Comparison of several survey-based algorithms to ascertain type 1 diabetes among US adults with self-reported diabetes

BMJ Open Diabetes Res Care. 2020 Dec;8(2):e001917. doi: 10.1136/bmjdrc-2020-001917.


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/m2, 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/m2) 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.

Keywords: algorithms; diabetes mellitus; epidemiology; surveys and questionnaires; type 1.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Adolescent
  • Adult
  • Algorithms
  • Diabetes Mellitus, Type 1* / diagnosis
  • Diabetes Mellitus, Type 1* / drug therapy
  • Diabetes Mellitus, Type 1* / epidemiology
  • Humans
  • Insulin / therapeutic use
  • Self Report
  • Surveys and Questionnaires


  • Insulin