healthmedicinet

This study aimed to address potential differences in utilities derived from the well-established
UK value set, as described by Dolan et al. 18], and the newly published Brazilian value set, obtained through a household-based
study conducted with 9,148 subjects in Minas Gerais state and Rio de Janeiro, Porto
Alegre and Recife cities 27]. Patients’ health status was assessed by using EQ-5D-3L and then the EQ-5D-3L data
were converted into a utility index using Brazilian and UK value sets. To our knowledge,
this is the first study that used the algorithm proposed by QALY Brazil group in a
Brazilian sample of patients with MS and also compared the findings with the most
used method in literature.

Patients participating in the study were mainly female with a mean age of 40.7 years
old. Demographic characteristics are comparable with those previously described for
Brazilian MS patients and in studies that assessed quality of life in MS worldwide
4]–16], 28]–31]. Most of the patients had relapsing-remitting MS, moderate disability and a mean
utility score of 0.59 (SD?=?0.22) and 0.56 (SD?=?0.32) for the Brazilian and UK algorithms,
respectively. Other studies with similar clinical characteristics described utility
scores ranging from 0.491 to 0.698 in MS patients 11], 13], 30].

Considering the total sample, statistical significant differences among the Brazilian
(0.59 [SD?=?0.22]) and UK (0.56 [SD?=?0.32]) algorithms were not observed (p?=?0.586, Wilcoxon test for paired samples). This finding is different compared to
results from studies comparing value sets for Argentina 32], Chile 32], Denmark 33], Japan 26], United States 26], 33], 34], UK 26], 32]–35] and Spain 35]. However, similar to the results described here, all studies so far have shown lower
values when UK algorithm was used for analysis (as compared to the local value set).
Statistical tests comparing distribution of data showed that most differences between
algorithms can be observed at lower utility scores as shown in this study and also
in previous studies comparing local value sets with the one from UK 26], 32]–34].

Differences among utility scores have been attributed in the literature to two main
factors: methods used to collect and to rate each of the EQ-5D-3L health status; and
cultural characteristics of the sample used 18], 27]. The most important differences among the methods used for UK and the QALY Brazil
group were the number of health states used to estimate the value sets and modifications
in the data collection process, both proposed by Kind (2009). However, the method
to value each of the health states was the same (the time-trade-off technique) 36]. The EQ-5D-3L questionnaire provides 243 possible health states and valuation studies
employ a subset of those health states and then apply statistical modelling to derive
the remaining states. The Brazilian valuation study used 99 health states while the
UK used 42 health states 27], 37]. The use of greater than 42 health states in the rating process was described only
by the Brazilian and South Korean studies and researchers have discussed that it may
provide the most simple and robust models 38]–47]. The protocol proposed by Kind 36] brings three main updates to the EQ-5D-3L health states valuation process, which
consists in shuffling cards describing the states before patients classify each one,
the exclusion of the “unconscious” health state and the procedure of giving all cards
at the same time to subjects. The rating of value sets is based on the time trade
off method, where patients determine how long they could live under the proposed health
state and whether it seems similar to death or perfect health. Cultural characteristics
may influence the final model of the developed algorithm. To investigate potential
cultural factors that may influence the difference in utility scores is not the scope
of the present analysis, but previous authors have suggested that this may be explained
by country-specific differences in the way people perceive and value health conditions
26], 32], 33], 35].

This study also assessed the role of disability (according to EDSS disability level),
fatigue (using MFIS-BR) and patient’s socio-demographic and clinical characteristics
relevant to MS natural history on the utility scores reported by Brazilian patients.
In terms of self-reported EDSS subgroups (0–3, 4–6.5, 7–9), the increase in self-perceived
disability level was accompanied by a decrease in the utility index for both Brazilian
and UK value set, which are similar with findings from previous studies 4]–16], 30], 31], 48]–50]. Regarding the assessment of self-reported impact of fatigue, the results observed
in our study using the MFIS-BR (59 %) differed from data previously described for
Brazil. Nogueira et al. (2009) found higher frequencies of self-reported impact of
fatigue (69 %, using the MFIS-BR) and Mendes et al. (2000) using the Fatigue Severity
Scale reported a frequency of 67.4 % 51], 52]. Despite this fact, an association between utility and fatigue was also observed,
as previously described by other authors who examined the same association using different
quality of life measures 52]. Other variables such as age, educational level, employment status, MS type and disease
duration were also significantly associated with utility scores. Those between-groups
differences were consistent for both Brazilian and UK values.

It is important to consider that this study presented some limitations. Although this
was a multicenter study, all study sites were from South and Southeastern Brazilian
regions, which are different from other regions in terms of socio-demographic characteristics;
and in terms of coverage and access to health care services. Thus, findings may not
be representative from the entire country. Another limitation of this study was the
self-reported approach to the data collection process, which can lead to memory bias
– but is the most adopted approach in patient-reported outcomes studies due to the
nature of targeted data. Regarding the variables assessed in this analysis, clinical
characteristics (type of MS, recurrence and disease duration) are probably the most
prone to bias if self-reported. Thus, the association between those variables and
utility scores in MS can be further addressed in studies using other source of data
or even combining different ones.

In spite of that, considering the widespread use of EQ-5D-3L in the decision process
for evaluating new therapies in health systems worldwide, through cost-utility analysis,
these findings could markedly be relevant for policy makers during the health technology
assessment of MS treatments that can affect patient’s quality of life by slowing disability
worsening and postponing progression to secondary progressive MS, reducing fatigue
symptoms, and favoring work productivity 53].