Using Pneumococcal Carriage Data to Monitor Postvaccination Changes in the Incidence of Pneumococcal Otitis Media

Stefan Flasche, Noga Givon-Lavi, Ron Dagan

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Pneumococcal conjugate vaccines (PCVs) have substantially reduced the burden of pneumococcal disease, including the incidence of otitis media (OM). However, in most countries, no surveillance exists to monitor the change in pneumococcal OM incidence after the introduction of PCVs. We explored whether measuring pneumococcal carriage was a useful surrogate for monitoring postvaccination changes in the incidence of pneumococcal OM. The 7-valent PCV was introduced to Israel's national immunization program in July 2009 and gradually replaced by the 13-valent PCV starting in November 2010. Each day since 2009, nasopharyngeal swabs have been obtained from the first 4 Bedouin children and the first 4 Jewish children who were younger than 5 years old and attended a pediatric emergency room in southern Israel. During the same time, OM surveillance in southern Israel included all children younger than 2 years of age who were diagnosed with OM and had undergone a middle-ear fluid culture. The relative change in the prevalence of vaccine-serotype (VT) pneumococcal carriage was predictive of the relative change in incidence of OM due to VT pneumococcus. However, the serotype replacement observed in non-VT carriage is not paralleled in the incidence of OM due to non-VT pneumococcus. This could indicate that there are more complex mechanisms of the immune response involved in preventing initial and consecutive episodes of OM, which has been changed through declining prevalence of the most virulent serotypes as a result of vaccination.

Original languageEnglish
Pages (from-to)652-659
Number of pages8
JournalAmerican Journal of Epidemiology
Volume184
Issue number9
DOIs
StatePublished - 1 Nov 2016
Externally publishedYes

Keywords

  • Streptococcus pneumoniae
  • nasopharyngeal carriage
  • otitis media
  • prediction model

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