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New ways of estimating excess mortality of chronic diseases from aggregated data: insights from the illness-death model
BACKGROUND: Recently, 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 ‘I...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6599235/ https://www.ncbi.nlm.nih.gov/pubmed/31253126 http://dx.doi.org/10.1186/s12889-019-7201-7 |
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author | Brinks, Ralph Tönnies, Thaddäus Hoyer, Annika |
author_facet | Brinks, Ralph Tönnies, Thaddäus Hoyer, Annika |
author_sort | Brinks, Ralph |
collection | PubMed |
description | BACKGROUND: Recently, 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. METHODS: After 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. RESULTS: In 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. CONCLUSIONS: The described approach is useful to estimate the excess mortality of a chronic condition from aggregated age-specific incidence and prevalence data. TRIAL REGISTRATION: The article does not report the results of any health care intervention. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12889-019-7201-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6599235 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65992352019-07-11 New ways of estimating excess mortality of chronic diseases from aggregated data: insights from the illness-death model Brinks, Ralph Tönnies, Thaddäus Hoyer, Annika BMC Public Health Technical Advance BACKGROUND: Recently, 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. METHODS: After 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. RESULTS: In 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. CONCLUSIONS: The described approach is useful to estimate the excess mortality of a chronic condition from aggregated age-specific incidence and prevalence data. TRIAL REGISTRATION: The article does not report the results of any health care intervention. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12889-019-7201-7) contains supplementary material, which is available to authorized users. BioMed Central 2019-06-28 /pmc/articles/PMC6599235/ /pubmed/31253126 http://dx.doi.org/10.1186/s12889-019-7201-7 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Technical Advance Brinks, Ralph Tönnies, Thaddäus Hoyer, Annika New ways of estimating excess mortality of chronic diseases from aggregated data: insights from the illness-death model |
title | New ways of estimating excess mortality of chronic diseases from aggregated data: insights from the illness-death model |
title_full | New ways of estimating excess mortality of chronic diseases from aggregated data: insights from the illness-death model |
title_fullStr | New ways of estimating excess mortality of chronic diseases from aggregated data: insights from the illness-death model |
title_full_unstemmed | New ways of estimating excess mortality of chronic diseases from aggregated data: insights from the illness-death model |
title_short | New ways of estimating excess mortality of chronic diseases from aggregated data: insights from the illness-death model |
title_sort | new ways of estimating excess mortality of chronic diseases from aggregated data: insights from the illness-death model |
topic | Technical Advance |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6599235/ https://www.ncbi.nlm.nih.gov/pubmed/31253126 http://dx.doi.org/10.1186/s12889-019-7201-7 |
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