Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 18 (1), 316

Identifying Diabetes Cases From Administrative Data: A Population-Based Validation Study

Affiliations

Identifying Diabetes Cases From Administrative Data: A Population-Based Validation Study

Lorraine L Lipscombe et al. BMC Health Serv Res.

Abstract

Background: Health care data allow for the study and surveillance of chronic diseases such as diabetes. The objective of this study was to identify and validate optimal algorithms for diabetes cases within health care administrative databases for different research purposes, populations, and data sources.

Methods: We linked health care administrative databases from Ontario, Canada to a reference standard of primary care electronic medical records (EMRs). We then identified and calculated the performance characteristics of multiple adult diabetes case definitions, using combinations of data sources and time windows.

Results: The best algorithm to identify diabetes cases was the presence at any time of one hospitalization or physician claim for diabetes AND either one prescription for an anti-diabetic medication or one physician claim with a diabetes-specific fee code [sensitivity 84.2%, specificity 99.2%, positive predictive value (PPV) 92.5%]. Use of physician claims alone performed almost as well: three physician claims for diabetes within one year was highly specific (sensitivity 79.9%, specificity 99.1%, PPV 91.4%) and one physician claim at any time was highly sensitive (sensitivity 93.6%, specificity 91.9%, PPV 58.5%).

Conclusions: This study identifies validated algorithms to capture diabetes cases within health care administrative databases for a range of purposes, populations and data availability. These findings are useful to study trends and outcomes of diabetes using routinely-collected health care data.

Keywords: administrative databases; diabetes; electronic medical record data; validation methods.

Conflict of interest statement

Ethics approval and consent to participate

The study protocol was approved by the Research Ethics Board at Sunnybrook Health Sciences Center.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Flow diagram of patients with diabetes from primary care electronic medical records

Similar articles

See all similar articles

Cited by 6 articles

See all "Cited by" articles

References

    1. Collaboration NCDRF Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet. 2016;387(10027):1513–1530. doi: 10.1016/S0140-6736(16)00618-8. - DOI - PMC - PubMed
    1. Clottey C, Mo F, LeBrun B, Mickelson P, Niles J, Robbins G. The development of the National Diabetes Surveillance System (NDSS) in Canada. Chronic Dis Can. 2001;22(2):67–69. - PubMed
    1. Blanchard JF, Ludwig S, Wajda A, et al. Incidence and prevalence of diabetes in Manitoba, 1986-1991. Diabetes Care. 1996;19(8):807–811. doi: 10.2337/diacare.19.8.807. - DOI - PubMed
    1. Hux JE, Ivis F, Flintoft V, Bica A. Diabetes in Ontario: Determination of prevalence and incidence using a validated administrative data algorithm. Diabetes Care. 2002;25(3):512–516. doi: 10.2337/diacare.25.3.512. - DOI - PubMed
    1. Lipscombe LL, Hux JE. Trends in diabetes prevalence, incidence, and mortality in Ontario, Canada 1995-2005: a population-based study. Lancet. 2007;369(9563):750–756. doi: 10.1016/S0140-6736(07)60361-4. - DOI - PubMed

Publication types

Grant support

Feedback