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2.2.1 Cost-minimization analysis (CMA)
When two or more programs generate the same outcome,
cost-minimization analysis can be used to guide resource allocation
decisions. By estimating and comparing the costs of alternative
programs, the analyst can identify which program costs less to
achieve a given outcome. Because outcomes are considered identical,
cost-minimization analysis is actually a special form of
cost-effectiveness analysis. Because alcohol and drug abuse
treatment programs involve multiple outcome measures,
cost-minimization analysis is rarely used to evaluate such programs
(French, 2001).
2.2.2 Cost-Effectiveness Analysis (CEA)
Cost-effectiveness analysis is the most common economic
evaluation method employed in health care. In this form of
analysis, costs are measured in dollars and consequences or outcomes
are measured in the most appropriate natural effects or physical
units such as life-years gained, disability-days saved, etc. In
alcohol or drug treatment studies the outcome measure may be the
degree of abstinence or the degree of drug or alcohol use.
Cost-effectiveness analysis can be performed on any alternatives
that have a common effect. As noted above, economic evaluations use
incremental analysis that relates the differences in the cost
between two alternatives (DC)
to the differences in program outputs (DO).
The results of such comparisons may be stated either in terms of
cost per unit of effect  or in terms of effects per unit of cost
.
The central purpose of CEA is to compare the relative
efficiency of different interventions (e.g., programs) in creating
better outcomes. A cost-effectiveness analysis provides information
that can help decision makers sort through alternatives to determine
which ones best serve their programmatic and financial needs (Gold,
et al. 1996). CEA can also be used by program managers to determine
the relative efficiency of the treatment components of their
programs. Such analysis can help them to redesign their program to
improve its overall efficiency (NIDA, 1999).
There are dangers in making cross-study comparisons of
CEAs (Gold et al., 1996). Ideally, one would wish to be able to
array all CEAs on a “league table,” where cost per health effect
gained using one intervention is assessed side by side with the cost
per health effect gained using others. Decision makers could then
make resource allocation decisions based on the relative
efficiencies of different health investments. The problem is that
studies vary with respect to the analyst’s perspective (which will
be discussed in the next chapter) and there are no uniform standards
for CEAs. For meaningful comparisons to be made, the studies must
use the same methodology, have the same analysts perceptive, define
and measure costs and outcomes similarly, and treat similar groups
of patients. According to NIDA (1999), it is easy to find an
apparent difference in the cost-effectiveness of different program
components or different programs, but it is much harder to show that
the difference is real. Gold, et al. (1996, p. 38) explains:
The ideal table of cost-effectiveness ratios would list all existing
and potential programs at all feasible levels of program scale and
intensity, for all population and patient groups, compared to all
feasible alternatives. This table would provide complete
cost-effectiveness information for decision making from which a
technically optimal allocation of resources could be identified,
given a budget constraint.
As shall be discussed in subsequent chapters, the noncomparability
of methods across evaluation studies of alcohol and drug abuse
programs and treatments has complicated the original intent of this
paper to identify the most efficient types of alcohol and drug abuse
programs and treatments. We shall base our estimates on the
effectiveness of such programs on those studies employing the
soundest methodology.
CEA studies generally focus on a single outcome
measure. Furthermore, no attempt is made to value the outcome in
terms of dollars or utility. In the case of programs having
multiple outcomes, CEA becomes problematic. Some CEA health
intervention studies present an array of output measures alongside
cost and leave it to the reader to form his own view of the relative
importance of these. Some analysts have used the term
“cost-consequences analysis” for the form of CEA (Gold, et al.,
1996).
2.2.3 Cost-utility Analysis (CUA)
Under cost-utility analysis the outcomes or consequences
of health care interventions are valued in terms of health state
preference scores or utility weights. In this sense
utility refers to the preferences individuals or society may have
for any particular set of health outcomes at a point in time or a
profile of states through time. There are several different methods
to measure health state preferences (see Drummond, et al., 1997,
chapter 6).
CUA is a particularly useful technique because it can
assess changes in the quality of life as well as changes in the
length of life resulting from health care interventions. The most
common CUA outcome is quality-adjusted life-years (QALYs), which is
calculated by adjusting the length of time affected through the
health outcome by the utility value (on a scale of 0 to 1) of the
resulting level of health status (Drummond, et al., 1997, pp.
