Taking multi-morbidity into account when attributing DALYs to risk factors: comparing dynamic modeling with the...

Area of Science:

• Epidemiology
• Public Health
• Health Economics

Background:

• Disability Adjusted Life Years (DALYs) measure healthy life loss from disease and premature death.
• Current DALY attribution methods incorrectly mix incidence-based and prevalence-based data.
• Existing methods conflate single-cause attribution with counterfactual elimination approaches.

Purpose of the Study:

• To introduce dynamic modeling as a purely counterfactual approach for DALY risk factor attribution.
• To compare dynamic modeling with the Global Burden of Disease 2010 (GBD2010) methodology.
• To highlight discrepancies in DALY estimates arising from different attribution methods.

Main Methods:

• Utilized the dynamic multistate disease table model (DYNAMO-HIA).
• Applied the model to 2011 Dutch smoking data for risk factor attribution.
• Conducted analyses on a synthetic population to isolate methodological differences.

Main Results:

• Dynamic modeling yielded substantially lower DALYs (398,000 vs. 607,000) compared to GBD2010 in a simplified scenario.
• The difference is largely due to dynamic modeling's inclusion of newly occurring morbidity in gained life years.
• Age-dependent risks and exposures introduce further distortions in prevalence-based DALY calculations.

Conclusions:

• The GBD2010 approach is a hybrid, resulting in less interpretable outcomes.
• Dynamic modeling provides a purely counterfactual and more understandable method for DALY estimation.
• This research advocates for the adoption of dynamic modeling for more accurate DALY attribution.