Volume 10 • Number 5 • 2007VA L U E I N H E A LT H
Principles of Good Practice for Budget Impact Analysis:
Report of the ISPOR Task Force on Good Research Practices—
Budget Impact Analysis
Josephine A. Mauskopf, PhD,1 Sean D. Sullivan, PhD,2 Lieven Annemans, PhD, MSc,3 Jaime Caro, MD,4
C. Daniel Mullins, PhD,5 Mark Nuijten, PhD, MBA, MD,6 Ewa Orlewska, MD, PhD,7 John Watkins, RPh, MPH,8
Paul Trueman, MA, BA9
1
RTI Health Solutions, Research Triangle Park, NC, USA; 2University of Washington, Seattle, WA, USA; 3IMS Health, Brussels, Belgium;
Caro Research, Concord, MA, USA; 5University of Maryland, Baltimore, MD, USA; 6Imta, Erasmus University, Rotterdam, The Netherlands;
7
Centre for Pharmacoeconomics, Warsaw, Poland; 8Premera Blue Cross, Bothell, WA, USA; 9York Health Economics Consortium,York, UK
4
A B S T R AC T
Objectives: There is growing recognition that a comprehensive economic assessment of a new health-care intervention at
the time of launch requires both a cost-effectiveness analysis
(CEA) and a budget impact analysis (BIA). National regulatory agencies such as the National Institute for Health and
Clinical Excellence in England and Wales and the Pharmaceutical Benefits Advisory Committee in Australia, as well as
managed care organizations in the United States, now require
that companies submit estimates of both the costeffectiveness and the likely impact of the new health-care
interventions on national, regional, or local health plan
budgets. Although standard methods for performing and presenting the results of CEAs are well accepted, the same
progress has not been made for BIAs. The objective of this
report is to present guidance on methodologies for those
undertaking such analyses or for those reviewing the results
of such analyses.
Methods: The Task Force was appointed with the advice and
consent of the Board of Directors of ISPOR. Members were
experienced developers or users of budget impact models,
worked in academia, industry, and as advisors to governments, and came from several countries in North America,
Oceana, Asia, and Europe. The Task Force met to develop
core assumptions and an outline before preparing a draft
report. They solicited comments on the outline and two
drafts from a core group of external reviewers and more
broadly from the membership of ISPOR at two ISPOR meetings and via the ISPOR web site.
Results: The Task Force recommends that the budget impact
of a new health technology should consider the perspective of
the specific health-care decision-maker. As such, the BIA
should be performed using data that reflect, for a specific
health condition, the size and characteristics of the population, the current and new treatment mix, the efficacy and
safety of the new and current treatments, and the resource
use and costs for the treatments and symptoms as would
apply to the population of interest. The Task Force
recommends that budget impact analyses be generated as a
series of scenario analyses in the same manner that sensitivity
analyses would be provided for CEAs. In particular, the input
values for the calculation and the specific cost outcomes
presented (a scenario) should be specific to a particular
decision-maker’s population and information needs. Sensitivity analysis should also be in the form of alternative scenarios
chosen from the perspective of the decision-maker. The
primary data sources for estimating the budget impact should
be published clinical trial estimates and comparator studies
for efficacy and safety of current and new technologies as
well as, where possible, the decision-maker’s own population
for the other parameter estimates. Suggested default data
sources also are recommended. These include the use of
published data, well-recognized local or national statistical
information and in special circumstances, expert opinion.
Finally, the Task Force recommends that the analyst use the
simplest design that will generate credible and transparent
estimates. If a health condition model is needed for the BIA,
it should reflect health outcomes and their related costs in the
total affected population for each year after the new intervention is introduced into clinical practice. The model should
be consistent with that used for the CEA with regard to
clinical and economic assumptions.
Conclusions: The BIA is important, along with the CEA, as
part of a comprehensive economic evaluation of a new health
technology. We propose a framework for creating budget
impact models, guidance about the acquisition and use of
data to make budget projections and a common reporting
format that will promote standardization and transparency.
Adherence to these proposed good research practice principles would not necessarily supersede jurisdiction-specific
budget impact guidelines, but may support and enhance local
Address correspondence to: Sean Sullivan, University of Washington, Pharmaceutical Outcomes Research and Policy Program, Box
357630, 1959 NE Pacific Street, Health Sciences Center, H-375, Seattle, WA 98195-7630, USA. E-mail: sdsull@u.washington.edu
10.1111/j.1524-4733.2007.00187.x
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© 2007, International Society for Pharmacoeconomics and Outcomes Research (ISPOR)
1098-3015/07/336
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Budget Impact Analysis Task Force Report
recommendations or serve as a starting point for payers
wishing to promulgate methodology guidelines.
Keywords: budget impact analysis, economic evaluation,
methodology, modeling.
Introduction
Whereas, CEA evaluates the costs and outcomes of
alternative technologies over a specified time horizon
to estimate their economic efficiency, BIA addresses the
financial stream of consequences related to the uptake
and diffusion of technologies to assess their affordability. Admittedly, both CEA and BIA share many of the
same data elements and methodological requirements,
but there are important differences in how these data
and methods are incorporated into the models because
of their different intended use. There may be circumstances where the CEA indicates an efficient technology while the BIA results indicate that it may not be
affordable. In such instances, there is, unfortunately,
no current scientific guidance on how to resolve this
dilemma.
