Epidemiology Consultant, London, UK.
Trop Med Int Health. 2010 May;15(5):540-6. doi: 10.1111/j.1365-3156.2010.02482.x. Epub 2010 Mar 8.
Vaccination programmes targeting disease elimination aim to achieve very high coverage levels (e.g. 95%). We calculated the precision of different clustered lot quality assurance sampling (LQAS) designs in computer-simulated surveys to provide local health officers in the field with preset LQAS plans to simply and rapidly assess programmes with high coverage targets.
We calculated sample size (N), decision value (d) and misclassification errors (alpha and beta) of several LQAS plans by running 10 000 simulations. We kept the upper coverage threshold (UT) at 90% or 95% and decreased the lower threshold (LT) progressively by 5%. We measured the proportion of simulations with < or =d individuals unvaccinated or lower if the coverage was set at the UT (pUT) to calculate beta (1-pUT) and the proportion of simulations with >d unvaccinated individuals if the coverage was LT% (pLT) to calculate alpha (1-pLT). We divided N in clusters (between 5 and 10) and recalculated the errors hypothesising that the coverage would vary in the clusters according to a binomial distribution with preset standard deviations of 0.05 and 0.1 from the mean lot coverage. We selected the plans fulfilling these criteria: alpha < or = 5% beta < or = 20% in the unclustered design; alpha < or = 10% beta < or = 25% when the lots were divided in five clusters.
When the interval between UT and LT was larger than 10% (e.g. 15%), we were able to select precise LQAS plans dividing the lot in five clusters with N = 50 (5 x 10) and d = 4 to evaluate programmes with 95% coverage target and d = 7 to evaluate programmes with 90% target.
These plans will considerably increase the feasibility and the rapidity of conducting the LQAS in the field.
针对消除疾病的疫苗接种计划旨在实现非常高的覆盖率（例如 95%）。我们计算了不同聚类批量质量保证抽样（LQAS）设计在计算机模拟调查中的精度，为现场的当地卫生官员提供预设的 LQAS 计划，以简单快速地评估具有高覆盖目标的计划。
我们通过运行 10,000 次模拟计算了几种 LQAS 计划的样本量（N）、决策值（d）和错误分类误差（alpha 和 beta）。我们将上限覆盖率阈值（UT）保持在 90%或 95%，并将下限阈值（LT）逐步降低 5%。如果覆盖率设置为 UT（pUT），则测量具有 <=d 个未接种或更低的个体的模拟比例（d 个个体未接种），如果覆盖率为 LT%（pLT），则测量具有 >d 个未接种个体的模拟比例（alpha）。我们将 N 分为簇（5 到 10 个），并假设簇中的覆盖率会根据预设的均值批覆盖率的 0.05 和 0.1 的标准偏差呈二项式分布进行变化，重新计算错误。我们选择符合以下标准的计划：在未聚类设计中，alpha <= 5%，beta <= 20%；当将批次分为 5 个集群时，alpha <= 10%，beta <= 25%。
当 UT 和 LT 之间的间隔大于 10%（例如 15%）时，我们能够选择精确的 LQAS 计划，将批次分为 5 个簇，N = 50（5x10）和 d = 4，以评估覆盖率目标为 95%的计划，以及 d = 7 以评估覆盖率目标为 90%的计划。
这些计划将大大提高现场进行 LQAS 的可行性和快速性。
