ArticlePopulation health metrics2016
The impact of individual-level heterogeneity on estimated infectious disease burden: a simulation study.
Article in Population health metrics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- Latent class analysis identifies risk groups to model the expected benefits of SARS-CoV-2 interventions among university students.Scientific reports · 2025Article
- Surveillance strategies for the detection of new pathogen variants across epidemiological contexts.PLoS computational biology · 2024Article
- Disaggregating proportional multistate lifetables by population heterogeneity to estimate intervention impacts on inequalities.Population health metrics · 2022Article
- Adjusting for comorbidity in incidence-based DALY calculations: an individual-based modeling approach.BMC medical research methodology · 2020Article
- Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDisease 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.
methodsSimulations 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 an a priori defined individual-level frailty (i.e., heterogeneity in disease risk) distribution, and DALYs were calculated.
resultsUnder 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%).
conclusionsThe consequences of ignoring population heterogeneity should be considered when defining transition parameters for natural history models and when interpreting the resulting disease burden estimates.
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