Before the introduction of the heptavalent pneumococcal conjugate vaccine (Prevnar-7), the relative prevalence of serotypes of Streptococcus pneumoniae was fairly stable worldwide. We sought to develop a statistical tool to predict the relative frequency of different serotypes among disease isolates in the pre- and post-Prevnar-7 eras using the limited amount of data that is widely available.
We initially used pre-Prevnar-7 carriage prevalence and estimates of invasiveness derived from case-fatality data as predictors for the relative abundance of serotypes causing invasive pneumococcal disease during the pre- and post-Prevnar-7 eras, using negative binomial regression. We fit the model to pre-Prevnar-7 invasive pneumococcal disease data from England and Wales and used these data to (1) evaluate the performance of the model using several datasets and (2) evaluate the utility of the country-specific carriage data. We then fit an alternative model that used polysaccharide structure, a correlate of prevalence that does not require country-specific information and could be useful in determining the postvaccine population structure, as a predictor.
Predictions from the initial model fit data from several pediatric populations in the pre-Prevnar-7 era. After the introduction of Prevnar-7, the model still had a good negative predictive value, though substantial unexplained variation remained. The alternative model had a good negative predictive value but poor positive predictive value. Both models demonstrate that the pneumococcal population follows a somewhat predictable pattern even after vaccination.
This approach provides a preliminary framework to evaluate the potential patterns and impact of serotypes causing invasive pneumococcal disease.
From the aDepartment of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, MA; bDepartment of Microbiological Surveillance and Research, Statens Serum Institute, Copenhagen, Denmark; cDepartment of Immunisation, Hepatitis and Blood Safety, Health Protection Agency, London, United Kingdom; dDepartment of Mathematics and Statistics, Strathclyde University, Glasgow, United Kingdom; eCentre for Geographic Medicine Research-Coast, Kilifi, Kenya; and fCenter for Communicable Disease Dynamics and Department of Epidemiology, Harvard School of Public Health, Boston, MA.
Submitted 19 May 2010; accepted 7 September 2010; posted 28 December 2010.
Supported by the NIH NRSA training program T32 A1007535 and by NIH research grant R01 AI048935 (to M.L.).
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Correspondence: Daniel M. Weinberger, 665 Huntington Ave, Boston, MA 02115. E-mail: firstname.lastname@example.org.