Model For Economic Analysis of Sickle Cell Cure (Measure)

Background and Objectives

Approximately 100,000 Americans have Sickle Cell Disease (SCD), a group of single-gene, autosomal recessive disorders that affect hemoglobin (Hb) structure and function. While treatment strides have been made, resulting in increased life expectancy and reduced disease complications, those with the most severe forms of SCD still have lifespans of 20-30 years shorter than those without SCD. Further, those living with SCD experience significant health and economic burden due to their disease. Currently, a wide array of approaches to curative gene therapy is being pursued in clinical trials, animal models, and tissue culture systems. While promising, the risks to patients vs. ultimate short and long-term benefits remain contested and largely unknown for these therapies.

Project Aims:

Aim 1: Conduct a landscape analysis for cost-of-illness studies in SCD and cost-effectiveness analysis of genetic therapies for SCD

We will carry out a landscape analysis to identify studies that estimate the cost of illness estimates for SCD, and, separately, studies that estimate the cost-effectiveness of emerging therapies for SCD. We will use established systematic review methods to search both the published and grey literature and publish our findings, as well as use them to inform our conceptual model.

Aim2: Develop a simulation model for economic analysis for the cure sickle cell initiative 

We will develop a simulation model to capture the progression of patients with SCD across relevant health states over their lifetimes. Our simulation model will follow patients from the age at diagnosis over their lifetimes and will produce lifetime estimates of outcomes and costs by modeling state-specific estimates of payoffs in health and healthcare utilization.

Aim 3: Employ data visualization techniques to promote and facilitate dissemination for the MEASURE model

We will present our model and results to various public conferences and settings. We will work with the Emmes Corporation and the NIH to identify other outlets for dissemination.

One important feature of our model will be to provide a web-based R-Shiny interface for our simulation model. Using this interface, any stakeholder interested in this area can generate estimates of comparative effectiveness and cost-effectiveness by varying parameter values for certain emerging therapies as compared to standard of care. Results will be presented through this web-interface using both tables and graphs. An example of our previous work on developing a simulation model and publicly disseminating the model through such an interface can be found at

Anirban Basu, PhD
Scott Ramsey, MD, PhD
Beth Devine, PharmD, PhD
Douglas Barthold, PhD
Micheal A Bender, MD
Aaron Winn, PhD

Graduate students
Boshen Jiao, MPH
Kate Johnson, MSc
Zizi Elsisi, MS

Connor Henry, MPH
Winona Wright

Publications (Selected)