Ethnic variation in asthma healthcare utilisation and exacerbation: systematic review and meta-analysis
Bibliographic record
Abstract
Background: Patients from ethnic minority groups (EMGs) frequently report poorer asthma outcomes; however, a broad synthesis summarising ethnic disparities is yet to be undertaken. What is the magnitude of ethnic disparities in asthma healthcare utilisation, exacerbations and mortality? Methods: MEDLINE, Embase and Web of Science databases were searched for studies reporting ethnic variation in asthma healthcare outcomes (primary care attendance, exacerbation, emergency department (ED) visits, hospitalisation, hospital readmission, ventilation/intubation and mortality) between White patients and those from EMGs. Estimates were displayed using forest plots and random-effects models were used to calculate pooled estimates. We conducted subgroup analyses to explore heterogeneity, including by specific ethnicity (Black, Hispanic, Asian and other). Results: 65 studies, comprising 699 882 patients, were included. Most studies (92.3%) were conducted in the United States of America (USA). Patients from EMGs had evidence suggestive of lower levels of primary care attendance (OR 0.72, 95% CI 0.48-1.09), but substantially higher ED visits (OR 1.74, 95% CI 1.53-1.98), hospitalisations (OR 1.63, 95% CI 1.48-1.79) and ventilation/intubation (OR 2.67, 95% CI 1.65-4.31) when compared to White patients. In addition, we found evidence suggestive of increased hospital readmissions (OR 1.19, 95% CI 0.90-1.57) and exacerbation rates (OR 1.10, 95% CI 0.94-1.28) among EMGs. No eligible studies explored disparities in mortality. ED visits were much higher among Black and Hispanic patients, while Asian and other ethnicities had similar rates to White patients. Conclusions: EMGs had higher secondary care utilisation and exacerbations. Despite the global importance of this issue, the majority of studies were performed in the USA. Further research into the causes of these disparities, including whether these vary by specific ethnicity, is required to aid the design of effective interventions.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".