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Clustered lot quality assurance sampling to assess immunisation coverage: increasing rapidity and maintaining precision
Author(s) -
Pezzoli Lorenzo,
Andrews Nick,
Ronveaux Olivier
Publication year - 2010
Publication title -
tropical medicine and international health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.056
H-Index - 114
eISSN - 1365-3156
pISSN - 1360-2276
DOI - 10.1111/j.1365-3156.2010.02482.x
Subject(s) - lot quality assurance sampling , statistics , quality assurance , sample size determination , confidence interval , acceptance sampling , negative binomial distribution , sampling (signal processing) , mathematics , medicine , cluster sampling , computer science , population , external quality assessment , filter (signal processing) , environmental health , poisson distribution , pathology , computer vision
Summary Objective 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. Methods 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 ≤ 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 ≤ 5% beta ≤ 20% in the unclustered design; alpha ≤ 10% beta ≤ 25% when the lots were divided in five clusters. Result 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 × 10) and d = 4 to evaluate programmes with 95% coverage target and d = 7 to evaluate programmes with 90% target. Conclusion These plans will considerably increase the feasibility and the rapidity of conducting the LQAS in the field.