Chapter 21
Cost-Effectiveness Analysis for Urban Health Research
Ahmed M. Bayoumi
1.0. INTRODUCTION
A frequently voiced concern of urban health researchers and practitioners relates to whether their program is “cost effective.” An assertion in favor of cost effectiveness may be viewed as a strong rhetorical argument for a program’s application, reflect- ing a practical recognition that multiple policies are competing for scarce dollars (particularly when they are publicly financed). Alternatively, critics of cost effective- ness analysis may argue that such considerations hamper social justice objectives by constraining policy decisions to arbitrary economic criteria. Regardless of which position one adopts, an understanding of the language and approach of cost effec- tiveness is essential for policy relevant research conducted in a setting of enhanced consciousness of costs and concerns about debt. A decision maker allocating resources to an intervention must consider the associated monetary costs, health effects, and the relationship between costs and effects. Cost-effectiveness analysis (CEA) is a formal method of analyzing the relationship of costs to health effects that can inform health care policy decision in meaningful ways (Gold, et al., 1996; Detsky and Naglie, 1990).
This chapter is structured as follows. First, we introduce the urban health per- spective used in this chapter and an example of a cost effectiveness analysis. Second, we review some conceptual issues and relevant definitions in cost effectiveness analysis. Third, we discuss the elements of a cost effectiveness analysis, including defining strategies, measuring both direct and indirect costs of an illness, discount- ing of future costs and effects, estimating life duration, and incorporating quality of life considerations. Fourth, we examine the data sources used by analysts, including the role of modeling and extrapolation. Fifth, we present methods used to analyze cost effectiveness analyses. Finally, we conclude by exploring the application of cost effectiveness analysis to urban health by posing several controversial ques- tions. Recent publications have made considerable strides towards establishing stan- dards for cost effectiveness analysis and are worthwhile sources of reference
431
material (Canadian Coordinating Office for Health Technology Assessment, 1997;
International Society for Pharmacoeconimics and Outcomes Research, 2004).
1.1. An Urban Health Perspective: Focus on the Disadvantaged
Urban health research and cost effectiveness analysis are both broad academic disci- plines that incorporate diverse theories and disciplines. Throughout this chapter, we adopt a perspective that is concerned with the health of urban dwelling individuals and focus on those who experience ill health as a consequence of social disadvan- tage. While the determinants of health of urban populations are broad, cities have special characteristics that are strongly linked to health. For example, the recent his- tory of urbanization is, strikingly, one of increasing concentration of poverty (Davis, 2004). Other health problems are not uniquely urban, but the conditions or their determinants are more prevalent in cities. For example, the human immunodefi- ciency virus (HIV) epidemic in North America is closely linked to the urban concen- tration of gay men, injection drug users, and sex trade workers. Internationally, the HIV epidemic has devastated several cities and remains an overwhelming challenge for many public health departments. This chapter will use an HIV-specific example to illustrate some issues in cost effectiveness analysis. More generally, this chapter assumes that the goal of economic analyses in urban health is to improve efficiency in resource allocation against a backdrop of social disadvantage and inequity.
To illustrate how cost effectiveness analysis can inform urban health (particu- larly when focused on the disadvantaged), this chapter will conclude by reviewing three questions relevant to this interaction. First, does cost effectiveness analysis dis- criminate against the disadvantaged? Second, are cost effectiveness analyses trans- ferable from one urban center to another? Third, how can other considerations, particularly those relating to health equity, be combined with cost effectiveness analysis by decision makers?
1.1.1. An Example: HIV Screening in Pregnant Wo m e n
Throughout this chapter, the following health care program will serve to illustrate
some of the advantages and limitations of cost effectiveness analysis, which will be
referred to as the “pregnant women” example. Screening pregnant women for HIV
infection offers opportunities to initiate prenatal antiretroviral therapy, a medical
intervention proven to decrease the rates of HIV transmission from mothers to
infants. Additionally, identifying women early in their infection provides opportuni-
ties for counseling about breast feeding and safe sex. The newborn baby also bene-
fits from early identification and appropriate therapy for HIV infection. However,
recent studies estimate about 25% of HIV-seropositive individuals are unaware of
their status (Fleming, et al., 2000). Furthermore, a significant proportion of women
are still not tested during pregnancy or in labor (Bulterys, et al., 2004; Lansky, et al.,
2001; Aynalem, et al., 2004). As an alternative to testing women (with consent),
some legislators have proposed routine mandatory testing of newborns for HIV
infection (in which case consent is not required). Others have objected to such pro-
grams on civil libertarian grounds (Powderly, 2001). Note that this objection focuses
solely on the issue of testing newborns for HIV and not on other related but simi-
larly complex issues such as mandatory testing of pregnant women or the optimal
choice or cost-effectiveness of antiretroviral therapy for expectant mothers
(de Zulueta, 2000; Phillips, et al., 2003; Clark, 2003; Lallemant, et al., 2004).
From an economic perspective, one might question whether programs of mandatory testing of newborns are an efficient use of resources, and how questions of efficiency relate to local prevalence patterns. A published cost effectiveness analy- sis suggests that a program to increase voluntary screening has a “cost effectiveness ratio” of $8,900 U.S. per life year gained (Zaric, et al., 2000). Furthermore, routine newborn HIV screening has a cost effectiveness ratio of $7,000 U.S. per life year gained compared to the status quo condition and $10,600 U.S. per life year gained if implemented after enhanced voluntary screening was implemented. The article concludes that such programs are cost effective. Throughout this chapter, we will return to this analysis to see how the analysts calculated the program’s cost effec- tiveness ratio, how they estimated the program’s costs and benefits, and how these findings should be interpreted to assist decision makers in policy making.
