Survival differences in ethnic minorities with dementia: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background In the US and UK, some minority ethnic (ME) groups are thought to be at higher risk for developing dementia while simultaneously accessing care less frequently. Despite this, studies from both countries have suggested that ME groups may actually survive longer after a dementia diagnosis. We conducted a systematic review and meta‐analysis to investigate survival differences in dementia between ethnic groups in the literature. Method We searched Embase, Global Health, Ovid MEDLINE, and PsycINFO from inception to November 2018 for longitudinal observational studies which quantified racial/ethnic differences in mortality in dementia. Studies were included if they used ethnicity or race as a predictor for all‐cause mortality and reported a comparison between multiple ethnic groups, regardless of whether this was the primary aim. Records were screened for inclusion in double. Study quality was assessed using the Newcastle‐Ottawa Scale. Narrative synthesis was used to analyze and report findings of all eligible studies. A subset of studies from the US was included in a random‐effects meta‐analysis with robust variance estimation. Result We identified 21 eligible articles: 17 from the US, three from the UK, and one from the Netherlands. Across countries, all studies but one found lower or no difference in survival in ME groups as compared to the reference population. In the US, five out of 12 studies reported lower mortality in African American/Black ethnic groups and seven out of nine reported lower mortality in Hispanic/Latino groups as compared to non‐Hispanic White groups. From the meta‐analysis (six studies; n=101,502), the pooled hazard ratio (HR) for all ME subgroups compared to reference was 0.77 (99% c.i. 0.48‐1.24), although heterogeneity was high. Separate meta‐analyses of Black/African American and Hispanic/Latino groups found lower hazard of mortality in both (Black/African American: HR=0.86, 95% c.i. 0.82‐0.91, I2 = 17%; Hispanic/Latino: HR=0.65, 95% c.i. 0.50‐0.84, I2 = 86%). Overall, study quality was mixed, and studies rarely reported how ethnicity was ascertained or defined. Conclusion Available literature indicates that ME groups, particularly Hispanic American and African American groups, may have lower risk of mortality in dementia. Lack of consistency in reporting ethnicity is an issue in the literature.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".