Task Force Co-Chairs
- Josephine A. Mauskopf PhD, RTI Health Solutions, RTI
International, Research Triangle Park, NC, USA
- Sean D. Sullivan PhD, RPh, MS, University of Washington, Seattle,
Task Force Core Members
- Lieven Annemans PhD, MSc, HEDM and
IMS Health, Brussels, Belgium
- J. Jaime Caro MD, Caro Research,
Concord, MA, USA
- C. Daniel Mullins PhD, University of
Maryland School of Pharmacy, Pharmaceutical Health Services
Research, Baltimore, MD, USA
- Mark Nuijten PhD, MBA, MD, Imta,
Erasmus University, Rotterdam, The Netherlands
- Ewa Orlewska MD, PhD, Centre for
Pharmacoeconomics, Warsaw, Poland
- Paul Trueman MA, BA, York Health Economics
Consortium, Heslington, York, UK
- John Watkins RPh, MPH, Premera Blue Cross, Bothell,
Principles of Good Practice for Budget Impact Analysis
OBJECTIVES: There is growing recognition that a comprehensive economic
assessment of a new health care intervention at the time of launch
requires both cost-effectiveness analysis (CEA) and a budget impact
analysis (BIA). National regulatory agencies such as the National
Institute for Health and Clinical Excellence (NICE) in England and Wales
and the Pharmaceutical Benefits Advisory Committee (PBAC) in Australia,
as well as managed care organizations (MCOs) in the USA, now require
that companies submit estimates of both the cost-effectiveness and the
likely impact of the new health care interventions on national,
regional, or local health plan budgets.
While 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
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 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. Uncertainty 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 are also recommended using published data
or national statistics.
Finally, the health condition model used for a budget impact analysis
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 cost-effectiveness analysis with regards to clinical
and economic assumptions.
CONCLUDING STATEMENT: Budget impact analysis is important, along with
cost-effectiveness analysis, 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 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 point
for payers wishing to promulgate methodology guidelines.
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), prior to
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 use of a technology will impact the
trajectory of spending on a particular health condition (see
It can be used for budget planning, forecasting and for computing the
impact of health technology changes on premiums in health insurance
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 information on the financial impact of
alternative health care interventions, yet each has different and
specific evidentiary requirements for data, methods and reporting.
BIA should be viewed as complementary to cost-effectiveness analysis (CEA),
not as a variant or replacement. Whereas, CEA evaluates the costs and
outcomes of alternative technologies over a specified time horizon to
determine their economic efficiency, BIA addresses the financial stream
of consequences related to the uptake and diffusion of technologies to
determine 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.
History of BIA
Mauskopf et al published an analytic framework for budget impact
modeling in 1998 . 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, USA), 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 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 , 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 (Mauskopf et al 2005). Some of these publications present
cost studies that focus on the annual, 2-, 3-year or lifetime costs for
a specific cohort of people or a representative individual being started
on competing treatments [17-21]. 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 [22-35]. 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 Co-Chairs 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 to participate by the Co-Chairs, 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 [37-8] 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 Co-Chairs, 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.
Purpose of the Document
The purpose of this document is:
1) to develop a coherent set of methodological 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, health care 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 budget impact analysis, a model of the health condition, its
treatment and outcomes is the essential component of the analytic
framework. The purpose of a budget impact analysis 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. The budget
impact analysis 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. Thus, the model outcomes should reflect scenarios that consist of
a set of specific assumptions and data inputs of interest to the
decision maker rather than a scientifically chosen “base” or “reference”
case based on assumptions and inputs intended to be generally
This section presents the Task Force recommendations for the key
elements of the analytic framework for budget impact analysis. It
addresses the: overall design, the perspective, the scenarios to be
compared, the population, time horizon, costing, uncertainty analysis,
discounting and validation.
Proper design of the analytic framework is a crucial step in budget
impact analysis. 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; the type of intervention (preventive, curative,
palliative, one-time, on-going, periodic, etc.). 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 modeling technique.
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
The type of health condition model that should be used 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, the
model should be constructed so that 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 should be considered. It is important that
researchers be alert to advances in modeling methods as well as
methodology requirements of payers rather than commit themselves 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.
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
cost-effectiveness analysis, where the recommended perspective is that
of society, which includes all cost implications of an intervention, a
budget impact analysis 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 decision maker what they
need to see, but also can extend beyond that to provide a more
comprehensive view of the fuller economic implications of the
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 reasonable 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 some or all of the current
interventions. These constitute the new scenarios.
The budget impact analysis 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
The population to be included in a budget impact analysis should be all
patients who might be given the new 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 off-label or 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. . 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.
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,
co-morbidities, age, gender, 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 epidemiologic
studies, local knowledge and so on.
Budget impact analyses should be performed first for the time horizon of
most relevance to the budget holder. This 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. 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 presented 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 financial stream of expenses and savings rather
than a single “net present value”.
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 resulted 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
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 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
The impact on productivity and other items outside the health care
system costs should not routinely be included in a budget impact
analysis as these are not generally relevant to the budget holder. One
exception may be when budget impact analyses are intended to inform the
decision making 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.
There is considerable uncertainty in a budget impact analysis.
