Cluster Lot Quality Assurance Sampling: Effect of Increasing the Number of Clusters on Classification Precision and Operational Feasibility

• Published in: The Journal of infectious diseases Vol. 210; no. suppl 1; pp. S341 - S346
• Main Authors: Okayasu, Hiromasa, Brown, Alexandra E., Nzioki, Michael M., Gasasira, Alex N., Takane, Marina, Mkanda, Pascal, Wassilak, Steven G. F., Sutter, Roland W.
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01.11.2014
• Subjects: Child, Preschool; EPIDEMIOLOGIC, VIROLOGIC, AND ENVIRONMENTAL SURVEILLANCE; Female; Health Services Research; Humans; Immunization; Immunization, Secondary - methods; Infant; Infant, Newborn; Local government; Lot quality assurance sampling; Lot Quality Assurance Sampling - methods; Male; Nigeria; Poliomyelitis; Poliomyelitis - prevention & control; Poliovirus vaccines; Poliovirus Vaccines - administration & dosage; Poliovirus Vaccines - immunology; Preventive medicine; Proportions; Sample size; Standard deviation; Vaccination; Nigeria; supplementary immunization activities; survey methods; cluster lot quality assurance sampling; Nigeria; polio eradication
• ISSN: 0022-1899; 1537-6613; 1537-6613
• DOI: 10.1093/infdis/jiu162

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.