Life course socioeconomic conditions and multimorbidity in old age – A scoping review
Bibliographic record
Abstract
Multimorbidity disproportionally affects individuals exposed to socioeconomic disadvantage. It is, however, unclear how adverse socioeconomic conditions (SEC) at different periods of the life course predict the occurrence of multimorbidity in later life. In this scoping review, we investigate the association between life course SEC and later-life multimorbidity, and assess to which extent it supports different life course causal models (critical period, sensitive period, accumulation, pathway, or social mobility). We identified four studies (25,209 participants) with the first measure of SEC in childhood (before age 18). In these four studies, childhood SEC was associated with multimorbidity in old age, and the associations were partially or fully attenuated upon adjustment for later-life SEC. These results are consistent with the sensitive period and the pathway models. We identified five studies (91,236 participants) with the first measure of SEC in young adulthood (after age 18), and the associations with multimorbidity in old age as well as the effects of adjustment for later-life SEC differed from one study to the other. Among the nine included studies, none tested the social mobility or the accumulation models. In conclusion, SEC in early life could have an effect on multimorbidity, attenuated at least partly by SEC in adulthood.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".