a
National Center for Chronic Disease Prevention and Health Promotion, Centers
for Disease Control and Prevention, Atlanta, Georgia, (Tyagi), USA bNational
Center for Environmental Health, Centers for Disease Control and Prevention,
Atlanta, Georgia, (Morris), USA
Corresponding author. Address correspondence to:
Anupam Tyagi, PhD, 1161 Greenbriar Circle, , Decatur GA 30033, USA.
*1 This work was done while
Tyagi was a Steven M. Teustch Prevention Effectiveness Fellow at the
Cardiovascular Health Branch, Division of Adult and Community Health, National
Center for Chronic Disease Prevention and Health Promotion, Centers for Disease
Control and Prevention (CDC) and Morris was an Epidemic Intelligence Service
Officer, Office of Genomics and Disease Prevention, National Center for
Environmental Health, CDC.
Family history may be a useful tool for
identifying people at increased risk of disease and for developing targeted
interventions for individuals at higher-than-average risk. This article
addresses the issue of how to examine the utility of a family history tool for
public health and preventive medicine. We propose the use of a decision analytic
framework for the assessment of a family history tool and outline the major
elements of a decision analytic approach, including analytic perspective, costs,
outcome measurements, and data needed to assess the value of a family history
tool. We describe the use of sensitivity analysis to address uncertainty in
parameter values and imperfect information. To illustrate the use of decision
analytic methods to assess the value of family history, we present an example
analysis based on using family history of colorectal cancer to improve rates of
colorectal cancer screening.
Family history (FH) of disease is a risk factor
for most diseases of public health significance.[1] Although
FH information is routinely collected in clinical settings, its systematic use
in public health and preventive medicine is largely absent. Other papers in
this issue attest to the usefulness of FH information. [2, 3,
4, 5, 6, 7,
8 and 9] This article addresses the use of
decision analysis to quantify the value of FH information. Questions we
consider are: (1) Of what use is FH information? and (2) How valuable is it?
At a simple level, the answer to the first is that FH can be used to
differentiate risk, motivate individuals to seek care or change behavior, and
target interventions more effectively. A simple answer to the second question
is that the value of FH is the improvement it brings about in desirable health
outcomes (taking into account the potential costs associated with obtaining
and using FH information). We start by outlining the main components of a
decision analytic approach and issues to consider when exploring the value of
FH. We then present an illustration based on using FH of colorectal cancer (CRC)
to improve rates of CRC screening.
Decision analysis is a systematic method for
making decisions when outcomes are uncertain. The basic building blocks of a
decision analysis are (1) decisions, (2) outcomes, and (3) probabilities. A decision
is a choice made by a person, group, or organization to select a course of
action from among a set of mutually exclusive alternatives. The decision maker
compares expected outcomes of available alternatives and chooses the best among
them. This choice is represented by a decision node, a square, with
branches representing the choices in the decision-tree diagram (for example, see
Figure 1 Because a decision is chosen and does not occur by
chance, no probability is attached to it. For example, after receiving
information that a person has FH of a disease, that person may decide (choose)
to seek medical advice or choose not to do so. Outcomes are the chance
events that occur in response to a decision. Outcomes can be intermediate or
final. Intermediate outcomes are followed by more decisions or chance events.
For example, if a person decides to seek medical care for hypertension, his or
her physician may advise behavior modification alone or a combination of
behavior modification and drug therapy. From the person's perspective, this is a
chance outcome; from a healthcare provider's perspective, it is a decision. An
outcome can be intermediate or final depending upon the context of the decision
problem. For example, hypertension control may be the final outcome in a
decision analysis focusing on hypertension as the health condition of interest,
but it may be an intermediate outcome in a decision analysis focusing on
myocardial infarction. In this essay, we define an outcome as an event
resulting from chance. This is represented by a chance node in a decision
tree, a circle, with branches representing different outcomes that occur by
chance, one and only one of which occurs. Each chance outcome has a probability
by which it can occur written below the branch in a decision-tree diagram. The
sum of probabilities for all outcomes that can occur at a chance node is one.
