#### Pages within the "support" section:

- Support home
- Glossary
- Model parameters
- Summary
- 1. Introduction
- 1.1 General framework
- 1.2 Relationship to the BRAFO tiered approach
- 2. Problem formulation
- 2.1 Dietary scenarios
- 2.2 Population
- 3. Common metrics for integration of risks & benefits
- 3.1 DALY versus QALY.
- 3.2 Alternative approaches for calculating DALY/QALYs
- 4. Main elements of the Qalibra framework
- 5. When is use of the Qalibra framework appropriate?
- 6. Calculation of DALYs for quantal health effects
- 7. Calculation of DALYs for continuous health effects
- 8. Calculation of QALYs for quantal and continuous health effects
- 9. Recurrent effects
- 10. Effects on the next generation
- 11. Multiple effects on the same health endpoint
- 12. Calculating the net health impact of a dietary change
- 13. Direct health loss calculations for a single individual
- 14. Data needed as inputs to the Qalibra framework
- 14.1 Individuals and their attributes
- 14.2 Life expectancy
- 14.3 Health effects to be quantified
- 14.4 Dietary intakes
- 14.5 Probability of quantal effects
- 14.6 Probability and magnitude of continuous effects
- 14.7 Probabilities of recovery and death
- 14.8 Duration of disease
- 14.9 Severity of effect (disease weights)
- 15. Addressing uncertainty in risk-benefit assessment
- 15.1 Qualitative evaluation of uncertainties
- 15.2 Quantitative evaluation of uncertainties
- 15.3 Probabilistic treatment of uncertainty in Qalibra
- 16. Treatment of variability in the Qalibra framework [current page]
- 17. Treatment of dependencies in Qalibra framework
- 18. Presentation of results
- 19. Interpretation of results
- 20. Risk management considerations
- 21. Final remarks
- Acknowledgements
- References

# 16. Treatment of variability in the Qalibra framework

The Qalibra framework and software model variability between individuals in their expected health outcomes. This variability is driven by two sources. The first source is variability between individuals in their diets and hence their intakes of the contaminants and nutrients considered in an assessment. This is represented in the Qalibra software by the user entering intakes for each individual as separate values. The second source of variability is individual differences in the other parameters of the risk-benefit calculation (dose-response, severity and duration of disease, probabilities of recovery and death, normal life expectancy). In the Qalibra software, these are not entered as separate values for every individual; however, the user has the option to make them a function of discrete covariates representing individual attributes that influence them. In principle it would be possible to specify a covariate that takes different values for every individual, but usually the covariates will be used to represent variability between groups or classes of individuals. Age is a required covariate, because it is a variable in equation 1. In effect, it is used as the age of onset in the annual directly attributable health loss approach. In general, both
*p
_{
effect
}*
and life expectancy are a function of age. Other covariates are optional. It may often be appropriate to specify gender as a covariate, as it is common that different dose-response relationships are reported for males and females. Qalibra requires covariates to be discrete: when a covariate is actually continuous (e.g. age), the user will need to divide it into a sufficiently large number of classes to adequately represent its influence on the risk-benefit calculation.