Incident and recurrent depression among older adults with asthma during the COVID-19 pandemic: Findings from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVES: (1) In a subsample of older adults with asthma without a history of depression, to determine the factors associated with developing depression during the COVID-19 pandemic; (2) in a subsample of older adults with asthma with a history of depression, to identify factors associated with recurrent depression during the pandemic. METHODS: Data came from four waves (Baseline [2011-2015], Follow-up 1 [2015-2018]; COVID Spring 2020, COVID Autumn 2020) of the Canadian Longitudinal Study on Aging's comprehensive cohort (n = 2,047 with asthma). The outcome of interest was a positive screen for depression based on the CES-D-10 during the autumn of 2020. Bivariate and multivariate logistic regression analyses were conducted. RESULTS: Among older adults with asthma without a history of depression (n = 1,247), approximately 1 in 7 (13.5%) developed depression for the first time during the COVID-19 pandemic. Among those with a history of depression (n = 770), approximately 1 in 2 (48.6%) experienced a recurrence of depression. The risk of incident depression and recurrent depression was higher among those who were lonely, those experiencing family conflict during the pandemic, and those who had difficulty accessing healthcare resources during the pandemic. The risk of incident depression only was higher among those who had difficulty accessing resources and/or loss of income during the pandemic. The risk of recurrent depression only was higher among those with functional limitations. CONCLUSIONS: There is a need for targeted interventions to support the mental health of older adults with asthma who have the above identified vulnerabilities during the pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".