Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Brighton, Brighton, United Kingdom, and Imperial College London, London, United Kingdom.
School of Life Sciences, University of Sussex, Brighton, Brighton, United Kingdom.
Clin Infect Dis. 2020 Jun 10;70(12):2476-2483. doi: 10.1093/cid/ciz678.
The 2013-2016 West African Ebola epidemic has been the largest to date with >11 000 deaths in the affected countries. The data collected have provided more insight into the case fatality ratio (CFR) and how it varies with age and other characteristics. However, the accuracy and precision of the naive CFR remain limited because 44% of survival outcomes were unreported.
Using a boosted regression tree model, we imputed survival outcomes (ie, survival or death) when unreported, corrected for model imperfection to estimate the CFR without imputation, with imputation, and adjusted with imputation. The method allowed us to further identify and explore relevant clinical and demographic predictors of the CFR.
The out-of-sample performance (95% confidence interval [CI]) of our model was good: sensitivity, 69.7% (52.5-75.6%); specificity, 69.8% (54.1-75.6%); percentage correctly classified, 69.9% (53.7-75.5%); and area under the receiver operating characteristic curve, 76.0% (56.8-82.1%). The adjusted CFR estimates (95% CI) for the 2013-2016 West African epidemic were 82.8% (45.6-85.6%) overall and 89.1% (40.8-91.6%), 65.6% (61.3-69.6%), and 79.2% (45.4-84.1%) for Sierra Leone, Guinea, and Liberia, respectively. We found that district, hospitalisation status, age, case classification, and quarter (date of case reporting aggregated at three-month intervals) explained 93.6% of the variance in the naive CFR.
The adjusted CFR estimates improved the naive CFR estimates obtained without imputation and were more representative. Used in conjunction with other resources, adjusted estimates will inform public health contingency planning for future Ebola epidemics, and help better allocate resources and evaluate the effectiveness of future inventions.
2013-2016 年西非埃博拉疫情是迄今为止规模最大的一次，受疫情影响的国家有超过 11000 人死亡。所收集的数据使我们对病死率（CFR）有了更深入的了解，以及其如何随年龄和其他特征而变化。然而，由于 44%的生存结果未报告，简单病死率的准确性和精密度仍受到限制。
我们使用增强回归树模型，对未报告的生存结果（即生存或死亡）进行推断，并对模型不完善进行校正，以估计未推断、推断和调整推断的 CFR。该方法使我们能够进一步识别和探讨 CFR 的相关临床和人口统计学预测因素。
模型的样本外表现（95%置信区间[CI]）良好：灵敏度为 69.7%（52.5-75.6%）；特异性为 69.8%（54.1-75.6%）；正确分类百分比为 69.9%（53.7-75.5%）；接受者操作特征曲线下面积为 76.0%（56.8-82.1%）。对 2013-2016 年西非疫情的调整后 CFR 估计值（95%CI）为：整体为 82.8%（45.6-85.6%），塞拉利昂、几内亚和利比里亚分别为 89.1%（40.8-91.6%）、65.6%（61.3-69.6%）和 79.2%（45.4-84.1%）。我们发现，地区、住院状态、年龄、病例分类和季度（病例报告按每三个月为间隔进行汇总）解释了简单 CFR 中 93.6%的差异。
调整后的 CFR 估计值改进了未推断的简单 CFR 估计值，更具代表性。与其他资源一起使用，调整后的估计值将为未来埃博拉疫情的公共卫生应急规划提供信息，并有助于更好地分配资源和评估未来发明的效果。
