Quantifying the contribution of modifiable risk factors to socio-economic inequities in cancer morbidity and mortality: a nationally representative population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Compared with those with a higher socio-economic position (SEP), individuals with a lower SEP have higher cancer morbidity and mortality. However, the contribution of modifiable risk factors to these inequities is not known. This study aimed to quantify the mediating effects of modifiable risk factors to associations between SEP and cancer morbidity and mortality. METHODS: This study used a prospective observational cohort design. We combined eight cycles of the Canadian Community Health Survey (2000/2001-2011) as baseline data to identify a cohort of adults (≥35 years) without cancer at the time of survey administration (n = 309 800). The cohort was linked to the Discharge Abstract Database and the Canadian Mortality Database for cancer morbidity and mortality ascertainment. Individuals were followed from the date they completed the Canadian Community Health Survey until 31 March 2013. Dates of individual first hospitalizations for cancer and deaths due to cancer were captured during this time period. SEP was operationalized using a latent variable combining measures of education and household income. Self-reported modifiable risk factors, including smoking, excess alcohol consumption, low fruit-and-vegetable intake, physical inactivity and obesity, were considered as potential mediators. Generalized structural equation modelling was used to estimate the mediating effects of modifiable risk factors in associations between low SEP and cancer morbidity and mortality in the total population and stratified by sex. RESULTS: Modifiable risk factors together explained 45.6% of associations between low SEP and overall cancer morbidity and mortality. Smoking was the most important mediator in the total population and for males, accounting for 15.5% and 40.2% of the total effect, respectively. For females, obesity was the most important mediator. CONCLUSIONS: Modifiable risk factors are important mediators of socio-economic inequities in cancer morbidity and mortality. Nevertheless, more than half of the variance in these associations remained unexplained. Midstream interventions that target modifiable risk factors may help to alleviate inequities in cancer risk in the short term. However, ultimately, upstream interventions that target structural determinants of health are needed to reduce overall socio-economic inequities in cancer morbidity and mortality.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".