Login / Signup

Meningococcal serogroup B vaccination series initiation in the United States: A real-world claims data analysis.

Elizabeth R PacknettNicole M ZimmermanPatricia NovyLaura C MorganNnenna ChimeParinaz K Ghaswalla
Published in: Human vaccines & immunotherapeutics (2023)
In the United States (US), meningococcal serogroup B (MenB) vaccination has been recommended for 16-23-year-olds (preferably 16-18 years) based on shared clinical decision-making since 2015. MenB vaccine coverage (≥1 dose) by age 17 years has been reported, but initiation at older ages and by insurance type is unknown. In this retrospective cohort study, MarketScan claims data were analyzed to assess MenB vaccine series initiation (i.e. receipt of a first dose) during 2017-2020 among US commercially insured and Medicaid-covered individuals aged 16-18 and 19-23 years. Kaplan-Meier curves were generated to estimate series initiation at various times from index (latest of 1/1/2017 or 16 th /19 th birthday, depending on the cohort). Multivariable analyses were conducted to identify factors associated with series initiation. Among 1,450,354 Commercial and 1,140,977 Medicaid 16-18-year-olds, MenB vaccine series initiation rates within 3 years of each person's first eligibility were estimated to be 33% and 20%, respectively; among 1,857,628 Commercial and 747,483 Medicaid 19-23-year-olds, 3% and 1%, respectively. Factors identified to be significantly associated with increased likelihood of initiating a MenB vaccine series included co-administration of meningococcal serogroups ACWY (MenACWY) vaccine, younger age, female sex, nonwhite race (Medicaid only), New England or Middle Atlantic location (Commercial only), urban residence, and previous influenza vaccination. MenB vaccine series initiation among the studied US adolescents and young adults was low. There is a need for continued efforts to better understand barriers to the uptake of vaccines that are recommended based on shared clinical decision-making.
Keyphrases
  • health insurance
  • affordable care act
  • decision making
  • data analysis
  • machine learning
  • quality improvement
  • middle aged