Effects of job stress on symptoms of bipolar spectrum disorder in an electronic parts manufacturing company

Job retention for people with bipolar disorder: A qualitative analysis

At least half of people with bipolar disorder, whose global prevalence is greater than 1% of the general population, do not have a stable occupation. The objective of this study is to identify the factors perceived by bipolar people as having an impact on retention in employment. Semi-directed individual interviews were offered to subjects with bipolar disorder, of working age, who had been seen by three different hospital departments in the south of France. These people had to be medically stable and have at least one occupational experience. Each interview was recorded and transcribed manually and then analyzed according to a thematic classification of verbatims. Nineteen people participated in the interviews. Three major themes identified were classified into intrinsic (working routine and working environment) and extrinsic (external aids) factors for the bipolar person. The majority believe that working regular hours is a balancing factor. For everyone, night work and stress factors can lead to relapses. All stressed the need to destigmatize the disease. Most believe that it is necessary to develop employment support organizations specific to mental illnesses. This research helps to inform people with bipolar disorder about the occupational factors that help balance their condition. Personalized multidisciplinary care pathways involving occupational medicine must be developed in order to promote the balance of the disease and job retention.

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

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 "multi-omic" measures, electronic health records), analyzed using advanced 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 point-of-care 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 developmentally-oriented 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, providing a synthesis of extant literature and recommendations for decision support tools and clinical metrics that can be implemented at point-of-care.