The building blocks of decision analysis––decisions, outcomes, and
probabilities––can be used to represent and examine complex decision
problems.
(15K)
Figure 1. A basic decision tree depicting the
alternative courses of action of (1) continuing with the current practice with
no explicit use of family history (the top "branch" emanating from
the square decision node) versus (2) use of a colorectal cancer (CRC) family
history tool for risk stratification and improved screening (the bottom
"branch" emanating from the square decision node). Chance events,
represented by branches emanating from circular chance nodes, are assumed to
occur with the probabilities shown under the respective branches. This
illustration assumes that the use of family history motivates 100% of
individuals with strong and moderate family histories to get screened.
The value of an FH tool can be assessed from the
perspectives of different stakeholders and may differ between an individual,
family, healthcare provider, public health policymaker, and society. For public
health decisions, a societal perspective is recommended.[10
and 11] Although framing a decision analysis using the
societal perspective is standard practice, examining the decision problem from
other perspectives may provide important information. For example, even if an
intervention such as the use of a particular FH tool is recommended on the basis
of a societal perspective, the question of whether such an intervention will be
acceptable to stakeholders other than public health decision makers is not
necessarily answered by a societal analysis. Looking at the decision problem
from the perspective of all major stakeholders may help to identify
implementation problems that a public health decision maker or a preventive
medicine practitioner may face. Examining the decision problem from perspectives
other than the societal perspective should be conducted as a sub-analysis of the
societal analysis. Such a sub-analysis is often useful before the societal
analysis because its results are often valuable sources of information for the
societal analysis. For instance, an analysis of an FH tool from the perspective
of a healthcare provider yields information needed for a societal perspective
analysis; for example, the question of whether and to what extent providers will
use the tool should be answered before examining the question about whether it
is a valuable intervention from a societal perspective.
The importance of multiple perspectives is
highlighted when individual characteristics not only stratify risk but also
affect participation and compliance. For example, this will be the case if a
public health decision maker wants to design programs to encourage participation
and compliance for an intervention, such as blood pressure and cholesterol
screening for low-income persons with FH of coronary heart disease. In this
case, the public health policymaker would want to vary program structure with
individual characteristics because these characteristics will affect the
participation and compliance decisions of individuals.
Both the quality and quantity of information
collected through an FH tool are important for decision making. A tradeoff
exists between keeping an FH tool simple and collecting all relevant
information.[1] More information may add disproportionately
more noise. Some individuals are more informed about their families, and some
diseases are discussed more openly than others. FH information may be inaccurate
or incomplete. [12 and 13] Even though a
simple decision analysis (such as our illustration below) may assume 100%
accuracy of FH information, in a realistic decision scenario the quality of FH
information will be an important consideration. Sensitivity analysis on the
probability that the information is accurate can be useful in shedding light on
how important the quality of the information is to a particular decision.
Although sensitivity analysis is usually performed after a decision analysis is
conducted, it can also be informative during the design of FH tools (e.g., by
illustrating the potential sensitivity of results to varying levels of detail in
FH information). Techniques for sensitivity analysis are discussed in more
detail below, and other papers in this issue deal explicitly with internal and
external validity of FH information.
To assess the value of FH information, we need to
measure its impact on desirable outcomes (positive health effects) and
undesirable outcomes (costs, negative health effects). Effectiveness of FH
information can be measured in different ways (Table 1),
depending on the possible intervention points and outcomes of interest.[14]
Table 1. Outcome and cost measures that
may be included in an assessment of family history tools
(21K)
The costs associated with using FH information
depend on how the information is obtained and the intervention that results from
its use. There are a number of nuances for the assessment of costs that are
detailed in the literature.[15 and 16] In
general, the costs in the decision to use an FH tool may include those shown in Table
1.
