title: Pandemic influenza A/H1N1pdm in Italy: age, risk and population susceptibility.
abstract: BACKGROUND
  A common pattern emerging from several studies evaluating the impact of the 2009 A/H1N1 pandemic influenza (A/H1N1pdm) conducted in countries worldwide is the low attack rate observed in elderly compared to that observed in children and young adults. The biological or social mechanisms responsible for the observed age-specific risk of infection are still to be deeply investigated.
  
  
  METHODS
  The level of immunity against the A/H1N1pdm in pre and post pandemic sera was determined using left over sera taken for diagnostic purposes or routine ascertainment obtained from clinical laboratories. The antibody titres were measured by the haemagglutination inhibition (HI) assay. To investigate whether certain age groups had higher risk of infection the presence of protective antibody (≥1∶40), was calculated using exact binomial 95% CI on both pre- and post- pandemic serological data in the age groups considered. To estimate age-specific susceptibility to infection we used an age-structured SEIR model.
  
  
  RESULTS
  By comparing pre- and post-pandemic serological data in Italy we found age- specific attack rates similar to those observed in other countries. Cumulative attack rate at the end of the first A/H1N1pdm season in Italy was estimated to be 16.3% (95% CI 9.4%-23.1%). Modeling results allow ruling out the hypothesis that only age-specific characteristics of the contact network and levels of pre-pandemic immunity are responsible for the observed age-specific risk of infection. This means that age-specific susceptibility to infection, suspected to play an important role in the pandemic, was not only determined by pre-pandemic levels of H1N1pdm antibody measured by HI.
  
  
  CONCLUSIONS
  Our results claim for new studies to better identify the biological mechanisms, which might have determined the observed pattern of susceptibility with age. Moreover, our results highlight the need to obtain early estimates of differential susceptibility with age in any future pandemics to obtain more reliable real time estimates of critical epidemiological parameters.
authors.affiliation: Predictive Models for Biomedicine & Environment, Bruno Kessler Foundation, Trento, Italy.
authors.forename: Stefano
authors.initials: S
authors.lastname: Merler
authors.forename: Marco
authors.initials: M
authors.lastname: Ajelli
authors.forename: Barbara
authors.initials: B
authors.lastname: Camilloni
authors.forename: Simona
authors.initials: S
authors.lastname: Puzelli
authors.forename: Antonino
authors.initials: A
authors.lastname: Bella
authors.forename: Maria Cristina
authors.initials: MC
authors.lastname: Rota
authors.forename: Alberto Eugenio
authors.initials: AE
authors.lastname: Tozzi
authors.forename: Maurizio
authors.initials: M
authors.lastname: Muraca
authors.forename: Marcello
authors.initials: M
authors.lastname: Meledandri
authors.forename: Anna Maria
authors.initials: AM
authors.lastname: Iorio
authors.forename: Isabella
authors.initials: I
authors.lastname: Donatelli
authors.forename: Caterina
authors.initials: C
authors.lastname: Rizzo
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D004198:Disease Susceptibility; D005260:Female; D006801:Humans; D015994:Incidence; D007223:Infant; D053118:Influenza A Virus, H1N1 Subtype / Q000276:immunology*; D007251:Influenza, Human / Q000453:epidemiology* / Q000276:immunology; D007558:Italy / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D012306:Risk