Psycho-socio-economic factors and cardiorenal multimorbidity in middle to older-aged adults: cross-sectional results from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Psycho-socio-economic factors (PSEFs) such as income, education, housing, and social support are known to influence health outcomes, yet their relationship with cardiorenal multimorbidity (CRM) remains poorly understood. This study aimed to estimate the prevalence of CRM and examine its associations with PSEFs in a large, nationally representative Canadian sample. We analyzed baseline data from 19,370 participants (mean age: 60 years; 49.8% men) in the Canadian Longitudinal Study on Aging, a prospective cohort of community-dwelling adults aged 45-85 years recruited between 2010 and 2015. CRM was defined as the co-existence of at least one cardiovascular disease and kidney disease. PSEFs assessed included household income, education, homeownership, marital status, employment status, and psychosocial variables. Survey-weighted multivariable logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for prevalent CRM. The overall prevalence of CRM was 0.83% (160 individuals), equivalent to 3.90 per 1000 individuals (95% CI 2.81-5.41). Prevalence increased with age and was higher among men than women (4.57 vs. 3.35 per 1000) and slightly higher in rural than urban areas (4.47 vs. 3.84 per 1000). Homeownership was associated with significantly lower odds of CRM (OR = 0.56; 95% CI 0.33-0.94). A household income of $50 K-99 K was associated with lower odds of CRM (OR = 0.63; 95% CI 0.39-1.00). No other PSEFs showed clear associations with prevalent CRM. CRM is relatively uncommon but shows variation by age, sex, and geography. Among the PSEFs assessed, homeownership and, to a lesser extent, moderate income were associated with reduced odds of prevalent CRM. These findings highlight the potential role of housing and economic stability in mitigating CRM risk. Longitudinal studies are needed to assess the impact of PSEFs on the development and progression of CRM over time.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".