Abstract 138: Socioeconomically Equitable Public Defibrillator Placement Using Mathematical Optimization
Bibliographic record
Abstract
Introduction: Mathematical optimization can be used to place automated external defibrillators (AEDs) in locations that maximize coverage of out-of-hospital cardiac arrests (OHCAs). However, the extent to which optimization strategies affect socioeconomically equitable distribution of AEDs is unknown. Methods: All suspected OHCAs and registered AEDs in Scotland between Jan. 2011 - Sept. 2017 with a recorded location were included and mapped across the quintiles of the Scottish Index of Multiple Deprivation (SIMD), a national measure of socioeconomic status. First, we maintained AEDs at current locations and modeled placing an equal number of additional AEDs to maximize “coverage” (i.e., AED located within 100 m) of suspected OHCAs. A second analysis determined optimal sites for relocating all existing AEDs to optimize coverage without additional AEDs. We computed the proportion of OHCAs covered in each SIMD quintile under each AED placement strategy. A Wilcoxon signed-rank test was used to test difference in coverage levels across all regions of Scotland. Results: We identified 49,692 suspected OHCAs and 1,532 AEDs. Existing AEDs covered 1,384 OHCAs (2.8%), with OHCA coverage peaking in quintile 3 (moderate deprivation), indicating a mismatch with the distribution of suspected OHCA. Adding an equal number of new AEDs in optimal locations covered 10,465 OHCAs (21.1%; P<0.001). Optimal relocation of existing AEDs with no additional units covered 9,464 OHCAs (19.0%; P<0.001). OHCA coverage under either optimization strategy peaked in quintile 1 (highest deprivation), aligning to the OHCA incidence distribution. Conclusion: Optimizing AED placement significantly increases OHCA coverage and better aligns coverage with OHCA incidence across SIMD quintiles, improving socioeconomic equity of OHCA coverage. Relocating existing AEDs could achieve similar coverage to doubling the number of devices.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".