Analysis of multiple casualty incidents - a prospective cohort study

Effects of mass casualty incidents on anxiety, depression, and post-traumatic stress disorder among doctors and nurses: a systematic review

OBJECTIVES

Doctors and nurses suffer different mental health conditions following traumatic incidents. We systematically synthesized existing evidence on the prevalence of anxiety, depression, and post-traumatic stress disorder (PTSD) and their associated risk factors among doctors and nurses following mass casualty incidents (MCIs).

STUDY DESIGN

Systematic review.

METHODS

Seven databases were searched (2010-2022) with peer-reviewed articles in English using the predefined keywords. Two reviewers screened the titles, abstracts, and full texts using the eligibility criteria and extracted data independently. We used the National Institutes of Health Quality Assessment Tools (NIH-QAT) and the Critical Appraisal Skills Programme checklist (CASP) to measure the quality appraisal of the included studies.

RESULTS

A total of 5170 articles were retrieved, and 2512 articles were assessed by title and abstract (53 were eligible for full-text review). Finally, we included 19 studies. Most were assessed as of fair quality with a considerable risk of bias. PTSD was the highest-reported mental health condition. Nurses reported higher mental conditions, particularly PTSD. Two sets of risk factors (personal and workplace) are associated with anxiety, depression, and PTSD were found.

CONCLUSIONS

MCIs have a significant impact on the mental health outcomes of emergency health workers. Preventive measures should be designed considering the high-risk group, personal, and organizational risk factors of mental health outcomes.

Evaluation of a Portable Blood Gas Analyzer for Prehospital Triage in Carbon Monoxide Poisoning: Instrument Validation Study

BACKGROUND

Carbon monoxide (CO) poisoning is an important cause of morbidity and mortality worldwide. Symptoms are mostly aspecific, making it hard to identify, and its diagnosis is usually made through blood gas analysis. However, the bulkiness of gas analyzers prevents them from being used at the scene of the incident, thereby leading to the unnecessary transport and admission of many patients. While multiple-wavelength pulse oximeters have been developed to discriminate carboxyhemoglobin (COHb) from oxyhemoglobin, their reliability is debatable, particularly in the hostile prehospital environment.

OBJECTIVE

The main objective of this pilot study was to assess whether the Avoximeter 4000, a transportable blood gas analyzer, could be considered for prehospital triage.

METHODS

This was a monocentric, prospective, pilot evaluation study. Blood samples were analyzed sequentially with 2 devices: the Avoximeter 4000 (experimental), which performs direct measurements on blood samples of about 50 µL by analyzing light absorption at 5 different wavelengths; and the ABL827 FLEX (control), which measures COHb levels through an optical system composed of a 128-wavelength spectrophotometer. The blood samples belonged to 2 different cohorts: the first (clinical cohort) was obtained in an emergency department and consisted of 68 samples drawn from patients admitted for reasons other than CO poisoning. These samples were used to determine whether the Avoximeter 4000 could properly exclude the diagnosis. The second (forensic) cohort was derived from the regional forensic center, which provided 12 samples from documented CO poisoning.

RESULTS

The mean COHb level in the clinical cohort was 1.7% (SD 1.8%; median 1.2%, IQR 0.7%-1.9%) with the ABL827 FLEX versus 3.5% (SD 2.3%; median 3.1%, IQR 2.2%-4.1%) with the Avoximeter 4000. Therefore, the Avoximeter 4000 overestimated COHb levels by a mean difference of 1.8% (95% CI 1.5%-2.1%). The consistency of COHb readings by the Avoximeter 4000 was excellent, with an intraclass correlation coefficient of 0.97 (95% CI 0.93-0.99) when the same blood sample was analyzed repeatedly. Using prespecified cutoffs (5% in nonsmokers and 10% in smokers), 3 patients (4%) had high COHb levels according to the Avoximeter 4000, while their values were within the normal range according to the ABL827 FLEX. Therefore, the specificity of the Avoximeter 4000 in this cohort was 95.6% (95% CI 87%-98.6%), and the overtriage rate would have been 4.4% (95% CI 1.4%-13%). Regarding the forensic samples, 10 of 12 (83%) samples were positive with both devices, while the 2 remaining samples were negative with both devices.

