Seventh Framework Programme - Health
European Comparative Effectiveness Research on Internetbased Depression Treatment
WP3 – Economic Modelling Studies
Deliverable D3.4
Grant Agreement no.
603098-2
Project acronym
E-COMPARED
Project title
European Comparative Effectiveness
Research on Internet-based Depression
Treatment
Funding scheme
Collaborative project
Representative of project’s
coordinator
Heleen Riper, Ph.D.
Professor eMental-Health/ clinical psychology
VU University Amsterdam
Tel.
+31 205988759
E-mail
[email protected]
WP3 leader
Karine Chevreul, M.D., Ph.D.
INSERM
Tel.
+33 140274148
E-mail
[email protected]
Project’s website
http://www.E-COMPARED.eu
E-COMPARED – WP3 Deliverable
This report was written by the following WP3 members:
-
for INSERM: Karine Chevreul, Amélie Prigent, Morgane Michel, Massinissa Aroun, Maya
Dorsey;
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for VU University Amsterdam: Judith Bosmans, Spyros Kolovos;
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for the University of Limerick: John Forbes, Maurice O’Connell.
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Table of contents
Table of contents ..................................................................................................................................... 3
Summary ................................................................................................................................................. 4
Background.............................................................................................................................................. 5
Section 1. Characteristics of the Markov and DES generic models ......................................................... 6
1.1. Generic features of the models .................................................................................................... 6
Health states.................................................................................................................................... 6
Inputs and data sources .................................................................................................................. 8
Outcomes ........................................................................................................................................ 9
1.2. Structure of the MM..................................................................................................................... 9
1.3. Structure of the DES model ........................................................................................................ 10
Section 2. Contents of the deliverable .................................................................................................. 12
2.1. Markov model ............................................................................................................................ 12
2.2. DES model................................................................................................................................... 13
Section 3. Perspectives, future updates ................................................................................................ 14
3.1. Common updates ....................................................................................................................... 14
3.2. Markov model ............................................................................................................................ 14
3.3. DES model................................................................................................................................... 14
References ............................................................................................................................................. 15
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Summary
The main objective of WP3 is to estimate the cost-effectiveness of implementation of internet-based
‘blended’ depression treatment in comparison with treatment as usual after five years. It was initially
planned to do so with a generic Markov model (MM) that simulates the natural course of depression.
However, the current literature on modelling techniques seems to indicate that discrete event
simulation (DES) modelling may be more suitable than MM to model the natural course of
depression, since DES modelling allows for incorporation of patient characteristics. Therefore,
Deliverable D3.4 was extended by WP3 members to include DES modelling in addition to the MM.
The present report describes the characteristics of both models along with the content of the
deliverable and future updates. The deliverable consists of five files: this report, the two files
containing the generic models, and two user guides (one for each model). These models will at a
later stage be made publicly available to decision makers in all countries, regardless of participation
in E-COMPARED, and can be adapted to reflect the specific setting the decision maker is operating in.
Both models include the following health states: major depressive episode, divided by severity level
(mild, moderate, moderately severe and severe), remission, recovery and death. To model the first
year, data from the E-COMPARED trials will be used and the Patient Health Questionnaire, 9 items
(PHQ-9) was chosen to define the health states. To extrapolate the cost-effectiveness data over a
period of 5 years, data from the literature will be obtained from a literature review to model the
evolution of patients through the different disease states (transition probabilities) after the end of
the trial. As not all studies will use the PHQ-9, similar cut-off scores for other instruments that may
be used to measure depressive symptoms have been collected.
Two outcome measures will be included in both models to assess the effectiveness of the blended
treatment in comparison with usual care: quality-adjusted life years (QALYs) based on the EQ-5D-5L
using the UK tariff, and cumulative time spent in remission, sustained remission and recovery. Costs
will be assessed from two separate perspectives: the societal and the healthcare perspective. Within
the healthcare perspective two scenarios will be taken into account: a statutory health insurance
scenario and an all healthcare payers scenario. Effectiveness and cost per health state will be derived
from the E-COMPARED clinical trials. Discounting at a rate of 3% will be applied for years 2 to 5 to
both costs and effectiveness.