2.0. CONCEPTS, TERMS, AND DEFINITIONS 2.1. Cost Effectiveness Analysis
Cost effectiveness analysis typically expresses the tradeoff between the costs of an intervention to the non-financial outcomes as a ratio. Cost effectiveness analysis is concerned with what is happening at the margins – that is, an accounting of the additional, or incremental, costs and effects of an intervention. Cost effectiveness analysis can inform health care decision making by indicating which of several alter- native demands for health care spending can improve health the most. For exam- ple, consider a city public health official who must decide between expanding one of three programs – smoking cessation, safe sex counseling, or food inspection – within a fixed budget. CEA can indicate which of the three programs will be associ- ated with the greatest improvement in health effects. These health effects could be measured as life years gained, hospitalizations averted, improvements in quality of life scores, or many other metrics. Real world decision making can highlight the complexities involved in making such decisions. For example, the gains from smok- ing cessation therapy may only be realized many years after the program is insti- tuted. Accordingly, the estimation of this program’s costs and health outcomes may be based on more extrapolations and assumptions than those related to food safety.
As well, smoking cessation and safe sex counseling may save many more lives than food inspections, although the latter may have important quality of life effects that are widely distributed over a population. Furthermore, a full economic analysis might consider not merely the funding of alternative public health programs, but also whether other non-health programs are a more efficient use of resources. We return to these issues below when we consider some of the limitations of cost effec- tiveness analysis.
2.2. A Note on Terminology
It is important to realize that the health economics literature, and more generally the field of health ser vices research, tends to use the term “cost effectiveness”
analysis in two distinct ways. The first terminology defines CEA as any analysis that calculates the incremental costs relative to a measure of incremental health effects, without consideration of the units with which the effects are measured.
For example, this approach would include studies that calculate the cost to avert a
case of HIV infection, the costs associated with housing a currently homeless per- son, or the cost per life year gained for a medical intervention. The second termi- nology restricts CEA to those analysis that use “life years gained” as the outcome measure. In keeping with the more common and general approach, we adopt the first convention. Thus, cost utility analysis, which is sometimes considered a dis- tinct alternative, will be treated here as a form of cost effectiveness analysis. In this approach, the outcomes are life years which have been “adjusted” by quality of life weights to yield Quality Adjusted Life Years (QALYs), which are discussed further below.
2.2.1. Quality Adjusted Life Years (QALYs)
QALYs are calculated by dividing the expected survival into discrete life states, assigning a quality of life weight to each health state, multiplying the quality of life weight of each state by the duration of time spent in the state, and summing across all health states (Zaric, et al., 2000; Carr-Hill, 1989). Consider a hypothetical 35-year- old HIV-positive woman who is projected to live for 12 years before developing acquired immune deficiency syndrome (AIDS) and 5 years afterwards. Although her life expectancy is 17 years (12 +5), these years will be spent in suboptimal health.
The quality adjusted life year (QALY) model “adjusts” this survival estimate by apply- ing quality weights to this patient’s life expectancy (Figure). If the health states our patient experiences before and after AIDS are assigned quality adjustment weights of 0.80 and 0.42, respectively 11, the quality adjusted life expectancy will be 11.7 QALYs (12 ×0.80 + 5×0.42). A major reason for the QALY model’s popularity is its incorporation of both survival and quality of life effects into a single outcome meas- ure. Alternative models, including the Disability Adjusted Life Year (DALY) and
1.0
0 3 6 9
AIDS HIV
12×0.80
= 9.6 QALYs
5×0.42
= 2.1 QALYs
0 0.2 0.4 0.6 0.8
18
12 15
Years Quality
Weight
Note: The projected life expectancy (17 years) of a person with HIV is illustrated. To calculate his projected QALYs, four steps are followed: 1) his projected life course is divided into discrete intervals defined by changes in health status (HIV and AIDS); 2) each interval is assigned a quality weight (0.80 and 0.42); 3) a quality adjusted survival for each interval is calculated by multiplying together the quality weight and duration (9.6 and 2.1); and 4) the quality adjusted survivals for all intervals are summed to yield a quality adjusted life expectancy. Thus, the QALY model asserts that our patient’s remaining 17 years of life in suboptimal health are equivalent to 11.7 (9.6 +2.1) years of life in best possible health.
Figure 1. Calculation of Quality Adjusted Life Years.
Healthy Year Equivalent, have proved less popular or tractable for modeling (Carr- Hill, 1989, Arnesen and Nord, 1999; Gold, et al., 2002; Fryback, 1993; Ried, 1998;
Mehrez and Gafni, 1989; Mehrez and Gafni, 1993).
2.2.2. Discounting
Discounting is an economic concept that reflects that people value future costs and health effects less than those that occur in the immediate future (Redelmeier and Heller, 1993). The rationale for discounting comes from the recognition that money invested today will be worth more in the future; similarly, money borrowed today will be paid back at a higher value because of interest charged on the loan.
For cost effectiveness analyses, where the tradeoff between money and health is being investigated, it makes sense to use the same discount rate for costs as for health effects. In the pregnant woman example, the estimated average gain in life expectancy for a newborn whose HIV infection was averted was 66.9 years. After applying a 3% annual discount rate, this gain was valued at 22.1 years.
