Children have unique medical needs compared to adults. Emergency medical services personnel need proper equipment and training to care for children. The purpose of this study is to characterize emergency medical services pediatric basic life support to help better understand the needs of children transported by ambulance. Pediatric basic life support patients were identified in this retrospective descriptive study. Descriptive statistics were used to examine incident location, possible injury, cardiac arrest, resuscitation attempted, chief complaint, primary symptom, provider's primary impression, cause of injury, and procedures performed during pediatric basic life support calls using the largest aggregate of emergency medical services data available, the 2013 National Emergency Medical Services Information System (NEMSIS) Public Release Research Data Set. Pediatric calls represented 7.4% of emergency medical services activations. Most pediatric patients were male (49.8%), White (40.0%), and of non-Hispanic origin (56.5%). Most incidents occurred in the home. Injury, cardiac arrest, and resuscitation attempts were highest in the 15 to 19 year old age group. Global complaints (37.1%) predominated by anatomic location and musculoskeletal complaints (26.9%) by organ system. The most common primary symptom was pain (30.3%) followed by mental/psychiatric (13.4%). Provider's top primary impression was traumatic injury (35.7%). The most common cause of injury was motor vehicle accident (32.3%). The most common procedure performed was patient assessment (27.4%). Median EMS system response time was 7 minutes (IQR: 5-12). Median EMS scene time was 12 minutes (IQR: 8-19). Median transport time was 14 minutes (IQR: 8-24). Median EMS total call time was 51 minutes (IQR: 33-77). The epidemiology of pediatric basic life support can help to guide efforts in both emergency medical services operations and training.
The ability to synthesize and analyze massive amounts of data is critical to the success of organizations, including those that involve global health. As countries become highly interconnected, increasing the risk for pandemics and outbreaks, the demand for big data is likely to increase. This requires a global health workforce that is trained in the effective use of big data. To assess implementation of big data training in global health, we conducted a pilot survey of members of the Consortium of Universities of Global Health. More than half the respondents did not have a big data training program at their institution. Additionally, the majority agreed that big data training programs will improve global health deliverables, among other favorable outcomes. Given the observed gap and benefits, global health educators may consider investing in big data training for students seeking a career in global health.
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