A decision analysis examining an FH tool must
explicitly state the time horizon for which costs and effects are
included in the analysis. For example, if measuring cost savings from avoided
future treatment resulting from reduction in disease risk, the time horizon is
the remaining lifetime of the individual. In this case, future cost savings
should be discounted to present costs because present is preferred over the
future. An important consideration is choosing the discount rate. Shadow prices
that correct for the failure of the market to reflect social valuation provide
the correct theoretical basis for valuation of costs and health effects and for
choosing a discount rate.[17, 18, 19
and 20] In practice, shadow price-based recommendations can be
difficult to implement. The Panel on Cost-Effectiveness in Health and Medicine [10]
recommends that costs and health effects be discounted at the same annual rate
of 3% and sensitivity analysis be done using a range of 0% to 7%. It is also
recommended that the costs be in real currency units after adjustment for
inflation. [20]
At times, the decision about the potential use of
an FH tool can be simple: whether to use the FH tool or not to use it. In such
simple cases, decision rules that rank alternatives can be used to select the
optimal alternative. However, a decision is often more complex where many FH
tools are available and they are not mutually exclusive (i.e., they can be used
in combination and collect different degrees of detail about FH). In such a
scenario, a decision algorithm can be used to rank FH tools or clusters of FH
tools that can be combined or used in sequence.[21 and 22]
In a decision analysis, considerations of returns to scale for implementation of
the FH tool can be important. The question is whether the costs of using an FH
tool increase proportionally when the tool is implemented in larger populations
or healthcare organizations. [22 and 23]
This issue can affect generalizability of results and alter the overall results
of a decision analysis if the cost of implementing an FH tool changes at a
different rate than the size of the healthcare setting (e.g., hospital or health
organization). The Panel on Cost-Effectiveness in Health and Medicine [10]
recommends assuming proportional changes unless these effects are likely to be
large. [6] Elbasha (Centers for Disease Control and Prevention,
unpublished observations, 2001) cites evidence for non-proportional change in
cost with change in the scale for healthcare inputs. An implication is that on
cost grounds alone, different FH tools or administration methods (e.g.,
self-administered vs. assisted) may be suitable for different sizes of
populations and settings. However, the cost of an FH tool alone, excluding
treatment changes, is likely to be relatively low. According to evidence from
the Utah Heart Tree Study, the cost of the FH tool was $27 for the
identification of a high-risk family. [24 and 25]
Parameter values (probabilities, costs, and
health effects) often are not known with certainty or are expected to change
over time or between settings. This uncertainty can be dealt with by using
sensitivity analysis. Decision analytic methods are particularly useful for
examining how the value of an intervention varies with changes in the input
factors. Sensitivity analysis can be used to answer questions such as,
"Which factors most affect the value of an FH tool?" and "Which
factors make a difference in the decision between alternatives?" The
sensitivity analysis can be one-way, in which only one parameter varies
at a time. For example, the effectiveness of an FH tool may depend on the
probability of screening, treatment, or control of a health condition in those
with a positive FH. Therefore, the decision about the effectiveness of an FH
tool is likely to be sensitive to the probability of adoption of healthy
behaviors or the probability of compliance with medical decisions. These
parameters can be varied one at a time over the range of their likely values to
see if the overall results of the decision analysis change. In real-life
scenarios, many parameters may change together. The sensitivity to this change
can be assessed with multi-way sensitivity analysis, in which two or more
parameters vary simultaneously. Because keeping track of the analysis becomes
difficult if too many parameters are varied together, carefully choosing a few
important parameters at a time is advisable.
When outcomes are continuous, statistical joint
confidence intervals can be used. The probabilistic approaches range from
those that rely on parametric assumptions, such as the delta method, to
simulation and re-sampling approaches that ease parametric assumptions, such as
the bootstrap method.[26, 27, 28,
29 and 30] For example, this approach could
be used for average time costs for patients using the FH tool, the proportion of
those who are screened for a condition after being identified by an FH tool, and
the average treatment costs after screening. Because a decision model is a
subjective representation of reality and the elements considered important by
the modeler, sensitivity of results to the model structure may also be examined.