2.2.3. The Incremental Cost-Effectiveness Ratio (ICER)
The results of a cost effectiveness analysis are typically expressed as an incremental cost effectiveness ratio (ICER). Mathematically, the incremental cost effective- ness of a program, designated B, relative to another program, designated A, is cal- culated as:
cost B - cost A effect B - effect A
Note that the costs and effects are net values; that is, they reflect both expenditures and savings in the cases of costs and gains and losses in the case of health effects.
Thus, the ICER is the cost of obtaining one extra unit of health effect * .
2.2.4. Dominance
An inter vention which results in lower net costs and enhanced health effects than an alternative strategy is deemed to dominate the alternative. However, the number of dominant inter ventions are relatively few (examples include prena- tal care for pregnant women and some immunizations) (Tengs, et al., 1995). For non-dominant inter vention, the decision maker must decide whether the incremental cost is low or high relative to the health effect; that is, whether the intervention represents good value for the money needed to implement the program.
*
CEAs generally assume that there is a constant return to scale. That is, the ratio of costs to effects is
constant regardless of the scale under which the intervention under evaluation is implemented. Such
assumptions are usually not problematic for large scale assessments, but may be problematic when
translating national CEA results to a smaller urban setting. Additionally, the assumption of a constant
return to scale may not hold for interventions where a threshold exists after which additional marginal
benefits are negligible. For example, the additional benefit for mass immunization campaigns likely
declines after a large proportion of the population has been immunized.
3.0. THE CONCEPT OF ECONOMIC ANALYSIS IN HEALTH CARE A tenet of economics is that resources are scarce relative to wants (Drummond, et al., 1987). That is, there will never be enough money available to pay for every- thing. Accordingly, decision makers require some guidance in how to optimally allo- cate resources in order to maximize well-being. Consider, for example, two different antiretroviral regimens to prevent transmission of HIV from pregnant women to their fetuses. If the regimens are equally effective but differ in costs, a rational deci- sion maker would clearly choose the less costly option. Unfortunately, decisions are rarely this easy. Consider a third antiretroviral regimen that is slightly more effective than the currently preferred regimen but a great deal more expensive. In this situa- tion, the decision about which regimen to use intrinsically incorporates a trade off between spending more money and averting more infections. Furthermore, money spent in this setting necessarily means that the money will be unavailable for other potential uses.
By posing the question “How much money must be spent to obtain a certain improvement in health?” CEAs directly assess the efficiency, from a cost perspective, of health care expenditures. CEAs can be complex, because to fully assess the inter- vention might require incorporating disparate outcomes into the costs or health effects, including medication toxicities, the potential for antiretroviral resistance for mothers and infants, productivity losses related to death and illness, and the societal cost of caring for orphans. The impact of such issues may be unknown for some issues and may be context-specific (different in urban vs. rural environments or resource-rich vs. resource-poor countries, for example). Although the methodol- ogy of cost-effectiveness continues to evolve, the techniques have obtained popular- ity as a quantitative method of technology assessment and an aid to policy making.
When a health benefit can only be obtained at a relatively large cost, policy makers may judge the health care program to be an unwise use of limited resources, although concerns about justice and equity may mean that decision makers are sometimes willing to trade-off some efficiency to promote other societal goals (See
“9.4. How can other considerations, particularly those relating to health equity, be combined with Cost Effectiveness Analysis by decision makers?” below).
Internationally, cost effectiveness considerations are now used in many jurisdictions when deciding about which medications to add to formularies and which health services to cover under social insurance programs (Gold, et al., 1996; Canadian Coordinatoring Office for Health Technology Assessment, 1997; International Society for Pharmacoeconomics and Outcomes Research, 2004). For example, at least 32 countries require a pharmacoeconomic component in submissions for drug reimbursement for publicly funded formularies (International Society for Pharmacoeconomics and Outcomes Research, 2004). Similarly, the United Kingdom’s National Institute for Clinical Excellence issues technology appraisals to guide public health insurance decisions, in which cost effectiveness analysis plays a central role (Wailoo, et al., 2004).
Returning to the pregnant women example, the authors of the article esti-
mated that enhanced prenatal screening would be associated with an additional
annual cost in 1997 of $50.83 million U.S. dollars (related to testing, counseling,
infant formula and HIV treatment) while saving $21.48 million U.S. dollars
(because of averted HIV infections), for a net cost of $29.35 (50.83-21.48) million
U.S. dollars. They further quantified the effectiveness by estimating how many
HIV infections would be averted with enhanced prenatal screening (150). Thus,
one measure of cost effectiveness is the marginal cost to avert one HIV infection;
in this study, the amount is $195,700 ($29.35 million ÷ 150). For comparison, the cost per HIV infection averted for a needle exchange program in 1996 was estimated at about $20,900 (Laufer, 2001). Alternative measures might include the cost per life year gained for infants, for mothers, or for infants and mothers combined.
3.1. Perspective in CEA
CEA can be performed from a number of perspectives. Considering the pregnant women example, the decision maker might be women paying for all or part of pre- natal services directly, decision makers at an urban health clinic where she receives prenatal care, state or provincial health officials responsible for hospital financing where the baby will be born and may be tested, or federal financing authorities responsible for funding and administering health care programs. The perspective of a cost effectiveness analysis indicates the point of view from which the analysis is conducted.
