Background: In France, as in many other countries, nationwide data on prevalence are rarely available and recent prevalence estimates of Chronic Myeloid Leukemia (CML) are scarce. Improved overall survival following the introduction of tyrosine kinase inhibitors (TKIs) is expected to have increased the prevalence of CML in Western countries.
Aim: We sought to estimate and analyze the prevalence of CML in France for the year 2014 using a large health care claim-based dataset.
Methods: Using the French national health insurance database that covers 98.8% of the French population (66 million people) we implemented a 3-step approach. First, focusing on the 2006-2014 period, we selected: 1) all patients treated with a TKI (ie, imatinib, dasatinib, nilotinib, bosutinib or ponatinib) and/or 2) identified by the ICD-10 diagnosis code C92.1 (Chronic Myeloid Leukemia, BCR/ABL-positive) among hospital discharge diagnoses and/or 3) identified by the ICD-10 diagnosis code C92 (myeloid leukemia) for coinsurance exemption. Then, we developed a claim-based algorithm to identify CML cases. Case definition was based on 1) identifying any TKI reimbursement lasting ≥ 2 months and 2) excluding patients receiving TKIs for diseases other than CML including Phi+ Acute Lymphoblastic Leukemia, Gastrointestinal Stromal Tumor, Stromal or other connective tissue tumor, and hypereosinophilic disease.
Finally, prevalent CML cases were those identified by the algorithm above and having ≥ 1 healthcare reimbursement during the year 2014 and still alive on December, 31st 2014. The internal validity of the algorithm was tested on a random sample of 100 potential CML cases fulfilling ≥ 1/3 selection criteria in step 1 by comparing the results of the algorithm with the opinion of two hematologists (gold standard). For each individual, hematologists reviewed patient demographics and the sequence of care from 2006 to 2014 including healthcare resource utilization (ie, all hospitalizations and ICD-10 diagnosis codes, all medication use, all specialist consultations with date and specialist type). In addition, we assessed the external validity of the algorithm by comparing the number of incident CML patients in 2014 as identified in the French national health insurance database with the number of incident CML cases recorded in the French cancer registries for respective departments (i.e. ~ 20% of the French territory).
Results: There were 10,789 prevalent CML cases in 2014 out of 68,067 individuals from the French national health insurance database who fulfilled the selection criteria for the overall 2006-2014 period. Eighty-nine percent of the prevalent CML cases were identified by at least two out of three selection criteria (TKI, ICD-10 code C92.1 among hospital discharge diagnoses, ICD-10 code C92 for coinsurance exemption). There was a 96% concordance rate (internal validity) between the algorithm and the opinion of the hematologists. For the year 2014, 162 and 150 incident CML patients were identified by the algorithm and the French cancer registries, respectively (high external validity).
Median age [Inter-Quartile Range] of the prevalent population of CML patients was 63 years [51-73], with slightly more males affected (55%). On December, 31st 2014, the crude prevalence of CML was estimated at 16.3 per 100,000 inhabitants [95% confidence interval (CI) 16.0-16.6]. The crude prevalence of CML was 18.5 per 100,000 in men (95% CI 18.0-19.0) and 14.2 per 100,000 in women (95% CI 13.8-14.6). The crude prevalence of CML was less than 1.6 per 100,000 (95% CI 1.2-2.0) before 20 years of age, progressively increasing to 19.4 per 100,000 (95% CI 18.1-20.7) among those with 50-54 and reaching a peak of 48.2 per 100,000 (95% CI 45.3-51.1) at 75-79 years. There was a male preponderance in CML prevalence in all age groups. The crude prevalence of CML varied in a ratio of one to two throughout the French territory (from 10.2 to 23.8 per 100,000 inhabitants).
Conclusion: Healthcare claims data are increasingly used to estimate epidemiological parameters worldwide. This approach is particularly relevant for rare diseases and administrative databases with high population coverage. Countries without national cohorts or cancer registries could easily use our algorithm to estimate their prevalence of CML.
Cony-Makhoul:Pfizer: Consultancy; BMS: Consultancy, Speakers Bureau; Incyte: Other: Travels for attending to Congress; Novartis: Consultancy, Other: Writing support, Travels for attending to Congress. Guerci-Bresler:Pfizer: Other: Fees for symposiums and boards; Novartis: Consultancy, Other: Fees for symposiums and boards; Incyte: Other: Fees for symposiums and boards; BMS: Other: Fees for symposiums and boards; Pfizer: Other: Travel fees for Congress. Delord:Incyte: Consultancy.
Asterisk with author names denotes non-ASH members.