ArticlePharmacoepidemiology and drug safety2014
Distinguishing incident and prevalent diabetes in an electronic medical records database.
Article in Pharmacoepidemiology and drug safety, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
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- Influence of comorbidities on the implementation of the fundus examination in patients with newly diagnosed type 2 diabetes.Japanese journal of ophthalmology · 2018Article
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- Association of Itraconazole, a Hedgehog Inhibitor, and Bladder Cancer.The Journal of urology · 2016Observational
- Validation of a coding algorithm for intra-abdominal surgeries and adhesion-related complications in an electronic medical records database.Pharmacoepidemiology and drug safety · 2016Article
- Article
- An algorithm for identification and classification of individuals with type 1 and type 2 diabetes mellitus in a large primary care database.Clinical epidemiology · 2016Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo develop a method to identify incident diabetes mellitus (DM) using an electronic medical records (EMR) database and test this classification by comparing incident and prevalent DM with common outcomes related to DM duration.
methodsIncidence rates (IRs) of DM (defined as a first diagnosis or prescription) were measured in 3-month intervals through 36 months after registration in The Health Improvement Network, a primary care database, from 1994 to 2012. We used Joinpoint regression to identify the point where a statistically significant change in the trend of IRs occurred. Further analyses used this point to distinguish those likely to have incident (n = 50 315) versus prevalent (n = 28 337) DM. Incident and prevalent cohorts were compared using Cox regression for all-cause mortality, cardiovascular disease (CVD), diabetic retinopathy, diabetic nephropathy, and diabetic neuropathy. Analyses were adjusted for age, sex, smoking, obesity, hyperlipidemia, hypertension, and calendar year.
resultsTrends in DM IRs plateaued 9 months after registration (p = 0.04). All cause-mortality was increased (hazard ratio (HR) 1.62, 95% CI 1.53-1.70) among patients diagnosed with DM prior to 9 months following registration (prevalent DM) compared to those diagnosed after 9 months (incident DM). Similarly, the risk of DM-related complications was higher in prevalent versus incident DM patients [CVD, HR 2.24 (2.08-2.40); diabetic retinopathy, HR 1.31 (1.24-1.38); diabetic nephropathy, HR 2.30 (1.95-2.72); diabetic neuropathy, HR 1.28 (1.16-1.41)].
conclusionJoinpoint regression can be used to identify patients with newly diagnosed diabetes within EMR data. Failure to exclude patients with prevalent DM can lead to exaggerated associations of DM-related outcomes.
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