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首页> 外文期刊>Journal of applied statistics >An elastic net penalized small area model combining unit-and area-level data for regional hypertension prevalence estimation
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An elastic net penalized small area model combining unit-and area-level data for regional hypertension prevalence estimation

机译:弹性净惩罚小区模型结合单位和区域级数据进行区域性高血压普遍估算

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摘要

Hypertension is a highly prevalent cardiovascular disease. It marks a considerable cost factor to many national health systems. Despite its prevalence, regional disease distributions are often unknown and must be estimated from survey data. However, health surveys frequently lack in regional observations due to limited resources. Obtained prevalence estimates suffer from unacceptably large sampling variances and are not reliable. Small area estimation solves this problem by linking auxiliary data from multiple regions in suitable regression models. Typically, either unit- or area-level observations are considered for this purpose. But with respect to hypertension, both levels should be used. Hypertension has characteristic comorbidities and is strongly related to lifestyle features, which are unit-level information. It is also correlated with socioeconomic indicators that are usually measured on the area-level. But the level combination is challenging as it requires multi-level model parameter estimation from small samples. We use a multi-level small area model with level-specific penalization to overcome this issue. Model parameter estimation is performed via stochastic coordinate gradient descent. A jackknife estimator of the mean squared error is presented. The methodology is applied to combine health survey data and administrative records to estimate regional hypertension prevalence in Germany.
机译:高血压是一种高度普遍的心血管疾病。它标志着许多国家卫生系统的成本因素。尽管它流行,但区域疾病分布往往是未知的,并且必须从调查数据估算。但是,由于资源有限,健康调查经常缺乏区域观察。获得的流行估计估计遭受了不可接受的大型采样差异,并且不可靠。小区估计通过将来自合适的回归模型中的多个区域链接到的辅助数据来解决这个问题。通常,为此目的考虑单位或区域级观察。但关于高血压,应使用两个水平。高血压具有特征性的合并症,与生活方式功能强烈相关,这是单位级信息。它还与通常在面积水平上测量的社会经济指标相关。但水平组合是具有挑战性的,因为它需要来自小样本的多级模型参数估计。我们使用多级小区域模型,具有级别特定的惩罚来克服这个问题。模型参数估计通过随机坐标梯度下降来执行。提出了平均方形错误的jackknife估计器。该方法适用于将健康调查数据和行政记录结合,以估算德国的区域高血压患病率。

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