BACKGROUND: The protection that an influenza vaccine offers can vary significantly from person to person due to differences in immune systems, body types, and other factors. The question, then, is what is the value of efforts to reduce this variability such as making vaccines more personalized and tailored to individuals. METHODS: We developed a compartment model of the United States to simulate different influenza seasons and the impact of reducing the variability in responses to the influenza vaccine across the population. RESULTS: Going from a vaccine that varied in efficacy (0-30%) to one that had a uniform 30% efficacy for everyone averted 16.0-31.2 million cases, $1.9-$3.6 billion in direct medical costs, and $16.1-$42.7 billion in productivity losses. Going from 0-50% in efficacy to just 50% for everyone averted 27.7-38.6 million cases, $3.3-$4.6 billion in direct medical costs, and $28.8-$57.4 billion in productivity losses. Going from 0-70% to 70% averted 33.6-54.1 million cases, $4.0-$6.5 billion in direct medical costs, and $44.8-$64.7 billion in productivity losses. CONCLUSIONS: This study quantifies for policy makers, funders, and vaccine developers and manufacturers the potential impact of efforts to reduce variability in the protection that influenza vaccines offer (eg, developing vaccines that are more personalized to different individual factors).
BACKGROUND: The protection that an influenza vaccine offers can vary significantly from person to person due to differences in immune systems, body types, and other factors. The question, then, is what is the value of efforts to reduce this variability such as making vaccines more personalized and tailored to individuals. METHODS: We developed a compartment model of the United States to simulate different influenza seasons and the impact of reducing the variability in responses to the influenza vaccine across the population. RESULTS: Going from a vaccine that varied in efficacy (0-30%) to one that had a uniform 30% efficacy for everyone averted 16.0-31.2 million cases, $1.9-$3.6 billion in direct medical costs, and $16.1-$42.7 billion in productivity losses. Going from 0-50% in efficacy to just 50% for everyone averted 27.7-38.6 million cases, $3.3-$4.6 billion in direct medical costs, and $28.8-$57.4 billion in productivity losses. Going from 0-70% to 70% averted 33.6-54.1 million cases, $4.0-$6.5 billion in direct medical costs, and $44.8-$64.7 billion in productivity losses. CONCLUSIONS: This study quantifies for policy makers, funders, and vaccine developers and manufacturers the potential impact of efforts to reduce variability in the protection that influenza vaccines offer (eg, developing vaccines that are more personalized to different individual factors).
Authors: F E Andre; R Booy; H L Bock; J Clemens; S K Datta; T J John; B W Lee; S Lolekha; H Peltola; T A Ruff; M Santosham; H J Schmitt Journal: Bull World Health Organ Date: 2008-02 Impact factor: 9.408
Authors: Timothy M Dall; Michael V Storm; April P Semilla; Neil Wintfeld; Michael O'Grady; K M Venkat Narayan Journal: Am J Prev Med Date: 2014-12-10 Impact factor: 5.043
Authors: Nicola Luigi Bragazzi; Vincenza Gianfredi; Milena Villarini; Roberto Rosselli; Ahmed Nasr; Amr Hussein; Mariano Martini; Masoud Behzadifar Journal: Front Public Health Date: 2018-03-05
Authors: Sarah M Bartsch; Michael S Taitel; Jay V DePasse; Sarah N Cox; Renae L Smith-Ray; Patrick Wedlock; Tanya G Singh; Susan Carr; Sheryl S Siegmund; Bruce Y Lee Journal: Vaccine Date: 2018-10-16 Impact factor: 3.641