CONCLUSIONS

The limited difference in COHb level measurements between the Avoximeter 4000 and the control device, which erred on the side of safety, and the relatively low overtriage rate warrant further exploration of this device as a prehospital triage tool.

When disaster strikes: staff recall and the use of staff recall systems during mass patient influx at Norwegian emergency primary health care centers - a cross-sectional study

BACKGROUND

In Norway, planning for disasters has been specifically emphasized since the incidents on July 22nd, 2011. Every municipality is now legislated to have a contingency plan that includes plans for staff recall during situations with mass influx of patients. Whether the primary health care services in Norway are prepared for mass influx of patients remains unclear.

AIMS OF THE STUDY

The aims of this study were (1) to assess the experiences of head doctors at emergency primary health care centers (EPHCC) in Norway with mass influx of patients, (2) to explore mass influx and staff recall procedures in use, (3) to assess head doctors' experiences with staff recall systems, and (4) to assess their perspective on automatized staff recall systems. We also wanted to assess whether there were differences between small and large EPHCCs regarding whether they had plans in place.

METHODS

The study had a cross-sectional, multicenter design, using a self-developed questionnaire. The questionnaire was developed utilizing recommendations from the Delphi technique, including an expert group and piloting. A purposive sampling strategy was used, including head doctors from Norwegian EPHCCs (n = 169). Data were analyzed using the Statistical Package for the Social Sciences, and included descriptive statistics, Chi-Square tests and Shapiro-Wilks. Free-text answers were analyzed by content analysis.

RESULTS

A total of 64 head doctors responded to the questionnaire. The results show that 25% of the head doctors had experienced mass influx of patients at their EPHCC. In total 54.7% of Norwegian EPHCCs did not have disaster plans that consider mass influx situations. The majority of EPHCCs plan to recall staff one by one (60.3%) or through Short-Message-Systems (34.4%). Most EPHCCs had available telephone "alarm" lists (81.4%), that are updated regularly (60.9%). However, only 17.2% had plans that consider loss of mobile phone connection or internet. In total, 67,2% of the head doctors reported to have little experience with automatized staff recall systems, and 59,7% reported to have little knowledge about such systems. There were no significant difference between small and large EPHCCs in having plans or not.

CONCLUSION

Even though our results show that few EPHCCs experience mass influx of patients, it is important to be prepared when such incidents do occur. Our results indicate that it is still potential for improvement regarding plans for staff recall and implementation of staff recall systems at Norwegian EPHCCs. Involving national disaster medicine experts in the process of generating tools or checklists could aid when constructing disaster plans. Education and implementation of training for mass influx situations at all levels should always be highlighted.

The Epidemiological Profile of Multiple Casualty Incidents in Northern Spain: 2014-2020

OBJECTIVE

To describe the epidemiological profile of multiple casualty incidents (MCI) and contribute to the better understanding of their impacts in Northern Spain.

METHOD

Retrospective, population-based observational study of MCI between 2014 and 2020 in 5 autonomous communities (Aragón, Castilla y León, Galicia, the Basque Country and Principado de Asturias) that participated in the MCI Database of Northern Spain. Inclusion criteria was any incident with 4 or more patients needing ambulance mobilization. A total of 54 variables were collected. This study presents the most relevant results.

RESULTS

There were 253 MCI. Of these, 79.8% were road traffic accidents, 12.3% fires or explosions, 2.0% poisonings and 5.9% defined as others. Monthly average was 2.9 (SD = 0.35; EEM = 15.90), average of victims by MCI was 6.8 (CI95% 6.16 - 7.60). There were significantly (P < 0.05) more victims in 3 types of MCI (fires, poisonings, and others). We saw 37.7% of MCI involved 4 victims, 18.8% 5 victims, and 37.9% more than 5. Mean response time was 30.8 minutes (95% CI 28.6 - 33.1), longer in maritime incidents. A total of 67% (95% CI 64.5 - 69.5) of victims were mild.

