Bayesian model averaging with change points to assess the impact of vaccination and public health interventions

Esra Kürüm*, Joshua L. Warren, Cynthia Schuck-Paim, Roger Lustig, Joseph A. Lewnard, Rodrigo Fuentes, Christian A.W. Bruhn, Robert J. Taylor, Lone Simonsen, Daniel M. Weinberger

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


Background: Pneumococcal conjugate vaccines (PCVs) prevent invasive pneumococcal disease and pneumonia. However, some low-and middle-income countries have yet to introduce PCV into their immunization programs due, in part, to lack of certainty about the potential impact. Assessing PCV benefits is challenging because specific data on pneumococcal disease are often lacking, and it can be difficult to separate the effects of factors other than the vaccine that could also affect pneumococcal disease rates. Methods: We assess PCV impact by combining Bayesian model averaging with change-point models to estimate the timing and magnitude of vaccine-associated changes, while controlling for seasonality and other covariates. We applied our approach to monthly time series of age-stratified hospitalizations related to pneumococcal infection in children younger 5 years of age in the United States, Brazil, and Chile. Results: Our method accurately detected changes in data in which we knew true and noteworthy changes occurred, i.e., in simulated data and for invasive pneumococcal disease. Moreover, 24 months after the vaccine introduction, we detected reductions of 14%, 9%, and 9% in the United States, Brazil, and Chile, respectively, in all-cause pneumonia (ACP) hospitalizations for age group 0 to <1 years of age. Conclusions: Our approach provides a flexible and sensitive method to detect changes in disease incidence that occur after the introduction of a vaccine or other intervention, while avoiding biases that exist in current approaches to time-trend analyses.

Original languageEnglish
Issue number6
Pages (from-to)889-897
Number of pages9
Publication statusPublished - 2017
Externally publishedYes

Bibliographical note

Funding Information:
Submitted 29 February 2016; accepted 19 July 2017. From the aDepartment of Statistics, University of California, Riverside, CA; bDepartment of Biostatistics, Yale School of Public Health, New Haven, CT; cSage Analytica, Bethesda, MD; dDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT; eDpto. de Epidemiología, DIPLAS, Ministerio de Salud, Chile; and fDepartment of Global Health, Milken Institute School of Public Health, George Wash-ington University, Washington, DC. This work was funded by Bill and Melinda Gates Foundation award number OPP1114733. This work was also partially funded by #P30AG021342 NIH/NIA (Scholar at the Claude D. Pepper Older Americans Indepen-dence Center at Yale University School of Medicine), UL1 TR000142, and R56 AI110449-01A1. D.M.W. had received an investigator-initiated research grant from Pfizer and consulting fees from Pfizer, Merck, and Affinivax. The other authors have no conflicts to report. Data and code availability: Data sets are available at, and R code is available as supplementary material. Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article ( Correspondence: Esra Kürüm, Department of Statistics, University of Cali-fornia, Riverside, CA 92521. E-mail: Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc.This is an open access article distributed under the Creative Commons Attribu-tion License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ISSN: 1044-3983/17/2806-0889 DOI: 10.1097/EDE.0000000000000719

Publisher Copyright:
© Copyright 2017 The Author(s).

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