Definition and Intended Use
Budget impact analysis (BIA) is an essential part of a
comprehensive economic assessment of a health-care
technology and is increasingly required, along with
cost-effectiveness analysis (CEA), before formulary
approval or reimbursement. The purpose of a BIA is to
estimate the financial consequences of adoption and
diffusion of a new health-care intervention within a
specific health-care setting or system context given
inevitable resource constraints. In particular, a BIA
predicts how a change in the mix of drugs and other
therapies used to treat a particular health condition will
impact the trajectory of spending on that condition (see
Fig. 1). It can be used for budget planning, forecasting
and for computing the impact of health technology
changes on premiums in health insurance schemes.
Users of BIA include those who manage and plan
for health-care budgets such as administrators of
national or regional health-care programs, administrators of private insurance plans, administrators of
health-care delivery organizations, and employers who
pay for employee health benefits. Each has a need for
clearly presented information on the financial impact
of alternative health-care interventions, yet each has
different and specific evidentiary requirements for
data, methods, and reporting.
Budget impact analysis should be viewed as complementary to CEA, not as a variant or replacement.
Figure 1 Budget impact schematic. Adapted
from Brosa et al. [39].
History of BIA
Mauskopf et al. published an analytic framework for
budget impact modeling in 1998 [1]. Others have
struggled with the need to include budget impact as
part of comprehensive economic evaluation [2–6].
Since the 1990s, several regions in the world including
Australia, North America (Canada, United States),
Europe (England and Wales, Belgium, France,
Hungary, Italy, Poland) and the Middle East (Israel),
have included a request for BIA alongside the CEA,
when submitting evidence to support national or local
formulary approval or reimbursement. Other countries have typically performed their own BI analysis
(The Netherlands) rather than requesting the BIA from
Mauskopf et al.
338
the manufacturer, although voluntary submission is
permitted. Country-specific guidelines for constructing
BIAs are also available [7–16]. These guidelines are
variable in terms of defining what constitutes a BIA
and most of them provide only limited details on the
important factors in a BIA. An exception are the Polish
guidelines [15], which provide precise recommendations on perspective, time horizon, reliability of data
sources, reporting of results, rates of adoption of new
therapies, probability of redeploying resources, inclusion of off-label use, and sensitivity analysis.
Despite the increased demand for BIA, a recent
literature review indicates that the number of studies
published in peer-reviewed journals is limited [17].
Some of these publications present cost studies that
focus on the annual, 2- to 3-year or lifetime costs for a
specific cohort of people or a representative individual
being started on competing treatments [18–22]. A
more limited number of published studies attempt to
estimate explicitly the financial and health-care service
impact of a new technology for a well-defined national
or health plan population [23–36]. There is ongoing
debate as to whether BIAs should be publicly available
for review and, if so, what parts should be published
and/or made available for review upon request.
Task Force Process
The cochairs of the ISPOR Task Force on Good
Research Practices––Budget Impact Analysis, Josephine A. Mauskopf and Sean D. Sullivan, were
appointed in 2005 by the ISPOR Board of Directors.
The members of the Task Force were invited by the
cochairs to participate, with advice and consent from
the ISPOR Board of Directors. Individuals were chosen
who were experienced as developers or users of budgetary impact models, who were recognized as scientific leaders in the field, who worked in academia,
industry, and as advisors to governments, and who
came from several countries. This document reflects
the authors’ own experiences developing budget
impact models and select publications, but is not
intended as a comprehensive review of the literature.
A reference group of ISPOR members from whom
comments would be sought also was identified. The
Task Force held its first meeting at the ISPOR 10th
Annual International Meeting in Washington DC in
2005 and held open Forums at the ISPOR 8th Annual
European Congress in Florence in 2005 and at the
ISPOR 11th Annual International Meeting in Philadelphia in 2006.
The Task Force reviewed other ISPOR guidance
documents that were developed to inform good scientific conduct [37,38] and National Guidelines for BIAs
[7–16]. The Task Force held teleconferences and used
electronic mail to exchange outlines and ideas during
the subsequent months. Sections of the report were
prepared by Task Force members and a draft of the
complete report was then prepared by the cochairs,
and circulated to the Task Force members for review. A
face-to-face meeting of the Task Force was held to
discuss the draft and make revisions. This draft report
was then sent to a group of primary reviewers chosen
to represent a broad range of perspectives. The reviewers are identified in the Acknowledgments section of
the report. Following this review, a new draft was
prepared by the Task Force members and made accessible for broader review by all ISPOR members. This
final report reflects the input from all of these sources
of comment.
Purposes of the Document
The purposes of this document are: 1) to develop a
coherent set of guidelines for those developing or
reviewing budget impact analyses; and 2) to develop a
format for presenting the results of budget impact
analyses that is useful for decision-makers.
The intended audience is research analysts who
perform budget impact analyses for health-care
decision-makers as well as health-care decision-makers
who are responsible for local or national budgets.
Others who may find this document useful include
members of the press, patient advocacy groups, healthcare professionals, drug and other technology manufacturers, and those developing guidelines for their
settings.
The panel recognizes that the methods for performing and reporting budget impact analyses continue to
develop. This report highlights areas of consensus as
well as areas where continued methodological development is needed. The guidance is divided into three
main sections: 1) analytic framework; 2) inputs and
data sources; and 3) reporting format.
