Associations between multiple long-term conditions and mortality in diverse ethnic groups
Bibliographic record
Abstract
Abstract Background Multiple conditions are more prevalent in some minoritised ethnic groups and are associated with higher mortality rate but studies examining differential mortality once conditions are established is US-based. Our study tested whether the association between multiple conditions and mortality varies across ethnic groups in England. Methods and Findings A random sample of primary care patients from Clinical Practice Research Datalink (CPRD) was followed from 1 st January 2015 until 31 st December 2019. Ethnicity, usually self-ascribed, was obtained from primary care records if present or from hospital records. Cox regression models were used to estimate mortality by number of long-term conditions, ethnicity and their interaction, with adjustment for age and sex for 532,059 patients with complete data. During five years of follow-up, 5.9% of patients died. Each additional long-term condition at baseline was associated with increased mortality. This association differed across ethnic groups. Compared with 50-year-olds of white ethnicity with no conditions, the mortality rate was higher for white 50-year-olds with two conditions (HR 1.77) or four conditions (HR 3.13). Corresponding figures were higher for 50-year-olds of Black Caribbean ethnicity with two conditions (HR=2.22) or four conditions (HR 4.54). The direction of the interaction of number of conditions with ethnicity showed higher mortality associated with long-term conditions in nine out of ten minoritised ethnic groups, attaining statistical significance in four (Pakistani, Black African, Black Caribbean and Black other ethnic groups). Conclusions The raised mortality rate associated with having multiple conditions is greater in minoritised ethnic groups compared with white people. Research is now needed to identify factors that contribute to these inequalities. Within the health care setting, there may be opportunities to target clinical and self-management support for people with multiple conditions from minoritised ethnic groups.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".