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Cost-Effectiveness Modelling of Multiple System Atrophy for Early Health Technology Assessment.

Tobias Sydendal Grand1,2, Shijie Ren3, Praveen Thokala3,4

  • 1Sheffield Centre for Health and Related Research (SCHARR), University of Sheffield, 30 Regent St, Sheffield City Centre, Sheffield, S1 4DA, UK. tsgrand1@sheffield.ac.uk.

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Summary
This summary is machine-generated.

Health-economic data for multiple system atrophy (MSA) is limited, hindering early health technology assessments. Literature searches for disease analogues and user-friendly modeling interfaces can help identify data gaps and guide future evidence generation for this rare disease.

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Area of Science:

  • Health Economics
  • Rare Diseases
  • Neurodegenerative Diseases

Background:

  • Multiple system atrophy (MSA) is a rare, rapidly progressing neurodegenerative disease with overlapping cerebellar and Parkinsonian motor phenotypes.
  • Limited health-economic data exists for MSA, posing challenges for early health technology assessments (HTAs).
  • The feasibility of early HTA and the utility of disease analogue modeling for rare diseases like MSA require investigation.

Purpose of the Study:

  • To assess the feasibility of conducting an early health technology assessment for multiple system atrophy (MSA).
  • To explore the utility of literature searches for disease analogues in minimizing data gaps and facilitating cost-effectiveness modeling for MSA.
  • To evaluate the potential for developing user-friendly model interfaces for rare disease health technology assessments.

Main Methods:

  • Conducted literature searches for economic evaluations and health-economic parameters (costs, utilities, natural history) for MSA.
  • Utilized the unified multiple system atrophy rating scale part IV (global disability scale) to inform model structure, informed by disease analogue studies.
  • Constructed a cost-effectiveness analysis from a UK National Health Service perspective using R Shiny for a user-friendly interface.

Main Results:

  • No direct cost-effectiveness analyses for MSA were identified; however, relevant studies on cost-of-illness, quality-of-life, and natural history were found.
  • Health-economic parameters, including utilities and transition probabilities, were estimated using registry data.
  • A cost-effectiveness analysis for a hypothetical intervention was developed despite data limitations, presented via an interactive R Shiny application.

Conclusions:

  • Literature searches for disease analogues are valuable for early modeling in rare diseases like MSA, aiding in identifying data gaps and guiding evidence generation.
  • Surrogate data from disease analogues can inform model structures when sufficient granularity is available.
  • Early conceptual modeling and iterative frameworks are crucial for addressing the uncertainty in cost-effectiveness results for rare diseases.