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首页> 外文期刊>Emerging themes in epidemiology >Performance of small cluster surveys and the clustered LQAS design to estimate local-level vaccination coverage in Mali
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Performance of small cluster surveys and the clustered LQAS design to estimate local-level vaccination coverage in Mali

机译：小集群调查的性能和集群LQAS设计以估计马里地方疫苗接种的覆盖率

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Background Estimation of vaccination coverage at the local level is essential to identify communities that may require additional support. Cluster surveys can be used in resource-poor settings, when population figures are inaccurate. To be feasible, cluster samples need to be small, without losing robustness of results. The clustered LQAS (CLQAS) approach has been proposed as an alternative, as smaller sample sizes are required. Methods We explored (i) the efficiency of cluster surveys of decreasing sample size through bootstrapping analysis and (ii) the performance of CLQAS under three alternative sampling plans to classify local VC, using data from a survey carried out in Mali after mass vaccination against meningococcal meningitis group A. Results VC estimates provided by a 10 × 15 cluster survey design were reasonably robust. We used them to classify health areas in three categories and guide mop-up activities: i) health areas not requiring supplemental activities; ii) health areas requiring additional vaccination; iii) health areas requiring further evaluation. As sample size decreased (from 10 × 15 to 10 × 3), standard error of VC and ICC estimates were increasingly unstable. Results of CLQAS simulations were not accurate for most health areas, with an overall risk of misclassification greater than 0.25 in one health area out of three. It was greater than 0.50 in one health area out of two under two of the three sampling plans. Conclusions Small sample cluster surveys (10 × 15) are acceptably robust for classification of VC at local level. We do not recommend the CLQAS method as currently formulated for evaluating vaccination programmes.
机译：背景技术评估本地疫苗接种覆盖率对于确定可能需要更多支持的社区至关重要。当人口数字不准确时，可以在资源匮乏的环境中使用聚类调查。为了可行，聚类样本必须很小，而不会失去结果的稳健性。由于需要较小的样本量，因此建议使用聚类LQAS（CLQAS）方法。方法我们使用在马里接种脑膜炎球菌疫苗后在马里进行的一项调查的数据，探索了（​​i）通过自举分析减少样本量的集群调查的效率，以及（ii）在三种替代抽样计划下对本地VC进行分类的CLQAS的性能。脑膜炎A组。结果由10×15整群调查设计提供的VC估计值相当合理。我们使用它们将卫生区域分为三类，并指导扫荡活动：i）不需要补充活动的卫生区域; ii）需要额外接种疫苗的卫生区; iii）需要进一步评估的健康领域。随着样本量的减少（从10×15到10×3），VC和ICC估计的标准误差越来越不稳定。对于大多数健康区域，CLQAS模拟的结果并不准确，在三个健康区域中，有一个健康区域的总体错误分类风险大于0.25。在三个抽样计划中的两个抽样计划中，在两个卫生区中的一个卫生区中，该值大于0.50。结论小样本聚类调查（10×15）对于地方级别的VC分类具有可接受的鲁棒性。我们不建议采用目前为评估疫苗接种计划而制定的CLQAS方法。

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