Presentation (CTI)

OBJECTIVES: Multiple sclerosis (MS) is a chronic, autoimmune-mediated neurological disease which often involves progressive deterioration and increased dependence on informal caregivers over time. Patient dependence on informal care leads to caregivers experiencing high levels of distress and reduced quality of life (QoL). Caregiver QoL is increasingly recognised by National Institute for Health and Care Excellence (NICE) committees as being relevant to decision-making in health technology assessments (HTAs). This study aimed to identify instances where caregiver QoL was modelled or incorporated into NICE technology appraisals (TAs) for MS interventions.
METHODS: TAs in MS published from the inception of the NICE HTA process until 5 June 2024 were systematically identified and reviewed. Final appraisal documents and committee papers were obtained and searched for the terms ‘caregiver’, ‘caregiver’, ‘family’, and ‘informal care’. TAs in which caregiver QoL was directly considered by NICE evaluation committees were also included.
RESULTS: In total, 15 HTAs have been considered by NICE in MS, 14 of which modelled caregiver QoL (13 single TAs and 1 multiple TA). In every submission identified, caregiver disutilities were modelled based on patient disease severity classified by Expanded Disability Status Scale (EDSS), meaning they were easily comparable. In addition, all submissions used the same number of caregivers (one). The NICE committee preferred assumptions where caregiver utility decrements increased with worsening patient EDSS health state. Caregiver QoL was accepted and included in the committee base-case in all 14 TAs.
CONCLUSIONS: The progressive, unpredictable nature of MS presents unique challenges to families and caregivers of people with the disease. MS is one of the few indications in which NICE committees have consistently accepted caregiver QoL in cost-utility analyses. As such, the methods used for deriving caregiver utility, and their applications in MS, can be used to inform evidence generation and modelling activities in other indications.