The Journal of infectious diseases · 2014
Cluster lot quality assurance sampling: effect of increasing the number of clusters on classification precision and operational feasibility.
СтатьяПоддержка исследования: вне правительства СШАПоддержка службы здравоохранения СШАНазвание и аннотация: Перевод в очереди
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Okayasu Hiromasa, Brown Alexandra E, Nzioki Michael M, Gasasira Alex N, Takane Marina, Mkanda Pascal, Wassilak Steven G F, Sutter Roland W
Аннотация
BACKGROUND: To assess the quality of supplementary immunization activities (SIAs), the Global Polio Eradication Initiative (GPEI) has used cluster lot quality assurance sampling (C-LQAS) methods since 2009. However, since the inception of C-LQAS, questions have been raised about the optimal balance between operational feasibility and precision of classification of lots to identify areas with low SIA quality that require corrective programmatic action.
METHODS: To determine if an increased precision in classification would result in differential programmatic decision making, we conducted a pilot evaluation in 4 local government areas (LGAs) in Nigeria with an expanded LQAS sample size of 16 clusters (instead of the standard 6 clusters) of 10 subjects each.
RESULTS: The results showed greater heterogeneity between clusters than the assumed standard deviation of 10%, ranging from 12% to 23%. Comparing the distribution of 4-outcome classifications obtained from all possible combinations of 6-cluster subsamples to the observed classification of the 16-cluster sample, we obtained an exact match in classification in 56% to 85% of instances.
CONCLUSIONS: We concluded that the 6-cluster C-LQAS provides acceptable classification precision for programmatic action. Considering the greater resources required to implement an expanded C-LQAS, the improvement in precision was deemed insufficient to warrant the effort.
METHODS: To determine if an increased precision in classification would result in differential programmatic decision making, we conducted a pilot evaluation in 4 local government areas (LGAs) in Nigeria with an expanded LQAS sample size of 16 clusters (instead of the standard 6 clusters) of 10 subjects each.
RESULTS: The results showed greater heterogeneity between clusters than the assumed standard deviation of 10%, ranging from 12% to 23%. Comparing the distribution of 4-outcome classifications obtained from all possible combinations of 6-cluster subsamples to the observed classification of the 16-cluster sample, we obtained an exact match in classification in 56% to 85% of instances.
CONCLUSIONS: We concluded that the 6-cluster C-LQAS provides acceptable classification precision for programmatic action. Considering the greater resources required to implement an expanded C-LQAS, the improvement in precision was deemed insufficient to warrant the effort.