A discrete choice experiment to derive health utilities for aromatic L-amino acid decarboxylase (AADC) deficiency in France
Smith AB, et al. Patient Relat Outcome Meas. 2022.
Publication Date | January 2022
Authors | Smith AB, Hanbury A, Whitty JA, Beitia Ortiz de Zarate I, Hammes F, de Pouvourville G, Buesch K.
Citation | Patient Relat Outcome Meas. 2022;13:21–30
Evaluating treatment cost-effectiveness requires the undertaking of patient health-related quality of life assessments (HRQoL) and robust health utility data.1 This can be difficult to ascertain in rare diseases such as aromatic L-amino acid decarboxylase (AADC) deficiency due to the rarity of the condition and the small patient population size.1 Commonly used instruments such as the EuroQoL 5D-5L (EQ-5D-5L) cannot be used in this situation, therefore, other methods need to be applied.1
Time trade-off (TTO) and standard gamble (SG) tasks are potential alternatives; however as these approaches are often used to provide input into health states in economic models, they tend to provide global utilities for the health states rather than utilities or disutilities for individual levels of the key symptoms.1,2 Discrete choice experiments (DCE) offer a potential solution to this, where participants are presented with choice sets, comprising symptoms (attributes) and degrees of severity (levels), and are asked to select from 1 of 2 profiles. In order to derive health utilities from DCE parameters, these need to be anchored to utilities to derive quality-adjusted life years (QALYs) such as TTO/SG utilities.2 The study carried out by Smith AB et al aimed to derive health state utility data for the key symptoms of AADC deficiency using a DCE in a representative French sample, to complement a vignette study.1,3
This DCE study was part of a larger study that has been described in detail elsewhere incorporating vignette studies from both the United Kingdom and France, and a DCE study in the UK.2–4 The French DCE study design replicated that of the UK study, where the DCE attributes or key AADC deficiency symptoms were identified from the vignette development.1,2 The 6 AADC deficiency attributes used to form the DCE sets were; mobility, muscle weakness or floppiness, oculogyric crises (OCG), feeding ability, cognitive impairment, and screaming (Table 1).1
A total of 1,001 participants, representative of the French general population, were asked to rate 5 health state vignettes describing AADC deficiency using TTO and SG.1,3 As part of the process, participants were required to rate the worst health state using the Health Utility Index version 3 (HUI3).1 Following this, each participant completed the choice sets for the DCE study described here.1 The relative importance of the symptom levels in driving health state choice was estimated through multinomial logit (MNL) regression analysis of the choice data.1 The mean utility values for the worst health state derived using the HUI3, and those for the best health state derived from the TTO and SG tasks were used to anchor the DCE health state preferences on to a utility scale (0–1 or deceased to perfect health, respectively).1
Able to sit unaided
Able to stand with support
Able to walk with assistance
Able to walk without assistance
|Muscle weakness/floppiness||Severe weakness
|Oculogyric crises (OCG)||Daily OGC
|Feeding support||Not able to feed themselves
Able to feed themselves
Not at all
Table 1: DCE symptoms and levels.1
Just over half of the participants (596, 59.5%) who completed the DCE provided consistent responses to the repeated choice task.1 Five models were evaluated, and one preference reversal (“head control”/“sitting unaided”) was identified in all models.1
From the vignette study, the estimated TTO utility weights for the best and worst health states were 0.5577 and 0.3891 respectively; for the SG task they were 0.7093 and 0.5534.1 The mean utility value for “bedridden” health state as rated on the HUI3 was 0.5263 (TTO) and 0.4924 (SG), and 0.5322 for the whole sample.1
The difference in utility value between the best and worst health states after rescaling was 0.169 (TTO).1 The largest relative disutility is associated with the “Mobility” attribute moving from “No problems walking” to “Bedridden” (−0.0533).1
The overall results of the study showed DCE parameters decreased or became more negative as attribute severity levels increased, with the associated disutilities following the same pattern.1 The “Mobility” attribute appeared to be the main driver for the disutilities, with “Head control” in particular being associated with the greatest disutility.1 Interestingly, the results contrast with those of the accompanying DCE undertaken with a UK sample where “Screaming” was the main driver, followed by mobility.1,3
In addition, participants in the UK study rated the health states, on average, higher than their French counterparts.1,3 For example, 0.494 and 0.7279 for the “Bedridden” and “Walking with assistance” health states respectively, compared to 0.3891 and 0.5577 in the French study sample.1,3 It appears French participants rated the health state, and consequently the symptoms and impact of AADC deficiency, more severe than participants in the UK study.1 These differences may be due to a number of reasons, such as cultural variation (e.g. relative preferences for the use of ‘head control’ [France] over ‘screaming’ [UK]), sociodemographic, and parental status, which may have also impacted on the differences observed at a country level.1
The study is the first to ascertain robust health utilities for AADC deficiency symptoms using a large sample from the general French population.1 The authors suggest these data could be utilised to enable improvements in health-related quality of life and be employed in a country-specific economic evaluation of a gene therapy for AADC deficiency.1
- Smith AB, et al. Patient Relat Outcome Meas. 2022;13:21–30.
- Smith AB, et al. Patient Relat Outcome Meas. 2021;12:97–106.
- Smith AB, et al. J Patient Relat Outcomes. 2021;12:237–46.
- Smith AB, et al. J Patient Rep Outcomes. 2021;5:130.
GL-AADC-1151 | May 2022