Future prevalence of type 2 diabetes-A comparative analysis of chronic disease projection methods.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2022
Historique:
received: 01 12 2021
accepted: 15 02 2022
entrez: 7 3 2022
pubmed: 8 3 2022
medline: 27 4 2022
Statut: epublish

Résumé

Accurate projections of the future number of people with chronic diseases are necessary for effective resource allocation and health care planning in response to changes in disease burden. To introduce and compare different projection methods to estimate the number of people with diagnosed type 2 diabetes (T2D) in Germany in 2040. We compare three methods to project the number of males with T2D in Germany in 2040. Method 1) simply combines the sex- and age-specific prevalence of T2D in 2010 with future population distributions projected by the German Federal Statistical Office (FSO). Methods 2) and 3) additionally account for the incidence of T2D and mortality rates using partial differential equations (PDEs). Method 2) models the prevalence of T2D employing a scalar PDE which incorporates incidence and mortality rates. Subsequently, the estimated prevalence is applied to the population projection of the FSO. Method 3) uses a two-dimensional system of PDEs and estimates future case numbers directly while future mortality of people with and without T2D is modelled independently from the projection of the FSO. Method 1) projects 3.6 million male people with diagnosed T2D in Germany in 2040. Compared to 2.8 million males in 2010, this equals an increase by 29%. Methods 2) and 3) project 5.9 million (+104% compared to 2010) and 6.0 million (+116%) male T2D patients, respectively. The results of the three methods differ substantially. It appears that ignoring temporal trends in incidence and mortality may result in misleading projections of the future number of people with chronic diseases. Hence, it is essential to include these rates as is done by method 2) and 3).

Sections du résumé

BACKGROUND
Accurate projections of the future number of people with chronic diseases are necessary for effective resource allocation and health care planning in response to changes in disease burden.
AIM
To introduce and compare different projection methods to estimate the number of people with diagnosed type 2 diabetes (T2D) in Germany in 2040.
METHODS
We compare three methods to project the number of males with T2D in Germany in 2040. Method 1) simply combines the sex- and age-specific prevalence of T2D in 2010 with future population distributions projected by the German Federal Statistical Office (FSO). Methods 2) and 3) additionally account for the incidence of T2D and mortality rates using partial differential equations (PDEs). Method 2) models the prevalence of T2D employing a scalar PDE which incorporates incidence and mortality rates. Subsequently, the estimated prevalence is applied to the population projection of the FSO. Method 3) uses a two-dimensional system of PDEs and estimates future case numbers directly while future mortality of people with and without T2D is modelled independently from the projection of the FSO.
RESULTS
Method 1) projects 3.6 million male people with diagnosed T2D in Germany in 2040. Compared to 2.8 million males in 2010, this equals an increase by 29%. Methods 2) and 3) project 5.9 million (+104% compared to 2010) and 6.0 million (+116%) male T2D patients, respectively.
CONCLUSIONS
The results of the three methods differ substantially. It appears that ignoring temporal trends in incidence and mortality may result in misleading projections of the future number of people with chronic diseases. Hence, it is essential to include these rates as is done by method 2) and 3).

Identifiants

pubmed: 35255104
doi: 10.1371/journal.pone.0264739
pii: PONE-D-21-38063
pmc: PMC8901066
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0264739

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

Theor Popul Biol. 2014 Mar;92:62-8
pubmed: 24333220
Pneumologie. 2010 Sep;64(9):535-40
pubmed: 20827635
Diabet Med. 2013 Aug;30(8):999-1008
pubmed: 23506452
Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2019 Aug;62(8):993-1003
pubmed: 31243489
Diabetologia. 2008 Dec;51(12):2187-96
pubmed: 18815769
Diabetologia. 2021 Jun;64(6):1288-1297
pubmed: 33665686
Dtsch Arztebl Int. 2016 Mar 18;113(11):177-82
pubmed: 27118665
Nat Commun. 2016 Aug 11;7:12408
pubmed: 27510634
BMJ Open Diabetes Res Care. 2020 May;8(1):
pubmed: 32475839
BMC Public Health. 2019 Jun 28;19(1):844
pubmed: 31253126
F1000Res. 2021 Jan 27;10:49
pubmed: 34136129
Hum Biol. 1951 Sep;23(3):205-41
pubmed: 14880165
BMJ Open Diabetes Res Care. 2020 Aug;8(1):
pubmed: 32784246
BMJ Open. 2021 Jan 6;11(1):e041508
pubmed: 33408205
Diabet Med. 2017 Jun;34(6):855-861
pubmed: 28199029
Lupus. 2014 Nov;23(13):1407-11
pubmed: 24928831
Stat Methods Med Res. 2018 Feb;27(2):414-427
pubmed: 26988925
PLoS One. 2019 Dec 17;14(12):e0226554
pubmed: 31846478
Popul Health Metr. 2021 Oct 11;19(1):38
pubmed: 34635124
Mov Disord. 2018 Jan;33(1):156-159
pubmed: 28590580
Eur J Epidemiol. 2012 Oct;27(10):791-7
pubmed: 22878939
Comput Math Methods Med. 2018 Sep 12;2018:5091096
pubmed: 30275874
Diabetes Care. 2001 Nov;24(11):1936-40
pubmed: 11679460
Lupus Sci Med. 2016 Nov 25;3(1):e000181
pubmed: 27933200
Diabet Med. 2019 Oct;36(10):1217-1225
pubmed: 30659656

Auteurs

Dina Voeltz (D)

Biostatistics and Medical Biometry, Medical School OWL, Bielefeld University, Bielefeld, Germany.
Department of Statistics, Ludwig-Maximilians-University Munich, München, Germany.

Thaddäus Tönnies (T)

Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich-Heine-University Duesseldorf, Duesseldorf, Germany.

Ralph Brinks (R)

Department of Statistics, Ludwig-Maximilians-University Munich, München, Germany.
Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich-Heine-University Duesseldorf, Duesseldorf, Germany.
Chair for Medical Biometry and Epidemiology, Faculty of Health/School of Medicine, Witten/Herdecke University, Witten, Germany.

Annika Hoyer (A)

Biostatistics and Medical Biometry, Medical School OWL, Bielefeld University, Bielefeld, Germany.

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