Measuring inequity in using routinely collected data in an emergency setting: a systematic review
Bibliographic record
Abstract
Abstract Background The international literature has highlighted many potential challenges in terms of inequitable access to care. In the last few years, health equity is becoming an increasingly important issue for policymakers, particularly in developed countries. The aim of this systematic review was to find how equity is measured and to identify some of its determinants. Methods We conducted a systematic review on all major databases (Medline Ovid SP, PubMed, Embase and Web of Science), following the PRISMA guidelines. We included published observational studies that reported on health equity and using administrative data, with a focus on emergency and unplanned hospital care. Study selection and data extraction were conducted independently and compared by two reviewers. Results In total, 223 records were screened and 39 articles met the inclusion criteria. Studies come from the United States (US) (23), United Kingdom (6), Canada (4), Australia (2) and some European countries (4). To measure health inequity, most of the studies used at least one of these 4 indicators: hospitalisations for chronic ambulatory care sensitive conditions (or preventable hospitalisations), emergency hospitalisation rate, readmissions or mortality. The most relevant health equity determinants concerned race/ethnicity (19), poverty (17), health insurance coverage (17) and gender (16). Race/ethnicity and gender are important determinants of inequities. Concerning poverty, despite the use of heterogeneous indicators, most of studies showed a socio-economic gradient of access to care. Health insurance coverage was often used but with conflicting results. Conclusions The use of indicators linking primary, emergency and hospital care seems to be particularly relevant to measure health inequity. Race/ethinicity, gender and socio-economic status are clear determinants of inequitable access to care. More studies are needed to explain and analyse the determinants of health equity. Key messages Health equity remains a major issue even for high-income countries health care system. Quantitative data about health equity still are needed to support policymaker’s recommendation.
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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 | 0.036 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| 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".