Cancer Medicine (Jun 2019)

Using healthcare claims data to analyze the prevalence of BCR‐ABL‐positive chronic myeloid leukemia in France: A nationwide population‐based study

  • Stéphanie Foulon,
  • Pascale Cony‐Makhoul,
  • Agnès Guerci‐Bresler,
  • Marc Delord,
  • Eric Solary,
  • Alain Monnereau,
  • Julia Bonastre,
  • Pascale Tubert‐Bitter

DOI
https://doi.org/10.1002/cam4.2200
Journal volume & issue
Vol. 8, no. 6
pp. 3296 – 3304

Abstract

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Abstract Background Data on Chronic Myeloid Leukemia (CML) prevalence are scarce. Here we provide an estimation of the prevalence of CML in France for the year 2014 using French national health insurance data. Methods We selected patients claiming reimbursement for tyrosine kinase inhibitors (TKI) or with hospital discharge diagnoses for CML, BCR/ABL‐positive or with full reimbursement of health care expenses for myeloid leukemia. We built an algorithm which we validated on a random sample of 100 potential CML patients by comparing the results obtained using the algorithm and the opinion of two hematologists who reviewed the patient demographics and sequence of care abstracted from claims data (internal validity). For external validity, we compared the number of incident CML patients identified using the algorithm with those recorded in French population‐based cancer registries in departments covered by such a registry. Results We identified 10 789 prevalent CML patients in 2014, corresponding to a crude prevalence rate of 16.3 per 100 000 inhabitants [95% confidence interval (CI) 16.0‐16.6]: 18.5 in men [18.0‐19.0] and 14.2 in women [13.8‐14.6]. The crude CML prevalence was less than 1.6 per 100 000 [1.2‐2.0] under age 20, increasing to a maximum of 48.2 [45.4‐51.2) at ages 75‐79. It varied from 10.2 to 23.8 per 100 000 across French departments. The algorithm showed high internal and external validity. Concordance rate between the algorithm and the hematologists was 96%, and the numbers of incident CML patients identified using the algorithm and the registries were 162 and 150, respectively. Conclusion We built and validated an algorithm to identify CML patients in administrative healthcare databases. In addition to prevalence estimation, the algorithm could be used for future economic evaluations or pharmaco‐epidemiological studies in this population.

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