across various information sources (e.g., hierarchical "multiomic" measures, electronic health records), analyzed using ad vanced computational methods (e.g., machine learning), will inform future diagnosis and treatment selection. In the interim, identifying clinically meaningful subgroups of persons with the disorder having differential response to specific treatments at pointofcare is an empirical priority. This paper endeavours to synthesize salient domains in the clinical characterization of the adult patient with bipolar disorder, with the overarching aim to improve health outcomes by informing patient management and treatment considerations. Extant data indicate that characterizing select domains in bipolar disorder provides actionable information and guides shared decision making. For example, it is robustly established that the presence of mixed features -especially during depressive episodes -and of physical and psychiatric comorbidities informs illness trajectory, response to treatment, and suicide risk. In addition, early environmental exposures (e.g., sexual and physical abuse, emotional neglect) are highly associated with more complicated illness presentations, inviting the need for developmentallyoriented and integrated treatment approaches. There have been significant advances in validating subtypes of bipolar disorder (e.g., bipolar I vs. II disorder), particularly in regard to pharmacological interventions. As with other severe mental disorders, social functioning, interpersonal/family relationships and internalized stigma are domains highly relevant to relapse risk, health outcomes, and quality of life. The elevated standardized mortality ratio for completed suicide and suicidal behaviour in bipolar disorder invites the need for characterization of this domain in all patients. The framework of this paper is to describe all the above salient domains, provid ing a synthesis of extant literature and recommendations for decision support tools and clinical metrics that can be implemented at pointofcare.

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“… 6 A Danish study found that patients diagnosed with bipolar disorder were less likely to be employed with an odds ratio (OR) of 0.16. 7 Three years before being diagnosed with bipolar disorder, 45% of the people with bipolar disorder were employed. However, this decreased to 34% five years after diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Relationship Between Employment Status and Unstable Periods in Outpatients with Bipolar Disorder: A Multicenter Treatment Survey for Bipolar Disorder in Psychiatric Outpatient Clinics (MUSUBI) Study

Ikenouchi¹,

Konno²,

Fujino³

et al. 2022

NDT

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Purpose To clarify the relationship between the length of unstable periods and employment status of patients with bipolar disorder. Patients and Methods Medical records of outpatients with bipolar disorder who visited 176 member clinics of the Japanese Association of Neuro-Psychiatric Clinics were investigated during September–October 2016, and details of their medical care and employment were surveyed using a questionnaire. The odds ratios (ORs) of length of unstable period and unemployment were analyzed with a logistic regression model. Results The study included 816 patients, of whom 707 were employed full-time (continuous employment) and 70 were unemployed (loss of employment). Univariate analysis showed that ORs were statistically significant for patients who were unstable for “almost all” of the year (OR = 10.4 [4.48–24.28] p < 0.001), but not for “few” unstable periods (OR = 1.06 [0.56–1.98] p = 0.849) and for “significant” unstable periods (OR = 1.65 [0.73–3.74] p = 0.231) were not significantly different. Multivariate analysis showed that ORs were statistically “significant” for patients who were unstable for “almost all” (OR = 12.1 [4.37–33.3] p < 0.001), but not for “few” unstable periods (OR = 1.07 [0.55–2.07] p = 0.846) and for “significant” unstable periods (OR = 1.62 [0.66–3.98] p = 0.290) did not differ significantly. Conclusion Patients with bipolar disorder with a long unstable period were associated with a higher risk of unemployment.

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Socio-economic functioning in patients with bipolar disorder and their unaffected siblings – results from a nation-wide population-based longitudinal study

Cited by 28 publications

References 33 publications

Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder

Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder

The clinical characterization of the adult patient with bipolar disorder aimed at personalization of management

Relationship Between Employment Status and Unstable Periods in Outpatients with Bipolar Disorder: A Multicenter Treatment Survey for Bipolar Disorder in Psychiatric Outpatient Clinics (MUSUBI) Study

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