Basic characteristics and representativeness of the German Disease Analyzer database

Int J Clin Pharmacol Ther. 2018 Oct;56(10):459-466. doi: 10.5414/CP203320
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Abstract

Purpose: The aim of this study was to evaluate the representativeness of diagnoses in the Disease Analyzer (DA) database for major chronic diseases (cancer, dementia, diabetes).

Materials and methods: DA contains anonymized longitudinal data on drug prescriptions, diagnoses as well as medical and demographic data directly obtained from the computer system of a representative sample of practices throughout Germany. DA contains data from 2,498 practices with 7.8 million patients (2017). The distribution and sex-specific incidence of various cancer subsites among new cancer cases, the age- and sex-specific prevalence of dementia, and the prevalence of diabetes were assessed. National reference data were obtained from official sources.

Results: Mean age (43 years) and sex distribution (47% men) of primary care patients in DA were similar to the German population. Among incident cancer cases, there was good agreement between DA data and national data with respect to the various cancer subsites (e.g., breast cancer: DA 17%; reference: 15%). Furthermore, sex distribution was largely similar. The age distribution of prevalent dementia was similar to national reference data, both in men (80 - 84 years: DA: 26.8%; reference: 27.0%) and in women (80 - 84 years: DA: 24.6%; reference: 24.1%). Diabetes prevalence in the DA (10.7%) was higher than in claims data from physicians (9.8%) or patients from statutory health insurances (9.9%).

Conclusion: There was a good agreement of the incidence or prevalence of major chronic diseases in the outpatient DA with German reference data. The higher diabetes prevalence in the DA is due to the increased number of outpatient visits of diabetes patients. .

MeSH terms

  • Databases, Factual*
  • Demography
  • Epidemiologic Methods
  • Germany
  • Humans
  • Pharmacoepidemiology / methods*
  • Physicians / statistics & numerical data
  • Practice Patterns, Physicians' / statistics & numerical data*
  • Risk Factors