[31] Another type of sensitivity analysis is called threshold
analysis. This analysis attempts to identify parameter values (one-way) or
combinations of parameters values (multi-way) at which the decision between
alternatives would change. Similarly, it is often useful to identify best-case
and worst-case scenarios and to examine the alternatives under extreme values of
the parameters. We also should keep in mind the uncertainty related to
generalizability and extrapolation of results of a decision analysis done in one
setting to other settings. Transfer of parameter values to other situations must
be followed by sensitivity analysis.[29] An important use of
sensitivity analysis is to guide better and more detailed data collection on
parameters for which there is high sensitivity. [32]
CRC is the second leading cause of cancer
mortality in the United States, with over 140,000 cases diagnosed and 56,000
deaths from the disease each year.[33] Average lifetime risks
of getting and dying from CRC are about 4.6% and 2.6%, respectively. An FH of
CRC is one of the strongest risk factors for the disease. [34]
The literature suggests that 5%–20% of people report an FH of CRC and that
this FH confers a relative risk of two to five––depending upon the number,
age, and relatedness of affected relatives [33, 34,
35, 36, 37, 38,
39, 40, 41, 42,
43, 44 and 45]––although
some studies report relative risks of up to nine. [38, 40
and 43] A moderate FH (defined as CRC diagnosed in one
relative after age 50 following the classification scheme of Scheuner et al. [45])
increases lifetime risk of CRC to about 6%, and a strong FH (defined as two or
more affected relatives or one in whom CRC is diagnosed before age 50) may
increase lifetime risk to 20% or more. [46]
Fortunately, CRC is one of the most preventable
cancers.[47] Evidence from the literature suggests that
regular endoscopic screening can reduce CRC incidence by 50% or more. [48
and 49] However, despite the widespread availability of
screening tests, the rate of screening remains well below that recommended by
the American Cancer Society [33, 34, 35,
36, 37, 38, 39,
40, 41, 42, 43,
4445, 46, 47,
48, 49 and 50] and the
U.S. Preventive Services Task Force, [51] with only 20%–40%
of persons aged 50 and older having received a recent endoscopic screening. [52
and 53] Improving the rate of screening is a fundamental
component of the strategy for decreasing CRC morbidity and mortality. Persons
with FHs of CRC may constitute an important target for FH education. If it can
be shown that individuals are more motivated to improve their health when they
know they are at a higher risk for CRC than the general population, then FH may
prove to be a valuable tool for promoting CRC screening.
The illustration that follows demonstrates a
decision analytic approach for evaluating the utility of an FH tool. To
demonstrate the approach, we use a decision analytic framework to explore the
value of using FH of CRC as a tool for risk stratification and improved disease
prevention. The premise of the example is that application of a CRC FH tool will
promote awareness of the increased risk of CRC associated with FH and motivate
persons with an FH to get screened for the disease, thereby reducing CRC
incidence. Using decision analytic methods, we illustrate how to compare the
"value added" of this FH intervention in terms of averted CRC cases
with the current practice in which no FH tool is systematically applied. Hence,
in this illustration, two alternatives exist: (1) use of an FH tool and (2)
current practice without an explicit FH tool. Although we have used reasonable
estimates of the parameters required in the decision analysis, this example is a
simplistic one designed to demonstrate decision analytic methods, and its
results should be considered illustrative only. Furthermore, in this
illustration we assume that the FH tool yields perfect information (i.e., that
the tool is completely accurate in stratifying people according to their FHs),
which is unlikely in real-world implementation of an FH tool.
