People With Cancer Experience Worse Psychosocial and Financial Consequences of COVID-19 Compared With Other Chronic Disease Populations: Findings From the International COVID-19 Awareness and Response Evaluation Survey Study
Bibliographic record
Abstract
PURPOSE The COVID-19 pandemic is likely to have profound psychosocial impacts across the globe. In this analysis of the International COVID-19 Awareness and Response Evaluation (iCARE) survey study, we comparatively investigated the psychosocial effects of COVID-19 on individuals with cancer and people with other chronic illness. METHODS iCARE study respondents were divided into two groups on the basis of self-reported health status: (1) active/current cancer (with or without any other chronic condition: heart disease, lung disease, hypertension, diabetes, severe obesity, immunity disease, and depressive or anxiety disorder); and (2) other chronic condition, but not cancer. Linear regressions were conducted to evaluate the associations between health status and outcomes. RESULTS Worldwide, 18,154 iCARE study respondents (mean age, 50.8 years) from 175 countries were included in the analysis. Among them, 3.8% (n = 677) identified as having active/current cancer and 96.2% (n = 17,477) identified as having other chronic condition. Multivariate analyses showed significant associations between having cancer and declined mental (β = .364; P < .0001) and physical (β = .317; P < .0001) health since the start of the COVID-19 pandemic, relative to those with other chronic illness. Moreover, individuals with cancer demonstrated a higher likelihood of reporting maladaptive coping mechanisms such as increased alcohol use (β = .457; P < .0001) and financial hardships such as not paying rent/mortgage (β = .476; P < .0001), compared with people with other chronic illness. CONCLUSION Individuals with cancer worldwide tended to have worse psychosocial and financial challenges during the COVID-19 pandemic, compared with other chronic disease populations. Clinicians need to be aware of the importance of attending to the specific mental health needs of individuals with cancer during and after COVID-19–related restrictions.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".