The Mental Health Toll of the COVID-19 Pandemic on Older Adults with Migraine: A Prospective Analysis of Depression Using the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Background: Individuals with migraine are recognized to have a heightened risk of depression compared to the general population. The COVID-19 pandemic and associated public health restrictions exacerbated several known risk factors for depression, but limited longitudinal research has examined the impact of the pandemic on the mental health of people with migraine. Aim: To examine the cumulative incidence of depression and recurrent depression among older adults with migraine, and to identify factors associated with depression among older adults with migraine during the pandemic. Methods: Data came from four waves of the Canadian Longitudinal Study on Aging's comprehensive cohort (n=2181 with migraine). 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: Older adults with migraine, both with and without a history of depression, experienced increases in depression when compared to pre-pandemic levels, and when compared to older adults without migraine. The risk of incident and recurrent depression was higher among those who felt lonely and those who experienced an increase in family conflict during the pandemic. The risk of incident depression only was higher among those who experienced difficulty accessing healthcare and those who experienced other family challenges, such as increased caregiving responsibilities. The risk of recurrent depression only was higher among those who felt left out socially, those with functional limitations, and those whose income did not satisfy their basic needs. Conclusion: Targeted interventions are needed to support the mental health of older adults with migraine.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".