Defining the perspective is important because this determination will establish which costs should be included in the analysis. For example, analyses conducted from the perspective of the pregnant woman may include only those costs borne by her (such as co-payments or lost wages due to time away from work) but would not include costs covered by her insurer. Alternatively, a CEA conducted from the per- spective of the community health clinic would address those costs incurred by the clinic but not those paid for at the regional or federal level. Most guidelines recom- mend that the optimal perspective for a CEA is the societal one, which comprehen- sively covers all of the costs, direct and indirect, included in treating individual patients (Gold, et al., 1996). To provide another example, a recent study estimated the societal costs associated with drug abuse include health care costs (including drug services and medical complications of drug use), productivity losses dues to premature death and illness as well as losses due to criminal activity, and costs related to the administration of the criminal justice and social welfare systems relat- ing to drug use (Cartwright, 1999). In this estimation, health care costs accounted for only 4.5% of total societal costs, illustrating the importance of a comprehensive cost accounting when using a societal perspective.
4.0. ELEMENTS OF A COST-EFFECTIVENESS ANALYSIS
The components of a CEA include a clear description of the alternative strategies under consideration, an estimate of the costs associated with each strategy, and an estimate of the corresponding health effects including survival and often, quality of life.
4.1. Strategies
It is important that CEAs adequately specify the strategies under consideration.
Typically, one strategy under consideration is the “status quo” condition; in some
instances, this may be doing nothing at all. In other situations, doing nothing may
be ethically objectionable and adding this strategy yields little additional informa-
tion. The strategies should be realistic and comprehensive. When more than two
strategies are compared, the comparisons between strategies should be reasonable and, when appropriate, combinations of non-mutually exclusive strategies should also be compared.
In the pregnant women example, the “status quo” condition reflects the current level of voluntary HIV testing; it does not reflect a condition of no testing since such a condition does not exist and would not be a reasonable policy to purse.
Furthermore, comparing interventions to a strategy of “no testing” would make them seem more attractive than they really are. The authors compared two strate- gies – enhanced voluntary testing and mandatory screening of newborns – each to the status quo condition. Importantly, they also analyzed a strategy of mandatory screening of newborns and enhanced voluntary screening to a strategy of enhanced voluntary screening alone, after demonstrating that voluntary screening was associ- ated with a favorable cost effectiveness ratio.
4.2. Costs
The costs associated with a health care intervention include the cost of the inter- vention itself, changes in the use of health care resources as a result of the interven- tion, changes in the use of non-health care resources, changes in the use of informal caregiver time, and changes in the use of patient time for treatment (Luce, et al., 1996). For example, a pregnant HIV-positive woman might incur costs related to the medication (the intervention), physician and nursing costs associated with her treatment (health care costs), costs associated with traveling to the clinic (non- health care costs), costs associated with her partner and friends taking time from work (informal caregiver time), and costs related to not being able to work while attending the clinic (patient time costs). An additional source of potential costs comes from decreased productivity related to her HIV status and to caring for her infant if the newborn is infected with HIV; U.S. guidelines on the conduct of cost effectiveness analyses recommended that these productivity losses be treated as health outcomes rather than costs (Gold, et al., 1996). Because CEAs are incremen- tal in their analysis (they are interested in an intervention’s extra, rather than total, costs), costs that can be shown or safely assumed to be equivalent between strategies need not be explicitly valued. Similarly, it is often not worthwhile to estimate costs that are small relative to the overall costs of a program, viewed from a societal per- spective. Some modeling assumptions (such as the exclusion of certain costs) may be controversial; their potential impact can be assessed by performing sensitivity analyses, in which the value of certain parameters are varied across a reasonable range, even if the true value for a parameter is unknown. If the results are robust to changing values, the analyst can have some assurance that the conclusions are not sensitive to the incorporated assumptions (See “7.1 Uncertainty in Cost Effectiveness Analysis” below).
Costing studies can be performed at the “micro” level, in which each resource used is identified, measured, and valued, or at the “gross” level, in which average costs from a large area are used (Luce, et al., 1996; Diehr, et al., 1999). For example, micro-costing an HIV test includes costing the materials used and the labor to per- form the test, while gross-costing a physician visit include counting the number of visits and assigning each an “average” cost based on reimbursement rates or other studies. Many analyses combine both micro and gross approaches.
In the cost effectiveness analyses of testing for HIV in pregnant women and
their newborns, the researchers included the costs of HIV testing, counseling
before and after an HIV test, infant formula, antiretroviral therapy, and future life- time healthcare costs. The analysis did not explicitly incorporate costs related to informal caregiver time or patient time for treatment, although some of these costs by be capture in the estimate of future costs and others may be negligible if, for example, the incremental patient time costs are small relative to the counseling and treatment costs.
4.3. Life Duration
Perhaps the most common health effect measure used in cost effectiveness analysis is survival. Although survival is virtually always an important outcome measure, there are several caveats associated with its use in an analysis. First, the available data for estimating cost effectiveness may be of limited duration. Hence, incorporating survival into the model necessitates extrapolations from intermediate outcomes and assumptions about future therapies, both of which may have considerable asso- ciated uncertainty. In the pregnant woman example, the investigators based their life expectancy assumptions on the best currently available therapy. Second, an intervention may have life-extending effects beyond those received by the person being treated. In the pregnant woman example, the effects on both the mother and her newborn baby are important to consider. A more extensive analysis may also have looked at the health benefits of diagnosing early HIV infection with the poten- tial for decreasing HIV transmission. Third, interventions may have important qual- ity of life effects that are not captured by focusing solely on survival. For example, an intervention may improve quality of life but have no effect on survival, or may even have divergent effects on quality of life and survival. For this reason, U.S. guidelines recommend the quality adjusted life year (QALY) as the preferred method of meas- uring health benefits, although this assertion remains controversial (McGregor, 2003; Carr-Hill, 1989; Freemantle, 2000; Loomes and McKenzie, 1989; Williams, 1991).
