title: Effectiveness of antiviral prophylaxis coupled with contact tracing in reducing the transmission of the influenza A (H1N1-2009): a systematic review.
abstract: BACKGROUND
  During the very early stage of the 2009 pandemic, mass chemoprophylaxis was implemented as part of containment measure. The purposes of the present study were to systematically review the retrospective studies that investigated the effectiveness of antiviral prophylaxis during the 2009 pandemic, and to explicitly estimate the effectiveness by employing a mathematical model.
  
  
  METHODS
  A systematic review identified 17 articles that clearly defined the cases and identified exposed individuals based on contact tracing. Analysing a specific school-driven outbreak, we estimated the effectiveness of antiviral prophylaxis using a renewal equation model. Other parameters, including the reproduction number and the effectiveness of antiviral treatment and school closure, were jointly estimated.
  
  
  RESULTS
  Based on the systematic review, median secondary infection risks (SIRs) among exposed individuals with and without prophylaxis were estimated at 2.1% (quartile: 0, 12.2) and 16.6% (quartile: 8.4, 32.4), respectively. A very high heterogeneity in the SIR was identified with an estimated I2 statistic at 71.8%. From the outbreak data in Madagascar, the effectiveness of mass chemoprophylaxis in reducing secondary transmissions was estimated to range from 92.8% to 95.4% according to different model assumptions and likelihood functions, not varying substantially as compared to other parameters.
  
  
  CONCLUSIONS
  Only based on the meta-analysis of retrospective studies with different study designs and exposure settings, it was not feasible to estimate the effectiveness of antiviral prophylaxis in reducing transmission. However, modelling analysis of a single outbreak successfully yielded an estimate of the effectiveness that appeared to be robust to model assumptions. Future studies should fill the data gap that has existed in observational studies and allow mathematical models to be used for the analysis of meta-data.
authors: School of Public Health, The University of Hong Kong, 100 Cyberport Road, Pokfulam, Hong Kong, China.
authors: Kenji
authors: K
authors: Mizumoto
authors: Hiroshi
authors: H
authors: Nishiura
authors: Taro
authors: T
authors: Yamamoto
mesh_terms: D000998:Antiviral Agents / Q000494:pharmacology* / Q000627:therapeutic use; D060085:Coinfection / Q000150:complications / Q000453:epidemiology; D016358:Contact Tracing; D004196:Disease Outbreaks / Q000517:prevention & control; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype / Q000187:drug effects* / Q000502:physiology; D007251:Influenza, Human / Q000188:drug therapy / Q000517:prevention & control* / Q000635:transmission* / Q000821:virology; D008270:Madagascar / Q000453:epidemiology; D008954:Models, Biological; D056990:Post-Exposure Prophylaxis; D012307:Risk Factors; D016896:Treatment Outcome
chemical_list: D000998:Antiviral Agents