The Association of Delayed Care With Depression Among US Middle-Aged and Older Adults During the COVID-19 Pandemic: Cross-sectional Analysis
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, the depression level among US adults has significantly increased. Age disparity in depression during the pandemic has been reported in recent studies. Delay or avoidance of medical care is one of the collateral damages associated with the COVID-19 pandemic, and it can lead to increased morbidity and mortality. OBJECTIVE: This study aimed to assess the prevalence of depression and delayed care among US middle-aged adults and older adults during the pandemic, as well as investigate the association of delayed care with depression among those 2 age groups. METHODS: This cross-sectional study used data from the 2020 Health and Retirement Study (HRS) COVID-19 Project (Early, Version 1.0). Univariate analyses, bivariate analyses, and binary logistic regression were applied. US adults older than 46 years were included. Depression was measured by the Composite International Diagnostic Interview-Short Form (CIDI-SF). Delayed care was measured by the following 4 items: delayed surgery, delayed seeing a doctor, delayed dental care, and other delayed care. RESULTS: A total of 3246 participants were identified. More than half of the participants were older than 65 years (n=1890, 58.2%), and 274 (8.8%) participants had depression during the pandemic. Delayed dental care was positively associated with depression among both middle-aged adults (OR 2.05, 95% CI 1.04-4.03; P=.04) and older adults (OR 3.08, 95% CI 1.07-8.87; P=.04). Delayed surgery was positively associated with depression among older adults (OR 3.69, 95% CI 1.06-12.90; P=.04). Self-reported pain was positively related to depression among both age groups. Middle-aged adults who reported higher education levels (some college or above) or worse self-reported health had a higher likelihood of having depression. While perceived more loneliness was positively associated with depression among older adults, financial difficulty was positively associated with depression among middle-aged adults. CONCLUSIONS: This study found that depression was prevalent among middle-aged and older adults during the pandemic. The study highlighted the collateral damage of the COVID-19 pandemic by identifying the association of delayed surgery and dental care with depression during the pandemic. Although surgery and dental care cannot be delivered by telehealth, telehealth services can still be provided to address patients' concerns on delayed surgery and dental care. Moreover, the implementation of telemental health services is needed to address mental health symptoms among US middle-aged and older adults during the pandemic. Future research that uses more comprehensive measurements for delayed care is needed to decipher the path through which delayed care is associated with depression.
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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 | 0.000 | 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 | 0.000 |
| 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".