Clustered lot quality assurance sampling: a pragmatic tool for timely assessment of vaccination coverage.
OBJECTIVES: To evaluate oral poliovirus vaccine (OPV) coverage of the November 2009 round in five Northern Nigeria states with ongoing wild poliovirus transmission using clustered lot quality assurance sampling (CLQAS). METHODS: We selected four local government areas in each pre-selected state and sampled six clusters of 10 children in each Local Government Area, defined as the lot area. We used three decision thresholds to classify OPV coverage: 75-90%, 55-70% and 35-50%. A full lot was completed, but we also assessed in retrospect the potential time-saving benefits of stopping sampling when a lot had been classified. RESULTS: We accepted two local government areas (LGAs) with vaccination coverage above 75%. Of the remaining 18 rejected LGAs, 11 also failed to reach 70% coverage, of which four also failed to reach 50%. The average time taken to complete a lot was 10 h. By stopping sampling when a decision was reached, we could have classified lots in 5.3, 7.7 and 7.3 h on average at the 90%, 70% and 50% coverage targets, respectively. CONCLUSIONS: Clustered lot quality assurance sampling was feasible and useful to estimate OPV coverage in Northern Nigeria. The multi-threshold approach provided useful information on the variation of IPD vaccination coverage. CLQAS is a very timely tool, allowing corrective actions to be directly taken in insufficiently covered areas.
Item Type | Article |
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Keywords | Child, Preschool, Cluster Analysis, Female, Humans, Immunization Programs, Infant, *Lot Quality Assurance Sampling/methods, Male, Nigeria, Poliovirus Vaccine, Oral/*administration & dosage, Quality Assurance, Health Care, Vaccination/standards/*statistics & numerical data, Child, Preschool, Cluster Analysis, Female, Humans, Immunization Programs, Infant, Lot Quality Assurance Sampling, methods, Male, Nigeria, Poliovirus Vaccine, Oral, administration & dosage, Quality Assurance, Health Care, Vaccination, standards, statistics & numerical data |
ISI | 292290300013 |