Estimating incidence of type 1 and type 2 diabetes using prevalence data: the SEARCH for Diabetes in Youth study.
Adolescents
Children
Epidemiology
Illness-death-model
Surveillance
Type 1 diabetes
Type 2 diabetes
Journal
BMC medical research methodology
ISSN: 1471-2288
Titre abrégé: BMC Med Res Methodol
Pays: England
ID NLM: 100968545
Informations de publication
Date de publication:
14 02 2023
14 02 2023
Historique:
received:
27
09
2022
accepted:
07
02
2023
entrez:
15
2
2023
pubmed:
16
2
2023
medline:
17
2
2023
Statut:
epublish
Résumé
Incidence is one of the most important epidemiologic indices in surveillance. However, determining incidence is complex and requires time-consuming cohort studies or registries with date of diagnosis. Estimating incidence from prevalence using mathematical relationships may facilitate surveillance efforts. The aim of this study was to examine whether a partial differential equation (PDE) can be used to estimate diabetes incidence from prevalence in youth. We used age-, sex-, and race/ethnicity-specific estimates of prevalence in 2001 and 2009 as reported in the SEARCH for Diabetes in Youth study. Using these data, a PDE was applied to estimate the average incidence rates of type 1 and type 2 diabetes for the period between 2001 and 2009. Estimates were compared to annual incidence rates observed in SEARCH. Precision of the estimates was evaluated using 95% bootstrap confidence intervals. Despite the long period between prevalence measures, the estimated average incidence rates mirror the average of the observed annual incidence rates. Absolute values of the age-standardized sex- and type-specific mean relative errors are below 8%. Incidence of diabetes can be accurately estimated from prevalence. Since only cross-sectional prevalence data is required, employing this methodology in future studies may result in considerable cost savings.
Sections du résumé
BACKGROUND
Incidence is one of the most important epidemiologic indices in surveillance. However, determining incidence is complex and requires time-consuming cohort studies or registries with date of diagnosis. Estimating incidence from prevalence using mathematical relationships may facilitate surveillance efforts. The aim of this study was to examine whether a partial differential equation (PDE) can be used to estimate diabetes incidence from prevalence in youth.
METHODS
We used age-, sex-, and race/ethnicity-specific estimates of prevalence in 2001 and 2009 as reported in the SEARCH for Diabetes in Youth study. Using these data, a PDE was applied to estimate the average incidence rates of type 1 and type 2 diabetes for the period between 2001 and 2009. Estimates were compared to annual incidence rates observed in SEARCH. Precision of the estimates was evaluated using 95% bootstrap confidence intervals.
RESULTS
Despite the long period between prevalence measures, the estimated average incidence rates mirror the average of the observed annual incidence rates. Absolute values of the age-standardized sex- and type-specific mean relative errors are below 8%.
CONCLUSIONS
Incidence of diabetes can be accurately estimated from prevalence. Since only cross-sectional prevalence data is required, employing this methodology in future studies may result in considerable cost savings.
Identifiants
pubmed: 36788497
doi: 10.1186/s12874-023-01862-3
pii: 10.1186/s12874-023-01862-3
pmc: PMC9930314
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
39Subventions
Organisme : NCCDPHP CDC HHS
ID : U18 DP006133
Pays : United States
Organisme : NCCDPHP CDC HHS
ID : U18 DP006134
Pays : United States
Organisme : NCCDPHP CDC HHS
ID : U18 DP006136
Pays : United States
Organisme : NCCDPHP CDC HHS
ID : U18 DP006138
Pays : United States
Organisme : NCCDPHP CDC HHS
ID : U18 DP006139
Pays : United States
Organisme : NCCDPHP CDC HHS
ID : U01 DP000246
Pays : United States
Organisme : NCCDPHP CDC HHS
ID : U18 DP002714
Pays : United States
Organisme : NCCDPHP CDC HHS
ID : U01 DP000247
Pays : United States
Organisme : NCCDPHP CDC HHS
ID : U01 DP000247
Pays : United States
Organisme : NCCDPHP CDC HHS
ID : U18 DP002708
Pays : United States
Organisme : NCCDPHP CDC HHS
ID : U01 DP000244
Pays : United States
Organisme : NCCDPHP CDC HHS
ID : U18 DP002710
Pays : United States
Organisme : NCCDPHP CDC HHS
ID : U01 DP000250
Pays : United States
Informations de copyright
© 2023. The Author(s).
Références
Diabetes Care. 2006 Feb;29(2):212-7
pubmed: 16443862
Theor Popul Biol. 2014 Mar;92:62-8
pubmed: 24333220
Stat Med. 2016 Feb 28;35(5):768-81
pubmed: 26376995
N Engl J Med. 2017 Apr 13;376(15):1419-1429
pubmed: 28402773
NCHS Data Brief. 2015 Nov;(219):1-8
pubmed: 26633046
JAMA. 2014 May 7;311(17):1778-86
pubmed: 24794371
J Pediatr. 2005 Jun;146(6):751-8
pubmed: 15973311
Nutr Metab Cardiovasc Dis. 2018 Sep;28(9):887-891
pubmed: 29960839
PLoS One. 2015 Mar 06;10(3):e0118955
pubmed: 25749133
Stat Med. 2013 May 30;32(12):2070-8
pubmed: 23034867
PLoS One. 2017 Feb 6;12(2):e0171027
pubmed: 28166298
Math Med Biol. 2015 Dec;32(4):425-35
pubmed: 25576933
F1000Res. 2021 Jan 27;10:49
pubmed: 34136129
Ann Epidemiol. 2019 Sep;37:37-42
pubmed: 31383511
Diabetes Care. 2014 Dec;37(12):3336-44
pubmed: 25414389
Epidemiol Rev. 1988;10:164-90
pubmed: 3066626
Diabetes Care. 2023 Feb 1;46(2):313-320
pubmed: 36580405
MMWR Morb Mortal Wkly Rep. 2017 May 19;66(19):502-505
pubmed: 28520705
J Diabetes Complications. 2018 Jun;32(6):545-549
pubmed: 29685480
PLoS One. 2016 Mar 29;11(3):e0152046
pubmed: 27023438
Control Clin Trials. 2004 Oct;25(5):458-71
pubmed: 15465616
Diabetes Care. 2014 Feb;37(2):402-8
pubmed: 24041677
Diabet Med. 2019 Oct;36(10):1217-1225
pubmed: 30659656
Lancet Public Health. 2017 May 19;2(6):e277-e285
pubmed: 28626830
Dtsch Arztebl Int. 2016 Mar 18;113(11):177-82
pubmed: 27118665