Ethnic disparities in the quality of hospital care in New Zealand, as measured by 30-day rate of unplanned readmission/death
Bibliographic record
Abstract
OBJECTIVE: To compare the quality of hospital care for New Zealand (NZ) Māori and NZ European adult patients, using the rate of unplanned readmission or death within 30 days of discharge as an indicator of quality. DESIGN: Retrospective cohort study. SETTING: NZ public hospitals. PARTICIPANTS: Data from 89 658 patients who were admitted for one of a defined set of surgical procedures at NZ public hospitals 2002-8 were obtained from the NZ Ministry of Health. Outcome The odds of readmission for NZ Māori when compared with NZ European patients were calculated using logistic regression, incorporating variables for age, sex, comorbidity, index procedure, hospital volume and socioeconomic position. RESULTS: NZ Māori had 16% higher odds of readmission or death when compared with NZ European patients (OR = 1.16; 95% CI 1.08-1.24) after adjusting for all covariates. Readmission or death was also associated with being female (OR = 1.09; 1.03-1.15), older age (OR = 1.33; 1.19-1.48, for >79 years compared with 18-39 years), higher comorbidity (OR = 2.08; 1.89-2.31, for Charlson score 3+ compared with 0) and higher hospital volume (OR = 0.81; 0.76-0.86, for lowest volume compared with highest). CONCLUSIONS: This study suggests ethnic disparities in the quality of hospital care in NZ using unplanned readmission rate as an indicator of quality. There are well-documented differences in health outcomes between Māori and NZ Europeans, and it is possible that differential treatment within the health system contributes to these health status inequalities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".