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Record W3135017615 · doi:10.1111/bdi.13069

COVID‐19 and older adults with bipolar disorder: Problems and solutions

2021· article· en· W3135017615 on OpenAlexaff
Osvaldo P. Almeida, Esther Jiménez, Soham Rej, Lisa T. Eyler, Martha Sajatovic, Annemiek Dols

Bibliographic record

VenueBipolar Disorders · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsMcGill University
Fundersnot available
KeywordsPandemicAnxietyMental healthMedicinePsychiatryAffect (linguistics)Depression (economics)DistressPublic healthDiseaseCoronavirus disease 2019 (COVID-19)GerontologyPsychologyClinical psychologyInfectious disease (medical specialty)

Abstract

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The implementation of public health measures designed to limit the spread of coronavirus disease 2019 (COVID-19) has been applied particularly stringently to people at high risk of complications, such as individuals older than 70 years and people with concurrent health morbidities like chronic respiratory or cardiovascular diseases. Older adults suffering from mental disorders are an especially high-risk group not only because of their age but also because the prevalence of chronic somatic conditions is disproportionately high in this group. Data on hospital admissions for people with mental disorders during the COVID-19 pandemic are yet to be published, although preliminary evidence suggests that adults with BD have been reporting increasing psychological distress and symptoms of anxiety and depression. Further emerging evidence suggests that the pandemic has been associated with a marked drop in hospital admissions for all causes other than COVID-19 related,1 raising concerns about how this might affect the management of people with chronic health conditions and their access to services, including people with BD. At this point in time, it is unclear how the pandemic will affect older adults with BD (OABD), although withholding action until more evidence becomes available is unlikely to be helpful. A high proportion of OABD have concurrent chronic somatic morbidities and cognitive deficits2 and therefore deserve particular attention as the medical and psychiatric community responds to the pandemic. We anticipate that the impact of the COVID-19 pandemic on this group will unfold along four overlapping stages (Table 1). Here, we will describe the expected complications of the pandemic and suggest mitigating measures for each stage. The first, direct impact, may result in a high number of OABD developing severe somatic complications as a result of the viral infection, leading to a large number of deaths among a group of people who already have decreased life expectancy. For example, about 35% of people infected with COVID-19 develop neurological symptoms during the course of their illness, including confusion, dizziness, headache, anosmia, encephalitis and stroke.3 Potentially lingering executive dysfunction after recovery has also been reported, but it is unclear whether these deficits are likely to persist in the long term. These findings raise concerns that infection with COVID-19 could worsen the cognitive deficits that are already present among people with OABD. Future research should clarify this issue and determine whether the direct and indirect effects of the COVID-19 pandemic have affected the course of OABD. Next, the worldwide introduction of measures designed to increase the capacity of health services to manage the acute medical complications of COVID-19 together with the various public health measures introduced to contain the spread of the virus in the community will most likely decrease access to specialized mental health services (resource restriction – stage 2).4 Access to acute treatment (such as ECT) may be hampered, and the regular monitoring of symptoms and of the possible adverse effects of medications, which are often magnified in late life, may be compromised. This, in turn, could increase the risk of medicine-related toxicity, sub-therapeutic use or discontinuation of treatment. The prolonged disruption of care (stage 3) may increase the risk of relapse or recurrence of affective symptoms, leading to functional decline and, potentially, health complications, increased general hospital admissions and increased cost of care. This, together with the widespread public health measures for social distancing and isolation, may strain social networks of support which, often, are already fragile. These factors could result in the collapse of social networks, loss of income and financial strain, increased use of substances (such as alcohol) and feelings of helplessness and hopelessness. An increase in the incidence of suicide attempts would not be unexpected (stage 4). However, this potentially bleak progression of events for OABD is not inevitable. The timely introduction of risk-mitigating strategies could circumvent most of the complications that OABD might experience in association with the COVID-19 pandemic (Table 1). Education and access to relevant resources is critical to maintaining engagement with management plans and social support. For example, active mental health surveillance and treatment are accessible via telehealth or web-based interventions and should be considered, even though supportive evidence of the efficacy of these approaches is currently limited. In addition, activity scheduling and the embracing of innovative approaches to encourage social interactions (e.g. web-based book club, choir or exercise group) would allow for enhancement of social engagement in the face of physical distancing and maintenance of a healthy lifestyle and sense of control. When access to such programs is not feasible, health services must consider alternative approaches to remain in active contact with OABD and safeguard the continuity of their care – some of these community-based approaches have already proven clinically useful.5 Osvaldo P. Almeida and Esther Jimenez, drafted the first version after the idea and outline for the manuscript was discussed by OA, EJ, SR, LE, MS and AD. All authors approved of the final version. This commentary does not contain original data.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.314
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
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