CONCLUSIONS

Road traffic accidents are the most frequent MCI and minor injuries predominate. More than 50% of the MCI have 5 or fewer patients. Fires had significantly more mild patients and significantly more resources deployed. Maritime incidents had a significantly longer response time.

SPECTRa: An Online Tool for Simulating Prehospital Patient Care

OBJECTIVES

To (1) develop a simulation software environment to conduct prehospital research during the COVID-19 pandemic on paramedics' teamwork and use of mobile computing devices, and (2) establish its feasibility for use as a research and training tool.

BACKGROUND

Simulation-based research and training for prehospital environments has typically used live simulation, with highly realistic equipment and technology-enhanced manikins. However, such simulations are expensive, difficult to replicate, and require facilitators and participants to be at the same location. Although virtual simulation tools exist for prehospital care, it is unclear how best to use them for research and training.

METHODS

We present SPECTRa-Simulated Prehospital Emergency Care for Team Research-an online simulated prehospital environment that lets participants care concurrently for single or multiple patients remotely. Patient scenarios are designed using Laerdal's SimDesigner. SPECTRa records data about scenario states and participants' virtual interaction with the simulated patients. SPECTRa's supporting environment records participants' verbal communication and their visual and physical interactions with their interface and devices using Zoom conferencing and audiovisual recording. We discuss a pilot research implementation to assess SPECTRa's feasibility.

RESULTS

SPECTRa allows researchers to systematically test small-team interaction in single- or multipatient care scenarios and assess the impact of mobile devices on participants' assessment and care of patients. SPECTRa also supports pedagogical features that could allow prehospital educators to provide individual trainees or teams with online simulation training and evaluation.

CONCLUSIONS

SPECTRa, an online tool for simulating prehospital patient care, shows potential for remote healthcare research and training.

Prehospital Response Time Delays for Emergency Patients in Events of Concurrent Mass Casualty Incidents

Objective

We investigated the extent of delays in the response time of emergency medical services (EMS) as an impact of mass casualty incidences (MCIs) in the same area.

Methods

We defined an MCI case as an event that resulted in 6 or more patients being transported by EMS, and prehospital response time as the time from the call to arrival at the scene. We matched patients before and after MCIs by dividing them into categories of 3 hours before, 0-1 hour after, 1-2 hours after, and 2-3 hours after the MCIs. We compared prehospital response times using multiple linear regression.

Results

A total of 33,276 EMS-treated patients were matched. The prehospital response time for the category of 3 hours before the MCIs was 8.8 minutes (SD: 8.2), treated as the reference, whereas that for the category of 0-1 hour after the MCI was 11.3 minutes (P<0.01). The multiple linear regression analysis revealed that prehospital response time increased by 2.5 minutes (95% CI: 2.3-2.8) during the first hour and by 0.3 minutes (95% CI: 0.1-0.6) during the second hour after MCIs.

Conclusion

There were significant delays in the prehospital response time for emergency patients after MCIs, and it lasted for 2 hours as the spillover effect. (Disaster Med Public Health Preparedness. 2018;12:94–100)

Identifying Factors That May Influence Decision-Making Related to the Distribution of Patients During a Mass Casualty Incident

OBJECTIVE

We aimed to identify and seek agreement on factors that may influence decision-making related to the distribution of patients during a mass casualty incident.

METHODS

A qualitative thematic analysis of a literature review identified 56 unique factors related to the distribution of patients in a mass casualty incident. A modified Delphi study was conducted and used purposive sampling to identify peer reviewers that had either (1) a peer-reviewed publication within the area of disaster management or (2) disaster management experience. In round one, peer reviewers ranked the 56 factors and identified an additional 8 factors that resulted in 64 factors being ranked during the two-round Delphi study. The criteria for agreement were defined as a median score greater than or equal to 7 (on a 9-point Likert scale) and a percentage distribution of 75% or greater of ratings being in the highest tertile.

RESULTS

Fifty-four disaster management peer reviewers, with hospital and prehospital practice settings most represented, assessed a total of 64 factors, of which 29 factors (45%) met the criteria for agreement.

CONCLUSIONS

Agreement from this formative study suggests that certain factors are influential to decision-making related to the distribution of patients during a mass casualty incident. (Disaster Med Public Health Preparedness. 2018;12:101-108).

