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Record W7162006195 · doi:10.82308/29978

Characterization of depression subtypes and depression chronicity in middle aged and older adults: An Analysis of the Canadian Longitudinal Study on Aging (CLSA)

2023· dissertation· en· W7162006195 on OpenAlexaboutno aff
G. Spiegler

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
Fundersnot available
KeywordsDepression (economics)Longitudinal studyStressorLogistic regressionAllostatic loadEpidemiologyDepressive symptomsCenter for Epidemiologic Studies Depression Scale

Abstract

fetched live from OpenAlex

Depression has heterogeneous symptom presentations and long-term courses, but little effort has been made to categorize depressive symptoms more finely among middle-aged and older adults, despite the increased tendency for chronic course of depression in older adults compared to younger adults. Adverse childhood experiences (ACE) and allostatic load (AL) are known to be associated with depression, but there is no comprehensive research linking these stressors to depression subtypes and its chronicity. The objectives of this research are: 1) to identify symptom-based depression subtypes at baseline among participants in the Canadian Longitudinal Study on Aging; 2) to assess their relationships with profiles of stress-related biological markers and early life adversities; and 3) to assess depression chronicity, its relationships with baseline depression subtypes, and its prognostic risk factors at three-year follow up. Participants with a baseline score of 10 or more on the Center for Epidemiological Studies Depression-10 item scale (CESD-10) were included in the analyses, and chronic depression was defined as a CESD-10 score of 10 more at both time points. Latent profile analyses were applied to baseline data on depressive symptoms, AL biomarkers and ACE, within the cross-sectional (n=3966) and longitudinal (n=3473) samples. In the cross-sectional study, multinominal logistic regression was used to determine the relationships between depression subtypes, stressors and other covariates. In the longitudinal study, chronic depression was regressed based on baseline variables using logistic regression. We identified four distinct depression subtypes, named positive affect, melancholic, typical and atypical, as well as three profiles of ACEs (low, moderate, physical abuse) and three profiles of AL (average, high-cardiovascular, low-cardiovascular). Depression subtypes had unique significant associations with stressor profiles. The strongest associations were observed for the atypical subtype (versus positive affect subtype) including a significantly lower relative risk (RRR 0.73, 95% CI: 0.57-0.93) for physical abuse-ACE, a higher risk for low-cardiovascular AL (RRR 1.31, 95%CI: 1.02-1.68), and a lower risk of high-cardiovascular AL (RRR 0.64, 95% CI: 0.49, 0.85), compared with the positive affect subtype. The prevalence of chronic depression was (46.6%), and was significantly associated with increased age group, total annual household income category, and chronic conditions score; decreased perceived social standing score; and current smoker status. Depression heterogeneity was identified, regarding symptom-based subtypes, their relationships with stress-related biological markers and early life adversities, and their relative risks for chronicity at three-year follow-up. Additionally, we characterized the prevalence of depression chronicity, relative to baseline factors including depression subtypes, which fills a gap in the literature regarding binary courses of depression subtypes within middle-aged and older adults. We found that prognostic factors for chronic depression are consistent with commonly identified depression incidence risk factors, and that stress profiles had distinct relationships with chronicity. Important factors for chronicity include baseline depressive symptom profiles, as well as ACE profile exposures and some AL profiles. Depression subtypes had distinct associations with stress-related biological markers and early life adversity profiles, as well as distinct risks for a more chronic course. Moreover, stressor exposures may not only have an impact on the profile of depressive symptoms experienced, but may also be significantly associated with depression chronicity. Such findings have implications for personalized clinical depression management strategies, earlier identification of depression, and potential primary and secondary intervention strategies

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.007
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
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 head0.060
GPT teacher head0.383
Teacher spread0.322 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes1
Has abstractyes

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