A cross-sectional study of health-related quality of life deficits in individuals with comorbid diabetes and cancer
Bibliographic record
Abstract
BACKGROUND: Numerous studies have identified a reduced health related quality of life (HRQL) in patients that have either diabetes or cancer. We assessed the HRQL burden in patients with these comorbid conditions, postulating that they would have even greater HRQL deficits. METHODS: Data from the Public Use File of the Canadian Community Health Survey (PUF CCHS) Cycle 1.1 (September 2000-November 2001) were used for this analysis. The total sample size of the CCHS PUF is 130,880 individuals. We used the Health Utilities Index Mark 3 (HUI3) to assess HRQL in patients with: 1) comorbid diabetes and cancer, 2) diabetes alone, 3) cancer alone, and 4) no diabetes or cancer. Analysis of covariance was used to compare the mean overall HUI3 score, controlling for age, sex, marital status, body mass index (BMI), physical activity level, smoking status, education level, depression status, and other chronic conditions. RESULTS: We identified 113,587 individuals (87%) with complete data for the analysis. The comorbid diabetes and cancer group were older and a larger proportion reported being obese, inactive, having less than a secondary education and more chronic conditions when compared to the other three cohorts (p < 0.0001). However, the diabetes and cancer cohort was less likely to be depressed (p < 0.0001). Overall HUI3 scores were significantly lower for the diabetes and cancer group (unadjusted mean (SD): 0.67 (0.30)), compared to diabetes (0.78 (0.27)), cancer (0.78 (0.25)), and the reference group (0.89 (0.18)) (p < 0.0001). After adjusting for covariates, the comorbid diabetes and cancer group continued to have significantly lower overall HUI3 scores than the reference group (unstandardized mean difference: -0.11, 95% CI: -0.13 to -.0.09) (p < 0.0001). CONCLUSION: Individuals with diabetes and cancer had a clinically important and significantly lower HRQL than those with either condition alone. A better understanding of the relationship between diabetes and cancer, and their associated comorbidities, complications, and HRQL deficits may have important implications for prevention and management strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".