Self-reported chronic conditions and COVID-19 public health measures among Canadian adults: an analysis of the Canadian longitudinal study on aging
Bibliographic record
Abstract
OBJECTIVES: During the COVID-19 pandemic, public health measures were used to reduce the spread of COVID-19; it is unknown whether people with chronic conditions differentially adhered to public health measures. The objectives of this study were to evaluate the association between chronic conditions and adherence and to explore effect modification by sex, age, and income. STUDY DESIGN: An analysis of data from the Canadian Longitudinal Study on Aging COVID-19 Questionnaires (from April to September 2020) was conducted among middle-aged and older adults aged 50-96 years (n = 28,086). METHODS: Self-reported chronic conditions included lung disease, diabetes, heart disease, cancer, obesity, anxiety, and depression. Multinomial logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between chronic conditions and low, medium, and high levels of adherence. Effect modification was evaluated using statistical interaction and stratification. RESULTS: Most people (n = 17,435; 62%) had at least one chronic condition, and 2866 (10%) had three to seven chronic conditions. Among those with high adherence to public health measures, 69% had one or more chronic condition (n = 2266). Having three to seven chronic conditions, compared with none, was associated with higher adherence to public health measures (OR: 2.14; 95% CI: 1.12-1.42). Higher adherence was also noted across chronic conditions, for example, those with diabetes had higher adherence (OR: 1.72; 95% CI: 1.53-1.93). There was limited evidence of effect modification by sex, age, or income. CONCLUSIONS: Canadians with chronic conditions were more likely to adhere to public health measures; however, future research is needed to understand whether adherence helped to prevent adverse COVID-19 outcomes and if adherence had unintended consequences.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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 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".