The influence of socioeconomic status on future risk for developing Type 2 diabetes in the Canadian population between 2011 and 2022: differential associations by sex
Bibliographic record
Abstract
BACKGROUND: Articulating future risk of diabetes at the population level can inform prevention strategies. While previous studies have characterized diabetes burden according to socioeconomic status (SES), none have studied future risk. METHODS: We quantified the influence of multiple constructs of SES on future diabetes risk using the Diabetes Population Risk Tool (DPoRT), a validated risk prediction algorithm that generates 10-year rates of new diabetes cases. We applied DPoRT to adults aged 30-64 in the 2011-2012 Canadian Community Health Survey (n = 65,372) and calculated risk for 2021-22. A multi-category outcome was created classifying risk as low (≤5%), moderate (greater than 5% and less than 20%), and high (≥20%), then assessed the impact of individual-level SES indicators, and area-level measures of marginalization on being moderate or high risk using multinomial logistic regression, stratified by sex. RESULTS: We found nuanced profiles of social determinants by sex, where women are more sensitive to social context. Women living in households where highest educational attainment was less than secondary school were at greater risk [odds ratio (OR) of high compared to low diabetes risk 3.10, 95% confidence interval (CI) 2.19-4.40, p < 0.0001). The same relationship was less pronounced for males (OR 2.17, 95% CI 1.42-3.32, p = 0.0004). Lower household income and being food insecure predicted high future diabetes risk for women (OR 1.37, 95% CI 1.01-1.86, p = 0.0418 comparing quintile 1 to quintile 5; OR 2.64, 95% CI 1.78-3.92, p < 0.0001 comparing severely food insecure to food secure), but not men (OR 1.15, 95% CI 0.84-1.57, p = 0.3818 and OR 1.22, 95% CI 0.71-2.10, p = 0.4815). At the area-level, material deprivation was significantly associated with increased future risk comparing the most to the least deprived (OR females 2.39, 95% CI 1.77-3.23; OR males 1.61, 95% CI 1.22-2.14). Additionally, a strong protective effect was observed for women living in ethnically dense areas (OR 0.75, 95% CI 0.63-0.89, p = 0.0011) which was not as pronounced for men (OR 0.95, 95% CI 0.76-1.18, p = 0.6351). CONCLUSIONS: This study characterized socio-contextual predictors for future diabetes risk, showing sex-specific effects. Diabetes prevention must consider factors beyond individual-level behavioral lifestyle change and actively take steps to mitigate the adverse impacts of socio-contextual factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".