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Population Attributable One Exposure Term Tool

Levin population attributable fraction under a single binary exposure and an applicable causal relative risk supplied externally.

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Description

Return prevalence×(risk-1) divided by that denominator.

Epidemiological model terms link supplied prevalence, rates, durations or risk ratios under explicit assumptions. A mathematical identity or approximation does not estimate those inputs or establish the causal effect of an exposure.

Return prevalence×(risk-1) divided by that denominator.

When to use this tool

Use Population Attributable One Exposure Term when you need one transparent population-level model term.

How to use the tool and how it works

  1. Enter exposurePrevalence, relativeRisk. Use the units and conventions below.
  2. Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
  3. Read populationAttributableFraction. Use the output convention below.

Require exposurePrevalence in [0, 1], nonnegative relativeRisk and positive 1+prevalence×(risk-1). Return prevalence×(risk-1) divided by that denominator.1

The population-attributable expression combines exposure prevalence with relative risk. Its causal interpretation requires an appropriately justified exposure-risk relationship, not merely a numerical association.2

Input fieldMeaning and unitsAccepted input
exposurePrevalenceExposure PrevalenceRequired. Finite number; minimum 0; maximum 1.
relativeRiskNonnegative supplied causal-model risk ratio, not an odds ratio automatically interchangeable with it.Required. Finite number; minimum 0.
Output fieldInterpretationReturned shape
populationAttributableFractionPopulation Attributable FractionRequired. Finite number.
json
{
  "input": {
    "exposurePrevalence": 0.25,
    "relativeRisk": 3
  },
  "output": {
    "populationAttributableFraction": 0.3333333333333333
  }
}

Limitations and assumptions

  • Protective risk below one can yield a negative fraction. Causal interpretation requires appropriate risk, exposure and confounding assumptions; this formula does not estimate a causal effect or support individual medical decisions.
  • Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.
  • Numeric calculations use binary floating point unless exact decimal-integer arithmetic is explicitly stated. No hidden measurement tolerance or decimal rounding is added; nonfinite or out-of-contract results are rejected. Numeric signed-zero results are exported as zero.

Alternative or Complementary methods

Use an appropriate epidemiological study and causal analysis for interpretation. Check steady-state assumptions, confounding, exposure definitions, compatible time units and uncertainty before applying a model term to health policy or individual care.

References

  1. Calculation and input contract: epidemiology/population-attributable-one-exposure-term — tool.io

  2. A Framework for Assessing the Effectiveness of Disease and Injury Prevention — CDC

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