Differences in survival and mortality in minority ethnic groups with dementia: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Objectives Although there are disparities in both risk of developing dementia and accessibility of dementia services for certain minority ethnic groups in the United States and United Kingdom, disparities in survival after a dementia diagnosis are less well‐studied. Our objective was to systematically review the literature to investigate racial/ethnic differences in survival and mortality in dementia. Methods We searched Embase, Ovid MEDLINE, Global Health and PsycINFO from inception to November 2018 for studies comparing survival or mortality over time in at least two race/ethnicity groups. Studies from any country were included but analysed separately. We used narrative synthesis and random‐effects meta‐analysis to synthesise findings. The Newcastle–Ottawa Scale was used to assess quality and risk of bias in individual studies. Results We identified 22 articles, most from the United States (n = 17), as well as the United Kingdom (n = 3) and the Netherlands (n = 1). In a meta‐analysis of US studies, hazard of mortality was lower in Black/African American groups (Pooled Hazard Ratio = 0.86, 95% CI = 0.82–0.91, I2 = 17%, from four studies) and Hispanic/Latino groups (Pooled HR = 0.65, 95% CI = 0.50–0.84, I2 = 86%, from four studies) versus comparison groups. However, study quality was mixed, and in particular, quality of reporting of race/ethnicity was inconsistent. Conclusion Literature indicates that Black/African American and Hispanic/Latino groups may experience lower mortality in dementia versus comparison groups in the United States, but further research, using clearer and more and consistent reporting of race/ethnicity, is necessary to understand what drives these patterns and their implications for policy and practice.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".