Alexander Monica, Zagheni Emilio, Barbieri Magali
Department of Demography, University of California, Berkeley, 2232 Piedmont Avenue, Berkeley, CA, 94720-2120, USA.
Department of Sociology, University of Washington, Seattle, 211 Savery Hall, Box 353340, Seattle, WA, 98195-3340, USA.
Demography. 2017 Dec;54(6):2025-2041. doi: 10.1007/s13524-017-0618-7.
Reliable subnational mortality estimates are essential in the study of health inequalities within a country. One of the difficulties in producing such estimates is the presence of small populations among which the stochastic variation in death counts is relatively high, and thus the underlying mortality levels are unclear. We present a Bayesian hierarchical model to estimate mortality at the subnational level. The model builds on characteristic age patterns in mortality curves, which are constructed using principal components from a set of reference mortality curves. Information on mortality rates are pooled across geographic space and are smoothed over time. Testing of the model shows reasonable estimates and uncertainty levels when it is applied both to simulated data that mimic U.S. counties and to real data for French départements. The model estimates have direct applications to the study of subregional health patterns and disparities.
可靠的国家以下层面死亡率估计对于研究一个国家内部的健康不平等至关重要。生成此类估计的困难之一在于存在人口较少的地区，其中死亡人数的随机变化相对较高，因此潜在的死亡率水平不明。我们提出了一种贝叶斯分层模型来估计国家以下层面的死亡率。该模型基于死亡率曲线中的特征年龄模式构建，这些模式是使用一组参考死亡率曲线的主成分构建的。死亡率信息在地理空间上汇总，并随时间进行平滑处理。对该模型的测试表明，当将其应用于模拟美国各县的模拟数据以及法国各省的真实数据时，能得出合理的估计值和不确定性水平。该模型估计值可直接应用于次区域健康模式和差异的研究。
