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

Endocrinol Nutr. 2016 Nov;63(9):440-448. doi: 10.1016/j.endonu.2016.07.004. Epub 2016 Sep 6.
[Article in English, Spanish]


Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: Atención primaria; Clasificación; Classification; Codificación; Coding; Diabetes; Primary care.

MeSH terms

  • 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


  • Hypoglycemic Agents