Recommendations for Analytic Framework
For BIA, a description of the health condition, its
treatment and outcomes, is the essential component of
the analytic framework. The purpose of a BIA is not to
produce exact estimates of the budget consequences of
an intervention, but to provide a valid computing
framework (a “model”) that allows users to understand the relation between the characteristics of their
setting and the possible budget consequences of a new
health technology (or a change in usage of current
health technologies). The BIA is a means of synthesizing the available knowledge at a particular point in
time for a particular decision-maker to provide a range
of predictions specific to that decision-maker’s information needs based on realistic estimates of the input
parameter values. Thus, the outcomes of the BIA
should reflect scenarios that consist of a set of specific
assumptions and data inputs of interest to the decisionmaker rather than a scientifically chosen “base” or
“reference” case based on assumptions and inputs
intended to be generally applicable.
Budget Impact Analysis Task Force Report
This section presents the Task Force recommendations for the key elements of the analytic framework
for BIA. It addresses the overall design, the perspective,
the scenarios to be compared, the population, time
horizon, costing, sensitivity analysis, discounting, and
validation.
Design
Proper design of the analytic framework is a crucial
step in BIA. The design must take into account the
current understanding of the nature of the health condition and the evidence regarding the current and new
technologies. There are several dimensions that must
be considered: acuteness of the health condition,
whether it is self-limiting, and the type of intervention
(preventive, curative, palliative, one-time, ongoing,
periodic). These dimensions will affect the degree to
which time-dependence is important in the design,
how the size of the population is estimated, the unit of
analysis (episode vs. patient, for example), how the
intervention uptake is addressed, and the choice of
computational framework.
These guidelines cannot address the details of
design of the analytic framework, but rather highlight
the key aspects to consider. It is important that whatever choices are made, they be clear, justified, and with
a view to the simplest design that will meet the needs of
the analysis.
Whether or not a health condition model is needed
depends on the type of health condition and interventions at issue. For a chronic health condition, where
time dependency tends to be a major concern, a health
condition model is likely to be needed. The model
should be constructed so that it is consistent both with
a coherent theory of the natural history of the health
condition and with available evidence regarding causal
linkages between variables. Techniques currently used,
such as Markov models, might be appropriate, but
newer techniques such as discrete event simulation,
agent-based simulation, and differential equations
models may be considered if they are likely to be
accepted by the decision-maker. It is important that
researchers be alert to advances in modeling methods
as well as to methodology requirements of payers
rather than commit them to a given technique exclusively. For acute, self-limiting health conditions where
the episode is the unit of analysis, simpler techniques
using deterministic calculations may be used.
All of these methods are supported by a variety of
software which is continually evolving. The software
chosen and the resulting model should be accessible
to the users in the sense that it should allow them
to review all the model calculation formulae and to
change the assumptions and other inputs interactively;
indeed, even the design of the model may result from
collaboration with the intended users.
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Perspective. Budget impact analyses are primarily
intended to inform health-care decision-makers, especially those who are responsible for national, regional,
or local health-care budgets. Therefore, the recommended perspective is that of the budget holder. Thus,
unlike a CEA, where the recommended perspective is
that of society, which includes all cost implications of
an intervention, a BIA needs to be flexible enough to
generate estimates that include various combinations
of health care, social service and other costs, depending on the audience.
The drawing of budget boundaries is a highly local
exercise. In particular, some budgets may have a very
narrow focus. For example, in one location the pharmacy budget holder will only be concerned with the
expenses for drugs but in another, this may be subsumed within a total hospital budget. Thus, the perspective of a given budget holder may cover very
different elements according to location. Whereas it is
mandatory for the analyst to address the needs of the
selected budget holders, it is also desirable for the
analytic framework to be able to encompass broader
(or even narrower) budgetary envelopes. In this way,
the analysis will not only be able to show the decisionmaker what they need to see, but also can extend
beyond that to provide a more comprehensive view of
the fuller economic implications of the intervention.
Scenarios to be compared. Budget impact analyses
generally compare scenarios defined by a set of interventions rather than specific individual technologies.
The reference scenario should be the current mix of
interventions for the chosen population and subgroups. The current mix may include no intervention
as well as interventions that might or might not be
replaced by the new intervention. It may also include
off-label use. Introduction of a new technology sets in
motion various marketplace dynamics, including
product substitution and possibly market expansion.
These need to be modeled explicitly with realistic and
justifiable assumptions before the comparisons among
scenarios can be made. Thus, the analysis should consider how the current mix of interventions is likely to
change when the new intervention is made available.
For example, the new intervention might be added to
all existing interventions or it might replace all of the
current interventions or only those in a particular drug
class. These constitute the new scenarios.
The BIA should be transparent regarding the
assumptions made about the current mix of interventions and the changes expected as the new intervention
is added to the mix. The budget impact model should
be designed to allow alternative assumptions regarding
the scenarios to be compared.
