Concordance between self-report and a survey-based algorithm for classification of type 1 and type 2 diabetes using the 2011 population-based Survey on Living with Chronic Diseases in Canada (SLCDC)-Diabetes component.
OBJECTIVE
There are 2 major forms of diabetes mellitus: types 1 and 2. A major limitation of most current population-based diabetes surveillance systems is the classification of diabetes types. Our objective was to examine the concordance of self-reported diabetes type with a previously developed classification algorithm, using a nationally representative survey sample.


METHODS
Self-reported data were available from 2544 adults with self-reported diabetes, aged ≥20 years and older, who responded to the diabetes component of the 2011 Survey of Living with Chronic Diseases in Canada. We examined the concordance of self-reported diabetes type with an algorithm based on self-reported, but objective, respondent characteristics, such as age of diagnosis and treatment patterns. Concordance was measured using kappa coefficients. Sensitivity, specificity and positive and negative predictive values were calculated using the algorithm as the reference "standard."


RESULTS
Approximately 11% of the estimated population did not self-report diabetes type; almost all of these respondents would be classified as having type 2 diabetes by the algorithm. Of those self-reporting diabetes type, we found moderate overall agreement between the algorithm and self-reported type (kappa, 0.52; 95% confidence interval [CI], 0.52 to 0.53). Perfect agreement was noted in the youngest age group (kappa, 1.0; 95% CI, 1.0-1.0) but agreement was poor for the oldest age group (kappa, 0.20; 95% CI, 0.19 to 0.20).


CONCLUSIONS
An algorithm based on self-reported, objective characteristics related to diabetes diagnosis and treatment patterns may have the potential to overcome limitations of simple self-report diabetes type for the classification of diabetes type in older adults.
Health Analysis Division, Statistics Canada, Ottawa, Ontario, Canada. Electronic address: edward.ng@statcan.gc.ca.
Edward
E
Ng
Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
Saskia E
SE
Vanderloo
Division of Diabetes Translation, US Centre for Disease Control, Atlanta, Georgia, United States of America.
Linda
L
Geiss
School of Public Health, University of Alberta, Edmonton, Alberta, Canada.
Jeffrey A
JA
Johnson
D000328:Adult; D000368:Aged; D000465:Algorithms; D002170:Canada / Q000453:epidemiology; D002908:Chronic Disease; D003625:Data Collection / Q000379:methods* / Q000592:standards; D003922:Diabetes Mellitus, Type 1 / Q000175:diagnosis* / Q000453:epidemiology; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis* / Q000453:epidemiology; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D062486:Public Health Surveillance / Q000379:methods*; D015203:Reproducibility of Results; D057566:Self Report; D055815:Young Adult
Surveillance; diabète de type 1; diabète de type 2; enquête; survey; type 1 diabetes; type 2 diabetes