Center for Demography and Population Health, Florida State University, Tallahassee, FL, 32306-2240, USA.
Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil.
Demography. 2018 Aug;55(4):1363-1388. doi: 10.1007/s13524-018-0695-2.
High sampling variability complicates estimation of demographic rates in small areas. In addition, many countries have imperfect vital registration systems, with coverage quality that varies significantly between regions. We develop a Bayesian regression model for small-area mortality schedules that simultaneously addresses the problems of small local samples and underreporting of deaths. We combine a relational model for mortality schedules with probabilistic prior information on death registration coverage derived from demographic estimation techniques, such as Death Distribution Methods, and from field audits by public health experts. We test the model on small-area data from Brazil. Incorporating external estimates of vital registration coverage though priors improves small-area mortality estimates by accounting for underregistration and automatically producing measures of uncertainty. Bayesian estimates show that when mortality levels in small areas are compared, noise often dominates signal. Differences in local point estimates of life expectancy are often small relative to uncertainty, even for relatively large areas in a populous country like Brazil.
高采样变异性使小区域人口统计数据的估计变得复杂。此外，许多国家的人口登记系统不完善，各地区的覆盖质量差异很大。我们开发了一种用于小区域死亡率表的贝叶斯回归模型，该模型同时解决了小局部样本和死亡漏报的问题。我们将死亡率表的关系模型与通过人口估计技术（如死亡分布方法）和公共卫生专家现场审计获得的关于死亡登记覆盖的概率先验信息结合起来。我们在巴西的小区域数据上测试了该模型。通过先验信息纳入外部人口登记覆盖估计值，可以通过登记不足来改进小区域死亡率估计，并自动生成不确定性度量。贝叶斯估计表明，当比较小区域的死亡率水平时，噪声通常会主导信号。即使在像巴西这样人口众多的国家，相对于不确定性而言，当地预期寿命的局部点估计差异通常也很小。