Population. The population to be included in a BIA
should be all patients who might be given the new
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intervention in the time horizon of interest. Specifying
who is included in this population is not straightforward. It depends, of course, on the approved indication, but it also reflects local intended restrictions on
use (and reimbursement), possible beyond-restriction
use, induced demand (i.e., the proportion of previously
untreated patients who now seek treatment because of
improved outcomes, greater convenience, or fewer side
effects), and the extent to which practitioners adopt
the technology or change patterns of use of existing
ones. The budget impact model must be designed to
allow for examination of the effect of alternative
assumptions about the nature and size of the treated
population as well changes in its nature and size over
time. The Task Force did not recommend inclusion of
off-label use of the new technology in these scenarios
since generally accepted methods for doing this are not
yet available.
Typically, these populations are open in the sense
that individuals enter or leave the population depending on whether they currently meet the analyst’s criteria for inclusion (e.g., by developing the indication,
meeting the intended restrictions, no longer having
symptoms, etc.). This is in contrast with CEA where
populations are closed (i.e., a cohort of patients is
defined at the start and all remain members throughout the analysis). For example, if one of the criteria
defining the population is a moderate severity of
illness, then patients with mild disease are not part of
the population but may enter when the disease
progresses; similarly, patients who are initially in the
population with moderate disease may exit as the
illness advances to a severe stage.
Subgroups. The analytic framework should allow for
subgroups of the population to be considered so that
budget impact information can be made specific to
these segments. Such aspects as disease severity or
stage, comorbidities, age, sex, and other characteristics
that might affect access to the new intervention, or its
impact on the budget, might be taken into account.
This may also inform decisions regarding use of the
new technology as a “first line” intervention or reserving for use in patients failing other alternatives. The
choice of subgroups must be founded on available
clinical and other evidence from epidemiological
studies, local knowledge, and so on.
Time horizon. Budget impact analyses should be presented for the time horizons of most relevance to the
budget holder. They should accord with the budgeting
process of the health system of interest, which is
usually annual. The framework should allow, however,
for calculating shorter and longer time horizons to
provide more complete information of the budgetary
consequences. A particularly useful extension of the
time horizon for a chronic health condition is to reflect
the impact that might be expected when a steady state
would be achieved if no further treatment changes are
assumed. This will vary with the condition and with
the impact of the new intervention, but will generally
be longer than the current budget period because of
costs and benefits that accrue over time. Although time
horizons that go beyond a few years are subject to
considerable assumptions, they may in exceptional
cases be required to cover the main implications of the
health condition (e.g., some vaccinations). In any case,
results should be available disaggregated over time in
periods appropriate to the budget holder (e.g., quarterly, annual, etc.). Hence, to be most useful, the
output must be the period by period level of expenses
and savings rather than a single “net present value.”
Costing
The steps in costing are identifying the resource use
that may change, estimating the amount of change,
and valuation of these changes. In a BIA, identification
must be done according to the perspective and interest
of the budget holder (see above). Moreover, the
resource use considered should be that which is relevant to the health condition and intervention of interest over the chosen time horizon. The Task Force
members did not reach agreement on whether or not
future costs should be included for other health conditions that might be incurred when the new intervention results in additional survival. On this point, the
Task Force proposes that the analyst should use her/his
best judgment, given payer requirements and perspectives, when including or excluding future unrelated
costs.
In general, the resource use profile should reflect the
actual usage and the way the budget holder values
these resources. Thus, the valuation of these resources
refers to the expenditures expected to accrue (in the
short-run variable costs only and in the long-run both
fixed and variable costs) rather than the opportunity
costs per se. It is the transaction prices that are relevant, including any rebates or other modifiers that
may apply. For example, in some countries, readmissions within a certain period will not generate another
payment and in other jurisdictions, the physician’s fee
depends on the number of times the patient is seen
within a period.
In some cases, the intervention alters resource use
and, thus, the capacity of the system, but this may have
no direct monetary consequence for the budget holder
because the system will not adjust financially within
the time horizon (e.g., personnel may not be redeployed or let go). It may still be desirable to describe
this impact on health services because it has implications for planning health system organization.
The impact on productivity and other items outside
the health-care system costs should not routinely be
included in a BIA as these are not generally relevant to
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Budget Impact Analysis Task Force Report
the budget holder. One exception may be when budget
impact analyses are intended to inform the decisionmaking of private health insurers or employers. Such
organizations may have a vested interest in maintaining a healthy and productive workforce and, thus, they
may be able to offset productivity gains against
increased health-care costs. Another exception may be
health-care systems relying on tax payments where lost
production due to morbidity could have important
implications for the payment of health.
Sensitivity Analysis
There is considerable uncertainty in a BIA. Therefore,
a single “best estimate” is not a sufficient outcome.
Instead, the analyst should compute a range of results
that reflect the plausible range of circumstances the
budget holder will face. Indeed, it might be argued that
the analytic framework itself is the most important
product of a BIA rather than any particular set of
results. It is useful to consider both a most optimistic
and most pessimistic scenario. Having said this, the
ranges to be presented must be based on realistic scenarios regarding the inputs and assumptions—a task
that should be done collaboratively with the decisionmakers because they are best placed to make many of
the key assumptions and to supply data for the ranges
of input parameter values.
Various forms of sensitivity analysis (univariate,
multivariate, probabilistic, etc.) may be carried out.
Their usefulness depends on the amount and quality of
available data and the needs of the decision-maker. For
example, there is little point to an extensive probabilistic sensitivity analysis when little is known about the
degree of variability and the extent of correlation
among parameters.
