Socioeconomic disparities in the prevalence of comorbid chronic conditions among Canadian adults with cancer
Bibliographic record
Abstract
To evaluate the prevalence of comorbid chronic conditions among Canadian adults with cancer and the impact of socioeconomic background on the distribution of these conditions. Canadian Community Health Survey (CCHS) 2017–2018 dataset was accessed and individuals with complete information about cancer history were reviewed. The prevalence of the following 10 chronic conditions was reviewed: asthma, chronic obstructive pulmonary disease, arthritis, hypertension, hypercholesterolemia/hyperlipidemia, heart disease, stroke, diabetes, mood disorder, and anxiety disorder. Stratification of the prevalence was done according to age, sex, and racial subgroups. Multivariable logistic regression analysis was done to evaluate the association between sociodemographic characteristics and having multiple comorbid conditions. A total of 104,362 participants were included in the current study (including 10,782 participants with a history of cancer; and 93,580 participants without a history of cancer). Among all age, sex, and race strata, participants with a history of cancer were more likely to have multiple chronic conditions (p History of cancer is associated with a higher probability of many comorbid conditions. This excess comorbidity burden seems to be unequally shouldered by individuals in the lower socioeconomic stratum as well as minority populations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".