ArticleBMC public health2019
New ways of estimating excess mortality of chronic diseases from aggregated data: insights from the illness-death model.
Article in BMC public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Future prevalence of type 2 diabetes-A comparative analysis of chronic disease projection methods.PloS one · 2022Article
- Impact of diagnostic accuracy on the estimation of excess mortality from incidence and prevalence: simulation study and application to diabetes in German men.F1000Research · 2021Article
- Assessing two methods for estimating excess mortality of chronic diseases from aggregated data.BMC research notes · 2020Article
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3 authors.
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Abstract
backgroundRecently, we have shown that the age-specific prevalence of a disease can be related to the transition rates in the illness-death model via a partial differential equation (PDE). The transition rates are the incidence rate, the remission rate and mortality rates from the 'Healthy' and 'Ill' states. In case of a chronic disease, we now demonstrate that the PDE can be used to estimate the excess mortality from age-specific prevalence and incidence data. For the prevalence and incidence, aggregated data are sufficient - no individual subject data are needed, which allows application of the methods in contexts of strong data protection or where data from individual subjects is not accessible.
methodsAfter developing novel estimators for the excess mortality derived from the PDE, we apply them to simulated data and compare the findings with the input values of the simulation aiming to evaluate the new approach. In a practical application to claims data from 35 million men insured by the German public health insurance funds, we estimate the population-wide excess mortality of men with diagnosed type 2 diabetes.
resultsIn the simulation study, we find that the estimation of the excess mortality is feasible from prevalence and incidence data if the prevalence is given at two points in time. The accuracy of the method decreases as the temporal difference between these two points in time increases. In our setting, the relative error was 5% and below if the temporal difference was three years or less. Application of the new method to the claims data yields plausible findings for the excess mortality of type 2 diabetes in German men.
conclusionsThe described approach is useful to estimate the excess mortality of a chronic condition from aggregated age-specific incidence and prevalence data.
trial registrationThe article does not report the results of any health care intervention.
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