Discounting
As the BIA presents financial streams over time, it is
not necessary to discount the costs. The computational
framework should be constructed so that the decisionmaker can readily discount these results according to
local practice back to a decision time point if they wish
to do so.
Validation
Like all models, those used for BIA must be valid
enough to provide useful information to the decisionmaker. The steps to be followed in validation are conceptually identical to those already identified in the
ISPOR Modeling Studies Task Force Report and are
therefore not repeated here [37].
Recommendations for Inputs and
Data Sources
There are six key elements requiring inputs for the
modeling framework of a BIA:
•
•
•
•
•
•
Size and characteristics of affected population;
Current intervention mix without the new
intervention;
Costs of current intervention mix;
New intervention mix with the new intervention;
Cost of the new intervention mix; and
Use and cost of other health condition- and
treatment-related health-care services.
These six elements can be combined to calculate the
budget impact of changing the treatment mix. The
Task Force recommends possible data sources for
deriving the inputs for each of these elements. Apart
from efficacy and safety which are assumed to be generalizable aspects of the interventions, the inputs are
local. In many jurisdictions, the required data may not
exist or may be difficult to obtain. Nevertheless, analyses should be as evidence-based as possible, with
expert opinion only used where alternative sources of
data are not readily available. If expert opinion is used,
care should be taken to frame the questions and choose
the experts in ways that generate reliable and generalizable information. For example, the experts should be
asked for responses to questions that they know the
answer to (e.g., how often do you schedule follow-up
visits for a certain type of patient). No matter what the
data source, the BIA should include measures of the
range of possible input parameter values.
Size and Characteristics of the Population
The estimated sizes of the population and of the relevant subgroups over time are critical for a determination of the budget impact. The ideal way to obtain this
estimate would be from the epidemiological data in the
decision-maker’s own population before and after the
introduction of the new technology. As these data are
not usually readily available even for the current technologies, various alternative methods can be used to
provide default estimates for a budget impact model.
One approach is to employ epidemiological data
from nationally representative populations, adapted to
the age, sex, and racial mix of the decision-maker’s
overall population. This generally involves the application of successively more restrictive inclusion criteria
to the decision-maker’s overall population. This
process requires rates such as the prevalence of the
condition, the proportion of patients with a particular
severity or usage pattern, and other relevant features
for the health condition and technologies being examined. In addition, change in prevalence over the time
horizon of the model because of new incident cases
and people leaving the population through death or
other changes in disease progression must be applied
over time to ensure that the size of the population
continues to reflect the prevalence with the current and
new technologies. This approach is relevant when
people are the unit of analysis. For some conditions,
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however, it is an episode of illness that is the unit of
analysis (e.g., a migraine attack), and then it is the
frequency of episodes in the population that must be
estimated with the current and new technologies.
Another approach is to obtain directly from providers their estimates of the number of people in their
setting who would be part of the relevant population
based on their current and anticipated new treatment
patterns and aggregating this up to the budget holder’s
level.
Regardless of the method used, it is important for
BIA to estimate not only the starting size of the population (or number of episodes) but also the way these
are likely to evolve over time with and without the new
technology. Hence, for the typically used open population, estimates of the inflows and outflows must be
made.
Given the difficulties in obtaining data to provide
accurate estimates of the population size, analysts
should consider multiple sources including national
statistics, published and unpublished epidemiological
data in the relevant, or similar, settings; registries;
naturalistic studies carried out for other purposes;
claims data; and even expert opinion. The calculations
used to derive the population estimate should be presented in disaggregated format so that a decisionmaker could adjust the calculations to reflect their
population.
Current Technology Mix
For each population subgroup, it is necessary to identify the interventions used currently and estimate the
proportion of patients using them, or proportion of
episodes in which they are used. Technologies may
include no active treatment as well as drugs, devices,
surgical or other modes of treatment. Some people
may receive more than one type of treatment which
should be recorded separately in the current technology mix table. Table 1 gives an example of what these
input parameters might look like. Although labeled
“current,” this technology mix may also evolve over
time even in the absence of the new technology and
this must be taken into account in budget impact
calculations.
Once again, the best data source for the current
technology mix for the different population subgroups
Table 1 Current technology mix
Drug name
Drug A (combination of drugs B and C)
Drugs B and C in separate doses
Drug B
Drug C
Drug D
Drugs C and D in separate doses
No therapy
Total
Percentage
Number
20.0
6.1
10.2
7.5
13.7
21.0
21.5
100
5,810
1,772
2,963
2,179
3,980
6,101
6,246
29,050
is the decision-maker’s own database. If these data are
not available, then published information on current
treatment patterns, such as the results of primary or
secondary data studies or medical text books or review
articles, can be used. In addition to these data sources,
market research data or expert opinion on current and
evolving treatment patterns may be used.
Cost of Current Intervention Mix
The cost of the current technology mix involves multiplying the decision-maker’s valuation of the technology by the number of people who receive each one in
each population subgroup. These costs should include
the acquisition of the product, administration or
implantation or other procedure costs as well as any
monitoring over the relevant time horizon. Costs of
managing any side effects should also be included in
the cost of current technology mix as a separate line
item.
The BIA should address the impact of compliance
and persistence with therapy on the cost of treatments.
