ESTIMATING THE UK POPULATION ELIGIBLE FOR LATE-LINE MULTIPLE MYELOMA THERAPIES USING AN EPIDEMIOLOGICAL MODEL
Author(s)
Kang Kang, PhD1, Shujun Li, MS2, Denise Zou, MS3.
1Thermo Fisher Scientific, Minneapolis, MN, USA, 2Thermo Fisher Scientific, Boston, MA, USA, 3Thermo Fisher Scientific, Wilmington, NC, USA.
1Thermo Fisher Scientific, Minneapolis, MN, USA, 2Thermo Fisher Scientific, Boston, MA, USA, 3Thermo Fisher Scientific, Wilmington, NC, USA.
OBJECTIVES: Given the evolving treatment pathway recommended by the National Institute for Health and Care Excellence (NICE) for chronic, incurable multiple myeloma (MM) and the continuous introduction of new therapies for heavily pretreated patients, accurate estimation of treatment-eligible populations is essential for healthcare system budgeting and resource planning. This study leveraged an epidemiological model to estimate the population in the United Kingdom (UK) eligible for late-line MM therapies.
METHODS: A previously published compartmental epidemiological model based on ordinary differential equations was utilized to calculate the number of patients by line of therapy (LOT). The model simulated disease progression from diagnosis through successive LOTs, accounting for treatment initiation, disease progression, mortality, and cumulative treatment exposure history. UK-specific demographic and epidemiological inputs were obtained from national population projections and published literature, while treatment patterns and outcomes were informed by real-world evidence. Deterministic and probabilistic sensitivity analyses were performed to evaluate uncertainty.
RESULTS: Based on an estimated UK population of 69 million, the model predicted 27,792 prevalent MM cases (approximately 40 cases per 100,000 population), consistent with published UK epidemiological estimates and the increasing incidence trends reported by Cancer Research UK. Of these, an estimated 1,040 patients were triple-class exposed (having received a proteasome inhibitor, an immunomodulatory agent, and an anti-CD38 monoclonal antibody), representing 3.7% of the prevalent MM population and approximately 1.5 patients per 100,000 UK residents. Sensitivity analyses identified survival assumptions in later LOTs and treatment sequencing patterns as the key drivers of uncertainty.
CONCLUSIONS: By explicitly incorporating treatment sequencing, treatment exposure history, and real-world epidemiological and clinical outcome data, the model provides credible estimates of the population eligible for late-line MM therapies. These estimates can support healthcare decision-making, budget impact assessments, and long-term resource planning.
METHODS: A previously published compartmental epidemiological model based on ordinary differential equations was utilized to calculate the number of patients by line of therapy (LOT). The model simulated disease progression from diagnosis through successive LOTs, accounting for treatment initiation, disease progression, mortality, and cumulative treatment exposure history. UK-specific demographic and epidemiological inputs were obtained from national population projections and published literature, while treatment patterns and outcomes were informed by real-world evidence. Deterministic and probabilistic sensitivity analyses were performed to evaluate uncertainty.
RESULTS: Based on an estimated UK population of 69 million, the model predicted 27,792 prevalent MM cases (approximately 40 cases per 100,000 population), consistent with published UK epidemiological estimates and the increasing incidence trends reported by Cancer Research UK. Of these, an estimated 1,040 patients were triple-class exposed (having received a proteasome inhibitor, an immunomodulatory agent, and an anti-CD38 monoclonal antibody), representing 3.7% of the prevalent MM population and approximately 1.5 patients per 100,000 UK residents. Sensitivity analyses identified survival assumptions in later LOTs and treatment sequencing patterns as the key drivers of uncertainty.
CONCLUSIONS: By explicitly incorporating treatment sequencing, treatment exposure history, and real-world epidemiological and clinical outcome data, the model provides credible estimates of the population eligible for late-line MM therapies. These estimates can support healthcare decision-making, budget impact assessments, and long-term resource planning.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
EPH23
Topic
Epidemiology & Public Health, Health Technology Assessment
Disease
Oncology