Combining cluster surveys to estimate vaccination coverage: Experiences from Nigeria's multiple indicator cluster survey / national immunization coverage survey (MICS/NICS), 2016–17. Issue 39 (3rd September 2020)

• Record Type: Journal Article
• Authors: Rhoda, Dale A.; Wagai, John Ndegwa; Beshanski-Pedersen, Bo Robert; Yusafari, Yusuf; Sequeira, Jenny; Hayford, Kyla; Brown, David W.; Danovaro-Holliday, M. Carolina; Braka, Fiona; Ali, Daniel; Shuaib, Faisal; Okposen, Bassey; Nwaze, Eric; Olarewaju, Isiaka; Adeniran, Adeyemi; Kassogue, Modibo; Jobin, Denis; Ryman, Tove K.
• Is Part Of: Vaccine. Volume 38:Issue 39(2020)
• Page Start: 6174
• Page End: 6183
• Publication Date: 2020-09-03
• Subjects: Nigeria -- Immunization -- Vaccination coverage -- Multiple Indicator Cluster Survey (MICS) -- Pooled data -- Cluster survey; Vaccines -- Periodicals; 615.372
• DOI: 10.1016/j.vaccine.2020.05.058
• Languages: English
• ISSNs: 0264-410X

Abstract

Highlights: Nigeria's immunization stakeholders cooperated on the 2016–17 MICS/NICS survey. Extra survey clusters were added in 20 states to improve outcome precision. Data from MICS & supplementary clusters were pooled after passing a statistical test. Combined results were used to guide policy, but not as precise as originally hoped. We explore organizational aspects of cooperation & technical aspects of pooled data. Abstract: In 2015 immunization stakeholders in Nigeria were proceeding with plans that would have fielded two nationally representative surveys to estimate vaccination coverage at the same time. Rather than duplicate efforts and generate either conflicting or redundant results, the stakeholders collaborated to conduct a combined Multiple Indicator Cluster Survey (MICS) / National Immunization Coverage Survey (NICS) with MICS focusing on core sampling clusters and NICS adding supplementary clusters in 20 states, to improve precision of outcomes there. This paper describes the organizational and technical aspects of that collaboration, including details on design of the sample supplement and analysis of the pooled dataset. While complicated, the collaboration was successful; it yielded a unified set of relevant coverage estimates and fostered some novel sub-national results dissemination work.