This must take into account whether the payer bears
the cost anyway (e.g., even if poorly compliant, the
patient still picks up the prescription). The assumptions regarding compliance rates and persistence with
treatment should be based on the best available evidence, which may come from database studies or specific date collection or expert opinion. The relative
compliance and persistence on therapy should be
reported at various time intervals. If patients do not fill
all the recommended prescriptions, then the cost of
treatment should be reduced. In addition, the cost to
the decision-maker should take into account drug discounts and patient deductibles and copays.
New Technology Mix
The new technology mix depends on the rate of uptake
of a new technology as well as the extent to which a
new technology replaces current technologies or is
added to them. The rate of uptake is likely to change
over time as physicians and patients become familiar
with a new technology. There are several ways to estimate the new technology mix. One way is to use the
producer’s estimates of market share over the first few
years after launch if these data are made available. An
assumption must then be made as to whether the new
intervention will be given in addition to current technologies or whether it will substitute for some or all of
the current technologies. For example, a new technology might reduce the use of a subset of the currently
used technologies equi-proportionately (e.g., all drugs
in a particular class) or it might be added to all of the
current technologies. The assumptions should be
transparent and the model structured so that the
budget impact of alternative assumptions about the
new technology mix can be calculated. Another way to
estimate the new technology mix is to incorporate
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Effectiveness (%)
97.89
96.51
94.25
90.53
84.38
74.25
57.54
30.00
20.00
10.00
0.00
100%
80%
Effectiveness
Adherence (%)
100
90
80
70
60
50
40
30
20
10
0
60%
40%
20%
0%
0%
20%
40%
60%
80%
100%
Adherence
Figure 2 Adherence and effectiveness. Notes:The relationship between effectiveness and adherence may be estimated based on observed data or expert
opinion or pharmacokinetic and pharmacodynamic data. The relationship in this figure is based on expert opinion:
Effectiveness Relative to Trial Data = Adherence rate (AR) if AR ⱕ 30%.
Effectiveness Relative to Trial Data = 1 – exp [-5 ¥ (AR – 0.2287)] if AR > 30%.
directly in the analytic framework usage rules that
account explicitly for the new treatment pathways
available, thus explicitly modeling how people switch
to the new drug. For example, they may only switch
when they have failed on current therapy. Other ways
of estimating the new technology mix involve extrapolating previous experience on product diffusion with
the same technology in other settings or with similar
interventions in the budget holder’s setting.
Cost of New Technology Mix
Costing of the new technology mix follows the same
process as for the current mix except that for technologies not yet on the market, the price may have to be
assumed if it is not yet set. In this case, we recommend
that the assumed technology cost be transparent and
justified. In addition, any uncertainty in the price
should be readily able to be incorporated into alternative scenarios for the sensitivity analyses.
Use and Cost of Other Condition-Related
Health-Care Services
Although the health outcomes associated with different technologies are not generally estimated explicitly
as part of a BIA, we recommend that they be estimated
and added to the BIA through changes in the cost of
treating the health condition of interest. Thus, alternative technology mixes are likely to result in changes in
the symptoms, duration, or disease progression rates
associated with the health condition and, thus, in
changes in the use of all other condition-related healthcare services. These changes will have an impact on the
health plan budget.
In order to compute these changes in health outcomes and the associated changes in costs over the
time horizon of the BIA, we recommend that estimation techniques similar to those described in the ISPOR
Modeling Studies Task Force Report and the Cost-
Effectiveness Analysis alongside Clinical Trials Task
Force Report be used but simplified where possible and
adapted so that the estimates of the health outcomes
are generated from a population perspective and presented for each year that is included in the BIA [37,38].
For an acute or episodic illness, this adaptation is
straightforward. For a chronic or progressive illness,
this adaptation may require an extension of the costeffectiveness health condition model to account for the
open population and time-dependencies required for a
BIA.
The BIA must be transparent about the assumptions
made about the impact of noncompliance or reduced
compliance on effectiveness and about safety issues
associated with underutilization or overutilization of
treatment and must allow them to be changed. If there
are no published data on the relationship between
compliance and health outcomes, then either pharmacokinetic or pharmacodynamic data or expert opinion
are possible alternative data sources. Figure 2 presents
a hypothetical example of the relationship between
adherence and effectiveness that was generated using
expert opinion.
Recommendations for Reporting Format
This section presents a recommended reporting format
for BIAs. The format presented below should be understood as the preferred ISPOR structure for the reporting
of any study regarding BIA. In view of the decisionmaker-specific scenario basis that we have recommended to be adopted for BIA, this format gives only
general directions for reporting.
Report Introduction
The introduction of the report of a BIA study should
contain all the necessary relevant epidemiological,
clinical, and economic information.
Mauskopf et al.
344
Epidemiology and treatment. The introduction of a
BIA study should present relevant aspects of the prevalence and incidence of the particular disease as well as
information on age, sex, and risk factors.
Clinical impact. The clinical information should
consist of a brief description of the pathology, including underlying pathophysiological mechanisms, and of
the prognosis, disease progression, and existing treatment options, all of which are relevant to the design of
the BIA study.
Economic impact. The economic impact information
should include any previous related studies on the
condition of interest and associated therapies, for
example, previous BIA studies in the condition of interest for another technology, cost-of-care studies, and
cost-effectiveness studies.
