Investigating the early impacts of the COVID-19 pandemic on modifiable risk factors for cancer and chronic disease: a repeated cross-sectional study in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVES: This study contributes to empirical evidence by examining the impact of the first and second waves of the COVID-19 pandemic on modifiable risk factors (MRF) and whether these patterns differ according to level of material deprivation among people living in Alberta. METHODS: Using data from a repeated cross-sectional provincial health survey (Alberta Community Health Survey (ACHS): 2018-2021), we conducted logistic regression analyses examining the impacts of the COVID-19 pandemic on meeting national guidelines on four MRFs (tobacco use, physical activity, fruit and vegetable consumption, alcohol use) (n=11,249). We compared population-level changes in MRFs from one year before the COVID-19 pandemic (March 2019-February 2020) to one year during the pandemic (March 2020-February 2021) in Alberta. We also assessed whether these trends differed by a measure of material deprivation. RESULTS: Compared to the pre-COVID-19 period, the fully adjusted odds of meeting recommended guidelines for fruit and vegetable consumption (OR=0.42) decreased during the pandemic. Individuals experiencing high material deprivation had lower odds of meeting recommended guidelines for physical activity (OR=0.65) and higher odds of not being current tobacco users (OR=1.36) during the pandemic versus during the pre-pandemic period. CONCLUSION: At a population level, analyses from the ACHS showed minimal impacts of the first year of the COVID-19 pandemic on MRFs, besides fruit and vegetable consumption. Yet, stratifying results showed statistically significant differences in pandemic impacts on MRFs by level of material deprivation. Therefore, understanding the influence of material deprivation on MRFs during the pandemic is key to tailoring future public health interventions promoting health and preventing cancer and chronic disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".