The association between total social exposure and incident multimorbidity: A population-based cohort study
Bibliographic record
Abstract
Background: Multimorbidity, the co-occurrence of two or more chronic conditions, is associated with the social determinants of health. Using comprehensive linked population-representative data, we sought to understand the combined effect of multiple social determinants on multimorbidity incidence in Ontario, Canada. Methods: Ontario respondents aged 20-55 in 2001-2011 cycles of the Canadian Community Health Survey were linked to administrative health data ascertain multimorbidity status until 2022. Additive total social exposure (TSE) was generated by summing 12 measures of social disadvantage captured from the survey. Weighted-additive TSE included 15 measures of social disadvantage summed across 5 equally weighted domains. Hazard ratios for the association between each TSE measure and multimorbidity were estimated using competing risk Cox-proportional hazards models. All analyses were sex-stratified. Results: Both additive and weighted-additive TSE were associated with an increased risk of multimorbidity among females and males. A social gradient was observed for multimorbidity risk in all models. While adjusted models were attenuated, an increased risk of multimorbidity was observed among those experiencing the most social disadvantage, compared to those with the least social disadvantage in additive (HR Females = 2.16; 95%CI = 1.63, 2.86; HR Males = 1.90; 95%CI = 1.52, 2.38) and weighted-additive (HR Females = 1.94; 95%CI = 1.49, 2.53; HR Males = 1.72; 95%CI = 1.41, 2.10) models. The observed social gradient was retained. Conclusions: These findings demonstrate the importance of considering the cumulative effects of multiple social determinants of health on multimorbidity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".