Life course socioeconomic conditions, multimorbidity and polypharmacy in older adults
Bibliographic record
Abstract
Abstract Many older adults have multiple chronic conditions (multimorbidity). With multimorbidity often comes the concurrent intake of multiple medications (polypharmacy). Our aims were to assess if childhood socioeconomic conditions (CSC) are associated with multimorbidity and polypharmacy in older adults, and how these associations change when adjusting for adulthood socioeconomic conditions (ASC). We used data from the European longitudinal Survey of Health, Ageing, and Retirement (SHARE), which follows individuals aged 50 years and over in 27 countries since 2004. We analysed data from 35,229 individuals with multimorbidity (mean age: women=64.1, men=65.4) and 20,757 individuals with polypharmacy (mean age: women=69.2, men=70.2). Multimorbidity was defined as 2 or more self-reported chronic conditions and polypharmacy as the intake of 5 or more medications. Confounder-adjusted multilevel logistic regression models were used to analyse associations of CSC (5 categories: most disadvantaged-most advantaged) and ASC with multimorbidity and polypharmacy. All analyses were adjusted for country, age, weight, alcohol consumption, smoking status and physical exercise. In addition, we adjusted the polypharmacy model with the number of chronic conditions, mental health, activities of daily living and living situation. We stratified the analyses by sex. 34% of women had multimorbidity and 26% received polypharmacy. In men, these percentages were 34% and 28%. All categories of CSC (except for disadvantaged CSC in men) were associated with multimorbidity. In both sexes, a higher advantage was associated with lower odds of multimorbidity. In women, advantaged and most advantaged CSC were associated with lower odds of polypharmacy (OR = 0.53, 95% CI 0.31-0.89; OR = 0.48, 95% CI 0.24-0.94). In men, CSC were not associated with polypharmacy. ASC attenuated the association of CSC with multimorbidity among women, but CSC remained associated. In men, CSC were attenuated after adjusting for ASC. Key messages Lower childhood socioeconomic conditions increase the odds of having multiple chronic conditions in older adults. In women, the odds of polypharmacy are marginally linked with CSC. Less disadvantaged socioeconomic conditions in adulthood may allow for compensating a more disadvantaged start in life, particularly in men.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".