title: Evaluation of targeted influenza vaccination strategies via population modeling.
abstract: BACKGROUND
  Because they can generate comparable predictions, mathematical models are ideal tools for evaluating alternative drug or vaccine allocation strategies. To remain credible, however, results must be consistent. Authors of a recent assessment of possible influenza vaccination strategies conclude that older children, adolescents, and young adults are the optimal targets, no matter the objective, and argue for vaccinating them. Authors of two earlier studies concluded, respectively, that optimal targets depend on objectives and cautioned against changing policy. Which should we believe?
  
  
  RESULTS
  In matrices whose elements are contacts between persons by age, the main diagonal always predominates, reflecting contacts between contemporaries. Indirect effects (e.g., impacts of vaccinating one group on morbidity or mortality in others) result from off-diagonal elements. Mixing matrices based on periods in proximity with others have greater sub- and super-diagonals, reflecting contacts between parents and children, and other off-diagonal elements (reflecting, e.g., age-independent contacts among co-workers), than those based on face-to-face conversations. To assess the impact of targeted vaccination, we used a time-usage study's mixing matrix and allowed vaccine efficacy to vary with age. And we derived mortality rates either by dividing observed deaths attributed to pneumonia and influenza by average annual cases from a demographically-realistic SEIRS model or by multiplying those rates by ratios of (versus adding to them differences between) pandemic and pre-pandemic mortalities.
  
  
  CONCLUSIONS
  In our simulations, vaccinating older children, adolescents, and young adults averts the most cases, but vaccinating either younger children and older adults or young adults averts the most deaths, depending on the age distribution of mortality. These results are consistent with those of the earlier studies.
authors.affiliation: National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA. jglasser@cdc.gov
authors.forename: John
authors.initials: J
authors.lastname: Glasser
authors.forename: Denis
authors.initials: D
authors.lastname: Taneri
authors.forename: Zhilan
authors.initials: Z
authors.lastname: Feng
authors.forename: Jen-Hsiang
authors.initials: JH
authors.lastname: Chuang
authors.forename: Peet
authors.initials: P
authors.lastname: Tüll
authors.forename: William
authors.initials: W
authors.lastname: Thompson
authors.forename: Mary
authors.initials: M
authors.lastname: Mason McCauley
authors.forename: James
authors.initials: J
authors.lastname: Alexander
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D007252:Influenza Vaccines / Q000008:administration & dosage* / Q000276:immunology; D007251:Influenza, Human / Q000276:immunology / Q000401:mortality / Q000517:prevention & control*; D008297:Male; D008875:Middle Aged; D011159:Population Surveillance; D015397:Program Evaluation; D014611:Vaccination; D055815:Young Adult
chemical_list: D007252:Influenza Vaccines