Hund Lauren, Pagano Marcello
Department of Family and Community Medicine, University of New Mexico, 2400 Tucker Avenue Northeast Albuquerque, NM 87131, U.S.A.
Stat Med. 2014 Jul 20;33(16):2746-57. doi: 10.1002/sim.6145. Epub 2014 Mar 17.
Lot quality assurance sampling (LQAS) has a long history of applications in industrial quality control. LQAS is frequently used for rapid surveillance in global health settings, with areas classified as poor or acceptable performance on the basis of the binary classification of an indicator. Historically, LQAS surveys have relied on simple random samples from the population; however, implementing two-stage cluster designs for surveillance sampling is often more cost-effective than simple random sampling. By applying survey sampling results to the binary classification procedure, we develop a simple and flexible nonparametric procedure to incorporate clustering effects into the LQAS sample design to appropriately inflate the sample size, accommodating finite numbers of clusters in the population when relevant. We use this framework to then discuss principled selection of survey design parameters in longitudinal surveillance programs. We apply this framework to design surveys to detect rises in malnutrition prevalence in nutrition surveillance programs in Kenya and South Sudan, accounting for clustering within villages. By combining historical information with data from previous surveys, we design surveys to detect spikes in the childhood malnutrition rate.
批质量保证抽样（LQAS）在工业质量控制中的应用历史悠久。LQAS经常用于全球卫生环境中的快速监测，根据指标的二元分类将地区分为表现不佳或可接受。从历史上看，LQAS调查依赖于从总体中抽取的简单随机样本；然而，对于监测抽样实施两阶段整群设计通常比简单随机抽样更具成本效益。通过将调查抽样结果应用于二元分类程序，我们开发了一种简单灵活的非参数程序，将整群效应纳入LQAS样本设计，以适当增加样本量，在相关时考虑总体中有限数量的群。然后，我们使用这个框架来讨论纵向监测项目中调查设计参数的原则性选择。我们应用这个框架来设计调查，以检测肯尼亚和南苏丹营养监测项目中营养不良患病率的上升，并考虑到村庄内的整群情况。通过将历史信息与以前调查的数据相结合，我们设计调查以检测儿童营养不良率的峰值。
