Javid Safeer Ahmad, Jamil Maheen, Noor Bushra, Umer Muhammad Rizwan, Asghar Areeba, Hassan Muddasir Reyaz, Akbar Amna, Hussain Shoukat, Khan Waleed
Trauma, Royal Sussex County Hospital, Brighton and Hove, GBR.
Medicine, William Harvey Hospital, East Kent Hospitals NHS Foundation Trust, Ashford, GBR.
Cureus. 2025 Jul 31;17(7):e89158. doi: 10.7759/cureus.89158. eCollection 2025 Jul.
This study presents a comprehensive retrospective analysis of 300 simulated mass casualty incidents (MCIs) in Pakistan from 2010 to 2024, aiming to evaluate emergency preparedness and response strategies. It is structured across four analytical domains: descriptive statistics, exploratory data analysis (EDA), inferential statistics, and predictive modeling. Findings reveal that terrorist attacks (n=115, 38.3%) and natural disasters (n=98, 32.7%) were the most common MCI types. Rural areas (n=104, 34.7%) experienced the highest mortality rate (mean = 17.2%) and morbidity burden (mean = 45%) due to prolonged EMS response times (mean = 39 minutes) and limited hospital infrastructure. EDA showed a moderate positive correlation between time to definitive care and mortality (r = 0.52), and between triage time and hospital stay duration (r = 0.43). Inferential tests confirmed significant associations between triage protocols and fatality outcomes (χ² = 14.23, p < 0.001). A linear regression model (adjusted R² = 0.61) identified time to care (β = 0.18), infrastructure status (β = 0.15), triage type (β = 0.12), and surge capacity gap (β = 0.17) as key predictors of mortality. Logistic regression showed that incidents under ICS or Unified Command (n=225, 75%) were 2.4 times more likely to lead to policy reform (p < 0.01), and those with post-incident reviews (n=156, 52%) had 1.8 times the likelihood of reform implementation. These results underscore the importance of structured command systems, standardized triage, and rural infrastructure investment in reducing MCI fatalities and strengthening system resilience.
本研究对2010年至2024年巴基斯坦300起模拟大规模伤亡事件（MCI）进行了全面的回顾性分析，旨在评估应急准备和应对策略。它分为四个分析领域：描述性统计、探索性数据分析（EDA）、推断性统计和预测建模。研究结果显示，恐怖袭击（n=115，38.3%）和自然灾害（n=98，32.7%）是最常见的MCI类型。农村地区（n=104，34.7%）由于紧急医疗服务响应时间延长（平均=39分钟）和医院基础设施有限，死亡率最高（平均=17.2%），发病负担也最重（平均=45%）。探索性数据分析表明，确定性治疗时间与死亡率之间存在中度正相关（r = 0.52），分诊时间与住院时间之间也存在中度正相关（r = 0.43）。推断性检验证实了分诊方案与死亡结果之间存在显著关联（χ² = 14.23，p < 0.001）。一个线性回归模型（调整后R² = 0.61）确定了治疗时间（β = 0.18）、基础设施状况（β = 0.15）、分诊类型（β = 0.12）和应急能力差距（β = 0.17）是死亡率的关键预测因素。逻辑回归显示，在ICS或统一指挥下的事件（n=225，75%）导致政策改革的可能性高出2.4倍（p < 0.01），而进行事后审查的事件（n=156，52%）实施改革的可能性高出1.8倍。这些结果强调了结构化指挥系统、标准化分诊和农村基础设施投资在降低MCI死亡率和增强系统复原力方面的重要性。
