The impact of individual-level heterogeneity on estimated infectious disease burden: a simulation study. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: McDonald, Scott; Devleesschauwer, Brecht; Wallinga, Jacco
• Is Part Of: Population health metrics. Volume 14:Issue 1(2016)
• Page Start: 1
• Page End: 9
• Publication Date: 2016-12
• Subjects: Infectious diseases -- Heterogeneity -- Disability-adjusted life years -- Markov model; Health status indicators -- Periodicals; Population -- Statistics -- Periodicals; Health status indicators -- Measurement; Health status indicators -- Statistical methods; 614.420727
• DOI: 10.1186/s12963-016-0116-y
• Languages: English
• ISSNs: 1478-7954

Abstract

Abstract Background Disease burden is not evenly distributed within a population; this uneven distribution can be due to individual heterogeneity in progression rates between disease stages. Composite measures of disease burden that are based on disease progression models, such as the disability-adjusted life year (DALY), are widely used to quantify the current and future burden of infectious diseases. Our goal was to investigate to what extent ignoring the presence of heterogeneity could bias DALY computation. Methods Simulations using individual-based models for hypothetical infectious diseases with short and long natural histories were run assuming either "population-averaged" progression probabilities between disease stages, or progression probabilities that were influenced by ana priori defined individual-level frailty (i.e., heterogeneity in disease risk) distribution, and DALYs were calculated. Results Under the assumption of heterogeneity in transition rates and increasing frailty with age, the short natural history disease model predicted 14% fewer DALYs compared with the homogenous population assumption. Simulations of a long natural history disease indicated that assuming homogeneity in transition rates when heterogeneity was present could overestimate total DALYs, in the present case by 4% (95% quantile interval: 1–8%). Conclusions The consequences of ignoring population heterogeneity should be considered when defining transition parameters for natural history models and when interpreting the resulting disease burden estimates.