Social Adversity Is Causally Linked to Multimorbidity Including Oral Conditions
Bibliographic record
Abstract
The fundamental cause theory posits social factors as causes of disease as they encompass access to important resources such as knowledge, wealth, and social networks. While these social factors have been consistently associated with oral and systemic diseases, causality remains unestablished. Here, we estimated the causal effect of social adversity, comprising low economic and social capital, on the development of (1) oral conditions (OC) and (2) multimorbidity including oral conditions (MIOC) in a cohort of middle-aged and older adults over a 7-y period and assessed whether effects varied by age or gender. We analyzed 2 waves from the Canadian Longitudinal Study on Aging (CLSA) (2011 and 2018). Social adversity comprised low economic (income) and social capital (community participation, social relationships). OC was defined as having 1 or more of poor self-reported oral health, lack of functional dentition (<20 natural teeth), or edentulism. Participants with an OC at baseline were excluded. MIOC was defined as having 2 or more chronic diseases and an OC. Logistic marginal structural models with inverse probability weighting estimated the causal odds ratio (OR) of developing both outcomes, controlling for sociodemographic and behavioral factors. In a total of 23,366 participants, 14% experienced social adversity at baseline, with a prevalence of 17% OC and 7% MIOC at follow-up. Social adversity significantly increased the odds of developing OC (OR = 1.9, 95% confidence interval [CI] 1.7, 2.2) and MIOC (OR = 1.7, 95% CI 1.5, 2.0) at follow-up. The observed effects were strongest in the middle-aged group, with similar odds observed in both men and women. Our findings indicate that social and economic capital are causally linked to the development of OC and MIOC over time. We suggest that policies for healthy aging should prioritize action on social and living conditions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".