Mortality patterns at a subnational level or across subpopulations are often used to examine the health of a population or for designing health policies. In large populations, the estimation of mortality indicators is rather straightforward. In small populations, however, death counts are driven by stochastic variation. In order to deal with this problem, demographers have proposed a variety of methods which all make use of knowledge about the shape of human mortality schedules. In practice, it is not readily clear how the methods relate to each other hindering informed decisions when choosing a method. We aim to provide guidance. First, we review recent demographic methods for the estimation of mortality schedules in small populations - emphasizing data requirements and ease of use. Second, by means of a simulation study, we evaluate the performance of three main classes of methods with respect to exposure size as well as sensitivity to the incorporated demographic knowledge. Often neglected by previous studies, we show that there is considerable variability in the performance across ages and regions and that this performance can depend on the choice of incorporated demographic knowledge.

翻译：在次国家层面或亚群体中的死亡率模式常被用于评估人口健康状况或制定卫生政策。对于大型人群，死亡率指标的估算相对直接。然而在小型人群中，死亡人数受随机变异驱动。为应对这一问题，人口学家提出了多种方法，这些方法均利用人类死亡率模式形状的先验知识。实践中，这些方法间的相互关系并不明确，阻碍了方法选择的科学决策。我们旨在提供指导：首先，系统梳理近年来用于小人群死亡率估计的人口学方法——重点阐述其数据需求与易用性；其次，通过模拟研究，评估三类主流方法在不同暴露人口规模下的表现及其对先验人口学知识的敏感性。本研究发现（此点常被前期研究所忽视）：不同年龄层与地区间的方法性能存在显著差异，且其表现可能取决于所整合先验人口学知识的选择。