Looking at the Map, Together: Modeling Treatment Center Location Selection and its Effects on Access to Gene Therapy in Brazil

Choosing at how many and which treatment centers to offer a gene therapy to patients is a crucial decision which impacts how far the treatment has to be transported and how far patients have to travel to receive treatment. Many gene therapies are for patients with severe diseases that make it difficult to travel. On the other hand, cold chain requirements make shorter transportation preferable for gene therapies, and few centers have prior experience handling them. Using multi-criteria optimization modeling paired with local input, this thesis explores different approaches to the gene therapy treatment center location selection decision and how these approaches would affect patients’ geographic accessibility to treatment. We focus on Brazil and a specific gene therapy product as our case study. We interview local pharmaceutical company employees to understand the stakeholders involved in this decision and the approaches being considered. We model how these approaches would affect patients’ geographic accessibility to treatment and discuss potential modifications to our model. Finally, by means of an interactive workshop, we explore the decision-making discussion between stakeholders in choosing which approach to follow. We find that the approaches under consideration result in a wide range of geographic accessibility for patients. Early stage decisions have impacts across stages, and even therapies, due to a reluctance to select new locations. Patients in the northwest of Brazil would need stakeholders to consider candidate locations beyond government reference centers or those with gene therapy experience, in order to have a treatment center nearby. Regarding facilitation, we find that quick, low-stakes modeling and joint discussion could allow stakeholders to consider approaches they might not otherwise consider.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Institute for Data, Systems, and Society

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Wertheimer, Sarah R.

Advisor dc:contributor.advisor

• Goentzel, Jarrod

Statement dc:rights

• Attribution 4.0 International (CC BY 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/157099

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/157099