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Related Experiment Videos

Calculating confidence intervals for impact numbers.

Mandy Hildebrandt1, Ralf Bender, Ulrich Gehrmann

  • 1Department of Medical Biometry, Institute for Quality and Efficiency in Health Care (IQWiG), Dillenburger Str, 27, 51105 Cologne, Germany. mandy.hildebrandt@iqwig.de

BMC Medical Research Methodology
|July 14, 2006
PubMed
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New impact numbers quantify population health effects, like smoking

Area of Science:

  • Epidemiology
  • Public Health
  • Biostatistics

Background:

  • Standard effect measures like risk difference and attributable risk are common in epidemiology.
  • Impact numbers, such as exposure impact number (EIN), case impact number (CIN), and exposed cases impact number (ECIN), offer new ways to express population impact.
  • Calculating confidence intervals for these novel impact numbers is crucial for understanding estimation uncertainty.

Purpose of the Study:

  • To present methods for calculating confidence intervals for new impact numbers in cohort studies.
  • To provide a framework for quantifying the uncertainty associated with impact number estimates.

Main Methods:

  • The study defines and considers three impact numbers: EIN (equivalent to number needed to treat), CIN (reciprocal of population attributable risk), and ECIN (reciprocal of attributable fraction among the exposed).

Related Experiment Videos

  • Confidence intervals for CIN and ECIN are derived by inverting and exchanging the confidence limits of population attributable risk and attributable fraction among the exposed, respectively.
  • Methods were applied to cohort studies examining smoking's association with coronary heart disease and stroke.

    • Main Results:

    • In a British cohort study, the case impact number (CIN) for smoking and coronary heart disease death was 6.46 (95% CI: 3.84–20.36).
    • The exposed cases impact number (ECIN) for the same association was 2.64 (95% CI: 1.76–5.29).
    • In a Japanese cohort study, the CIN for smoking and stroke was 6.67 (95% CI: 3.80–27.27), and the ECIN was 4.89 (95% CI: 2.86–16.67).

    Conclusions:

    • Impact numbers provide valuable additional information for interpreting epidemiological study results, particularly in public health.
    • The calculation of confidence intervals for impact numbers is feasible using established methods for attributable risk measures.
    • Estimated impact numbers should always be reported with corresponding confidence intervals to convey estimation uncertainty.