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A hospital budget impact model to compare stem cell mobilisation strategies: impact of primary research and direct stakeholder engagement
  1. Ivar S Jensen1,
  2. Ronald J Halbert2,
  3. Giuseppe Rossi3,
  4. Sarah Naoshy4,
  5. Sheikh Usman Iqbal4,
  6. Zhimin Xiao4,
  7. Peter A McSweeney5
  1. 1Department of Health Economics, ICON, Cambridge, Massachusetts, USA
  2. 2Department of Health Economics, ICON, El Segundo, California, USA
  3. 3Haematology Department, Spedali Civili di Brescia, Brescia, Italy
  4. 4Global Health Economics & Outcomes Research, Sanofi, Cambridge, Massachusetts, USA
  5. 5Colorado Blood Cancer Institute, Denver, Colorado, USA
  1. Correspondence to Ivar S Jensen, Health Economics, ICON, Cambridge, MA 02142, USA; ijensen{at}pricespective.com

Abstract

Objectives There is a dearth of published health economic evidence on stem cell mobilisation (SCM) that can be leveraged effectively for decision making. Our objective was to develop a budget impact model (BIM) that accurately represented the preferences of key decision makers in estimating the total financial impact of adopting plerixafor for patients undergoing autologous peripheral stem cell transplantation for multiple myeloma and lymphoma. The BIMs were developed for France, Germany, Italy, Spain, UK and USA.

Methods Prior to BIM development, a targeted literature review to identify key aspects of the SCM process was conducted in addition to in-depth interviews in Europe (n=33) and the USA (n=20), to determine the most influential decision maker(s) for choosing a mobilisation regimen. Inputs and outputs that are critical for decision making at the hospital level were determined.

Results Primary research revealed that the centre director and treating physician are the most influential decision makers, while hospital administrators, transplant coordinators, pharmacy and apheresis directors have a more limited role. Clinical inputs most critical for assessment were drug/regimen use, apheresis days and success/failure rates, and economic inputs were costs of mobilisation, drug, apheresis and hospitalisation. Model outputs include first mobilisation success and total mobilisation budget impact. The model has flexibility to assess costs of multiple regimens including granulocyte-colony stimulating factor (G-CSF), G-CSF+plerixafor, G-CSF+chemotherapy and G-CSF+chemotherapy+plerixafor.

Conclusions Conducting interviews with key stakeholders and using the latest clinical practice information to identify model inputs and outputs is useful for developing a representative BIM that is likely to be accepted by hospital decision makers evaluating adoption of plerixafor for SCM.

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