THE INFLUENCE OF SOCIO-ECONOMIC DEPRIVATION ON MULTIMORBIDITY: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Introduction: Multimorbidity poses a current global health challenge due to its increasing prevalence and burden on individuals and health systems. Evidence suggests that more socially disadvantaged individuals share a disproportionate burden of multimorbidity. The evidence on the relationship between area-level socioeconomic disadvantage and multimorbidity is unclear. Thus, the aim of the current study is to synthesise evidence on the association between area-level socio-economic disadvantage and multimorbidity. Methods: A systematic review was conducted of published literature from inception to January 2020. Search strategy was applied to identify evidence on PubMed (Medline), Ovid (Medline, Embase, Psycinfo) and Web of Science. Studies were selected according to the inclusion and exclusion criteria. Newcastle Ottawa Scale for observational studies was used for quality assessment of included studies. Evidence was synthesised narratively. Results: We identified eight out of 2588 studies identified in the search as per the inclusion and exclusion criteria. Out of the eight studies, five studies confirmed a positive association between area-level socio-economic disadvantage and multimorbidity, two studies presented a negative association, and one study presented no association. Three studies found individuals in deprived areas to have higher multimorbidity than those in affluent areas. Two studies established that individuals in rural areas had higher multimorbidity than their urban counterparts. Two studies found individuals in urban areas to have a higher multimorbidity than those in rural areas. Conclusion: Evidence shows that association between area-level socioeconomic disadvantage and multimorbidity exist. Except for area of residence, clear positive associations were confirmed between area deprivation and multimorbidity.
 
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Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".