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Different screening strategies (single or dual) for the
diagnosis of suspected latent tuberculosis: a costeffectiveness analysis
This section gives a brief description of the structure of the decision tree model used in
the cost analysis (figures 1-3), and also further explains the choice of the probability and
cost estimates (table 1) as well as the effectiveness measures used.
Decision Tree Models
Five different screening scenarios were investigated in this cost analysis: (1) TST alone,
(2) the T-SPOT.TB assay alone, (3) TST followed by T-SPOT.TB assay when TST was
positive, (4) Quantiferon-TB-Gold-In-Tube (QFT-GIT) alone, and (5) TST followed by
QFT-GIT when TST was positive. As the T-SPOT.TB and QFT-GIT are different
versions of the IFN-γ release assay (IGRA), the decision tree for each version of the
assay are interchangeable. Hence the screening steps in scenarios (2) and (4) and
scenarios (3) and (5) are identical.
When screening with the TST (figure 1), a contact may or may not return to have the test
interpreted (‘TST read’ and ‘TST not read’). If the TST is not read then a contact may
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have LTBI which can remain latent (‘TB remains latent’) or progress to cause active TB
(‘post-exposure TB’). For a contact returning for TST interpretation, a test result can be
either positive or negative. For the T-SPOT.TB or QFT-GIT scenario (figure 2), the
screening steps are identical to the TST decision tree except that, as there is no need for a
return visit, all contacts would produce either a positive or negative test result. In the dual
strategies (figure 3), the T SPOT.TB or QFT-GIT is only performed on contacts with a
positive TST. The positive predictive value (PPV) refers to the probability that a positive
result is truly positive. Therefore a positive test can be truly positive (PPV) or falsely
positive (1-PPV). Similarly, the negative predictive value (NPV) refers to the probability
that a negative result is truly negative. Therefore a negative test can be a truly negative
(NPV) or falsely negative (1-NPV).
True Positive (PPV)
A contact with a truly positive test result is assumed to have LTBI and can either accept
(‘start INH treatment’) or refuse isoniazid treatment (‘no INH treatment’). If treatment is
refused, TB can remain latent or progress to active TB. In a contact receiving INH
treatment, there is a risk of severe isoniazid-induced hepatotoxicity (‘severe hepatitis’),
which can result in death (‘death due to hepatitis’), or resolution of the hepatitis
(‘hepatitis resolved’). If the hepatitis is resolved then the LTBI treatment can be
successful and completely eliminate the disease (‘LTBI fully cured’) or unsuccessful
allowing the TB to remain uncured (‘LTBI not cured’). If not cured, the TB can remain
latent or can reactivate into active TB.
False Positive (1-PPV)
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It is assumed that a person with a falsely positive result is not infected with latent TB.
However, in the model, they will still be offered isoniazid treatment. If treated, there is
still a risk of developing hepatitis which can be resolved or cause death. If treatment is
refused, there is no further consequence. It is assumed that those who give a falsely
positive test result undergo the same length of treatment as those with a truly positive
result. Therefore, ‘hepatitis resolved’ is associated with the cost of partial course (3
months) of isoniazid treatment, whereas ‘no hepatitis’ is associated with the cost of a full
course (6 months) of isoniazid treatment.
True Negative (NPV)
It is assumed that a contact with a negative test result has no further investigation. A
contact that has a truly negative result is free of infection with no further consequence.
False negative (1-NPV)
No treatment is offered for a falsely negative result. However, it is assumed the person is
latently infected. As a result, the TB can remain latent or can progress to active TB
Probabilities
All probability estimates were taken from the published literature.
LTBI prevalence
3
Estimates of LTBI prevalence can vary widely depending on the study population and the
duration of exposure to the index case. Previous studies have shown an estimated 30 –
40% [1, 2] prevalence of LTBI in contact tracing studies conducted in the USA and the
UK. A baseline estimate of 30% was used and a wider range was examined in the
sensitivity analysis in order to cover different contact tracing scenarios.
Bayesian statistics were used to calculate the probability of a positive and negative test
result from LTBI prevalence and test specificity and sensitivity:
Probability of a positive result = (Sensitivity * Prevalence) + ((1 - Specificity) * (1 Prevalence))
Probability of a negative result = (Specificity * (1 - Prevalence)) + ((1 - Sensitivity) *
Prevalence)
Furthermore, positive predictive value and negative predictive values for each test were
subsequently calculated:
Positive Predictive Value = Sensitivity * Prevalence / Probability of a positive result
Negative Predictive Value = Specificity * (1 - Prevalence) / Probability of a negative
result
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In the dual screening strategies, the prevalence of LTBI in the TST positive cohort will be
higher than the background prevalence. As such, the probability of a positive and
negative result, as well as the PPV and NPV, of the IGRA given a positive TST is
different than these estimates in the IGRA only strategies. These values are shown in
table 1.
