Out-of-hospital cardiac arrests and bystander response by socioeconomic disadvantage in communities of New South Wales, Australia
Bibliographic record
Abstract
BACKGROUND & AIM: Bystander response to out-of-hospital cardiac arrest (OHCA) may relate to area-level factors, including socioeconomic status (SES). We aimed to examine whether OHCA among individuals in more disadvantaged areas are less likely to receive bystander cardiopulmonary resuscitation (CPR) compared to those in more advantaged areas. METHODS: We analysed data on OHCAs in New South Wales, Australia collected prospectively through a statewide, population-based register. We excluded non-medical arrests; arrests witnessed by a paramedic; occurring in a medical centre, nursing home, police station; or airport, and among individuals with a Do-Not-Resuscitate order. Area-level SES for each arrest was defined using the Australian Bureau of Statistics' Index of Relative Socioeconomic Disadvantage and its relationship to likelihood of receiving bystander CPR was examined using hierarchical logistic regression models. RESULTS: Overall, 39% (6622/16,914) of arrests received bystander CPR (71% of bystander-witnessed). The OHCA burden in disadvantaged areas was higher (age-standardised incidence 76-87/100,000/year in more disadvantaged quintiles 1-4 versus 52 per 100,000/year in most advantaged quintile 5). Bystander CPR rates were lower (38%) in the most disadvantaged quintile and highest (42%) in the most advantaged SES quintile. In adjusted models, younger age, being bystander-witnessed, arresting in a public location, and urban location were all associated with greater likelihood of receiving bystander CPR; however, the association between area-level SES and bystander CPR rate was not significant. CONCLUSIONS: There are lower rates of bystander CPR in less advantaged areas, however after accounting for patient and location characteristics, area-level SES was not associated with bystander CPR. Concerted efforts to engage with communities to improve bystander CPR in novel ways could improve OHCA outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".