Epidemiology of special incidents: Results from national mortality and morbidity registry and the associated factors in Iran in 2014

Background

Special incidents are harmful events that can result in people’s death or injury. Despite registering special incidents’ data in Iran, no study has yet been conducted to identify the types, rates, mortality and morbidity of such incidents and their associated factors. The present study was conducted to assess the epidemiology of incidents and their associated factors during 2014 in Iran.

Methods

In this cross-sectional study, all special incidents of 2014 were examined. Data were initially collected by universities of medical sciences nationwide and then sent to the Disaster and Emergency Management Center in the Ministry of Health and Medical Education. The collected data were analyzed in this study using statistical tests of Chi-square and Pearson’s correlation coefficient using SPSS ver. 14.5.

Results

Out of 6,892 special incidents that occurred during 2014 in Iran, 6,781 cases were included, of which, the most prevalent were traffic crashes (71%), carbon monoxide poisoning (14%), drowning (3.5%), and other cases (11.5%) (which included suspicious deaths, explosions, group poisoning, quarrels, fires, falls from height, and building collapses). The incidents led to 37,313 injuries and 3,259 deaths, of which 78% of injuries and 75% of deaths were due to road traffic incidents.

Conclusion

Given to relationship between occurrence of the incidents and special holidays; such incidents can be reduced through preventive planning and education. We recommend annual monitoring of special incidents and further studies on the associated factors.

Usefulness of a multiplying factor in predicting the final number of victims during a mass casualty incident

OBJECTIVE

Whenever a mass casualty incident (MCI) occurs, it is essential to anticipate the final number of victims to dispatch the adequate number of ambulances. In France, the custom is to multiply the initial number of prehospital victims by 2-4 to predict the final number. However, no one has yet validated this multiplying factor (MF) as a predictive tool. We aimed to build a statistical model to predict the final number of victims from their initial count.

METHODS

We observed retrospectively over 30 years of MCIs triggered in a large urban area. We considered three types of events: explosions, fires, and road traffic accidents. We collected the initial and final numbers of victims, with distinction between deaths, critical victims (T1), and delayed or minimal victims (T2-T3). The MF was calculated for each category of victims according to each type of event. Using a Poisson multivariate regression, we calculated the incidence risk ratio (IRR) of the final number of T1 as a function of the initial deaths and the initial T2-T3 counts, while controlling for potential confounding variables.

RESULTS

Sixty-eight MCIs were included. The final number of T1 increased with the initial incidence of deaths [IRR: 1.8 (1.4-2.2)], the initial number of T2-T3 being greater than 12 [IRR: 1.6 (1.3-2.1)], and the presence of one or more explosion [IRR: 1.4 (1.1-1.8)].

CONCLUSION

The MF seems to be an appealing decision-making tool to anticipate the need for ambulance resources. In explosive MCIs, we recommend multiplying T1 by 1.4 to estimate final count and the need for supplementary advanced life support teams.

Epidemiology of Emergency Medical Services-Assessed Mass Casualty Incidents according to Causes

To effectively mitigate and reduce the burden of mass casualty incidents (MCIs), preparedness measures should be based on MCIs' epidemiological characteristics. This study aimed to describe the epidemiological characteristics and outcomes of emergency medical services (EMS)-assessed MCIs from multiple areas according to cause. Therefore, we extracted the records of all MCIs that involved ≥ 6 patients from an EMS database. All patients involved in EMS-assessed MCIs from six areas were eligible for this study, and their prehospital and hospital records were reviewed for a 1-year period. The EMS-assessed MCIs were categorized as being caused by fire accidents (FAs), road traffic accidents (RTAs), chemical and biological agents (CBs), and other mechanical causes (MECHs). A total of 362 EMS-assessed MCIs were identified, with a crude incidence rate of 0.6-5.0/100,000 population. Among these MCIs, 322 were caused by RTAs. The MCIs involved 2,578 patients, and 54.3% of these patients were women. We observed that the most common mechanism of injury varied according to MCI cause, and that a higher number of patients per incident was associated with a longer prehospital time. The highest hospital admission rate was observed for CBs (16 patients, 55.2%), and most patients in RTAs and MECHs experienced non-severe injuries. The total number of deaths was 32 (1.2%). An EMS-assessed MCI database was established using the EMS database and medical records review. Our findings indicate that RTA MCIs create a burden on EMS and emergency department resources, although CB MCIs create a burden on hospitals' resources.

