Abstract
Global prevalence of cardiopulmonary resuscitation and automated external defibrillator training: a systematic review and meta-analysis Aim: Sudden cardiac arrest exerts a large disease burden, which may be mitigated by bystander cardiopulmonary resuscitation (CPR) and automated external defibrillation (AED). The global prevalence and distribution of laypersons trained in these skills are poorly understood. We aimed to estimate the global prevalence of CPR and AED training, and identify their determinants. Method: We searched Medline, Embase and Cochrane Library from inception to October 24th, 2021, for cross-sectional studies reporting the prevalence of CPR or AED training from representative samples of laypersons. Prevalences were pooled using random effects models. We examined varying definitions of being identified as CPR-trained, namely CPR-V (valid training within 2-years), CPR-E (ever been trained). Subgroup analysis and meta-regression were used to examine determinants of interest. Results: 28 studies were included, representing 53,397 laypersons. Among national studies, the prevalence of CPR-V training was 10.02% (95% CI 6.60 to 14.05), prevalence of CPR-E training was 39.64% (95%CI 29.11 to 50.67), and prevalence of AED training was 15.70% (95% CI 10.17 to 22.18). Subgroup analysis revealed differences in prevalences between continents,with prevalence highest in Oceania and lowest in Asia (p<0.01). Meta-regression showed a positive correlation between a country’s income and CPR-E training prevalence ( p=0.0325). Comparative meta-analysis showed that the prevalence of CPR-E training was higher among the employed (p<0.00001), and the more highly educated (p<0.00001). Conclusion: Large regional variation exists in the data availability, and prevalence of CPR and AED training amongst laypersons. Socioeconomic status correlated with prevalence of CPR training and regional disparities were apparent between continents. As such, community bystander training should be promoted, particularly in Asia and low-income regions, while data availability should be encouraged from under-represented regions.
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