Both models will provide expected incremental cost-effectiveness ratios (ICERs) at five years for
QALYs gained and days spent in remission or recovery. Each ICER will be calculated for each cost
scenario (national health insurance only, all payers, societal perspective). Deterministic and
probabilistic sensitivity analyses will complete the results to assess the uncertainty surrounding the
ICER estimations.
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Background
The main objective of WP3 is to estimate the cost-effectiveness of implementation of internet-based
‘blended’ depression treatment in primary care and specialized care in comparison with Treatment
As Usual (TAU) over a five-year time horizon. Specific objectives are:
-
To build a generic Markov model (MM) to simulate the natural course of depression;
-
To populate the Markov model with cost and effect estimates of internet-based blended
depression treatment and TAU in both primary care and specialized care settings and to
assess the cost-effectiveness and budget-impact of the implementation of web-based
‘blended’ depression treatment in comparison with TAU from different perspectives using
different scenarios;
-
To show the feasibility and generic applicability of the model to assess the cost-effectiveness
of implementation of web-based ‘blended’ depression treatment in comparison with TAU for
countries that do not participate in the trials in WP2.
Two perspectives were chosen for the costs:
-
The perspective of the healthcare payers, including two scenarios: all payers, i.e. statutory
health insurance, complementary health insurances and patient out-of-pocket expenses, and
only public payers, i.e. only statutory health insurance or government;
-
The societal perspective which in addition to all payers takes into account indirect costs of
the effect of treatment, in particular productivity losses.
However, a recently published literature review concluded that discrete event simulation (DES)
modelling would be more suitable than MM to perform cost-effectiveness analyses when individual
patient history may considerably impact future events (Afzali, 2012; Karnon, 2014; Standfield et al.,
2014). Therefore, the WP3 team decided to extend the work and resulting deliverables to also
include DES modelling as stated in Periodic Report 1. In addition, a literature review was conducted
to systematically review model-based studies evaluating the cost-effectiveness of treatments for
depression with specific attention to the differences between MM and DES models.
Overall, advantages and disadvantages of the two models identified in the literature may be
summarized as follows: the DES model has better ability to take into account patient history, events
can occur simultaneously, and it has greater face validity due to the more realistic representation of
disease course. Conversely, this type of model may be complex to develop and time-consuming to
run. The MM is more transparent, which makes it easier to understand leading to more confidence
among decision makers about the results, and it has been used frequently in literature which allows
for better comparisons with previous studies.
Within the E-COMPARED project, both a MM and the DES model will be developed. The differences
in results and in resource allocation decisions will be compared and related to the differences in
terms of complexity to develop and time needed to run the model.
The characteristics of the two generic models developed by WP3 are described in Section 1 of the
document. Section 2 describes the files contained in Deliverable 3.4, and Section 3 describes the
future adaptations that will be made to both models.
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Section 1. Characteristics of the Markov and DES generic models
1.1. Generic features of the models
Health states
Based on the existing literature and discussion among the E-COMPARED Consortium, the following
health states were used to describe the course of depression over time:
Major depressive episode, divided by severity level1:
-
Mild
-
Moderate
-
Moderately severe
-
Severe
Remission: a period during which the patient is either symptom free or has no more than minimal
symptoms (Afzali et al. 2012).
Recovery: an extended asymptomatic phase, which lasts more than 6 months (Afzali et al., 2012).
Death
Definition of health states: year 1
To model the first year, data from the E-COMPARED trials will be used. In the trials, the Patient
Health Questionnaire, 9 items (PHQ-9) (Kroenke et al., 2001) and Quick Inventory of Depressive
Symptomatology (Self-Report) (QIDS-SR16) will be available at inclusion, 3 months, 6 months and 12
months. The MINI International Neuropsychiatric Interview (M.I.N.I) will be assessed at inclusion and
12 months.
The PHQ-9 is a nine-item questionnaire that can be used to screen and diagnose patients with
depressive disorders. The 9 items are each scored on a 0–3 scale with the total score ranging from 0
to 27, with higher scores indicating more severe depression. The PHQ-9 has shown to have good
psychometric properties (Wittkampf et al., 2007).
