Four health states were defined: GINA Step 3, GINA Step 2, asthma
without medications (complete withdrawn of all asthma medications) and
any-cause death (absorbing Markov state). GINA Step 3 and GINA Step 2
states were defined as medium and low dose of ICS with salbutamol,
respectively. An asthma-related death state was not included in the
model due to the low frequency of this event in this level of disease
severity. All-cause mortality probabilities were calculated based on
vital statistics data of the Colombian National Administrative
Department of Statistics (20). A Markov cycle of three months was used
in the model as it is the minimum period of time in which asthma
medications can be modified according to the clinical response of
patients as recommended by the GINA report (18). A decision tree was
embedded in the model to include exacerbations as events (Figure 1).
Within the model the following transitions were possible: medication
step-down (from GINA Step 3 to GINA Step 2), remission (from GINA Step 2
to asthma without medications) and death (from any health state). The
following assumptions were considered: 1) patients start in the GINA
Step 3 state and end in the asthma remission state, 2) transition from
GINA Step 3 state to the asthma remission state was not allowed, 3)
hospitalizations and ED visits were included in GINA Step 3 state, and
only ED visits in the GINA Step 2 state, 4) transition probabilities of
medication step-down and remission generated by the SCIT+ICS would be
constant during the first five years, and a reduction in exacerbations
during the complete time horizon, 5) the ICS strategy would generate
medication-step-down and remission in the first two years of the time
horizon, 7) no adverse events were considered in the model due to their
relative low frequency and impact in terms of HCRU, costs and quality of
life, 8) an exacerbation would require medical attention through ED or
hospitalization.
Our model is based on the assumption that SCIT+ICS therapy generates
higher disease remission and reduction of exacerbations compared to ICS
without SCIT (21,22). As patients move among health states (i.e. as
disease severity is reduced) they accumulate health-state and
event-specific costs, and gains in utilities that are translated into
QALYs. The negative effect of asthma exacerbations on quality of life
was expressed through disutilities.
Inputs and data sources
Transition probabilities
Table 1 shows the parameters used in the decision model. A literature
review was conducted to obtain effectiveness parameters and were
subsequently validated with clinical experts. The probability of asthma
remission for the ICS strategy was defined as the proportion of patients
that discontinued all asthma medications in the control group of an
observational study by Sánchez et al. in Colombia that included patients
with moderate persistent asthma (23). Although authors do not specify
the ICS used by patients, we assumed that the majority of patients were
treated with Beclomethasone dipropionate (BDP) as it is the only IC
covered by the Colombian health care system (24). The probability of
medication step-down was obtained from the control arm of an RCT
conducted by Zielen et al. in which the steroid-sparing effect of SCIT
was evaluated and the proportion of patients achieving a controller
medication step reduction was reported (25). This was the only study
retrieved in our search that used the reduction in medication steps as
an outcome to evaluate the steroid-sparing effect of SCIT + ICS and ICS.
As authors used fluticasone propionate as the controller treatment, we
assumed similar effectiveness between BDP and fluticasone propionate
(26). Probabilities of medication step-down and remission for the
SCIT+ICS cohort were obtained from the study by Sánchez et al in which a
sample of 122 patients received SCIT + ICS over a 3-year period and the
proportion of patients with a reduction/complete withdrawn of asthma
medications was reported (23). All initial probabilities were converted
as rates using their periodicity and then re-expressed as probabilities
with a 3-month interval (cycle length) (27)