Estimating the mortality associated with a specific mortality crisis event (for example, a pandemic, natural disaster, or conflict) is clearly an important public health undertaking. In many situations, deaths may be directly or indirectly attributable to the mortality crisis event, and both contributions may be of interest. The totality of the mortality impact on the population (direct and indirect deaths) includes the knock-on effects of the event, such as a breakdown of the health care system, or increased mortality due to shortages of resources. Unfortunately, estimating the deaths directly attributable to the event is frequently problematic. Hence, the excess mortality, defined as the difference between the observed mortality and that which would have occurred in the absence of the crisis event, is an estimation target. If the region of interest contains a functioning vital registration system, so that the mortality is fully observed and reliable, then the only modeling required is to produce the expected deaths counts, but this is a nontrivial exercise. In low- and middle-income countries it is common for there to be incomplete (or nonexistent) mortality data, and one must then use additional data and/or modeling, including predicting mortality using auxiliary variables. We describe and review each of these aspects, give examples of excess mortality studies, and provide a case study on excess mortality across states of the United States during the COVID-19 pandemic.

中文翻译：

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超额死亡率估计


估计与特定死亡危机事件（例如，大流行、自然灾害或冲突）相关的死亡率显然是一项重要的公共卫生工作。在许多情况下，死亡可能直接或间接归因于死亡危机事件，这两种贡献都可能引起人们的兴趣。死亡率对人口的总影响（直接和间接死亡）包括事件的连锁反应，例如医疗保健系统的崩溃，或由于资源短缺而增加的死亡率。不幸的是，估计直接归因于该事件的死亡人数经常是有问题的。因此，超额死亡率（定义为观察到的死亡率与在没有危机事件的情况下可能发生的死亡率之间的差值）是一个估计目标。如果感兴趣区域包含功能正常的生命登记系统，以便充分观察和可靠地观察死亡率，那么唯一需要的建模是生成预期的死亡计数，但这是一项非同寻常的工作。在低收入和中等收入国家，死亡率数据不完整（或不存在）是很常见的，因此必须使用额外的数据和/或建模，包括使用辅助变量预测死亡率。我们描述和回顾了这些方面中的每一个，给出了超额死亡率研究的示例，并提供了 COVID-19 大流行期间美国各州超额死亡率的案例研究。