As an example of how decision analysis can be
used to examine the utility of an FH tool, consider the basic decision tree
depicted in Figure 1. This decision tree graphically
represents the two alternative intervention strategies of (1) continuing with
the current practice with no explicit use of FH (the Current Practice branch at
the square decision node) versus (2) use of a CRC FH tool for risk
stratification and improved screening (the Family History Stratification branch
at the square decision node). Outcomes are represented by branches from circular
chance nodes and are assumed to occur with the probabilities shown under the
respective branches. Assumptions used in this decision analysis example are
summarized in Table 2 and described below.
Table 2. Assumptions used to assess
utility of a hypothetical family history (FH) tool for colorectal cancer (CRC)
(8K)
Under the Current Practice alternative, 20% of
all individuals get screened. Those who undergo screening reduce their risk by
50%, from 4.6% to 2.3%. Of those who do not get screened, the risk of developing
CRC is approximated by the population-wide average of 4.6%.
Under the Family History Stratification
alternative, individuals are stratified according to their FHs of CRC. We assume
that 15% of individuals have FHs of CRC, including 13% who possess moderate FHs
and 2% who possess strong FHs. Of those without FH, risk of CRC is 4% (i.e.,
slightly lower than the population average). Individuals with moderate or strong
FHs incur CRC risks of 6% or 20%, respectively. Screening is assumed to reduce
risk by 50% regardless of degree of FH.
Under the Family History Stratification
alternative, the rate of screening among persons with moderate or strong FHs
increases relative to the Current Practice alternative, based on the assumption
that individuals are more likely to get screened if they perceive they have a
higher risk for CRC than the average population. In this simplistic example, we
assume that the rate of screening for those with FHs increases to 100%,
representing the best-case scenario of complete adherence to screening
guidelines among those with FHs.
As described earlier under Measures of Outcomes
and Cost, decision analytic methods can be used to compare alternative
strategies with a variety of metrics. To illustrate decision analytic methods in
this example, the Family History Stratification alternative is evaluated in
terms of disease incidence. Strictly speaking, each of the alternatives is
associated with an expected lifetime risk of CRC. This risk is obtained by
taking a weighted average of the lifetime CRC risk associated with each
potential consequence of that alternative. For ease of presentation, the
expected lifetime CRC risk is converted to CRC cases per 100,000 individuals.
Table 3 and
Figure
2 present results of the illustrative decision analysis, in which risk
stratification using FH is compared to the current practice of no explicit use
of FH. The alternative strategies are evaluated in terms of total expected CRC
cases and number of cases averted per 100,000 individuals. Because strategies of
No Screening and 100% Population-wide Screening are intuitive benchmarks against
which to compare the Family History Stratification and Current Practice
alternatives, results for these two additional strategies are also presented. We
caution that these results are illustrative in nature and intended merely to
illustrate the type of quantitative information that decision analytic
methods can provide in an assessment of an FH tool.
Table 3. Results of the illustrative
decision analysis
(10K)
N/A, = not applicable.
(8K)
Figure 2. Results of the illustrative
decision analysis. The "value added" of using family history (FH) is
presented in terms of the additional colorectal cancer (CRC) cases averted per
100,000 individuals using an FH tool to increase screening among persons with
FH compared with CRC cases averted using current practice (in which 20% of
people are screened without regard to FH)
Two scenarios were evaluated for the Family
History Stratification alternative: (1) screening increases to 100% among
persons with strong FHs, and (2) screening increases to 100% among persons with
any FH (including strong and moderate). In this example, the use of FH to
stratify risk and increase screening to 100% among those with strong FHs leads
to an additional 160 cases averted per 100,000 compared with the current
practice (i.e., population-wide screening rate of 20%). The use of FH to
increase screening to100% among persons with any FH results in an additional 472
cases averted per 100,000 compared with the current practice.
