Evaluating the Effectiveness of Vaccines Using a Regression Discontinuity Design

Nicole E. Basta, M. Elizabeth Halloran

Research output: Contribution to journalReview articlepeer-review


The regression discontinuity design (RDD), first proposed in the educational psychology literature and popularized in econometrics in the 1960s, has only recently been applied to epidemiologic research. A critical aim of infectious disease epidemiologists and global health researchers is to evaluate disease prevention and control strategies, including the impact of vaccines and vaccination programs. RDDs have very rarely been used in this context. This quasi-experimental approach using observational data is designed to quantify the effect of an intervention when eligibility for the intervention is based on a defined cutoff such as age or grade in school, making it ideally suited to estimating vaccine effects given that many vaccination programs and mass-vaccination campaigns define eligibility in this way. Here, we describe key features of RDDs in general, then specific scenarios, with examples, to illustrate that RDDs are an important tool for advancing our understanding of vaccine effects. We argue that epidemiologic researchers should consider RDDs when evaluating interventions designed to prevent and control diseases. This approach can address a wide range of research questions, especially those for which randomized clinical trials would present major challenges or be infeasible. Finally, we propose specific ways in which RDDs could advance future vaccine research.

Original languageEnglish (US)
Pages (from-to)987-990
Number of pages4
JournalAmerican journal of epidemiology
Issue number6
StatePublished - Jun 1 2019

Bibliographical note

Funding Information:
Author affiliations: Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota (Nicole E. Basta); Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington (M. Elizabeth Halloran); and Department of Biostatistics, University of Washington, Seattle, Washington (M. Elizabeth Halloran). This work was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (awards R01 AI132496 (PI: N.E.B.) and R37 AI032042 (PI: M.E.H.)).

Publisher Copyright:
© The Author(s) 2019.


  • Causal Inference
  • Effectiveness
  • Efficacy
  • Quasi-Experimental Methods
  • Regression Discontinuity
  • Vaccines

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