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Comparison of several survey-based algorithms to ascertain type 1 diabetes among US adults with self-reported diabetes
INTRODUCTION: Defining type of diabetes using survey data is challenging, although important, for determining national estimates of diabetes. The purpose of this study was to compare the percentage and characteristics of US adults classified as having type 1 diabetes as defined by several algorithms...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7733112/ https://www.ncbi.nlm.nih.gov/pubmed/33298431 http://dx.doi.org/10.1136/bmjdrc-2020-001917 |
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author | Casagrande, Sarah S Lessem, Sarah E Orchard, Trevor J Bullard, Kai McKeever Geiss, Linda S Saydah, Sharon H Menke, Andy Imperatore, Giuseppina Rust, Keith F Cowie, Catherine C |
author_facet | Casagrande, Sarah S Lessem, Sarah E Orchard, Trevor J Bullard, Kai McKeever Geiss, Linda S Saydah, Sharon H Menke, Andy Imperatore, Giuseppina Rust, Keith F Cowie, Catherine C |
author_sort | Casagrande, Sarah S |
collection | PubMed |
description | INTRODUCTION: Defining type of diabetes using survey data is challenging, although important, for determining national estimates of diabetes. The purpose of this study was to compare the percentage and characteristics of US adults classified as having type 1 diabetes as defined by several algorithms. RESEARCH DESIGN AND METHODS: This study included 6331 respondents aged ≥18 years who reported a physician diagnosis of diabetes in the 2016–2017 National Health Interview Survey. Seven algorithms classified type 1 diabetes using various combinations of self-reported diabetes type, age of diagnosis, current and continuous insulin use, and use of oral hypoglycemics. RESULTS: The percentage of type 1 diabetes among those with diabetes ranged from 3.4% for those defined by age of diagnosis <30 years and continuous insulin use (algorithm 2) to 10.2% for those defined only by continuous insulin use (algorithm 1) and 10.4% for those defined as self-report of type 1 (supplementary algorithm 6). Among those defined by age of diagnosis <30 years and continuous insulin use (algorithm 2), by self-reported type 1 diabetes and continuous insulin use (algorithm 4), and by self-reported type 1 diabetes and current insulin use (algorithm 5), mean body mass index (BMI) (28.6, 27.4, and 28.5 kg/m(2), respectively) and percentage using oral hypoglycemics (16.1%, 11.1%, and 19.0%, respectively) were lower than for all other algorithms assessed. Among those defined by continuous insulin use alone (algorithm 1), the estimates for mean age and age of diagnosis (54.3 and 30.9 years, respectively) and BMI (30.9 kg/m(2)) were higher than for other algorithms. CONCLUSIONS: Estimates of type 1 diabetes using commonly used algorithms in survey data result in varying degrees of prevalence, characteristic distributions, and potential misclassification. |
format | Online Article Text |
id | pubmed-7733112 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-77331122020-12-21 Comparison of several survey-based algorithms to ascertain type 1 diabetes among US adults with self-reported diabetes Casagrande, Sarah S Lessem, Sarah E Orchard, Trevor J Bullard, Kai McKeever Geiss, Linda S Saydah, Sharon H Menke, Andy Imperatore, Giuseppina Rust, Keith F Cowie, Catherine C BMJ Open Diabetes Res Care Epidemiology/Health services research INTRODUCTION: Defining type of diabetes using survey data is challenging, although important, for determining national estimates of diabetes. The purpose of this study was to compare the percentage and characteristics of US adults classified as having type 1 diabetes as defined by several algorithms. RESEARCH DESIGN AND METHODS: This study included 6331 respondents aged ≥18 years who reported a physician diagnosis of diabetes in the 2016–2017 National Health Interview Survey. Seven algorithms classified type 1 diabetes using various combinations of self-reported diabetes type, age of diagnosis, current and continuous insulin use, and use of oral hypoglycemics. RESULTS: The percentage of type 1 diabetes among those with diabetes ranged from 3.4% for those defined by age of diagnosis <30 years and continuous insulin use (algorithm 2) to 10.2% for those defined only by continuous insulin use (algorithm 1) and 10.4% for those defined as self-report of type 1 (supplementary algorithm 6). Among those defined by age of diagnosis <30 years and continuous insulin use (algorithm 2), by self-reported type 1 diabetes and continuous insulin use (algorithm 4), and by self-reported type 1 diabetes and current insulin use (algorithm 5), mean body mass index (BMI) (28.6, 27.4, and 28.5 kg/m(2), respectively) and percentage using oral hypoglycemics (16.1%, 11.1%, and 19.0%, respectively) were lower than for all other algorithms assessed. Among those defined by continuous insulin use alone (algorithm 1), the estimates for mean age and age of diagnosis (54.3 and 30.9 years, respectively) and BMI (30.9 kg/m(2)) were higher than for other algorithms. CONCLUSIONS: Estimates of type 1 diabetes using commonly used algorithms in survey data result in varying degrees of prevalence, characteristic distributions, and potential misclassification. BMJ Publishing Group 2020-12-09 /pmc/articles/PMC7733112/ /pubmed/33298431 http://dx.doi.org/10.1136/bmjdrc-2020-001917 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Epidemiology/Health services research Casagrande, Sarah S Lessem, Sarah E Orchard, Trevor J Bullard, Kai McKeever Geiss, Linda S Saydah, Sharon H Menke, Andy Imperatore, Giuseppina Rust, Keith F Cowie, Catherine C Comparison of several survey-based algorithms to ascertain type 1 diabetes among US adults with self-reported diabetes |
title | Comparison of several survey-based algorithms to ascertain type 1 diabetes among US adults with self-reported diabetes |
title_full | Comparison of several survey-based algorithms to ascertain type 1 diabetes among US adults with self-reported diabetes |
title_fullStr | Comparison of several survey-based algorithms to ascertain type 1 diabetes among US adults with self-reported diabetes |
title_full_unstemmed | Comparison of several survey-based algorithms to ascertain type 1 diabetes among US adults with self-reported diabetes |
title_short | Comparison of several survey-based algorithms to ascertain type 1 diabetes among US adults with self-reported diabetes |
title_sort | comparison of several survey-based algorithms to ascertain type 1 diabetes among us adults with self-reported diabetes |
topic | Epidemiology/Health services research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7733112/ https://www.ncbi.nlm.nih.gov/pubmed/33298431 http://dx.doi.org/10.1136/bmjdrc-2020-001917 |
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