4.4. Quality of Life Weights
Quality of life weights are usually derived from utilities, which are values that sum- marize preferences for health states in a single number (Torrance, 1987; Froberg and Kane, 1989a). Utility elicitation methods are based on decision science theory.
Formally, utilities are numbers that describes an individual’s strength of preference for outcomes under conditions of uncertainty (Torrance, 1987; Froberg and Kane, 1989b; Torrance 1986). The rationale for using utilities as quality of life weights in cost effectiveness analysis is that both the need for health interventions and the ben- efit from the intervention are usually unknown; as such, preference weights from the field of decision sciences, which focuses on judgments under conditions of uncertainty, are appealing.
In health care, the utility scale is typically anchored at 0, representing death, and 1, representing best possible health. Several methods are commonly used for measuring utilities, which typically involve asking individuals to choose between hypothetical alternative and calculating their utilities from their responses.
Although the methods are widely used, several observations can lead investigators to approach their use cautiously. First, alternative methods for measuring utility for health states often differ, sometimes greatly (Bayoumi and Redelmeier, 1999;
Dolan, et al., 1996; Hornberger, et al., 1992, Read, et al., 1984; Stiggelbout, et al.,
1994; Bombardier, 1982; Martin, et al., 2000, Torrance, 1976). Second, the assess- ment tasks can be cognitively demanding (Hershey, et al., 1988; Bleichrodt, 2002;
Llewellyn-Thomas, et al., 1984). Third, some individuals may object to the form of the task and refuse to answer (for example, religious people sometimes object to the notion that they could shorten or extend their life). Fourth, the methods can be internally inconsistent (Llewellyn-Thomas, et al., 1982).
U.S. guidelines recommend that community preferences (from representatives of the general population) be surveyed when conducting cost effectiveness analyses from a societal perspective, since these will most accurately reflect “societal” values (Gold, et al., 1996). However, there are some situations in which it may be difficult for community members to adequately characterize a health condition. As well, it may be difficult to find an appropriate proxy to evaluate health states for some health states (like dementia) or stages of life (like infancy). In the pregnant women example, the authors did not measure utilities or incorporate quality of life weights as their main outcome measure was the health effects of children and mothers com- bined. Thus, even if quality of life weights for infants could have been applied, the authors would have faced the additional formidable problem of ascertaining that these weights were measured on the same scale as that used for mothers. More generally, CEAs incompletely capture some benefits, including the benefit obtained by others from an individual’s well-being, both directly (for example, the benefit to children from saving the life of a parent) and indirectly expressed as altruism (for example, the well-being felt by men from the existence of a program of screening for cervical cancer among low income women).
5.0. DATA SOURCES
CEA generally integrate data from a number of sources. Primary data collection has been used to conduct economic analyses parallel to randomized controlled trials or observational studies. Additionally, many studies have used primary data collection to measure utilities. Systematic reviews of the medical literature are an effective method of synthesizing large amounts of data to answer specific clinical questions.
Secondary analyses of either primary research data or administrative databases have also proven invaluable. Finally, when no data is available, CEA have made use of expert consensus opinion.
5.1. The Role of Modeling
Although analysts can estimate effectiveness from empiric estimations, the time frame of CEAs often exceeds that of clinical trials or observational databases. Additionally, CEAs may address questions or include populations that are more general than those included in traditional studies of effectiveness. Accordingly, CEAs are usually inte- grative studies that combine data from several sources into a mathematical model to simulate the natural history of a condition. While models necessarily simplify rep- resentations of complex processes, the selection of an appropriate model is not always straightforward (Sculpher, et al., 2000). Salient points to consider include the com- plexity of the populations and disease under consideration, the importance of includ- ing individual or population level outcomes, and the degree of uncertainty associated with the outcome. Quantifying the latter concern can help prioritize future research.
How results from different models differ is a largely unexplored area.
6.0. ANALYZING THE RESULTS
The incremental cost effectiveness ratio (ICER) will estimate the amount needed for a gain in one unit of health effect. But what constitutes an attractive ICER? No consensus exists on the appropriate threshold at which an intervention stops being cost effective, although many decision makers consider interventions with a cost- effectiveness ratio of greater than $50,000 to $100,000 per quality adjusted life year (QALY) to be economically unattractive for interventions when analyzed from the societal perspective (Owens, 1998; Laupacis, et al., 1993). Analyses performed in different urban settings, countries (particularly resource poor states) or from other perspectives may adopt alternative thresholds. Some analysts have criticized the threshold approach to designating interventions as cost effective, arguing that such an approach does not offer decision makers an effective means to control budget- ary expenditures.
7.0. TECHNICAL AND ALLOCATIVE EFFICIENCY
The ICER is a measure of efficiency. It is useful to consider two types of efficiency:
technical and allocative (Oliver, et al., 2002). Technical (also called productive) effi- ciency is the comparison of costs to outcomes, whether as a ratio (in cost-consequence and cost-utility analyses) or as a difference (in cost benefit analyses). An interven- tion is technically efficient when, for the same amount of money, no additional health benefits can be achieved. In the pregnant woman example, implementing 71% of the routine newborn screening strategy would yield the same costs (about
$29 million U.S.) as the enhanced prenatal screening strategy but would avert 41 additional HIV infections. Hence, from a technical efficiency perspective, a strategy of enhanced prenatal screening is not preferred.
