Influence of Socioeconomic Status on Functional Outcomes After Stroke: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: This review aimed to quantify the impact of socioeconomic status on functional outcomes from stroke and identify the socioeconomic status indicators that exhibit the highest magnitude of association. METHODS AND RESULTS: We performed a systematic literature search across Medline and Embase from inception to May 2022, to identify observational studies (n≥100, and in English). Risk of bias was assessed using the modified Newcastle Ottawa Scale. Random effects meta-analysis was used to pool data. We included 19 studies (157 715 patients, 47.7% women) reporting functional outcomes measured with modified Rankin Scale or Barthel index, with 10 assessed as low risk of bias. Measures of socioeconomic status reported were education (11 studies), income (8), occupation (4), health insurance status (3), and neighborhood socioeconomic deprivation (3). Pooled data suggested that low socioeconomic status was significantly associated with poor functional outcomes, including incomplete education or below high school level versus high school attainment and above (odds ratio [OR], 1.66 [95% CI, 1.40-1.95]), lowest income versus highest income (OR, 1.36 [95% CI, 1.02-1.83]), a manual job/being unemployed versus a nonmanual job/working (OR, 1.62 [95% CI, 1.29-2.02]), and living in the most disadvantaged socioeconomic neighborhood versus the least disadvantaged (OR, 1.55 [95% CI, 1.25-1.92]). Low health insurance status was also associated with an increased risk of poor functional outcomes (OR, 1.32 [95% CI, 0.95-1.84]), although this was association was not statistically significant. CONCLUSIONS: Despite great strides in stroke treatment in the past decades, social disadvantage remains a risk factor for poor functional outcome after an acute stroke. Further research is needed to better understand causal mechanisms and disparities.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".