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Abstract
Background:
Ambulance services treat over 32,000 patients sustaining an out-of-hospital cardiac arrest annually, receiving over 90,000 calls. The definitive treatment for out-of-hospital cardiac arrest is defibrillation. Prompt treatment with an automated external defibrillator can improve survival significantly. However, their location in the community limits opportunity for their use. There is a requirement to identify the optimal location for an automated external defibrillator to improve out-of-hospital cardiac arrest coverage, to improve the chances of survival.
Methods:
This was a secondary analysis of data collected by the Out-of-Hospital Cardiac Arrest Outcomes registry on historical out-of-hospital cardiac arrests, data held on the location of automated external defibrillators registered with ambulance services, and locations of points of interest.
Walking distance was calculated between out-of-hospital cardiac arrests, registered automated external defibrillators and points of interest designated as potential sites for an automated external defibrillator. An out-of-hospital cardiac arrest was deemed to be covered if it occurred within 500 m of a registered automated external defibrillator or points of interest.
For the optimisation analysis, mathematical models focused on the maximal covering location problem were adapted.
A de novo decision-analytic model was developed for the cost-effectiveness analysis and used as a vehicle for assessing the costs and benefits (in terms of quality-adjusted life-years) of deployment strategies.
A meeting of stakeholders was held to discuss and review the results of the study.
Results:
Historical out-of-hospital cardiac arrests occurred in more deprived areas and automated external defibrillators were placed in more affluent areas. The median out-of-hospital cardiac arrest – automated external defibrillator distance was 638 m and 38.9% of out-of-hospital cardiac arrests occurred within 500 m of an automated external defibrillator.
If an automated external defibrillator was placed in all points of interests, the proportion of out-of-hospital cardiac arrests covered varied greatly. The greatest coverage was achieved with cash machines. Coverage loss, assuming an automated external defibrillator was not available outside working hours, varied between points of interest and was greatest for schools.
Dividing the country up into 1 km2 grids and placing an automated external defibrillator in the centre increased coverage significantly to 78.8%.
The optimisation model showed that if automated external defibrillators were placed in each points-of-interest location out-of-hospital cardiac arrest coverage levels would improve above the current situation significantly, but it would not reach that of optimisation-based placement (based on grids). The coverage efficiency provided by the optimised grid points was unmatched by any points of interest in any region.
An economic evaluation determined that all alternative placements were associated with higher quality-adjusted life-years and costs compared to current placement, resulting in incremental cost-effectiveness ratios over £30,000 per additional quality-adjusted life-year. The most appealing strategy was automated external defibrillator placement in halls and community centres, resulting in an additional 0.007 quality-adjusted life-year (non-parametric 95% confidence interval 0.004 to 0.011), an additional expected cost of £223 (non-parametric 95% confidence interval £148 to £330) and an incremental cost-effectiveness ratio of £32,418 per quality-adjusted life-year.
The stakeholder meeting agreed that the current distribution of registered publicly accessible automated external defibrillators was suboptimal, and that there was a disparity in their location in respect of deprivation and other health inequalities.
Conclusions:
We have developed a data-driven framework to support decisions about public-access automated external defibrillator locations, using optimisation and statistical models. Optimising automated external defibrillator locations can result in substantial improvement in coverage. Comparison between placement based on points of interest and current placement showed that the former improves coverage but is associated with higher costs and incremental cost-effectiveness ratio values over £30,000 per additional quality-adjusted life-year.
Study registration:
This study is registered as researchregistry5121.
Funding:
This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: NIHR127368) and is published in full in Health and Social Care Delivery Research; Vol. 13, No. 5. See the NIHR Funding and Awards website for further award information.
Plain language summary
Ambulance services of the NHS treat over 32,000 people whose heart suddenly stops pumping effectively, a condition known as cardiac arrest. Despite ambulance services’ best efforts fewer than 1 in 10 survive. Electric shock treatment, known as defibrillation, is one of the most effective treatments, and if it is given within a few minutes of the heart stopping, over half the people treated survive. It is now possible for public to use an automatic machine (defibrillator) to safely give an electric shock to the heart before the emergency services arrive. For the public to make best use of these machines they need to be in the right places.
In this study, we attempted to work out the best places to put defibrillators in communities, making them more accessible to use. We showed that defibrillators currently are sited disproportionately in more affluent areas of the country, and not used despite being within an accessible distance from where a cardiac arrest occurs. We assessed that if a defibrillator was installed at various points of interest the number of cardiac arrests that were covered increased significantly. We then used a computer to model the best locations for new defibrillators and calculate the optimal number needed. Placement based on this model showed that, for a smaller number of defibrillators, a similar improvement in coverage could be achieved.
A health economic analysis that considered the cost of purchasing and installing defibrillators showed that installing additional defibrillators in specific points of interest improved coverage, but it was also more costly compared to current defibrillator placement.
This research showed that significant improvement in cardiac arrest coverage could be achieved if defibrillators were placed intelligently in public settings. We also created a system that uses data to decide where to place public-access defibrillators in the community.
Contents
- Scientific summary
- Chapter 1. Background
- Chapter 2. Project aims and objectives
- Chapter 3. Methods
- Chapter 4. Results
- Chapter 5. Discussion
- Chapter 6. Conclusions
- Chapter 7. Consideration for deployment and future research
- Chapter 8. Impact
- Additional information
- References
- Appendix 1. Ambulance service AED operational radii; POI numbers and hours of operation
- Appendix 2. Data used in cost-effectiveness analysis
- Appendix 3. Temporal variability of out-of-hospital cardiac arrest occurrence (numbers and proportions)
- Appendix 4. Logistic regression results for out-of-hospital cardiac arrest
- Appendix 5. Neighbourhood characteristics of locations of automated external defibrillators registered on The Circuit
- Appendix 6. Neighbourhood characteristics of automated external defibrillator locations in each ambulance service region
- Appendix 7. Logistic regression results for automated external defibrillator location
- Appendix 8. Rural–urban distribution of out-of-hospital cardiac arrest and automated external defibrillator locations
- Appendix 9. Out-of-hospital cardiac arrest coverage by POIs and ambulance service region
- Appendix 10. Out-of-hospital cardiac arrest coverage gains and coverage levels using optimisation and POI approaches in English ambulance services
- Appendix 11. Additional results from the cost-effectiveness analysis
- List of abbreviations
About the Series
Article history
The research reported in this issue of the journal was funded by the HSDR programme or one of its preceding programmes as award number NIHR127368. The contractual start date was in October 2019. The draft manuscript began editorial review in April 2023 and was accepted for publication in March 2024. The authors have been wholly responsible for all data collection, analysis and interpretation, and for writing up their work. The HSDR editors and production house have tried to ensure the accuracy of the authors’ manuscript and would like to thank the reviewers for their constructive comments on the draft document. However, they do not accept liability for damages or losses arising from material published in this article.
Last reviewed: April 2023; Accepted: March 2024.
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