Community cardiac awareness teaching in a rural area: the potential for a health promotion message

Community Interventions for Out-of-Hospital Cardiac Arrest in Resource-Limited Settings: A Scoping Review Across Low, Middle, and High-Income Countries

BACKGROUND

Out-of-hospital cardiac arrest (OHCA) is a major global health challenge, characterized by poor survival outcomes worldwide. Resource-limited settings are burdened with suboptimal emergency response and worse outcomes than high-resource areas. Engaging the community in the response to OHCA has the potential to improve outcomes, although an overview of community interventions in resource-limited settings has not been provided.

OBJECTIVE

This review evaluated the scope of community-based OHCA interventions in resource-limited settings.

METHODS

Literature searches in electronic databases (MEDLINE, EMBASE, Global Health, CINAHL, Cochrane Central Register of Controlled Clinical Trials) and grey literature sources were performed. Abstract screening, full-text review, and data extraction of eligible studies were conducted independently by two reviewers. The PCC (Population, Concept, and Context) framework was used to assess study eligibility. Studies that evaluated community-based interventions for laypeople (Population), targeting emergency response activation, cardiopulmonary resuscitation (CPR), or automated external defibrillator (AED) use (Concept) in resource-limited settings (Context) were included. Resource-limited settings were identified by financial pressures (low-income or lower-middle-income country, according to World Bank data on year of publication) or geographical factors (setting described using keywords indicative of geographical remoteness in upper-middle-income or high-income country).

RESULTS

Among 14,810 records identified from literature searches, 60 studies from 28 unique countries were included in this review. Studies were conducted in high-income (n = 35), upper-middle-income (n = 2), lower-middle-income (n = 22), and low-income countries (n = 1). Community interventions included bystander CPR and/or AED training (n = 34), community responder programs (n = 8), drone-delivered AED networks (n = 6), dispatcher-assisted CPR programs (n = 4), regional resuscitation campaigns (n = 3), public access defibrillation programs (n = 3), and crowdsourcing technologies (n = 2). CPR and/or AED training were the only interventions evaluated in low-income, lower-middle-income, and upper-middle-income countries.

CONCLUSIONS

Interventions aimed at improving the community response to OHCA in resource-limited settings differ globally. There is a lack of reported studies from low-income countries and certain continental regions, including South America, Africa, and Oceania. Evaluation of interventions other than CPR and/or AED training in low- and middle-income countries is needed to guide community emergency planning and health policies.

Where there is no paramedic: the Sachigo Lake Wilderness Emergency Response Education Initiative

Aaron Orkin and colleague describe their collaboration that developed, delivered, and studied a community-based first response training program in a remote indigenous community in northern Canada.

Influence of sociodemographic and neighbourhood factors on self rated health and quality of life in rural communities: findings from the Agriproject in the Republic of Ireland

OBJECTIVE

To examine the influence of sociodemographic and neighbourhood factors on self rated health, quality of life, and perceived opportunities for change (as one measure of empowerment) in rural Irish communities.

DESIGN

Pooled data from cross sectional surveys two years apart.

SETTING

Respondents in four randomly selected rural district electoral divisions with a population size of between 750 and 2000.

PARTICIPANTS

1738 rural dwellers aged 15-93, 40.5% men, interviewed at two time points.

MAIN OUTCOME MEASURES

Determinants of self rated health (SRH), quality of life (QOL), and perceived opportunities for change, rated on a closed option Likert scale and assessed in multivariate logistic regression models.

MAIN RESULTS

Overall 23.8% of the sample reported poor SRH, 22.2% poor QOL, and 50.1% low perceived opportunities for change. Low financial security and dissatisfaction with work were each significantly associated with poor SRH (OR = 1.96 (1.50 to 2.56) and 1.54 (1.11 to 2.14)), with poor QOL (OR = 2.04 (1.56 to 2.68) and 1.87 (1.34 to 2.61). Concern about access to public services was significantly predictive of SRH (OR = 1.47 (1.11 to 1.94)) rather than access to health care (that is, hospital and GP services). There were distinct sex specific patterns and a generational effect for educational status in men. Variables associated with social networks and social support were less strongly predictive of SRH and QOL when economic measures were accounted for.

CONCLUSION

Inter-relations between indicators of health status, wellbeing, and deprivation are not well studied in rural communities. Material deprivation has a direct influence on both health status and quality of life, although immediate sources of support are relatively well preserved.

A geographic information system simulation model of EMS: reducing ambulance response time

Response time is a very important factor in determining the quality of prehospital EMS. Our objective was to model the response by Israeli ambulances and to offer model-derived strategies for improved deployment of ambulances to reduce response time. Using a geographic information system (GIS), a retrospective review of computerized ambulance call and dispatch logs was performed in two different regional districts, one large and urban and the other rural. All calls that were pinpointed geographically by the GIS were included, and their data were stratified by weekday and by daily shifts. Geographic areas (polygons) of, at most, 8 minutes response time were simulated for each of these subgroups to maximize the timely response of calls. Before using the GIS model, mean response times in the Carmel and Lachish districts were 12.3 and 9.2 minutes, respectively, with 34% and 62% of calls responded within 8 minutes. When ambulances were positioned within the modeled polygons, more than 94% of calls met the 8-minute criterion. The GIS simulation model presented in this study suggests that EMS could be more effective if a dynamic load-responsive ambulance deployment is adopted, potentially resulting in increased survival and cost-effectiveness.