TST return rate
Reported TST return rates were available mainly from U.S. studies on populations of
drug users, ranging from 47% to 93% [3, 4]. In the UK, Bothamley reported a 73% return
rate among a population of newly screened immigrants [5]. We assumed a 90% rate of
return for TST readings (as contacts are more likely to be followed up), similar to
previous cost analyses [6, 7], with lower rates explored in the sensitivity analysis.
Test sensitivity and specificity
There is no gold standard test for LTBI diagnosis. Therefore, as in most previous studies,
sensitivity was obtained from a population of confirmed TB cases and specificity was
obtained from a population of healthy contacts at low risk for exposure. T-SPOT.TB and
QFT-GIT sensitivity and specificity values can vary depending on the screening
population and study methods. In order to best approximate our UK study population,
estimates of IGRA sensitivity and specificity were used from studies conducted in the
UK or with similar population dynamics to the UK (low TB burden, moderate to high
levels of immigration, BCG vaccination). Furthermore, T-SPOT.TB estimates were taken
from IGRA studies using the ELISPOT platform [8-11] while QFT-GIT estimates were
taken from studies using ELISA versions of the assay [12, 13]. Additionally, estimates
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from studies on immunosuppressed populations and those using other versions of the
IGRA (prolonged incubation times, use of protein antigens) were omitted.
Sensitivity values for the T-SPOT.TB ranged from 83% to 97% [8-11]. In two UK
studies, T-SPOT.TB sensitivities were 92% and 96% [9, 11]. A sensitivity of 95% was
used for the base-case estimate. Specificity of the T-SPOT.TB ranges from 92%-100%
with 2 UK studies [9, 11] and one German study[14] reporting 100%. Thus a base-case
value of 100% was chosen for this analysis. Reported sensitivity estimates of the QFTGIT were lower than the T-SPOT.TB [15], with values of 85% and 89% in two studies
[12, 13]. We used the higher sensitivity value of 89% in the base-case analysis. Similarly,
reported specificity of the QFT-GIT is slightly lower than in the T-SPOT.TB. Two
studies report values of 97% and 90% [12, 13]. We used a base-case estimate of 95% in
our analysis. Sensitivity of the TST was shown to be the same as or lower than that of the
IGRA assays in similar populations, with values ranging from 69%-90% [8-10, 16]. A
higher estimate of 85% was used as the base-case estimate. TST specificity also tends to
be lower especially in BCG vaccinated populations. Reported figures fall between 79%88% [17, 18]. We assumed a high degree of BCG vaccination therefore a baseline value
of 80% was used for this analysis. High and low estimates for these parameters were
explored in the sensitivity analysis.
Isoniazid-related hepatitis
The incidence of isoniazid-induced hepatitis is highly age dependent with persons > 50
years of age exhibiting higher rates of 2.3% [19]. Furthermore, while older studies have
reported rates as high as 4.1% [20], more recent studies show rates tend to be lower once
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proper patient selection and treatment guidelines are followed. Two meta-analyses
reported rates of 0.6% [21] and 0.36% [22]. A rate of 0.3% was used in our analysis but
higher and lower rates were explored in the sensitivity analysis.
Death from isoniazid-related hepatitis
Salpeter reported an average rate of isoniazid hepatitis-related death of 0.01% from
studies where anti-TB treatment was not monitored [23]. However, if current treatment
monitoring guidelines are followed, unlike in the older studies, then much lower rates of
0.001% to 0.003% are found [23]. For this analysis, an isoniazid-hepatitis fatality rate of
0.002% was assumed but ranges were examined in sensitivity analyses.
Efficacy of Isoniazid
The probability of being fully cured is based on the efficacy of either a complete or
incomplete course of isoniazid. The literature quotes estimates of isoniazid effectiveness
ranging from 50% to 93% based on treatment durations of 6 months to one year [22, 2429]. The IUAT study, a large randomized-controlled trial which investigated isoniazid
treatment duration, reported an effectiveness of 65% for a 6 month course of isoniazid
treatment (effectiveness of a 12 month course was 75%). [24]. These estimates assumed
heterogeneity in the adherence and completion rates of those taking INH treatment.
