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BMC Health Services Research
Published in: BMC Health Services Research 1/2018

Open Access 01-12-2018 | Research article

Identifying diabetes cases from administrative data: a population-based validation study

Authors: Lorraine L. Lipscombe, Jeremiah Hwee, Lauren Webster, Baiju R. Shah, Gillian L. Booth, Karen Tu

Published in: BMC Health Services Research | Issue 1/2018

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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.
Appendix
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Literature
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):1513–30.CrossRef
2.
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):67–9.PubMed
3.
Blanchard JF, Ludwig S, Wajda A, et al. Incidence and prevalence of diabetes in Manitoba, 1986-1991. Diabetes Care. 1996;19(8):807–11.CrossRefPubMed
4.
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):512–6.CrossRefPubMed
5.
Lipscombe LL, Hux JE. Trends in diabetes prevalence, incidence, and mortality in Ontario, Canada 1995-2005: a population-based study. Lancet. 2007;369(9563):750–6.CrossRefPubMed
6.
Feig DS, Hwee J, Shah BR, Booth GL, Bierman AS, Lipscombe LL. Trends in incidence of diabetes in pregnancy and serious perinatal outcomes: a large, population-based study in ontario, Canada, 1996-2010. Diabetes Care. 2014;37(6):1590–6.CrossRefPubMed
7.
8.
9.
Booth GL, Creatore MI, Moineddin R, et al. Unwalkable neighborhoods, poverty, and the risk of diabetes among recent immigrants to Canada compared with long-term residents. Diabetes Care. 2013;36(2):302–8.CrossRefPubMedPubMedCentral
10.
Lysy Z, Booth GL, Shah BR, Austin PC, Luo J, Lipscombe LL. The impact of income on the incidence of diabetes: a population-based study. Diabetes Res Clin Pract. 2013;99(3):372–9.CrossRefPubMed
11.
Lipscombe LL, Chan WW, Yun L, Austin PC, Anderson GM, Rochon PA. Incidence of diabetes among postmenopausal breast cancer survivors. Diabetologia. 2013;56(3):476–83.CrossRefPubMed
12.
Booth GL, Kapral MK, Fung K, Tu JV. Relation between age and cardiovascular disease in men and women with diabetes compared with non-diabetic people: a population-based retrospective cohort study. Lancet. 2006;368(9529):29–36.CrossRefPubMed
13.
Booth GL, Kapral MK, Fung K, Tu JV. Recent trends in cardiovascular complications among men and women with and without diabetes. Diabetes Care. 2006;29(1):32–7.CrossRefPubMed
14.
Booth GL, Hux JE. Relationship between avoidable hospitalizations for diabetes mellitus and income level. Arch Intern Med. 2003;163(1):101–6.CrossRefPubMed
15.
Lind M, Garcia-Rodriguez LA, Booth GL, et al. Mortality trends in patients with and without diabetes in Ontario, Canada and the UK from 1996 to 2009: a population-based study. Diabetologia. 2013;56(12):2601–8.CrossRefPubMed
16.
Lipscombe LL, Hux JE, Booth GL. Reduced screening mammography among women with diabetes. Arch Intern Med. 2005;165(18):2090–5.CrossRefPubMed
17.
Shah BR, Hux JE, Laupacis A, Zinman B. van WC. Clinical inertia in response to inadequate glycemic control: do specialists differ from primary care physicians? Diabetes Care. 2005;28(3):600–6.CrossRefPubMed
18.
Cheung A, Stukel TA, Alter DA, et al. Primary Care Physician Volume and Quality of Diabetes Care: A Population-Based Cohort Study. Ann Intern Med. 2017;166(4):240–7.CrossRefPubMed
19.
Manuel DG, Rosella LC, Stukel TA. Importance of accurately identifying disease in studies using electronic health records. BMJ. 2010;341:c4226.CrossRefPubMed
20.
Carnahan RM. Mini-Sentinel's systematic reviews of validated methods for identifying health outcomes using administrative data: summary of findings and suggestions for future research. Pharmacoepidemiol Drug Saf. 2012;21(Suppl 1):90–9.CrossRefPubMed
21.
van Walraven C, Bennett C, Forster AJ. Administrative database research infrequently used validated diagnostic or procedural codes. J Clin Epidemiol. 2011;64(10):1054–9.CrossRefPubMed
22.
Tu K, Mitiku TF, Ivers NM, et al. Evaluation of Electronic Medical Record Administrative data Linked Database (EMRALD). Am J Manag Care. 2014;20(1):e15–21.PubMed
23.
Tu K, Widdifield J, Young J, et al. Are family physicians comprehensively using electronic medical records such that the data can be used for secondary purposes?A Canadian perspective. BMC Med Inform Decis Mak. 2015;15:67.CrossRefPubMedPubMedCentral
24.
Tu K, Manuel D, Lam K, Kavanagh D, Mitiku TF, Guo H. Diabetics can be identified in an electronic medical record using laboratory tests and prescriptions. J Clin Epidemiol. 2011;64(4):431–5.CrossRefPubMed
25.
Ivers NM, Tu K, Francis J, et al. Feedback GAP: study protocol for a cluster-randomized trial of goal setting and action plans to increase the effectiveness of audit and feedback interventions in primary care. Implement Sci. 2010;5:98.CrossRefPubMedPubMedCentral
26.
Guttmann A, Nakhla M, Henderson M, et al. Validation of a health administrative data algorithm for assessing the epidemiology of diabetes in Canadian children. Pediatr Diabetes. 2010;11(2):122–8.CrossRefPubMed
27.
Lee WJ, Lee TA, Pickard AS, Shoaibi A, Schumock GT. Using linked electronic data to validate algorithms for health outcomes in administrative databases. J Comp Eff Res. 2015;4(4):359–66.CrossRefPubMed
Metadata
Title
Identifying diabetes cases from administrative data: a population-based validation study
Authors
Lorraine L. Lipscombe
Jeremiah Hwee
Lauren Webster
Baiju R. Shah
Gillian L. Booth
Karen Tu
Publication date
01-12-2018
Publisher
BioMed Central
Published in
BMC Health Services Research / Issue 1/2018
Electronic ISSN: 1472-6963
DOI
https://doi.org/10.1186/s12913-018-3148-0

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