论文标题
小面积估计人口率的空间方差平滑区域水平模型
A spatial variance-smoothing area level model for small area estimation of demographic rates
论文作者
论文摘要
准确的次国健康和人口统计指标的准确估计对于告知健康政策决策至关重要。许多国家使用复杂的家庭调查收集相关数据，但是当数据有限时，直接调查的加权估计小面积比例可能是不可靠的。将这些直接估计值视为响应数据的区域级别模型可以提高精度，但通常需要所有区域的直接估计器的已知采样差异。实际上，通常估算采样差异，因此标准方法不能解释不确定性的关键来源。为了说明估计采样方差的可变性，我们提出了一个层次的贝叶斯空间区域水平模型，该模型平滑了估计的均值和采样方差，以产生小面积比例的点和间隔估计值。我们的模型明确针对小面积比例的估计，而不是连续变量的手段，我们考虑了中度和低患病率事件的示例。我们通过模拟和应用程序来证明我们的方法的性能，以从人口统计和健康调查中进行疫苗接种覆盖率和HIV患病率数据。
Accurate estimates of subnational health and demographic indicators are critical for informing health policy decisions. Many countries collect relevant data using complex household surveys, but when data are limited, direct survey weighted estimates of small area proportions may be unreliable. Area level models treating these direct estimates as response data can improve precision but often require known sampling variances of the direct estimators for all areas. In practice, the sampling variances are typically estimated, so standard approaches do not account for a key source of uncertainty. In order to account for variability in the estimated sampling variances, we propose a hierarchical Bayesian spatial area level model that smooths both the estimated means and sampling variances to produce point and interval estimates of small area proportions. Our model explicitly targets estimation of small area proportions rather than means of continuous variables and we consider examples of both moderate and low prevalence events. We demonstrate the performance of our approach via simulation and application to vaccination coverage and HIV prevalence data from the Demographic and Health Surveys.
