New ways of estimating excess mortality of chronic diseases from aggregated data: insights from the illness-death model.
Bayes estimation
Chronic disease epidemiology
Dementia
Diabetes
Incidence
Multi-state model
Partial differential equation
Prevalence
Journal
BMC public health
ISSN: 1471-2458
Titre abrégé: BMC Public Health
Pays: England
ID NLM: 100968562
Informations de publication
Date de publication:
28 Jun 2019
28 Jun 2019
Historique:
received:
05
04
2019
accepted:
19
06
2019
entrez:
30
6
2019
pubmed:
30
6
2019
medline:
13
9
2019
Statut:
epublish
Résumé
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. 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. 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. The described approach is useful to estimate the excess mortality of a chronic condition from aggregated age-specific incidence and prevalence data. The article does not report the results of any health care intervention.
Sections du résumé
BACKGROUND
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
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
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
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
BACKGROUND
The article does not report the results of any health care intervention.
Identifiants
pubmed: 31253126
doi: 10.1186/s12889-019-7201-7
pii: 10.1186/s12889-019-7201-7
pmc: PMC6599235
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
844Références
Theor Popul Biol. 1999 Aug;56(1):76-90
pubmed: 10438670
Neurology. 2000;54(11 Suppl 5):S4-9
pubmed: 10854354
Neurology. 2000;54(11 Suppl 5):S10-5
pubmed: 10854355
Diabetologia. 2008 Dec;51(12):2187-96
pubmed: 18815769
BMJ. 2010 Aug 05;341:c3584
pubmed: 20688840
Theor Popul Biol. 2013 Dec;90:29-35
pubmed: 24084064
Theor Popul Biol. 2014 Mar;92:62-8
pubmed: 24333220
Math Med Biol. 2015 Dec;32(4):425-35
pubmed: 25576933
PLoS One. 2015 Mar 06;10(3):e0118955
pubmed: 25749133
PLoS One. 2016 Mar 29;11(3):e0152046
pubmed: 27023438
Dtsch Arztebl Int. 2016 Mar 18;113(11):177-82
pubmed: 27118665
Ann Rheum Dis. 2018 Apr;77(4):e19
pubmed: 28765122
Lifetime Data Anal. 2018 Oct;24(4):743-754
pubmed: 29374340
Nutr Metab Cardiovasc Dis. 2018 Sep;28(9):887-891
pubmed: 29960839
Comput Math Methods Med. 2018 Sep 12;2018:5091096
pubmed: 30275874