The QIDS-16-SR US Translation (Rush et al., 2003) was used in the clinical trial in addition to the PHQ9 because it is a promising questionnaire to assess depressive symptoms specifically in a specialized
mental health care setting whereas the PHQ-9 is developed for use in primary care. It consists of 16
items (each item scores 0-3) and includes symptom domains of Major Depressive Disorders (MDD)
based on Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and Research Diagnostic
Criteria (RDC). The QIDS has shown good psychometric properties (Reilly et al., 2015).
The M.I.N.I. is a structured diagnostic interview based on the DSM-IV and the International
Classification of Diseases (ICD-10) criteria. The interview compares well with Structural Clinical
Interview for DSM-IV disorders (SCID) (Sheehan et al., 1998) and the Composite International
Diagnostic Interview (CIDI) (Lecrubier et al., 1997; Sheehan et al., 1998).
1
Following discussions among E-COMPARED members, we decided to include four levels of depression severity
in the generic model. However, this number could be reduced to three depending on the data available in the
clinical trial and the literature.
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The PHQ-9 was chosen to define the health states in the first year of the model as it is available at
different points in time in the E-COMPARED trials and was developed for use in primary care setting,
which is the care setting the most represented in the trials. Sensitivity analyses will be carried out
using the QIDS-SR16 data and the retrospective M.I.N.I data.
The following cut-offs were chosen to define the health states with the PHQ-9 in the first year of the
trial:
HEALTH STATES
PHQ-9 CUT-OFF SCORES
Depressive episode
Mild
5-9
Moderate
10-14
Moderately severe
15-19
Severe
20-27
Remission
<5
Sustained remission
< 5 following remission
Recovery
< 5 following sustained remission
Definition of health states: Years 2-5, after the end of the trial
To extrapolate the cost-effectiveness data over a period of 5 years, data from the literature will be
obtained to model the evolution of patients through the different disease states (transition
probabilities) after the end of the trial. A literature review will be carried out to estimate the
remission rate, recovery rate, recurrence rate, relapse rate and death (and suicide) rate over time
and depending on the number of previous depressive episodes for patients with depression.
As it is likely that not all studies will use the PHQ-9 to define severity of depressive episodes or
remission and recovery, we have collected similar cut-off scores for other instruments that may be
used to measure depressive symptoms (see overview below).
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E-COMPARED – WP3 Deliverable
Cut-off scores for health states related to depressive symptom severity
Measures
Remission
Mild
Moderate
depression
depression
Moderately
Severe
severe
depression
depression
PHQ-9 1,2
0-4
5-9
10-14
15-19
20-27
MADRS 3,4
0-8
9-18
19-26
27-34
35-60
CES-D 5-7
0-15
16-19
20-25
26-30
31-60
IDS-SR 8,9
0-13
14-25
26-38
39-48
49-84
HADS-D 10,11
0-7
8-13
14-19
20-25
26-52
1
Kroenke et al., 2001; 2 Manea et al., 2012; 3 Rush et al., 2000; 4 Carmody et al., 2006; 5 Smaar et al.,
2011; 6 Radloff, 1977; 7 Haringsma et al., 2004; 8 Trivedi et al., 2004; 9 Kessler et al., 2003; 10 Fava et
al., 2003; 11 Snaith et al., 2003
Inputs and data sources
The following outcome measures will be included in both health-economic models to assess the
effectiveness of the blended treatment in comparison with usual care:
-
Quality-adjusted life years (QALYs) based on the EQ-5D-5L using the UK tariff;
-
Cumulative time spent in remission, sustained remission and recovery.
Costs will be assessed from two separate perspectives: the societal and the healthcare perspective.
The healthcare perspective only includes healthcare costs, whereas the societal perspective also
includes patient costs and lost productivity costs. Within the healthcare perspective two scenarios
will be taken into account: a statutory health insurance scenario and an all healthcare payers
scenario.
Healthcare resource utilization will be estimated using data from the E-COMPARED trials and will
include primary care utilization (e.g. general practitioner and physical therapist consultations),
primary (e.g. psychologist and psychiatrist consultations) and secondary (e.g. full-time and part-time
psychiatric hospitalization) mental healthcare utilization, secondary care provided for somatic
disorders, and home care. Productivity loss will comprise time not worked (absence from paid work)
as well as decrease in performance at work (presenteeism) because of depression. The associated
unit costs will be assessed in each country involved in the E-COMPARED project. Costs associated
with each health state will be calculated by multiplying unit costs with the number of resources used.
