scholarly journals Characteristics of Hemophilia A Patients Treated with Emicizumab: an Early View using Claims Database Analysis

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 4928-4928
Author(s):  
Arash Mahajerin ◽  
Erru Yang ◽  
Anisha M. Patel

Introduction Hemophilia A is a genetic bleeding disorder characterized by bleeding episodes due to deficiency of factor VIII (FVIII). Emicizumab (EMI) is a recombinant, humanized, bispecific factor IXa- and factor X-directed monoclonal antibody, indicated for routine prophylaxis in all persons with hemophilia A (PwHA) with or without FVIII inhibitors. This study aimed to provide an early view of the characteristics of PwHA who are treated with emicizumab. Methods This was a retrospective cohort study using US commercial insurance claims data from MarketScan Commercial Research Database and PharMetrics Plus Database from 11/16/17 to 12/30/18 (MarketScan data available until 9/30/18 at the time of analysis). The study cohort included PwHA with ≥1 emicizumab claim during the study period. Emicizumab claims were identified using NDC or HCPCS codes (Q9995). The index date was defined as the date of first emicizumab claim. However, this index date was adjusted to an earlier date during the study period, if an appropriate miscellaneous/unclassified drug or biologic HCPCS J-code claim was identified indicating emicizumab use prior to the specific NDC or Q-code claim. The study sample was required to have ≥12 months of continuous insurance enrollment prior to the index date i.e. prior to starting emicizumab (pre-EMI period). Demographics, all-cause health care resource utilization and clinical characteristics including major bleeds, arthropathy, and any pain diagnosis were examined in the pre-EMI period. Major bleeds were identified using a previously developed algorithm (Shrestha et al. 2017) while arthropathy and pain were identified using International Classification of Diseases, 9th Revision, Clinical Modification or ICD-9-CM/ICD-10-CM diagnosis codes. FVIII and bypassing agents (BPAs) were identified using NDC or HCPCS codes. Results We identified a total of 47 PwHA taking emicizumab with ≥12 months of prior continuous insurance enrollment. The mean age of these individuals was 20.4 years (standard deviation [SD]=16.7, range=1-61y); 19.1% (n=9) were under 5 years of age and 36.2% (n=17) ages 6-17 years. All individuals were male (100%), and the majority were in the Southern region of the US (44.7%, n=21) and covered with Preferred Provider Organization insurance plans (78.7%, n=37). In the pre-EMI period, 25.5% (n=12) of the cohort had evidence of inhibitors (i.e. claim for a BPA); 27.7% (n=13) had evidence of a major bleed, with an average of 2.8 bleeds (SD=2.3; range=1-8) among those with ≥1 major bleed-related claim; 21.3% (n=10) had diagnosis of any arthropathy; and 19.1% (n=9) had diagnosis of any pain. A total of 76.6% (n=36) and 25.5% (n=12) of PwHA had evidence of FVIII or BPA use in the pre-EMI year, with an average of 12.8 (SD=15.1) and 11.5 (SD=8.6) prescriptions/ administrations among those with ≥1 FVIII or BPA claim, respectively. Overall, 36.2% (n=17) of the cohort had ≥1 emergency room visit (mean=0.8, SD = 1.6); 17.0% (n=8) had ≥1 inpatient hospital stay (mean=0.2, SD=0.6), with a mean length of stay of 1.3 days (SD=3.3); 80.9% (n=38) had ≥1 outpatient hospital visit (mean=5.4, SD=14.2); and 83.0% (n=39) had ≥1 office visit (mean=7.3, SD=9.3) in the pre-EMI period. Conclusions This is the first study to provide an understanding of the disease and treatment characteristics of PwHA who are initiating treatment with emicizumab using insurance claims data. Based on these findings, emicizumab is being used across all age groups and in patients with different clinical characteristics. Availability of more longitudinal data following treatment initiation with emicizumab will allow for an assessment and comparison of real-world treatment outcomes in PwHA. Disclosures Mahajerin: Spark: Speakers Bureau; Alexion: Speakers Bureau; Kedrion: Membership on an entity's Board of Directors or advisory committees; Genentech: Consultancy, Speakers Bureau. Yang:Genentech: Employment, Equity Ownership. Patel:Genentech: Employment; Roche/Genentech: Equity Ownership.

2021 ◽  
Vol Volume 13 ◽  
pp. 969-980
Author(s):  
Khulood Al Mazrouei ◽  
Asma Ibrahim Almannaei ◽  
Faiza Medeni Nur ◽  
Nagham Bachnak ◽  
Ashraf Alzaabi

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Michael Stucki ◽  
Janina Nemitz ◽  
Maria Trottmann ◽  
Simon Wieser

Abstract Background Decomposing health care spending by disease, type of care, age, and sex can lead to a better understanding of the drivers of health care spending. But the lack of diagnostic coding in outpatient care often precludes a decomposition by disease. Yet, health insurance claims data hold a variety of diagnostic clues that may be used to identify diseases. Methods In this study, we decompose total outpatient care spending in Switzerland by age, sex, service type, and 42 exhaustive and mutually exclusive diseases according to the Global Burden of Disease classification. Using data of a large health insurance provider, we identify diseases based on diagnostic clues. These clues include type of medication, inpatient treatment, physician specialization, and disease specific outpatient treatments and examinations. We determine disease-specific spending by direct (clues-based) and indirect (regression-based) spending assignment. Results Our results suggest a high precision of disease identification for many diseases. Overall, 81% of outpatient spending can be assigned to diseases, mostly based on indirect assignment using regression. Outpatient spending is highest for musculoskeletal disorders (19.2%), followed by mental and substance use disorders (12.0%), sense organ diseases (8.7%) and cardiovascular diseases (8.6%). Neoplasms account for 7.3% of outpatient spending. Conclusions Our study shows the potential of health insurance claims data in identifying diseases when no diagnostic coding is available. These disease-specific spending estimates may inform Swiss health policies in cost containment and priority setting.


2006 ◽  
Vol 48 (10) ◽  
pp. 1054-1061 ◽  
Author(s):  
Mark R. Cullen ◽  
Sally Vegso ◽  
Linda Cantley ◽  
Deron Galusha ◽  
Peter Rabinowitz ◽  
...  

2019 ◽  
Vol 51 (2) ◽  
pp. 327-334 ◽  
Author(s):  
Chirag M. Lakhani ◽  
Braden T. Tierney ◽  
Arjun K. Manrai ◽  
Jian Yang ◽  
Peter M. Visscher ◽  
...  

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