Completeness and Representativeness of the PHARMO General Practitioner (GP) Data: A Comparison with National Statistics

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Purpose: When using incomplete or non-representative real-world data (RWD), bias is more likely to occur. The aim of the current study was to assess the completeness and representativeness of the PHARMO GP data for the Dutch population. Patients and Methods: A cross-sectional study was performed. The PHARMO GP data comprise data from electronic health records registered by GPs. Data on the Dutch population were obtained from Statistics Netherlands (CBS), which offers publicly available data on several themes. The standardized difference (std.diff) was used to compare proportions between the PHARMO GP population and the Dutch population. An absolute std.diff >0.2 was considered a difference. Results: On January 1st, 2018, 3,466,321 persons were included in the PHARMO GP data (mean age: 41.6 years, 49.7% males). The sex and age distribution was similar to the Dutch population. The PHARMO GP data captured less not urbanized areas compared to the Dutch population (not urbanized areas: 9.4% vs 17.1% [std.diff: −0.23]). Regarding medication use, only the pharmacological subgroups “viral vaccines” and “hormonal contraceptives for systemic use” differed (std.diff >0.2); use in the GP data was more complete than in the Statistics Netherlands (CBS) data. No differences were observed regarding diagnoses. Conclusion: The PHARMO GP data are representative of the Dutch population with regard to the demographic characteristics and diagnoses in primary care. Medication data in the PHARMO GP data are more complete than national statistics, and differences are related to reimbursement. Use of the data and interpretation of results based on these sources should be done with experts on the data sources, the Dutch healthcare system and (pharmaco)epidemiology.
Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalClinical Epidemiology
Publication statusPublished - 2023


  • PHARMO GP data
  • RWD
  • completeness
  • data quality
  • real-world evidence
  • representativeness

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