Discounting at a rate of 3% will be applied for years 2 to 5 to both costs and effectiveness. Sensitivity
analyses using different discount rates will also be performed.
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E-COMPARED – WP3 Deliverable
Outcomes
Both models will provide expected incremental cost-effectiveness ratios (ICERs) at five years for
QALYs gained and days spent in remission or recovery. Each ICER will be calculated for each cost
scenario (national health insurance only, all payers, societal perspective), resulting in a total of six
ICERs:
-
cost for the national health insurance per QALY gained;
-
cost for all payers per QALY gained;
-
societal cost per QALY gained;
-
cost for the national health insurance per additional day spent in remission or recovery;
-
cost for all payers per additional day spent in remission or recovery;
-
societal cost per additional day spent in remission or recovery.
The ICERs will be represented on cost-effectiveness planes. Both deterministic and probabilistic
sensitivity analyses will complete the results to assess the uncertainty surrounding the ICER
estimations. To show decision uncertainty, cost-effectiveness acceptability curves will be estimated.
1.2. Structure of the MM
The structure of the MM is presented in Figure 1. A cycle length of three months was chosen for the
MM as it allows us to take into account the evolution of depression (both the natural history and the
clinical evolution with treatment) and the availability of data in the E-COMPARED trial (collected at
inclusion, 3 months, 6 months and 12 months).
In order to account for the levels of severity of depressive disorders and for the number of
recurrences (which are expected to affect transition probabilities, effectiveness and resources used),
we introduced four health states corresponding to different severity levels in a semi-Markov model,
with transition probabilities varying over time. Varying transition probabilities allows us to model the
increased risk of relapse/recurrence as the number of recurrences increases, based on the
hypothesis that the number of recurrences increases over time. The use of a semi-Markov model
avoids introducing too many health states in the MM, the number of health states having to be
coherent with the expected sample size of the trial.
A “sustained remission” health state was added to the model. It is a “tunnel” state that allows
patients to remain 6 months in a remission state in order to reach recovery as the cycle length is 3
months.
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Figure 1. Structure of the Markov model
1.3. Structure of the DES model
The structure of the DES model is presented in Figure 2. The model has four different events/ health
states related to depression severity (i.e. mild, moderate, moderately severe and severe depression).
Individuals move between these health states based on their depressive symptom severity. When
the individuals have a PHQ-9 score lower than 5 they move to the remission state. From this state
the individuals can go back to any of the four depression states after a relapse to depression. The
individuals that stay in remission for more than 6 months move to the recovery health state.
Subsequently, they can stay in this state or go back to one of the depression states after a recurrence
of depression.
The DES model does not require fixed cycles. A clock is constantly running in the background and
monitors the individual’s trajectory in the model. The transition probabilities between all the
events/health states are influenced from the individuals’ attributes (such as number of previous
depressive episodes, comorbidities, age etc.), which are entered at the start of the model and are
updated periodically.
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E-COMPARED – WP3 Deliverable
Figure 2. Structure of the DES model.
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Section 2. Contents of the deliverable
The current deliverable consists of five files: the present report, the two files containing the generic
models that model the cost-effectiveness of blended treatment for depression in comparison with
usual care over a period of 5 years, and two user guides (one for each model, the MM user guide in
Word format and the DES user guide in video format2). At a later point in the project these models
will be made publicly available to decision makers in all countries, regardless of participation in ECOMPARED. They can be adapted to reflect the specific setting the decision maker is operating in.
This section briefly describes the two models and is completed by the user guides.
2.1. Markov model
The generic MM was implemented in Excel and currently includes only “fake” data. It has several
tabs. In the first one, the user is given a choice among several options before running the model:
-
Regarding the TAU: based on the treatment of depression in the country under
consideration, the decision maker must choose which TAU present in the E-COMPARED trial
is closest to his/her country. Eight options are given, both in primary (n=5) and secondary
care (n=3) settings. The corresponding transition probabilities, effectiveness criteria and
resources consumption will be all derived from the clinical trial and from the literature.
-
Regarding blended treatment: the decision maker must choose in which setting the blended
treatment will be administered if it is implemented (primary care or secondary care).
