Is diabetes mellitus correctly registered and classified in primary care? A population-based study in Catalonia, Spain

To assess the prevalence of miscoding, misclassification, misdiagnosis and under-registration of diabetes mellitus (DM) in primary health care in Catalonia (Spain), and to explore use of automated algorithms to identify them. In this cross-sectional, retrospective study using an anonymized electroni...

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Veröffentlicht in:Endocrinologia y nutricion Jg. 63; H. 9; S. 440 - 448
Hauptverfasser: Mata-Cases, Manel, Mauricio, Dídac, Real, Jordi, Bolíbar, Bonaventura, Franch-Nadal, Josep
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Spain 01.11.2016
Schlagworte:
Adult
Algorithms
Clinical Coding
Cross-Sectional Studies
Databases, Factual
Diabetes Mellitus - classification
Diabetes Mellitus - drug therapy
Diabetes Mellitus - epidemiology
Electronic Health Records
Female
Humans
Hypoglycemic Agents - therapeutic use
Male
Middle Aged
Prediabetic State - epidemiology
Prevalence
Primary Health Care
Retrospective Studies
Spain - epidemiology
Atención primaria
Coding
Codificación
Classification
Diabetes
Primary care
Clasificación
ISSN:1579-2021
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Zusammenfassung:To assess the prevalence of miscoding, misclassification, misdiagnosis and under-registration of diabetes mellitus (DM) in primary health care in Catalonia (Spain), and to explore use of automated algorithms to identify them. In this cross-sectional, retrospective study using an anonymized electronic general practice database, data were collected from patients or users with a diabetes-related code or from patients with no DM or prediabetes code but treated with antidiabetic drugs (unregistered DM). Decision algorithms were designed to classify the true diagnosis of type 1 DM (T1DM), type 2 DM (T2DM), and undetermined DM (UDM), and to classify unregistered DM patients treated with antidiabetic drugs. Data were collected from a total of 376,278 subjects with a DM ICD-10 code, and from 8707 patients with no DM or prediabetes code but treated with antidiabetic drugs. After application of the algorithms, 13.9% of patients with T1DM were identified as misclassified, and were probably T2DM; 80.9% of patients with UDM were reclassified as T2DM, and 19.1% of them were misdiagnosed as DM when they probably had prediabetes. The overall prevalence of miscoding (multiple codes or UDM) was 2.2%. Finally, 55.2% of subjects with unregistered DM were classified as prediabetes, 35.7% as T2DM, 8.5% as UDM treated with insulin, and 0.6% as T1DM. The prevalence of inappropriate codification or classification and under-registration of DM is relevant in primary care. Implementation of algorithms could automatically flag cases that need review and would substantially decrease the risk of inappropriate registration or coding.
Bibliographie:ObjectType-Article-1
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ISSN:1579-2021
DOI:10.1016/j.endonu.2016.07.004