We assumed that the use of the FH tool resulted
in 100% screening among individuals with FHs of CRC. But what happens to the
value of the FH tool in the more realistic situation in which the FH tool
increases screening but to a lesser extent than 100%? Using sensitivity
analysis, we can vary the assumed level of screening among individuals with FHs
and examine the value of the FH tool in each case. In practice, computer
programs perform these calculations for us. Figure 3 depicts
the results of a sensitivity analysis in which we independently varied the rate
of screening among persons with strong and moderate FHs. The upper
(cross-hatched) region of Figure 3 represents combinations
of screening rates that result in the Family History Stratification leading to
fewer expected CRC cases than the Current Practice. For screening rates among
those with FHs of above 50%, the Family History Stratification is clearly
superior to Current Practice. The Family History Stratification remains superior
if screening rates are 30% in individuals with strong and moderate FH; in this
case, however, the Family History Stratification leads to only an additional 83
cases averted per 100,000, compared with Current Practice. Although these
scenarios and data are merely illustrative, they provide insight into the power
of sensitivity analysis in exploring the utility of an FH tool.
(42K)
Figure 3. Results from an example
sensitivity analysis in which the probability of screening varies from 0 to 1
(analogous to rates of 0 to 100%) among those with strong family histories
(along the x-axis) and moderate family histories (along the y-axis). The
probability of screening under the Current Practice alternative is held
constant at 20%. The upper (cross-hatched) region represents combinations of
screening rates for which the Family History Stratification leads to fewer
expected CRC cases than the Current Practice. The smaller (white) region at
the lower left represents combinations of screening rates for which the
Current Practice leads to fewer CRC cases. Although this sensitivity analysis
varies only the screening rates among those with family histories, the
comparison of the strategies takes into account all cancers, not just cancers
among those with family histories.
In this example, Family History Stratification is
preferred to Current Practice as long as more than 20% of persons with FH get
screened. This makes sense given our baseline assumption that no decrement
exists in screening among individuals with no FH. But what would happen if
individuals who perceive a negative or null FH become complacent and thus less
likely to be screened than if they had not been made aware of their FH? We can
use sensitivity analysis to examine the implications of this possibility. For
example, assume that persons with strong and moderate FHs have a 50% screening
rate. Sensitivity analysis indicates that the Family History Stratification
would be preferred as long as the rate of screening in those with negative FHs
is greater than 10%. If the rate of screening among persons with negative FHs
falls below 10%, the Current Practice would result in fewer expected CRC cases
than would the Family History Stratification.
We have illustrated sensitivity analysis for only
a few of the many parameters that may influence the utility of an FH tool for
CRC. Other parameters for which sensitivity analysis would be recommended in an
evaluation of a specific CRC FH tool would include the prevalence of FH,
proportion of people who are aware of their FHs, risk of CRC, risk reduction
achieved by screening, and prevalence of adverse effects from screening.
In this paper, we have outlined the main elements
of decision analytic methods and described how those methods can be used to
assess the value of an FH tool. Using a colorectal cancer example, we
demonstrated how decision analytic methods might be applied to examine the
utility of an FH tool. In addition to illustrating the decision-tree approach,
we provided an example of sensitivity analysis, with the intent of demonstrating
how such an analysis can be used to quantitatively evaluate the influence of
multiple factors on the overall utility of FH tools. While use of decision
analytic method requires a planned collection of information and attention to
nuances of methodology, we hope readers will consider these methods; we also
encourage interested readers to consult comprehensive texts and current
literature for discussions of the necessary methods and mechanics.[2,
3, 46, 47 and 48]
This approach could be applied to any risk factor, not just FH, where cases
averted will depend both on prevalence of the risk factor and associated
relative risk. By providing an example of the type of information that decision
analytic methods can provide, we hope to have provided motivation for and
insight into how these methods can be applied for systematically evaluating the
use of FH information in public health and preventive medicine. [54,
55 and 56]
The
authors wish to acknowledge support from the Office of Genomics and
Disease Prevention, Cardiovascular Health Branch, and the Prevention
Effectiveness Branch, Epidemiology Program Office, Centers for Disease
Control and Prevention. The authors also thank participants of the Family
History Workshop, Atlanta GA (May 1–2, 2002) for helpful comments.
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