ArticleClinical epidemiology2016
An algorithm for identification and classification of individuals with type 1 and type 2 diabetes mellitus in a large primary care database.
Article in Clinical epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Developing a Computable Phenotype for Identifying Children, Adolescents, and Young Adults With Diabetes Using Electronic Health Records in the DiCAYA Network.Diabetes care · 2025Article
- Pathophysiological hallmarks in type 2 diabetes heterogeneity (review).Diabetology international · 2025Review
- Developing an automated algorithm for identification of children and adolescents with diabetes using electronic health records from the OneFlorida+ clinical research network.Diabetes, obesity & metabolism · 2025Article
- Screening for Hypoglycaemia Risk and Medication Changes in Diabetes Patients Using Pharmacy Dispensing Data.Journal of clinical medicine · 2024Article
- Dietary patterns, socioeconomic disparities, and risk of type 2 diabetes in the Sister Study.Diabetes research and clinical practice · 2023Article
- UK research data resources based on primary care electronic health records: review and summary for potential users.BJGP open · 2023Article
- Polygenic Scores Help Reduce Racial Disparities in Predictive Accuracy of Automated Type 1 Diabetes Classification Algorithms.Diabetes care · 2023Article
- Incremental value of risk factor variability for cardiovascular risk prediction in individuals with type 2 diabetes: results from UK primary care electronic health records.International journal of epidemiology · 2022Article
- Cardiovascular outcomes of type 2 diabetic patients treated with DPP‑4 inhibitors versus sulphonylureas as add-on to metformin in clinical practice.Scientific reports · 2021Article
- Prediction of Cardiovascular Disease Risk Accounting for Future Initiation of Statin Treatment.American journal of epidemiology · 2021Article
- A retrospective epidemiological study of type 1 diabetes mellitus in wales, UK between 2008 and 2018.International journal of population data science · 2021Article
- A prospective study of type 2 diabetes, metformin use, and risk of breast cancer.Annals of oncology : official journal of the European Society for Medical Oncology · 2021Article
- Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.JMIR medical informatics · 2021Article
- Validation of a type 1 diabetes algorithm using electronic medical records and administrative healthcare data to study the population incidence and prevalence of type 1 diabetes in Ontario, Canada.BMJ open diabetes research & care · 2020Article
- Development and validation of algorithms to classify type 1 and 2 diabetes according to age at diagnosis using electronic health records.BMC medical research methodology · 2020Article
- Population-calibrated multiple imputation for a binary/categorical covariate in categorical regression models.Statistics in medicine · 2019Article
- Ethnic Differences in the Prevalence of Type 2 Diabetes Diagnoses in the UK: Cross-Sectional Analysis of the Health Improvement Network Primary Care Database.Clinical epidemiology · 2019Article
- Observational studies of treatment effectiveness: worthwhile or worthless?Clinical epidemiology · 2019Article
- Emerging Approaches in Surveillance of Type 1 Diabetes.Current diabetes reports · 2018Review
- Landmark Models for Optimizing the Use of Repeated Measurements of Risk Factors in Electronic Health Records to Predict Future Disease Risk.American journal of epidemiology · 2018Article
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Authors and funding
4 authors.
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Abstract
backgroundResearch into diabetes mellitus (DM) often requires a reproducible method for identifying and distinguishing individuals with type 1 DM (T1DM) and type 2 DM (T2DM).
objectivesTo develop a method to identify individuals with T1DM and T2DM using UK primary care electronic health records.
methodsUsing data from The Health Improvement Network primary care database, we developed a two-step algorithm. The first algorithm step identified individuals with potential T1DM or T2DM based on diagnostic records, treatment, and clinical test results. We excluded individuals with records for rarer DM subtypes only. For individuals to be considered diabetic, they needed to have at least two records indicative of DM; one of which was required to be a diagnostic record. We then classified individuals with T1DM and T2DM using the second algorithm step. A combination of diagnostic codes, medication prescribed, age at diagnosis, and whether the case was incident or prevalent were used in this process. We internally validated this classification algorithm through comparison against an independent clinical examination of The Health Improvement Network electronic health records for a random sample of 500 DM individuals.
resultsOut of 9,161,866 individuals aged 0-99 years from 2000 to 2014, we classified 37,693 individuals with T1DM and 418,433 with T2DM, while 1,792 individuals remained unclassified. A small proportion were classified with some uncertainty (1,155 [3.1%] of all individuals with T1DM and 6,139 [1.5%] with T2DM) due to unclear health records. During validation, manual assignment of DM type based on clinical assessment of the entire electronic record and algorithmic assignment led to equivalent classification in all instances.
conclusionThe majority of individuals with T1DM and T2DM can be readily identified from UK primary care electronic health records. Our approach can be adapted for use in other health care settings.
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