15-17).
One might think that CUAs of alcohol and drug abuse
treatment programs would be commonplace since the technique provides
a generic outcome measure, which could be summed for multiple
outcomes and used for comparison of cost and outcomes in different
programs. Our literature search revealed that few if any CUA of
alcohol and drug abuse treatment programs has been conducted or
published. There are several possible explanations for this.
Whereas the analyst’s perspective in most health care intervention
evaluations is from the perspective of the patient or society as a
whole, the analyst’s perspective in most economic evaluations of
alcohol and drug abuse treatment programs is from the perspective of
the government or taxpayer. The health care studies focus on the
changes in the value of outputs to the individual patient. The
alcohol and drug abuse program evaluations focus on the cost-savings
to tax payers. The health care evaluations use micro data and
examine the long-term or lifetime effects of interventions. The
alcohol and drug abuse studies frequently use aggregate data and
examine the short-term effects per episode of treatment.
2.2.4 Cost-benefit analysis (CBA)
Cost-benefit analysis evaluates changes in the outcomes
of alternative health care interventions in money terms. The most
common methods of assigning dollar value to health consequences are
willingness to pay and human capital (Gold et al. (1996, p. 40).
Willingness to pay can be derived from a survey approach known as
“contingent valuation, or it can be inferred from decisions actually
made that involve tradeoffs between health and money. Human capital
essentially values health in terms of the productive value (i.e.,
earnings) of people in the economy. Many health care researchers
have avoided CBA on the grounds that the methods used to monitize
the value of outcomes raise ethical concerns; they prefer CEA,
albeit at some sacrifice of generalizability (Gold, et al., 1996).
In theory, CBA can be used to ascertain whether the
beneficial consequences of a program justify the costs and to
determine how government funds should be allocated to health care
programs and non-health care programs to obtain the highest rate of
return on investments. CBA provides information on the absolute
benefit of programs, in addition to information on their relative
performance. Cost-benefit analysis provides an estimate of the
value of resources used up by each program compared to the value of
resources the program might save or create. Often CBA implicitly
assumes that each program is being compared to a do-nothing
alternative that entails no costs and no benefits, even when this is
not true (Drummond et al. 1997, p. 13). Under CBA all relevant
costs and consequences are supposed to be valued in money terms. In
practice, many of the cost-benefit analyses published to date are
limited to a comparison of those costs and consequences that can
easily be expressed in money terms.
Alcohol and drug abuse treatment programs lend
themselves to some form of CBA since they produce measurable
monetary outcomes like increased days of legitimate employment and
decreased job absences. In addition, such programs may reduce
patients’ use of food stamps, public health services, and other
public assistance – a potentially huge cost savings. Substance
abuse programs also produce beneficial indirect or secondary effects
on crime related costs. In Chapter 3, we will review the so-called
cost-of-illness studies dealing with the problems of alcohol and
drug abuse and the potential cost-savings associated with programs
to treat their problems.
The difficulty associated with estimating the dollar
value of program outcomes such as abstinence, reductions in alcohol
and drug use, and improved family life has resulted in relatively
few CBA studies in the alcohol and drug abuse treatment literature
(French, 1995 and 2001). There have been, however, a number of
cost-offset analysis studies which examine dollar changes in income,
reduced social services and associated costs, reduced expenditures
for other services for alcohol and drug abusers or their families,
and increased economic productivity resulting from alcohol and drug
abuse treatment programs (Holder, 1987 and Holder, Longabough,
Miller, and Rubonis, 1991). In reality, cost-offset analysis is a
partial cost-benefit analysis because it compares the cost of a
program with the dollar value of avoided future health care costs
(French 2001).
One final point should be made with respect to
cost-benefit analysis. CBA results are often expressed as a ratio (Dbenefits/Dcosts).
This approach should be avoided because the costs of averted illness
are sometimes viewed as a benefit and placed in the numerator or
sometimes viewed as a negative cost and placed in the denominator.
It is preferable to calculate the net benefit of a program by
subtracting the Dcosts from
the Dbenefits, to make
studies more comparable (Gold et al. 1996).
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