亚国家级麻疹常规免疫覆盖率估计的时空平滑模型及其复杂调查数据应用
Space-time smoothing models for sub-national measles routine immunization coverage estimation with complex survey data
AI总结：
针对亚国家级常规免疫覆盖率估计问题，提出一种利用复杂调查数据并整合补充免疫活动信息的时空平滑模型，用于尼日利亚MCV1覆盖率估计，可高效实现。
摘要：
尽管全球麻疹疫苗接种取得了显著进展，但在许多低收入和中等收入国家，麻疹疾病负担仍然很高。在此类高负担环境中，控制麻疹的一项关键公共卫生策略是开展补充免疫活动（SIAs），以大规模疫苗接种运动的形式进行，同时通过常规免疫（RI）计划提供定期接种。为了实现RI和SIAs的均衡实施，对亚国家级RI特异性覆盖率进行稳健测量至关重要。在本文中，我们开发了一种时空平滑模型，利用复杂调查数据估计亚国家级含麻疹疫苗第一剂（MCV1）的RI特异性覆盖率。本研究的应用动机是估计尼日利亚36个州和联邦首都特区的RI特异性MCV1覆盖率。数据来源于2003年至2018年间在尼日利亚进行的四次人口与健康调查、三次多指标类集调查和两次国家营养与健康调查。我们的方法整合了世界卫生组织发布的SIA日历信息，并考虑了SIAs对横断面调查所测量的总体MCV1覆盖率的影响。该模型可用于分析具有不同数据收集方案的多次调查数据，并构建反映各种抽样设计的不确定性的覆盖率估计。我们的方法可以通过集成嵌套拉普拉斯近似（INLA）高效实现。
英文摘要：
Despite substantial advances in global measles vaccination, measles disease burden remains high in many low- and middle-income countries. A key public health strategy for controling measles in such high-burden settings is to conduct supplementary immunization activities (SIAs) in the form of mass vaccination campaigns, in addition to delivering scheduled vaccination through routine immunization (RI) programs. To achieve balanced implementations of RI and SIAs, robust measurement of sub-national RI-specific coverage is crucial. In this paper, we develop a space-time smoothing model for estimating RI-specific coverage of the first dose of measles-containing-vaccines (MCV1) at sub-national level using complex survey data. The application that motivated this work is estimation of the RI-specific MCV1 coverage in Nigeria's 36 states and the Federal Capital Territory. Data come from four Demographic and Health Surveys, three Multiple Indicator Cluster Surveys, and two National Nutrition and Health Surveys conducted in Nigeria between 2003 and 2018. Our method incorporates information from the SIA calendar published by the World Health Organization and accounts for the impact of SIAs on the overall MCV1 coverage, as measured by cross-sectional surveys. The model can be used to analyze data from multiple surveys with different data collection schemes and construct coverage estimates with uncertainty that reflects the various sampling designs. Implementation of our method can be done efficiently using integrated nested Laplace approximation (INLA).
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