Sustained reduction in vaccine-type invasive pneumococcal disease despite waning effects of a catch-up campaign in Kilifi, Kenya: A mathematical model based on pre-vaccination data

dc.contributor.authorOjal J
dc.contributor.authorFlasche S
dc.contributor.authorHammitt LL
dc.contributor.authorAkech D
dc.contributor.authorKiti MC
dc.contributor.authorKamau T
dc.contributor.authorAdetifa I
dc.contributor.authorNurhonen M
dc.contributor.authorScott JAG
dc.contributor.authorAuranen K
dc.contributor.organizationfi=tilastotiede|en=Statistics|
dc.contributor.organization-code1.2.246.10.2458963.20.42133013740
dc.converis.publication-id27628433
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/27628433
dc.date.accessioned2022-10-27T12:17:02Z
dc.date.available2022-10-27T12:17:02Z
dc.description.abstractBackground: In 2011, Kenya introduced the 10-valent pneumococcal conjugate vaccine together with a catch-up campaign for children aged <5 years in Kilifi County. In a post-vaccination surveillance study based in Kilifi, there was a substantial decline in invasive pneumococcal disease (IPD). However, given the continued circulation of the vaccine serotypes, it is possible that vaccine-serotype disease may re-emerge once the effects of the catch-up campaign wear off.Methods: We developed, a compartmental, age-structured dynamic model of pneumococcal carriage and invasive disease for three serotype groups: the 10-valent vaccine serotypes and two groups of non vaccine serotypes based on their susceptibility to mutual competition. The model was calibrated to age- and serotype-specific data on carriage and IPD in the pre-vaccination era and used to predict carriage prevalence and IPD up to ten years post-vaccination in Kilifi. The model was validated against the observed carriage prevalence after vaccine introduction.Results: The model predicts a sustained reduction in vaccine-type pneumococcal carriage prevalence from 33% to 8% in infants and from 30% to 8% in 1-5 year olds over the 10-year period following vaccine introduction. The incidence of IPD is predicted to decline across all age groups resulting in an overall reduction of 56% in the population, corresponding to 10.4 cases per 100,000 per year. The vaccine-type IPD incidence is estimated to decline by 83% while non-vaccine-type IPD incidence is predicted to increase by 52%. The model's predictions of carriage prevalence agrees well with the observed data in the first five years post-vaccination.Conclusion: We predict a sustained and substantial decline in IPD through PCV vaccination and that the current regimen is insufficient to fully eliminate vaccine-serotype circulation in the model. We show that the observed impact is likely to be sustained despite waning effects of the catch-up campaign. (C) 2017 The Author(s). Published by Elsevier Ltd.
dc.format.pagerange4561
dc.format.pagerange4568
dc.identifier.jour-issn0264-410X
dc.identifier.olddbid174440
dc.identifier.oldhandle10024/157534
dc.identifier.urihttps://www.utupub.fi/handle/11111/34328
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0264410X17309155?via=ihub
dc.identifier.urnURN:NBN:fi-fe2021042717561
dc.language.isoen
dc.okm.affiliatedauthorAuranen, Kari
dc.okm.discipline112 Statistics and probabilityen_GB
dc.okm.discipline3111 Biomedicineen_GB
dc.okm.discipline3121 Internal medicineen_GB
dc.okm.discipline112 Tilastotiedefi_FI
dc.okm.discipline3111 Biolääketieteetfi_FI
dc.okm.discipline3121 Sisätauditfi_FI
dc.okm.internationalcopublicationinternational co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA1 ScientificArticle
dc.publisherELSEVIER SCI LTD
dc.publisher.countryUnited Kingdomen_GB
dc.publisher.countryBritanniafi_FI
dc.publisher.country-codeGB
dc.relation.doi10.1016/j.vaccine.2017.07.019
dc.relation.ispartofjournalVaccine
dc.relation.issue35
dc.relation.volume35
dc.source.identifierhttps://www.utupub.fi/handle/10024/157534
dc.titleSustained reduction in vaccine-type invasive pneumococcal disease despite waning effects of a catch-up campaign in Kilifi, Kenya: A mathematical model based on pre-vaccination data
dc.year.issued2017

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