Therefore, a single “best estimate” is not a sufficient outcome.
Instead, the analyst should compute a range of results that reflects 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 reasonable
scenarios regarding the inputs and assumptions — a task that should be
done collaboratively with the decision makers because they are best
placed to make many of the key assumptions, and supply data for them.
Various forms of sensitivity analysis (Univariate, Multivariate,
Probabilistic, etc.) should 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.
As the BIA presents financial streams over time, it is not necessary to
discount the costs. The decision maker can discount these results
according to local practice back to a decision time point if they wish
to do so.
Like all models, those used for BIA must be valid enough to provide
useful information to the decision maker. 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
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
• Use and cost of other health condition- and treatment-related health
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, restricting the use of expert
opinion to a minimum. Likewise, they should incorporate uncertainty in
the scenarios and in the analyses themselves.
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 epidemiologic data from nationally
representative populations, adapted to the age, gender, 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, however, it is an episode of
illness that is the unit of analysis (for example, 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 epidemiologic
data in the relevant, or in 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 decision maker 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 is the decision maker’s own data base. If
these data are not available, then published information on current
treatment patterns, such as medical text books or review papers, can be
used. In addition to these data sources, market research data 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 budget impact analysis 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. 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.
New Technology Mix
The new technology mix depends on the rate of uptake of the new
technology as well as the extent to which the 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 the
new technology. There are several ways to estimate the new technology
mix. The first way is to use the producer’s estimates of market share
over the first few years after launch. 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, the new technology might reduce the
use of a subset of the currently used technologies equi-proportionately
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 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 budget impact analysis,
we recommend that they be estimated and added to the budget impact
analysis 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 health care 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 the
estimation techniques described in the ISPOR Modeling Studies Task Force
Report and the Cost-Effectiveness Analysis alongside Clinical Trials
Task Force Report be used but 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 budget impact analysis
[37-8]. For an acute or episodic illness, this calculation is
straightforward. For a chronic or progressive illness, this calculation
will require adaptation of the cost-effectiveness health condition model
to account for the open population and time-dependencies required for a
budget impact analysis.
The BIA must be transparent about the assumptions made about the impact
of non-compliance or reduced compliance on effectiveness and about
safety issues associated with under or over-utilization 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 data or expert opinion might be the only available data
sources. Table 2 presents a hypothetical example of the relationship
between compliance and health outcomes.
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
decision-maker-specific scenario basis that we have recommended be
adopted for budget impact analysis, this format gives only general
directions for reporting.
The introduction of the report of a BIA study should contain all the
necessary relevant epidemiological, clinical and economic information.
Epidemiology and treatment
The introduction of a BIA study should present relevant aspects of the
prevalence and incidence of the particular disease, information on age,
gender and risk factors. 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.
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.
This section should contain a detailed description of the
characteristics of the new technology compared to 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.
The objective of the BIA should be clearly stated. This will be tied to
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:
This paragraph should clearly specify the study population. The report
should identify and justify differences between the clinical trial
populations and the BIA population.
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 country-specific treatment patterns and clinical guidelines
and this choice should be justified.
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
Perspective and Target audience
This paragraph should clearly identify the perspective(s) from which the
study is performed, the costing that is accomplished 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
silos of interest to these parties. In all cases, the perspective should
be clearly stated and transparent to the budget holder.
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.
The parameter values assumed for all the clinical data items and all the
cost data items for all the scenarios modelled should be presented in
the report. The level of detail should be such that the reader could
duplicate all the calculations in the model.
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.
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).
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.
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 (disaggregated 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 costs over time after applying costing information to the
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 decision makers. 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
uncertainty analysis of the key variables from the perspective of the
decision maker. We recommend that the results of all uncertainty
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
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 Uncertainty
Analysts should be encouraged to use diagrams (such as Tornado diagrams)
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 programmed so that the user can restore the original default
The model should be programmed 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.
Budget impact analysis is important, along with cost-effectiveness
analysis, 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 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 point for payers
wishing to promulgate methodology guidelines.
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The following individuals provided suggestions and comments on the first
draft of the Task Force Report:
Sang-Eun Choi, PhD, MPH, Health Insurance Review
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
Ulf Persson PhD, The Institute for Health Economics,
Jose-Manuel Rodriguez Barrios PharmD, MPH, MSc, Medtronic Iberia, Spain
Rod Taylor PhD, MSc, University of
David Thompson PhD, i3 Innovus
Research Inc., USA
Jill van den Bos MA, Milliman USA, USA
Johan van Luijn, RPh, Health Care Insurance
Board, The Netherlands
The authors also wish to thank 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.
Adapted from: Brosa M, Gisbert R, Rodríguez Barrios JM y Soto J.
Principios, métodos y aplicaciones del análisis del impacto
presupuestario en sanidad. Pharmacoeconomics Spanish Research Articles
2005; 2: 65-79.
The relationship between effectiveness and adherence may be estimated
based on observed data or expert opinion or pharmacokinetic data. The
relationship in this Figure is based on expert opinion:
Effectiveness Relative to Trial Data = Adherence rate (AR) if AR <= 30%
Effectiveness Relative to Trial Data = 1 – exp(-5 * (AR – 0.2287)) if AR
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