PTSD is considered the most common negative psychological reactions among survivors following an earthquake. The present study sought to find out the determinants of PTSD in earthquake survivors using a systematic meta-analysis. Four electronic databases (PubMed, Embase, Web of Science, and PsycInfo) were used to search for observational studies about PTSD following earthquakes. The literature search, study selection, and data extraction were conducted independently by two authors. 52 articles were included in the study. Summary estimates, subgroup analysis, and publication bias tests were performed on the data. The prevalence of PTSD after earthquakes ranged from 4.10% to 67.07% in adults and from 2.50% to 60.00% in children. For adults, the significant predictors were being female, low education level or socio-economic status, prior trauma; being trapped, experiencing fear, injury, or bereavement during the disaster. For children, the significant predictors were being older age, high education level; being trapped, experiencing fear, injury, or bereavement, witnessing injury/death during the earthquakes. Our study provides implications for the understanding of risk factors for PTSD among earthquake survivors. Post-disaster mental health recovery programs that include early identification, on-going monitoring, and sustained psychosocial support are needed for earthquake survivors.

A number of studies have shown a range of negative psychological symptoms (e.g. depression) after exposure to natural disasters. The aim of this study was to determine risk factors for depression in both children and adults who have survived natural disasters. Four electronic databases (PubMed, Embase, Web of Science, and PsychInfo) were used to search for observational studies (case–control, cross-sectional, and cohort studies) about depression following natural disasters. The literature search, study selection, and data extraction were conducted independently by two authors. Thirty-one articles were included in the study, of which twenty included adult participants and eleven included child participants. Summary estimates were obtained using random-effects models. Subgroup analysis, sensitivity analysis, and publication bias tests were performed on the data. The prevalence of depression after natural disasters ranged from 5.8% to 54.0% in adults and from 7.5% to 44.8% in children. We found a number of risk factors for depression after exposure to natural disasters. For adults, the significant predictors were being female ;not married;holding religious beliefs; having poor education; prior trauma; experiencing fear, injury, or bereavement during the disaster; or losing employment or property, suffering house damage as a result of the disaster. For children, the significant predictors were prior trauma; being trapped during the disaster; experiencing injury, fear, or bereavement during the disaster; witnessing injury/death during the disaster; or having poor social support. The current analysis provides evidence of risk factors for depression in survivors of natural disasters. Further research is necessary to design interventions to improve the mental health of survivors of natural disasters.

Background: Description of the anatomical severity of injuries in trauma patients is important. While the Injury Severity Score has been regarded as the “gold standard” since its creation, several studies have indicated that the New Injury Severity Score is better. Therefore, we aimed to systematically evaluate and compare the accuracy of the Injury Severity Score and the New Injury Severity Score in predicting mortality. Methods: Two researchers independently searched the PubMed, Embase, and Web of Science databases and included studies from which the exact number of true-positive, false-positive, false-negative, and true-negative results could be extracted. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies checklist criteria. The meta-analysis was performed using Meta-DiSc. Meta-regression, subgroup analyses, and sensitivity analyses were conducted to determine the source(s) of heterogeneity and factor(s) affecting the accuracy of the New Injury Severity Score and the Injury Severity Score in predicting mortality. Results: The heterogeneity of the 11 relevant studies (total n = 11,866) was high (I² > 80%). The meta-analysis using a random-effects model resulted in sensitivity of 0.64, specificity of 0.93, positive likelihood ratio of 5.11, negative likelihood ratio of 0.27, diagnostic odds ratio of 27.75, and area under the summary receiver operator characteristic curve of 0.9009 for the Injury Severity Score; and sensitivity of 0.71, specificity of 0.87, positive likelihood ratio of 5.22, negative likelihood ratio of 0.20, diagnostic odds ratio of 24.74, and area under the summary receiver operating characteristic curve of 0.9095 for the New Injury Severity Score. Conclusion: The New Injury Severity Score and the Injury Severity Score have similar abilities in predicting mortality. Further research is required to determine the appropriate use of the Injury Severity Score or the New Injury Severity Score based on specific patient condition and trauma type.

The aim of this paper is to investigate medical efforts and injury profiles of victims of the Lushan earthquake admitted to three military hospitals. This study retrospectively investigated the clinical records of 266 admitted patients evacuated from the Lushan earthquake area. The 2005 version of the Abbreviated Injury Scale (AIS-2005) was used to identify the severity of each injury. Patient demographic data, complaints, diagnoses, injury types, prognosis, means of transportation, and cause of injury were all reviewed individually. The statistical analysis of the study was conducted primarily using descriptive statistics. Of the 266 patients, 213 (80.1%) were admitted in the first two days. A total of 521 injury diagnoses were recorded in 266 patients. Earthquake-related injuries were primarily caused by buildings collapsing (38.4%) and victims being struck by objects (33.8%); the most frequently injured anatomic sites were the lower extremities and pelvis (34.2%) and surface area of the body (17.9%). Fracture (41.5%) was the most frequent injury, followed by soft tissue injury (27.5%), but crush syndrome was relatively low (1.2%) due to the special housing structures in the Lushan area. The most commonly used procedure was suture and dressings (33.7%), followed by open reduction and internal fixation (21.9%).The results of this study help formulate recommendations to improve future disaster relief and emergency planning in remote, isolated, and rural regions of developing countries.

Background: Prehospital trauma triage is essential to get the right patient to the right hospital. However, the national field triage guidelines proposed by the American College of Surgeons have proven to be relatively insensitive when identifying severe traumas. Objective: This study aimed to build a prehospital triage model to predict severe trauma and enhance the performance of the national field triage guidelines. Methods: This was a multisite prediction study, and the data were extracted from the National Trauma Data Bank between 2017 and 2019. All patients with injury, aged 16 years of age or older, and transported by ambulance from the injury scene to any trauma center were potentially eligible. The data were divided into training, internal, and external validation sets of 672,309; 288,134; and 508,703 patients, respectively. As the national field triage guidelines recommended, age, 7 vital signs, and 8 injury patterns at the prehospital stage were included as candidate variables for model development. Outcomes were severe trauma with an Injured Severity Score ≥16 (primary) and critical resource use within 24 hours of emergency department arrival (secondary). The triage model was developed using an extreme gradient boosting model and Shapley additive explanation analysis. The model’s accuracy regarding discrimination, calibration, and clinical benefit was assessed. Results: At a fixed specificity of 0.5, the model showed a sensitivity of 0.799 (95% CI 0.797-0.801), an undertriage rate of 0.080 (95% CI 0.079-0.081), and an overtriage rate of 0.743 (95% CI 0.742-0.743) for predicting severe trauma. The model showed a sensitivity of 0.774 (95% CI 0.772-0.776), an undertriage rate of 0.158 (95% CI 0.157-0.159), and an overtriage rate of 0.609 (95% CI 0.608-0.609) when predicting critical resource use, fixed at 0.5 specificity. The triage model’s areas under the curve were 0.755 (95% CI 0.753-0.757) for severe trauma prediction and 0.736 (95% CI 0.734-0.737) for critical resource use prediction. The triage model’s performance was better than those of the Glasgow Coma Score, Prehospital Index, revised trauma score, and the 2011 national field triage guidelines RED criteria. The model’s performance was consistent in the 2 validation sets. Conclusions: The prehospital triage model is promising for predicting severe trauma and achieving an undertriage rate of <10%. Moreover, machine learning enhances the performance of field triage guidelines.