Pattern of utilization of pegfilgrastim in patients with chemotherapy-induced neutropenia: A retrospective analysis of administrative claims data

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 9624-9624
Author(s):  
A. T. Skarin ◽  
F. Vekeman ◽  
F. Laliberté ◽  
O. Afonja ◽  
M. Lafeuille ◽  
...  

9624 Background: Pegfilgrastim is a long-acting granulocyte colony-stimulating factor (G-CSF) used to prevent or treat febrile neutropenia associated with myelosuppressive anticancer therapies. According to the prescribing information, pegfilgrastim should not be administered within 14 days before or 24 hours after cytotoxic chemotherapy because of the potential for myeloid toxicity. This study examined use patterns of pegfilgrastim in real-life practice. Methods: Analysis of health insurance claims data in 2000- 2007 from > 35 large health plans across the US was conducted. Patients who had a cancer diagnosis and chemotherapy within 120 days of their first pegfilgrastim injection were identified. The proportion of pegfilgrastim injections that were followed by administration of chemotherapy within 11 and 9 days was calculated. Analysis was also stratified by cancer type [Non-Hodgkin's lymphoma (NHL), lung, breast]. Results: A total of 13,526 cancer patients received 57,118 pegfilgrastim injections. NHL, lung, and breast cohorts comprised 2,722, 2,772, and 4,955 patients, respectively. Mean age (SD) was 55.0 (11.6) and women represented 65.9% of study population. Among all cancer types, 19.2% of pegfilgrastim injections had a chemotherapy claim within the following 11 days. This pattern of use was the highest in NHL (18.9%), followed by lung (17.1%), and breast (16.2%). Similar results were observed in the 9-day sensitivity analysis (see Table ). Conclusions: Based on the retrospective analysis of this administrative claims database, the use of pegfilgrastim within 11 days of an administration of chemotherapy was observed in 15–20% of cases which is inconsistent with the recommended guidelines. Pegfilgrastim use in these situations may have the potential to increase sensitivity of rapidly dividing myeloid cells to cytotoxic chemotherapy. Further research is being conducted to assess the related clinical and economic impact of this pattern of usage. [Table: see text] [Table: see text]

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.


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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