Now consider the combination of enhanced screening and routine screening of newborns. Such a program would result in additional costs ($32 million U.S.) and 135 additional HIV infections averted. Would an alternative use of this $32 mil- lion U.S. result in better health outcomes? * Because other interventions likely will not measure outcomes as the number of HIV infections averted, it is not possible to compares these alternative financial allocations directly. Thus, assessing this type of efficiency, known as allocative efficiency, necessitates a common outcome measure for economic analyses. The strong preference for the QALY by official agencies is in part because it is not specific to any one health condition (or even to life-extending conditions) and therefore can be used to assess both technical and allocative decisions within the health sector.
Allocative efficiency asks whether there is a net gain in health by assigning resources from one intervention to another. Assuming that budgets are fixed, there will necessarily be some losses in health effects for a group whose intervention is deprived of funds. Although decision makers may rationally want information about allocative efficiency to make their decisions, few economic analyses present their results in this way. Furthermore, because non-health interventions are typi- cally not measured using QALYs, allocative efficiency between health and other sectors cannot be readily assessed with the QALY model.
*
Economists refer to the “opportunity cost” of a decision – the cost of the next best alternative which is
foregone as a result of the decision made.
7.1. Uncertainty in Cost Effectiveness Analysis
Several aspects of a cost effectiveness analyses are often uncertain. For example, the precise values of some of the parameters in the analysis may be uncertain. Many models incorporate assumptions and extrapolations. U.S. guidelines recommend looking at three types of uncertainty (Gold, et al., 1996). Parameter uncertainty refers to examining how model results vary when parameter values are changed.
Parameter uncertainty is best examined by performing sensitivity analysis, in which changing one or two parameters at a time can yield important insights into which model parameters most influence the model results while sophisticated methods of varying multiple model parameters simultaneously are often the best method of assessing overall model uncertainty. Model structural uncertainty refers to changing the model structure to examine the robustness of the model to modeling assump- tions. Model process uncertainty refers to examining results from different models to see if divergent approaches yield consistent results.
Although the analysis of uncertainly around an ICER, a ratio statistic without a defined distribution, has historically been difficult, recent advances have used transformations, graphical representations, and re-sampling methods (Fenwick, et al., 2001). These advances now enable analysts to derive reasonable confidence intervals for cost effectiveness statistics. Nevertheless, some have argued that the reason to assess uncertainty in the ICER is not to preclude decision making, but rather to point to areas in which additional research is needed for more definitive conclusions (Claxton, 1999).
8.0. ALTERNATIVES TO COST EFFECTIVENESS ANALYSIS Cost effectiveness analysis has been endorsed as the preferred method of economic analysis by several national technology assessment authorities. Several alternatives to cost effectiveness analysis have been proposed, of which the most common are summarized below (Gold, et al., 1996):
• Cost minimization analysis is a form of analysis in which the effectiveness of the interventions under consideration are assumed to be equivalent. Thus, the goal of the analysis is to find the strategy that minimizes costs. Because it is rarely the case that effectiveness is truly equivalent across strategies, cost minimization analysis is not commonly used.
• Cost consequence analysis is a form of analysis in which costs and consequences of the interventions being considered are presented in a disaggregated form. The advantage of such an approach is that the consequences that can be considered are comprehensive and there is no attempt to combine diver- gent outcomes, as in the QALY model.
• Cost benefit analysis is closely related to cost effectiveness analysis, except that the health effects are valued in dollar amounts (McIntosh, et al., 1999;
O’Brien and Gafni, 1996; Klose, 1999; Diener, et al., 1998; Olsen and Smith, 2001). Although cost benefit analysis has been considerably less popular in health services research than cost effectiveness analysis, there are some clear advantages for the former approach. For example, it is easy to com- pare allocative efficiency across different programs or even across sectors and it is also straightforward to include non-health benefits in the analysis.
The greatest limitation of cost benefit analysis is the requirement for health
effects to be expressed in dollar amounts. This raises measurement prob- lems as well as ethical ones. For example, health problems of the rich may be valued more highly than the poor simply because they can afford to pay more to address them. For some features of cost benefit analysis, there are both advantages and drawbacks. For example, cost benefit analysis is directly grounded in welfare economic theory, which appeals to economists working from this perspective but may be objectionable to others (see below). Because of the concerns about cost benefit analysis, U.S. guidelines have endorsed cost effectiveness analysis as the preferred method for economic analysis in health care (Gold, et al., 1996).
9.0. CONTROVERSIES IN COST EFFECTIVENESS ANALYSIS AND URBAN HEALTH
9.1. Does Cost Effectiveness Analysis Discriminate against the Disadvantaged?
To the extent that urban health researchers and decision makers are focused on helping the urban disadvantaged, they may be particularly interested whether there are aspects of CEA that may be discriminatory. Briefly, there are five main consider- ations: (1) The use of community ratings of quality of life may further disempower the disadvantaged and introduce biases due to stigmatization; (2) The cognitive and experiential requirements of utility surveys may be disadvantageous for populations with these limitations; (3) The use of QALYs as a health outcome may introduce biases against the elderly and disabled; (4) The reliance on welfare economic theory may favor resource distribution to the rich; (5) Cost effectiveness analysis is silent about the status quo distribution of resources. Each of these are presented in turn.
