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Accuracy of rheumatoid arthritis diagnosis coding in primary care: a validation study
Jamal Roberton1, Douglas White2, Vicki Quincey3
1Faculty of Medical and Health Sciences, The University of Auckland, Auckland, Aotearoa New Zealand; Rheumatology Department, Waikato Hospital, Hamilton, Aotearoa New Zealand; School of Health Equity and Innovation, The University of Waikato, Hamilton, Aotearoa New Zealand.
The New Zealand Medical Journal
|July 30, 2026
Summary
Accurate rheumatoid arthritis (RA) coding in primary care has a high positive predictive value (PPV) of 86.3%. However, relying solely on codes can lead to misclassification, highlighting the need for validated case definitions for reliable RA research.
Area of Science:
- Rheumatology
- Health Informatics
- Epidemiology
Background:
- Accurate identification of rheumatoid arthritis (RA) in routinely collected health data is crucial for surveillance, planning, and research.
- Primary care records are a key source for RA data, but the accuracy of coded diagnoses needs validation.
Purpose of the Study:
- To determine the proportion of recorded rheumatoid arthritis (RA) diagnoses in primary care that meet predefined validation criteria.
- To assess the accuracy of SNOMED-coded RA diagnoses in a real-world primary care setting.
Main Methods:
- Retrospective diagnostic validation study using primary care records with SNOMED-coded RA diagnoses (2018-2025).
- RA status assessed via a hierarchical pathway including rheumatology specialist involvement, disease-modifying antirheumatic drug (DMARD) exposure, and serology (RF, aCCP).
- Positive predictive value (PPV) calculated against a composite reference standard, with independent review for equivocal cases.
Main Results:
- Out of 3,831 subjects with coded RA, 3,306 were confirmed, yielding a PPV of 86.3%.
- PPV was highest (94.4%) with documented rheumatology specialist involvement.
- Misclassification primarily occurred in cases lacking specialist input, DMARD exposure, or serological data.
Conclusions:
- SNOMED-coded RA diagnoses in primary care show a high PPV but are not infallible.
- Sole reliance on coding can lead to misclassification, impacting epidemiological research and health service planning.
- Validated case definitions and improved diagnostic coding practices are essential for accurate RA data.
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