An epidemiological approach to mass casualty incidents in the Principality of Asturias (Spain)

Background

Mass Casualty Incidents (MCI) have been rarely studied from epidemiological approaches. The objective of this study is to establish the epidemiological profile of MCI in the autonomous region of the Principality of Asturias (Spain) and analyse ambulance deployment and severity of patients.

Methods

This is a population-based prospective study run in 2014. Inclusion criteria for MCI is “every incident with four or more people affected that requires ambulance mobilisation”.

Results

Thirty-nine MCI have been identified in Asturias in 2014. Thirty-one (79 %) were road traffic accidents, three (7.5 %) fires and five (12.8 %) other types. Twenty-one incidents (56.7 %) had four patients, and only three of them (8 %) had seven or more patients. An average of 2.41 ambulances per incident were deployed (standard error = 0.18). Most of the patients per incident were minor injured patients (mean = 4; standard error = 0.2), and 0,26 were severe patients (standard error = 0.08). There was a positive significant correlation (p < 0.01) between the total number of patients and the total number of ambulances deployed and between the total number of patients and Advanced Life Support (ALS) ambulances deployed (p < 0.001). The total number of non-ALS ambulances was not related with the total number of patients.

Discussion

Population based research in MCI is essential to define MCI profile. Quantitative definition of MCI, adapted to resources, avoid selection bias and present a more accurate profile of MCI. As espected, road traffic accidents are the most frequent MCI in our region. This aspect is essential to plan training and response to MCI. Analysis of total response to MCI shows that for almost an hour, we should plan extra resources for daily emergencies. This data is an important issue to bear in mind when planning MCI response. The fact that most patients are classified as minor injured and more advanced life support units than needed are deployed shows that analysis of resources deployment and patient severity helps us to better plan future MCI response.

Conclusions

Road traffic accidents with minor injured patients are the most frequent MCI in our region. More advanced life support units than needed have been initially deployed, which might compromise response to daily emergencies during an MCI.

Comparing the Accuracy of Three Pediatric Disaster Triage Strategies: A Simulation-Based Investigation

Background

It is unclear which pediatric disaster triage (PDT) strategy yields the best accuracy or best patient outcomes.

Methods

We conducted a cross-sectional analysis on a sample of emergency medical services providers from a prospective cohort study comparing the accuracy and triage outcomes for 2 PDT strategies (Smart and JumpSTART) and clinical decision-making (CDM) with no algorithm. Participants were divided into cohorts by triage strategy. We presented 10-victim, multi-modal disaster simulations. A Delphi method determined patients’ expected triage levels. We compared triage accuracy overall and for each triage level (RED/Immediate, YELLOW/Delayed, GREEN/Ambulatory, BLACK/Deceased).

Results

There were 273 participants (71 JumpSTART, 122 Smart, and 81 CDM). There was no significant difference between Smart triage and CDM. When JumpSTART triage was used, there was greater accuracy than with either Smart (P<0.001; OR [odds ratio]: 2.03; interquartile range [IQR]: 1.30, 3.17) or CDM (P=0.02; OR: 1.76; IQR: 1.10, 2.82). JumpSTART outperformed Smart for RED patients (P=0.05; OR: 1.48; IQR: 1.01,2.17), and outperformed both Smart (P<0.001; OR: 3.22; IQR: 1.78,5.88) and CDM (P<0.001; OR: 2.86; IQR: 1.53,5.26) for YELLOW patients. Furthermore, JumpSTART outperformed CDM for BLACK patients (P=0.01; OR: 5.55; IQR: 1.47, 20.0).