9.1.1. Community Ratings of Quality of Life
Who should provide quality of life weights for use in cost effectiveness analyses? From an urban health perspective, the recommendation that community ratings be used raises two significant concerns. First, some health situations may be insufficiently familiar to members of the general population (Froberg and Kane, 1989a). Thus, editing and misinformation biases may result in respondents not truly evaluating the intervention of interest, but rather their (mis)interpretation thereof. For example, urban health researchers may be concerned that few members of the population would have sufficient experience to rate conditions associated with homelessness, such as scabies. More broadly, the use of community references raises questions of agency; that is, ought members of the general population be judging the health con- ditions of a disadvantaged minority? This intensely political question may trouble community health researchers who are unwilling to cede such judgments to external evaluators, with the accompanying concerns about power and control.
Another concern with community ratings relates to biased responses that
reflect how respondents feel about the potential beneficiaries of an interven-
tion rather than how they rate the health state, particularly if the beneficiaries
are stigmatized. For example, respondents may rate a health condition highly if the
potential respondents are newborn babies out of a desire to appear gracious.
Alternatively, respondents may rate a health condition too harshly if the potential beneficiaries are stigmatized. For example, respondents may provide low ratings for health conditions associated with hepatitis C if they harbor discriminatory feelings against injection drug users. For such reasons, the use of community ratings, and the attendant potential for introducing biases, should be carefully considered in each valuation study.
9.1.2. Cognitive and Experiential Requirements of Utility Surveys
For some outcome measures, providing utility ratings may be cognitively or emo- tionally challenging. The utility elicitation methods with the strongest theoretical bases require individuals to consider scenarios in which they must trade off a chance of death or future survival. Individuals who have a hard time understanding such scenarios, or find them too psychologically disturbing, may give responses that are systematically too high (reflecting an unwillingness to accept the trade off, for example). Similarly, religiosity – which is associated with ethnicity – may influence individual ratings (Tsevat, et al., 1996). The result of such a valuation would be that these ratings, if taken at face value, would indicate that individuals with such characteristics these are not experiencing adverse quality of life and hence, may be less deserving of funding priorities. Thus, utility elicitation studies should carefully assess the role of biases related to cognition, emotion, and religiosity when reporting their results.
9.1.3. Q A LYs and Discrimination
Another concern relating to the disadvantaged stems from the use of QALYs to measure health effects and individuals’ potential for increasing quality of life or sur- vival. Consider three individuals who are being considered for a given treatment.
One is 40 years old and otherwise healthy, another is 30 years old with HIV infection with AIDS complications, and the third is 70 years old with some mild chronic med- ical problems. Although all may benefit from the treatment, the 40 year old has the greatest potential QALY improvement since his potential for improvement and life prolongation is not limited by comorbidity (such as AIDS) or advanced age. Indeed, ethicists have criticized resource allocation decisions that aim to only maximize QALYs (without considering other patient characteristics) as being inherently dis- criminatory against the disabled and elderly (Harris, 1987; Singer, et al., 1995) a point to which we return below when discussing equity considerations in resource allocation decisions.
9.1.4. W elfare Economic Theory
Other critics have raised questions about welfare economic theory as a basis for cost effectiveness analysis. While a full review of theor y is available elsewhere, I restrict my comments here to considerations that are important for applying CEA to urban health. Welfare theory is a branch of economics that examines the desirability of alternative allocation of resources (Garber and Phelps, 1997;
Brouwer and Koopmanschap, 2000). (It is not at all about “welfare”, financial
assistance to disadvantaged individuals, as social policy). The underlying assump-
tion of welfare economics is that each individual in a society, acting as rational
agents, would use the resources available to him or her to maximize their own
welfare, or well-being. The way in which these resources are combined are deter- mined at the individual level, according to each person’s preference (or utility) function. For example, some individuals would spend their money on cars while others would spend it on housing, but all would maximize their utility (happi- ness) by allocating their personal resources appropriately. Welfare economic the- ory asserts that the overall welfare of a society is determined through aggregating individual welfare.
Under welfare economics, the optimal allocation of resources is obtained when each individual’s welfare is maximized. When such conditions are met, some individuals may have worse health than others, but if this distribution reflects individual differences in preferences for health, then the inequality in health is unimportant. That is, what welfare economics seeks to maximize is aggre- gate utility not aggregate health. This approach accepts that individual prefer- ences for health often vary. For example, some people may have decided to accept some other benefit (such as more money) while trading off health, but this com- bination of characteristics maximizes their happiness. Welfare economics uses a hypothetical “compensation test” to ascertain if the allocation of resources is truly optimal. This test assumes that with the introduction of an intervention there will be winners (for example, those who benefit from the intervention) and losers (for example, those who pay for the intervention but do not benefit). The compensa- tion test asserts that if the amount the winners are willing to pay exceeds the amount the losers demand for compensation, then the reallocation of resources is worthwhile.
Three salient criticisms of welfare theory are notable. First, the compensation test as a basis for resource allocation is controversial, since resources are often allo- cated in such a way that the winners benefit but the losers are not compensated, or insufficiently so. As such, the compensation test has been criticized as a basis for resource allocation since there is no assurance that resource allocation will meet this internal standard of fairness (Weinstein and Stason, 1977). Of course, decision makers may want even more rigorous standards to be considered. An additional concern about the compensation test is that it may actually lead to less aggregate health, particularly if the losers are giving up more health than the winners are buy- ing. If the losers are poor and the winners are rich, then society may have maxi- mized individual preferences but worsened overall health and exacerbated class differences in health.
