The impact of COVID-19 on the lives of Canadians with and without non-communicable chronic diseases: results from the iCARE Study
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic and its prevention policies have taken a toll on Canadians, and certain subgroups may have been disproportionately affected, including those with non-communicable diseases (NCDs; e.g., heart and lung disease) due to their risk of COVID-19 complications and women due to excess domestic workload associated with traditional caregiver roles during the pandemic. Aims/Objectives We investigated the impacts of COVID-19 on mental health, lifestyle habits, and access to healthcare among Canadians with NCDs compared to those without, and the extent to which women with NCDs were disproportionately affected. Methods As part of the iCARE study ( www.icarestudy.com ), data from eight cross-sectional Canadian representative samples (total n = 24,028) was collected via online surveys between June 4, 2020 to February 2, 2022 and analyzed using general linear models. Results A total of 45.6% (n = 10,570) of survey respondents indicated having at least one physician-diagnosed NCD, the most common of which were hypertension (24.3%), chronic lung disease (13.3%) and diabetes (12.0%). In fully adjusted models, those with NCDs were 1.18–1.24 times more likely to report feeling lonely, irritable/frustrated, and angry ‘to a great extent’ compared to those without (p’s < 0.001). Similarly, those with NCDs were 1.22–1.24 times more likely to report worse eating and drinking habits and cancelling medical appointments/avoiding the emergency department compared to those without (p’s < 0.001). Moreover, although there were no sex differences in access to medical care, women with NCDs were more likely to report feeling anxious and depressed, and report drinking less alcohol, compared to men with NCDs (p’s < 0.01). Conclusion Results suggest that people with NCDs in general and women in general have been disproportionately more impacted by the pandemic, and that women with NCDs have suffered greater psychological distress (i.e., feeling anxious, depressed) compared to men, and men with NCDs reported having increased their alcohol consumption more since the start of COVID-19 compared to women. Findings point to potential intervention targets among people with NCDs (e.g., prioritizing access to medical care during a pandemic, increasing social support for this population and mental health support).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".