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Estimating incidence of type 1 and type 2 diabetes using prevalence data: the SEARCH for Diabetes in Youth study

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 su...

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Autores principales: Hoyer, Annika, Brinks, Ralph, Tönnies, Thaddäus, Saydah, Sharon H., D’Agostino, Ralph B., Divers, Jasmin, Isom, Scott, Dabelea, Dana, Lawrence, Jean M., Mayer-Davis, Elizabeth J., Pihoker, Catherine, Dolan, Lawrence, Imperatore, Giuseppina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9930314/
https://www.ncbi.nlm.nih.gov/pubmed/36788497
http://dx.doi.org/10.1186/s12874-023-01862-3
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author Hoyer, Annika
Brinks, Ralph
Tönnies, Thaddäus
Saydah, Sharon H.
D’Agostino, Ralph B.
Divers, Jasmin
Isom, Scott
Dabelea, Dana
Lawrence, Jean M.
Mayer-Davis, Elizabeth J.
Pihoker, Catherine
Dolan, Lawrence
Imperatore, Giuseppina
author_facet Hoyer, Annika
Brinks, Ralph
Tönnies, Thaddäus
Saydah, Sharon H.
D’Agostino, Ralph B.
Divers, Jasmin
Isom, Scott
Dabelea, Dana
Lawrence, Jean M.
Mayer-Davis, Elizabeth J.
Pihoker, Catherine
Dolan, Lawrence
Imperatore, Giuseppina
author_sort Hoyer, Annika
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01862-3.
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spelling pubmed-99303142023-02-16 Estimating incidence of type 1 and type 2 diabetes using prevalence data: the SEARCH for Diabetes in Youth study Hoyer, Annika Brinks, Ralph Tönnies, Thaddäus Saydah, Sharon H. D’Agostino, Ralph B. Divers, Jasmin Isom, Scott Dabelea, Dana Lawrence, Jean M. Mayer-Davis, Elizabeth J. Pihoker, Catherine Dolan, Lawrence Imperatore, Giuseppina BMC Med Res Methodol Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01862-3. BioMed Central 2023-02-14 /pmc/articles/PMC9930314/ /pubmed/36788497 http://dx.doi.org/10.1186/s12874-023-01862-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Hoyer, Annika
Brinks, Ralph
Tönnies, Thaddäus
Saydah, Sharon H.
D’Agostino, Ralph B.
Divers, Jasmin
Isom, Scott
Dabelea, Dana
Lawrence, Jean M.
Mayer-Davis, Elizabeth J.
Pihoker, Catherine
Dolan, Lawrence
Imperatore, Giuseppina
Estimating incidence of type 1 and type 2 diabetes using prevalence data: the SEARCH for Diabetes in Youth study
title Estimating incidence of type 1 and type 2 diabetes using prevalence data: the SEARCH for Diabetes in Youth study
title_full Estimating incidence of type 1 and type 2 diabetes using prevalence data: the SEARCH for Diabetes in Youth study
title_fullStr Estimating incidence of type 1 and type 2 diabetes using prevalence data: the SEARCH for Diabetes in Youth study
title_full_unstemmed Estimating incidence of type 1 and type 2 diabetes using prevalence data: the SEARCH for Diabetes in Youth study
title_short Estimating incidence of type 1 and type 2 diabetes using prevalence data: the SEARCH for Diabetes in Youth study
title_sort estimating incidence of type 1 and type 2 diabetes using prevalence data: the search for diabetes in youth study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9930314/
https://www.ncbi.nlm.nih.gov/pubmed/36788497
http://dx.doi.org/10.1186/s12874-023-01862-3
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