Estimates were higher in those who complied to and completed treatment [30] As such,
65% was used as the baseline value for 6 month INH efficacy in this model and the full
reported range was investigated in the sensitivity analysis. Ranges for the effectiveness of
a 3 month isoniazid course was reported to be between 12 and 30% [24, 27]. The IUAT
trial reported 21-31% effectiveness for a 3 month period of INH [24].We therefore used a
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value of 21% for the efficacy of an incomplete course (3 months) and explored the ranges
in the sensitivity analysis.
Post exposure TB
There is wide variation in the reported risk of post exposure TB mainly due to differences
in the study population and length of time since the initial infection. It is widely accepted
that the greatest risk of progression occurs immediately following initial infection and
this risk drops as time passes. An estimated 50% of LTBI cases that may progress to
active disease do so within two years of primary infection [6]. As this model focuses on
the first 2 years after infection, a rate of 2.5% was used as the base-case, as in previous
cost analyses [6, 31]. Higher and lower estimates from the published data were examined
in the sensitivity analysis.
Costs
Unit costs were taken either from UK national sources or from published studies using
the most available current estimates (table 1). All costs were updated to 2008 GPB using
the Bank of England Consumer Price Index [32]. There was no time discounting of future
costs as the time period of the model was only 2 years.
IGRA
The Royal Free Hospital in London estimated the cost of performing the T-SPOT.TB at
£50. The reported cost of performing the QFT-GIT was £40 (estimate from Royal
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Blackburn Hospital). These costs include sample processing and performing of the test
itself. Cost of phlebotomy was taken from national sources [33] and estimated to be £5.
The cost of performing the IGRA is more expensive in private laboratories, with some
laboratories charging £100 per test. As a result we have included a wide range of costs in
the sensitivity analysis.
TST
Costs associated with the TST (either Heaf or Mantoux) were taken from the National
Institute of Health and Clinical Excellence (NICE) [34]. TST cost components from the
NICE analysis were updated to current estimates [32, 35].
LTBI Treatment
Estimates for a 6 month course of isoniazid were taken from NICE and included the cost
of 6 months of isoniazid, chest X-rays, culture tests and clinic visits [34]. We calculated
the cost of a 3 month isoniazid course by incorporating the cost of a 3 month supply of
isoniazid course rather than a 6 month supply. Halving and doubling of the total
treatment cost was used as the higher and lower estimates in the sensitivity analysis.
Treatment for severe INH hepatotoxicity
According to Schwartzmann and Menzies [18], the treatment of INH related
hepatotoxicity requires 2 additional clinic visits and 2 full blood counts and biochemisty
panels. Additionally, up to 20% of individuals require hospitalization for one week. The
cost of the hospital stay, clinic visits and blood tests were taken directly from NHS
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sources [33]. Halving and doubling of the total treatment cost was used as the higher and
lower estimates in the sensitivity analysis.
Active TB Treatment
This refers to the cost associated with treating a case of post-exposure TB. White and
Moore-Gillion estimated the overall costs of treating a case of active TB in the UK,
which was updated to 2008 costs [36]. Despite the costs of active TB being incurred in
the second year after initial infection, we chose not to discount these costs back to 2008
GBP as the rate of inflation used by the consumer price index (3.9%) is similar the rate of
discounting typically used in cost analyses (3.5%). Halving and doubling of the cost
variables was used as the higher and lower estimates in the sensitivity analysis.
Effectiveness measures
The number of active TB cases prevented by each screening strategy was calculated by
subtracting the number of active cases arising from a ‘no screening’ scenario from the
cases arising from each screening strategy in the two year time-frame of the model.
Additionally, the number needed to treat (NNT) which is the number of contacts that
undergo LTBI treatment to prevent a case of active TB was calculated (table 3).
Cost-effectiveness
10
The primary measure of cost effectiveness used in our analysis was the incremental cost
per active TB case prevented. This was calculated using incremental costs, defined as the
additional costs of a particular strategy over and above the costs spent on the ‘no
screening’ scenario (£57,148), divided by the number of cases of active TB prevented by
that strategy.
i.e.
Incremental cost per
Cost of screening strategy – Cost of no screening
active TB case prevented = ___________________________________________
Active TB cases prevented by screening strategy
Cost-effectiveness was also measured as the total cost per case of active TB prevented
and was calculated using the total cost of each strategy divided by the number of active
TB cases prevented by that strategy. The savings associated with each strategy when
compared with the TST only screening strategy was also reported (Table 4).
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