Validity of Using Prescription Medications to Classify Disease - A Retrospective Observational Study Using Routinely Collected Electronic Health Records from the UK

Journal article

BackgroundEpidemiological studies rely on valid classifications of patients' disease status. However, in the absence of perfect information on every patients' health status, researchers use proxy information with variable and often unknown validity.MethodsTo investigate the validity of using prescription records for disease classification, we conducted a retrospective observational study on a UK-wide database of medical prescriptions and clinical records (Optimum Patient Care Research Database). We used electronic health records of 25,000 randomly selected patients for each year between 2004 and 2020 (total N=425,000) and compared disease classification of 18 different chronic conditions based on clinical records for a period of three years (gold standard) with disease classification based on prescription records for the same period. We then used logistic regression to analyse if positive and negative predicted values (PPV and NPV) were associated with known predictors of disease.ResultsResults showed large variations in PPV ranging from 8% (heart failure) to 94% (all type diabetes) and smaller variations in NPV ranging from 96% (anxiety) to 100% (Type 1 diabetes). Age, sex, ethnicity, and year but not socio-economic status were associated with variations in validity, especially in classifying dementia, diabetes, and depression.DiscussionVarying validity can partly be explained by different (stratum-specific) prevalence of disease. Additionally, conditions like heart failure can be treated with medication that can also be prescribed for other conditions or can be treated without medication. However, varying validity can also be attributed to imperfect clinical records, which we used as gold standard. As a consequence of low validity, the apparent prevalence based on using prescription records was between 1.3 times lower (all-type diabetes) and up-to 11 times higher (heart failure) than the true prevalence based on the clinical records.ConclusionStudies using prescription data to classify disease status run a substantial risk of misclassification bias.

Authors: Schnier, C; Busby, J; Sheikh, A; Quint, JK; Price, DB

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Taylor and Francis Group
• Journal: Pragmatic and Observational Research
• Volume: 17
• Pages: 1-14
• Publication date: 2026-01-15
• Acceptance date: 2025-11-10
• DOI: 10.2147/por.s553011
• EISSN: 1179-7266
• ISSN: 1179-7266
• Pmid: 41567609
• Language: English
• Keywords: Electronic Health Records; Misclassification Bias; Optimum Patient Care Research Database; Validation Study; Diagnostic Code Lists