title: Dangers of vaccine refusal near the herd immunity threshold: a modelling study.
abstract: BACKGROUND
  Childhood vaccination remains the focus of heated public debate. Parents struggle to understand the potential risks associated with vaccination but both parents and physicians assume that they understand the risks associated with infection. This study was done to characterise how modern vaccination practices have altered patient risks from infection.
  
  
  METHODS
  In this modelling study, we use mathematical analysis to explore how modern-era vaccination practices have changed the risks of severe outcomes for some infections by changing the landscape for disease transmission. We show these effects using published data from outbreaks in the USA for measles, chickenpox, and rubella. Calculation of risk estimation was the main outcome of this study.
  
  
  RESULTS
  Our calculations show that negative outcomes are 4·5 times worse for measles, 2·2 times worse for chickenpox, and 5·8 times worse for rubella than would be expected in a pre-vaccine era in which the average age at infection would have been lower.
  
  
  CONCLUSIONS
  As vaccination makes preventable illness rarer, for some diseases, it also increases the expected severity of each case. Because estimates of case risks rely on data for severity generated during a pre-vaccine era they underestimate negative outcomes in the modern post-vaccine epidemiological landscape. Physicians and parents should understand when making decisions about their children's health and safety that remaining unvaccinated in a predominantly vaccine-protected community exposes their children to the most severe possible outcomes for many preventable diseases.
  
  
  BACKGROUND
  None.
authors.affiliation: Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ, USA; Center for Discrete Mathematics and Theoretical Computer Science (DIMACS), Rutgers University, Piscataway, NJ, USA; Tufts University Initiative for the Forecasting and Modeling of Infectious Diseases, Tufts University, Medford, MA, USA. Electronic address: fefferman@aesop.rutgers.edu.
authors.forename: Nina H
authors.initials: NH
authors.lastname: Fefferman
authors.affiliation: Center for Discrete Mathematics and Theoretical Computer Science (DIMACS), Rutgers University, Piscataway, NJ, USA; Tufts University Initiative for the Forecasting and Modeling of Infectious Diseases, Tufts University, Medford, MA, USA; Department of Civil and Environmental Engineering, Tufts University, Medford, MA, USA.
authors.forename: Elena N
authors.initials: EN
authors.lastname: Naumova
mesh_terms: D000293:Adolescent; D000328:Adult; D002644:Chickenpox / Q000276:immunology / Q000517:prevention & control; D002648:Child; D002675:Child, Preschool; D005239:Fear; D005260:Female; D007722:Health Knowledge, Attitudes, Practice; D006801:Humans; D032521:Immunity, Herd / Q000276:immunology*; D007223:Infant; D008297:Male; D008457:Measles / Q000276:immunology / Q000517:prevention & control; D008875:Middle Aged; D008962:Models, Theoretical; D010290:Parents; D010820:Physicians; D045808:Refusal to Participate; D012306:Risk; D012409:Rubella / Q000276:immunology / Q000517:prevention & control; D012720:Severity of Illness Index; D014611:Vaccination / Q000379:methods; D014612:Vaccines / Q000276:immunology*; D055815:Young Adult
chemical_list: D014612:Vaccines