Transition probabilities and efficacy results from the trial are pooled together by type of
setting. Resource consumption and costs are not pooled.
-
Regarding costs: unit costs specific to the country under consideration may be substituted to
the unit costs of the country of the chosen TAU or they can be kept as is if the decision maker
deems them close enough to his own country. Resource consumption is always dependent
on the chosen country of reference as it is linked to specific organisation of health care.
-
Additionally, decision makers may change the composition of the population under
evaluation in terms of severity levels at inclusion if it is expected that blended therapy will
only be offered to less severe patients.
-
Regarding the cost-effectiveness analysis itself, the discount rates for both effectiveness
criteria and costs as well as the cost-effectiveness threshold may be changed to reflect
recommendations in the country under consideration.
The second tab includes the structure of the model. The third and fourth tabs present the data
(transition probabilities, effectiveness and costs) included in the model for both the TAU arm and the
blended treatment arm based on the user’s choice in Tab 1. Tabs 5 to 7 present the results of the
MM, first in the TAU arm, then in the blended treatment arm, and finally as a comparison of the two
with the ICER. Additional hidden tabs include the transition probabilities, effectiveness criteria and
costs associated with each health state for each treatment and each country. A final tab consists of
the data which the user may change or add based on his choice in the first tab (discount rates, costeffectiveness threshold, severity of depression in the cohorts, unit costs).
2
A more elaborate version of the DES model’s video guide is available upon request by email.
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E-COMPARED – WP3 Deliverable
2.2. DES model
The generic DES model was implemented in Matlab. The files can be opened with the Octave
software which can be downloaded here: https://www.gnu.org/software/octave/download.html.
Initial data has been manually simulated to reflect the progression of depression over one year based
on PHQ-9 scores. Events and waiting times are estimated jointly for each individual based on the
number of days and the length of time over the first year that a patient has spent in each health
state. A multinomial distribution is fitted individually for each patient that simulates the next event
based on the proportion of time a patient spent in each of the health states over the first year (for
instance see diagram 1). Death is not considered a health state in the current version of the model.
Given the simulated state, the waiting time in that state is jointly estimated with an exponential
distribution fitted for each individual's data. For example, if the event mild depression is simulated,
the waiting time is jointly simulated using an exponential distribution of waiting times based on the
average episode of mild depression for that individual in the past. Currently, only Dutch unit costs are
included in the model.
Diagram 1. Patient simulation in DES model
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Section 3. Perspectives, future updates
3.1. Common updates
Updates that are planned for both models are described below.
-
Unit costs will be added to the generic models for years 1-5 once WP2 members have filled in
the unit cost questionnaire for their country that will be sent to them.
-
Transition probabilities between depression states over the long term will be obtained from
the literature.
-
When trial data become available, real effectiveness and resource consumption will be
added to the generic models.
Once we have obtained all required data, we may also update the models to downgrade the number
of severity levels from four to three.
In the final models, different utility tariffs will be incorporated to allow decision makers to select the
tariff applying to their specific country.
Sensitivity analyses using the QIDS-SR16 data and the retrospective MINI data instead of the PHQ-9
to define health states will also be carried out.
In a last step, the two models will be compared in order to provide knowledge about the impact of
the modelling technical choice on the cost-effectiveness analysis results, the resulting resource
allocation decision and the time, data and human resources requested.
-
If the MM and the DES model give similar results and lead to the same resource allocation
decision, and the DES model turns out to require more time, data and human resources to
develop and run, we will recommend the use of MM for policy decision making in the field of
depression.
-
If the DES model appears to be more relevant to describe the natural course of depression
and provides more accurate results than the MM, then the empirical comparison of the two
models in terms of results and practicability (time, data and human resources requested) will
allow assessing the incremental cost of providing more precise information.
3.2. Markov model
Both deterministic and probabilistic sensitivity analyses will be added to the MM as soon as we have
enough data to do so.
3.3. DES model
Various patient demographic (e.g. age, gender) and clinical (e.g. comorbidity, number of previous
depressive episodes) characteristics, which may influence the prognosis of depression, will be added
in the DES model.
In addition, sensitivity analysis in specific subgroups of patients will be conducted at a later point.
This analysis will be performed to identify subgroups of patients for whom the Internet-based
intervention is more (cost-) effective.
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