CDC’s Advisory Committee on Immunization Practices (ACIP)
provides annual recommendations for the prevention and control
of influenza. The ACIP Influenza Work Group* meets monthly
throughout the year to discuss newly published studies, review current
guidelines, and consider potential revisions to the recommendations.
As they review the annual recommendations for consideration of the
full ACIP, members of the Work Group consider a variety of issues,
including burden of influenza illness, vaccine efficacy and effectiveness,
safety and coverage in groups recommended for vaccination,
feasibility, cost-effectiveness, and anticipated vaccine supply. Work
group members also request periodic updates on antiviral production,
supply, safety, efficacy, and effectiveness from clinician researchers,
regulatory agencies, public health epidemiologists, and manufacturers
and review influenza surveillance and antiviral resistance data
obtained from CDC’s Influenza Division.
Published, peer-reviewed studies are the primary source of data
used by ACIP in making recommendations for the prevention
and control of influenza, but unpublished data that are relevant
to issues under discussion also are considered. The best evidence
for antiviral efficacy comes from randomized, controlled trials that
assess laboratory-confirmed influenza virus infection as an outcome
measure. However, randomized, placebo-controlled trials might be
difficult to perform in populations for which antiviral treatment
already is recommended. Observational studies that assess outcomes
associated with laboratory-confirmed influenza virus infection can
provide important antiviral effectiveness data but are more subject to
biases and confounding that can affect validity and the size of effects
measured. Randomized, placebo-controlled clinical trials are the best
source of antiviral safety data for common adverse events; however,
such studies do not have the power to identify rare but potentially
serious adverse events. In cited studies that included statistical comparisons,
a difference was considered to be statistically significant if
the p-value was