TY - JOUR
T1 - Using healthcare claims data to analyze the prevalence of BCR-ABL-positive chronic myeloid leukemia in France
T2 - A nationwide population-based study
AU - Foulon, Stéphanie
AU - Cony-Makhoul, Pascale
AU - Guerci-Bresler, Agnès
AU - Delord, Marc
AU - Solary, Eric
AU - Monnereau, Alain
AU - Bonastre, Julia
AU - Tubert-Bitter, Pascale
N1 - Publisher Copyright:
© 2019 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.
PY - 2019/6/1
Y1 - 2019/6/1
N2 - 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.
AB - 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.
KW - cancer registries
KW - chronic myeloid leukemia
KW - epidemiology
KW - insurance claims database
KW - prevalence
UR - http://www.scopus.com/inward/record.url?scp=85067180566&partnerID=8YFLogxK
U2 - 10.1002/cam4.2200
DO - 10.1002/cam4.2200
M3 - Article
C2 - 31038849
AN - SCOPUS:85067180566
SN - 2045-7634
VL - 8
SP - 3296
EP - 3304
JO - Cancer Medicine
JF - Cancer Medicine
IS - 6
ER -