Technology
This section should contain a detailed description of
the characteristics of the new technology compared
with the current technologies: indication, onset of
action, efficacy, side effects, serious adverse events,
intermediate outcomes, and adherence. A summary of
the clinical trials is given, including information on the
design, study population, follow-up period, and clinical outcomes.
Objectives
The objective of the BIA should be clearly stated. This
will be tied to the perspective(s).
Study Design and Methods
The report should specify the design of the BIA, which
will usually involve a modeling study. The following
characteristics of the model should be described.
Patient population. This paragraph should clearly
specify the study population. The report should identify and justify differences between the clinical trial
populations and the BIA population.
Technology mix. The chosen technology mix with and
without the new technology should be discussed and
justified. The choice of the technology mix is primarily
based on the local treatment patterns and clinical
guidelines and this choice should be justified.
Time horizon. The time horizon(s) for the study
should be presented and its choice justified. The choice
for the study period should be appropriate to the
budget holder.
Perspective and target audience. This paragraph
should clearly identify the perspective(s) from which
the study is performed, the costing that is accom-
plished and the target audience (i.e., for which
decision-making body the study is intended). Ideally,
the model should be flexible enough to model the
perspective of the budget holder and those of other
stakeholders with whom the budget holder must interact. This requires disaggregation into the various cost
components and categories of interest to these parties.
In all cases, the perspective should be clearly stated and
transparent to the budget holder.
Model description. This section should contain a complete description of the structure of the BIA model,
including a figure of the model. The description should
allow the reader to identify outcomes for all treated
patients during the study period, including patients
with treatment failure.
Input data. The parameter values assumed for all the
clinical data items and all the cost data items for all the
scenarios modeled should be presented in the report.
The level of detail should be such that the reader could
duplicate all the calculations in the model.
Data sources. The sources of model inputs should be
described in detail. The strengths, weaknesses, and
possible sources of bias, that may be inherent in the
data sources used in the analysis, should be described.
Selection criteria for studies and databases should be
discussed and an indication is given of the direction
and magnitude of potential bias in the data sources
which were used.
Data collection. The methods and processes for
primary data collection (e.g., for a Delphi panel) and
data abstraction (e.g., for a database) should be
described and explained. The data collection forms
which were used in the study should be included in the
appendix of the report (e.g., the questionnaire for
the Delphi panel, or the abstraction protocol for the
database).
Analyses. A description of the methods used to
perform budget total and incremental analyses should
be provided. The choice of all of the scenarios
presented in the results should be documented and
justified.
Results
Both total and incremental budget impact should be
presented for each year of the time horizon. Both
annual resource use and annual costs should be presented. The estimates of resource use should be listed
in a table (if possible classified by technology application, technology side effects, and condition related)
which shows the change in use for each year of the
time horizon. Another table should show the aggregated and disaggregated (e.g., pharmacy, physician
345
Budget Impact Analysis Task Force Report
visit, outpatient tests, inpatient care, and home care)
costs over time after applying costing information to
the resource use. In general, budget impact estimates
should be presented as a range of values, based on
alternative possible scenarios rather than a single point
estimate.
Annual health outcomes for each year of the time
horizon do not need to be reported, but may be presented if these results are of interest to the decisionmakers. For example, the health outcomes might be of
interest to the decision-makers when a large budget
impact is accompanied by large health benefits.
The results of the scenarios (sets of assumptions and
inputs and outcomes) analyzed should be described.
These scenarios may consist of optimistic, pessimistic,
and most likely input values determined from the sensitivity analysis of the key variables from the perspective of the decision-maker. We recommend that the
results of all sensitivity analyses be presented as a
Tornado diagram.
Inclusion of Graphics
Graphical snapshots of the model’s structure and data
can be useful in summarizing for the user, who may
wish to copy them for inclusion in their own internal
reporting. Use of the following tools is recommended:
Figure of the model. A graphical representation of the
model structure makes it easier for the budget holder
to understand what is represented by the outputs.
Simple flow diagrams are recommended to be included
with the model description.
Table of assumptions. Listing the major assumptions
in tabular form can improve the transparency of the
model, particularly to the relatively inexperienced user
and should be included with the model description.
Tables of inputs and outputs. Similarly, collecting the
model inputs and their data sources and outputs in
tables provides a useful snapshot for the user and
should be included with the text on input data and
data sources.
Schematic representation of sensitivity analysis. Analysts should be encouraged to use diagrams (such as
Tornado diagrams which show graphically the impact
on the budget impact of feasible ranges of each input
parameter) as a simple way of capturing the key
drivers of the model and presenting them to the user
and should be included along with the text on the
results of the scenario analyses.
Appendices and References
The enclosure of relevant appendices to reports is
encouraged. The appendices may cover the intermediate results (e.g., of individual Delphi panel rounds),
study audit reports and the names and addresses of
participating experts and investigators.
Budget Impact Computer Model
Because budget impact models need to be flexible
enough to provide budget impact estimates for different health-care decision-makers, it is critical that the
software used to perform the model calculations is
designed with both default input parameter values
based on credible national or local values and with the
capability for the user to enter values that represent
their own particular situation. The model should be
programed so that the user can restore the original
default parameters easily.
The model should be programed as easy-to-use
spreadsheets. For example, all input parameters would
be presented on one input worksheet and outputs displayed in one or more worksheets in a logical manner
that summarizes the findings for the user. Graphical
output is often useful in the model. Introductory worksheets should be included to describe the structure,
assumptions, and use of the model. All sources and
assumptions associated with input parameters should
be displayed with the parameters themselves and full
references should be included on a reference worksheet. The model calculations should be accessible to
the user and clearly and comprehensively presented.