Conclusion

Our simulation-based comparison suggested that JumpSTART triage outperforms both Smart and CDM. JumpSTART outperformed Smart for RED patients and CDM for BLACK patients. For YELLOW patients, JumpSTART yielded more accurate triage results than did Smart triage or CDM. (Disaster Med Public Health Preparedness. 2016;10:253–260)

Abnormal breathing of sudden cardiac arrest victims described by laypersons and its association with emergency medical service dispatcher-assisted cardiopulmonary resuscitation instruction

Background

Current guidelines for cardiopulmonary resuscitation (CPR) emphasise that emergency medical service (EMS) dispatchers should identify sudden cardiac arrest (CA) with abnormal breathing and assist lay rescuers performing CPR. However, lay rescuers description of abnormal breathing may be inconsistent, and it is unclear how EMS dispatchers provide instruction for CPR based on the breathing status of the CA victims described by laypersons.

Methods and results

To investigate the incidence of abnormal breathing and the association between the EMS dispatcher-assisted CPR instruction and layperson CPR, we retrospectively analysed 283 witnessed CA cases whose information regarding breathing status of CA victims was available from population-based prospective cohort data. In 169 cases (59.7%), laypersons described that the CA victims were breathing in various ways, and that the victims were ‘not breathing’ in 114 cases (40.3%). Victims described as breathing in various ways were provided EMS dispatch-instruction for CPR less frequently than victims described as ‘not breathing’ (27.8% (47/169) vs 84.2% (96/114); p<0.001). Multivariate logistic regression showed that EMS dispatch-instruction for CPR was associated significantly with layperson CPR (adjusted OR, 11.0; 95% CI, 5.72 to 21.2).

Conclusions

This population-based study indicates that 60% of CA victims showed agonal respiration, which was described as breathing in various ways at the time of EMS call. Although EMS dispatch-instruction was associated significantly with an increase in layperson CPR, abnormal breathing was associated with a much lower rate of CPR instruction and, in turn, was related to a much lower rate of bystander CPR.

Scientific framework for research on disaster and mass casualty incident in Korea: building consensus using Delphi method

We aimed to determine the scientific framework for research on disaster and mass casualty incident (MCI) in Korea, especially Korean terminology, feasible definition, and epidemiologic indices. The two staged policy Delphi method was performed by instructors of National Disaster Life Support (NDLS®) with the constructed questionnaire containing items based on the literature review. The first-stage survey was conducted by 11 experts through two rounds of survey for making issue and option. The second-stage survey was conducted by 35 experts for making a generalized group based consensus. Experts were selected among instructors of National Disaster Life Support Course. Through two staged Delphi survey experts made consensus: 1) the Korean terminology "jaenan" with "disaster" and "dajung-sonsang-sago" with "MCI"; 2) the feasible definition of "disaster" as the events that have an effect on one or more municipal local government area (city-county-district) or results in ≥ 10 of death or ≥ 50 injured victims; 3) the feasible definition of MCI as the events that result in ≥ 6 casualties including death; 4) essential 31 epidemiologic indices. Experts could determine the scientific framework in Korea for research on disaster medicine, considering the distinct characteristics of Korea and current research trends.

Incidence and mortality rates of disasters and mass casualty incidents in Korea: a population-based cross-sectional study, 2000-2009

The objective of study was to evaluate the incidence and mortality rates of disasters and mass casualty incidents (MCIs) over the past 10 yr in the administrative system of Korea administrative system and to examine their relationship with population characteristics. This was a population-based cross-sectional study. We calculated the nationwide incidence, as well as the crude mortality and injury incidence rates, of disasters and MCIs. The data were collected from the administrative database of the National Emergency Management Agency (NEMA) and from provincial fire departments from January 2000 to December 2009. A total of 47,169 events were collected from the NEMA administrative database. Of these events, 115 and 3,079 cases were defined as disasters and MCIs that occurred in Korea, respectively. The incidence of technical disasters/MCIs was approximately 12.7 times greater than that of natural disasters/MCIs. Over the past 10 yr, the crude mortality rates for disasters and MCIs were 2.36 deaths per 100,000 persons and 6.78 deaths per 100,000 persons, respectively. The crude injury incidence rates for disasters and MCIs were 25.47 injuries per 100,000 persons and 152 injuries per 100,000 persons, respectively. The incidence and mortality of disasters/MCIs in Korea seem to be low compared to that of trend around the world.