Operations Research Center, University Miguel Hernández de Elche, Elche, Spain.
Department of Economic and Social Statistics, Trier University, Trier, Germany.
Psychometrika. 2022 Mar;87(1):344-368. doi: 10.1007/s11336-021-09808-8. Epub 2021 Sep 6.
Major depression is a severe mental disorder that is associated with strongly increased mortality. The quantification of its prevalence on regional levels represents an important indicator for public health reporting. In addition to that, it marks a crucial basis for further explorative studies regarding environmental determinants of the condition. However, assessing the distribution of major depression in the population is challenging. The topic is highly sensitive, and national statistical institutions rarely have administrative records on this matter. Published prevalence figures as well as available auxiliary data are typically derived from survey estimates. These are often subject to high uncertainty due to large sampling variances and do not allow for sound regional analysis. We propose a new area-level Poisson mixed model that accounts for measurement errors in auxiliary data to close this gap. We derive the empirical best predictor under the model and present a parametric bootstrap estimator for the mean squared error. A method of moments algorithm for consistent model parameter estimation is developed. Simulation experiments are conducted to show the effectiveness of the approach. The methodology is applied to estimate the major depression prevalence in Germany on regional levels crossed by sex and age groups.
重度抑郁症是一种严重的精神障碍，与死亡率显著升高有关。对其在区域层面上的流行程度进行量化，是公共卫生报告的一个重要指标。此外，它还为进一步研究该疾病的环境决定因素提供了重要的基础。然而，评估人群中重度抑郁症的分布情况具有一定的挑战性。该主题非常敏感，国家统计机构通常很少有关于这方面的行政记录。已公布的流行率数据以及现有的辅助数据通常是通过调查估计得出的。由于抽样方差较大，这些数据往往存在较大的不确定性，无法进行可靠的区域分析。我们提出了一种新的区域水平泊松混合模型，该模型考虑了辅助数据中的测量误差，以弥补这一差距。我们推导出了模型下的最佳经验预测值，并提出了一种用于均方误差的参数 bootstrap 估计量。还开发了一种用于一致模型参数估计的矩算法。通过模拟实验验证了该方法的有效性。该方法应用于估计德国按性别和年龄组划分的区域水平的重度抑郁症流行率。