In many cases, the budget holder will be interested
in modeling from more than one perspective. In such
cases, model developers are encouraged to design the
user interface so that the user can toggle between the
different perspectives easily.
The user should be able to change easily any of the
input parameters. Color coding the input cells is a
useful way of doing this. Changing the inputs allows
the user to test various input scenarios. It may be
useful to provide sample scenarios.
Finally, we recommend that the model be programed so that the user can readily perform sensitivity
analyses of relevance to their population.
Concluding Statement
Budget impact analysis is important, along with CEA,
as part of a comprehensive economic evaluation of a
new health technology. Some published examples of
budget impact analyses are described in the review by
Mauskopf et al. [17]. We propose here a framework
for creating budget impact models, guidance about the
acquisition and use of data to make budgetary projections and a common reporting format that will
promote standardization and transparency. Adherence
to these proposed good research practice principles
would not necessarily supersede jurisdiction-specific
budget impact guidelines, but may support and
enhance local recommendations or serve as a starting
346
point for payers wishing to promulgate methodology
guidelines.
The following individuals provided suggestions and comments on the first draft of the Task Force Report: Sang-Eun
Choi, PhD, MPH, Health Insurance Review Agency, Korea;
Karen Lee, MA, Canadian Agency for Drugs and Technologies in Health, Canada; Maurice McGregor, MD, McGill
University, Canada; Penny Mohr, MA, Centers for Medicare
and Medicaid Services, USA; Ulf Persson, PhD, The Institute
for Health Economics, Sweden; Jose-Manuel Rodriguez
Barrios PharmD, MPH, MSc, Medtronic Iberia, Spain; Rod
Taylor, PhD, MSc, University of Birmingham, UK; David
Thompson, PhD, i3 Innovus Research Inc., USA; Jill van den
Bos, MA, Milliman USA, USA; and Johan van Luijn, RPh,
Health Care Insurance Board, The Netherlands. The authors
wish to thank the 23 ISPOR members from 11 countries who
provided detailed comments on an earlier version of the
report, Jerusha Harvey from the ISPOR office for her excellent administrative support in all aspects of the Task Force
process and Executive Director of ISPOR, Dr Marilyn Dix
Smith, PhD, for her institutional support.
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Pre-Class Exercises – Weekend 1
Objectives
1. Review the basic excel functions needed to establish a Budget Impact
Model.
2. Discuss the basic background on a BIA/BIM and how to develop a basic
model structure.
Exercises
1. View the 1 pre-weekend video presented by Dr. Snyder
2. Read the weekend #1 pre-class reading posted to BB
3. Review information from HCDA 520 or review of the book, “A Practical Guide
to Pharmacoeconomics”. (Please review these materials to ensure you
understand the difference between BIMs, CBAs, and CEAs.)
Answer the following questions:
1. Define a Budget Impact Analysis?
A budget impact analysis (BIA) is an economic assessment that estimates the
financial consequences of adopting a new intervention.A budget impact
analysis evaluates whether the high-value intervention is affordable.
Predicts how a change in the mix of interventions used for a condition affects
the trajectory of spending on that condition.
2. Describe how a population funnel is used in developing a BIA Model?
A population funnel in BIA helps prioritize business processes, assess
criticality, and plan for operational continuity amid disruptions, ensuring
resilience.
3. What role does the Market Share worksheet serve in the BIA/BIM?
The Market Share worksheet in BIA/BIM assesses how disruptions affect an
organization’s market position, guiding decisions for business continuity and
risk management.
4. What strategies can be used to calculate prevalence/incidence data in a
BIA/BIM?
…………………………………………………………………………………………
…………………………………………………………………………………………
…………………………………………………………………………………………
…………………………………………………………………………………………
5. According to the reading, what are 3 key concepts to consider when
designing a Budget Impact Model?
a. the nature of the health condition
b. the evidence regarding the current
c. new technologies
6. According to the reading, what are 6 key elements that require inputs for
model framework in a Budget Impact Model?
a.
b.
c.
d.
e.
f.
…………………………………………………………………………………
………………………………………………………………………………
…………………………………………………………………………………
…………………………………………………………………………………
…………………………………………………………………………………
…………………………………………………………………………………
7. Describe how to design the new technology mix of a BIA? What are some key
considerations?
…………………………………………………………………………………………
…………………………………………………………………………………………
…………………………………………………………………………………………
…………………………………………………………………………………………
8. What results are recommended to provide as part of the output report in a
BIA/BIM?
…………………………………………………………………………………………
…………………………………………………………………………………………
…………………………………………………………………………………………
…………………………………………………………………………………………
9. What/who are the two typical perspectives considered by a BIA/BIM in the
United States? Why is perspective important to consider when building a
BIM?
a. ………………………………………………………
b. ………………………………………………………
c. Why? ……………………………………………………………………… …
………………………………………………………
10. What are the 2 strategies for estimating market size? Describe each.
a. …………………………………………………………………………………
…………………………………………………………………………………
…………………………………………………………………………………
b. …………………………………………………………………………………
…………………………………………………………………………………
…………………………………………………………………………………
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