A second criticism of welfare theory is that there is unlikely to be any accept- able means of aggregating utilities across individuals, even if good methods of meas- uring utilities were available. Third, some have argued that health is a “primary social good” that should be treated differently than other commodities. That is, we may be happy as a society to let individuals determine what basket of consumer goods maximizes individual utility, but we may be unwilling to let individuals trade off health to improve wealth. Proponents of this view argue that health is different from other goods, since good health is frequently a precondition for enjoyment of other aspects of life. Accordingly, health is a good that everyone should be entitled to, regardless of their willingness to pay. This last view, which has been termed
“extra-welfarist” seeks to maximize health, rather than utility, when allocating socie- tal resources (Birch and Donalson, 2003; Wagstaff, 1991).
To illustrate this difference, consider society’s response to the question of indi-
viduals interested in selling their body organs for money. An extra-welfarist perspec-
tive might agree to ban this sale, since the goal of resource allocation is to maximize
health. Accordingly, individuals who voluntarily sacrifice some element of their health would be acting contrary to society’s best interest. In contrast, the welfarist perspective might argue that, assuming the seller has full information of the inher- ent risk, such a sale might result in a net increase in personal, and hence societal, utility and is unobjectionable.
Why should urban health researchers care about the tension between the wel- farist and extra-welfarist viewpoints? It is important to recognize that this discrepancy should not be interpreted as a rejection of CEA as a method as a health technology assessment tool. Rather, the debate informs how QALYs are interpreted and how cost-effectiveness analyses are used for resource allocation decisions. Adopting an extra-welfarist orientation leads to the conclusion that the goal of cost-effectiveness analysis is to maximize health, not utility. While health maximization does not imply that QALYs are a good measure of health, analysts who adopt this perspective will be considerably less concerned about whether the QALYs are themselves utilities or whether the QALY model violates the tenets of welfare economic theory (Bleichrodt, 1997). Accordingly, this approach views CEA less as an overarching theory of resource allocation and more as a guide to decision making, to be incorporated with other pertinent information (Brouwer and Koopmanschap, 2000).
9.1.5. Cost Effectiveness Analysis and Social Justice
As discussed above, cost effectiveness analysis will be most helpful to decision mak- ers when addressing allocative efficiency – that is, are the health gains from reallo- cating resources from one group to another efficient in maximizing health (or utility)? Importantly, such analyses do not question the underlying distribution of financial resources. That is, the “winners” in a resource allocation decision are chosen based on their potential for health improvement. Even when analysts have considered applying equity considerations to cost effectiveness decision making (see below), the concern has been for health equity; that is, reducing inequalities in health need, access, or status. The application of cost effectiveness analysis to address issues of societal equity – that is, to examine social justice issues – is almost certainly beyond such analyses’ capabilities.
9.2. Are Cost Effectiveness Analyses Transferable from One Urban Center to Another?
The application of the results of cost effectiveness analysis conducted in one loca- tion to another is not automatic (Owens and Nease, 1997). Urban health researchers considering applying a cost effectiveness analysis conducted in another situation to their own should consider the following questions:
9.2.1. Is the Perspective of the Analysis Similar?
Although the societal perspective is preferred for societal level decision making, it
may not be the most relevant perspective when applying decisions in a specific con-
text. For example, urban public health departments may want to adopt a narrower
perspective when deciding on policy implications. Of note, many analyses that
assume a “payer” (public insurer) perspective adopt that of a state, provincial, or
federal and much less commonly, an urban perspective. Although it is common to
assume that an intervention can be easily scaled up or down without changing the
results, it is worthwhile to explicitly interrogate this assumption before applying analyses conducted at a large-scale level to a more local environment. For example, consider a local public health department considering a program of directly observed therapy for people infected with tuberculosis. From the perspective of the urban health department, some costs may be excluded in a particular local situation (for example, if travel costs were not paid for directly by the local health depart- ment). Furthermore, the startup costs related establishing such an infrastructure may vary considerably between locations. As a result, simply scaling down national cost effectiveness estimates can be misleading.
9.2.2. Which Parameters Need to Be Adapted to the Local Level?
Many parameters in a cost effectiveness analysis may need to be adapted to a local situation. In the pregnant women example, the prevalence of HIV infection and the rate of maternal to infant transmission is a key determinant of cost effectiveness. In the tuberculosis example cited above, the health effects may depend on the preva- lence of multi-drug resistant tuberculosis and medication non-adherence.
Accordingly, the application of this analysis would require careful customization of the results to local situations. Similarly, local costs may differ considerably from those in an analysis. If the analysis incorporates community ratings of quality of life, it is worthwhile to ask if local ratings would be similar to those in an analysis. Finally, risk adjustment to local considerations may be important. In the pregnant women example, if the distribution of prognostic factors is different in a local population than in that described in the analysis, the projected life expectancies may also vary.
An analysis of model uncertainty can often be helpful if the parameter values exam- ined in sensitivity analysis are similar to those of the local setting.
Apart from model parameters, two other considerations are noteworthy when applying CEAs to the local level, particularly when approaching such issues from a global perspective. First, the approach to the analysis, particularly if based on a model, should be carefully examined. Are the interventions described feasible to implement at a local level? Is the description of the health care provided in the analysis different enough to warrant changes in the model structure? Second, is the cost effectiveness threshold similar across settings? That is, the cost per incremental health gain may be very different in an economically well off country and one that is resource poor. Thus, even if the analysis is accepted without major changes, the interpretation of the results requires careful attention.
9.2.3. What Are the Budgetary and Allocative Implications of the Decisions?
Most cost effectiveness analyses only report an incremental cost effectiveness ratio (ICER). Decision makers will ordinarily require at least two additional pieces of information before making a decision about allocating resources to the interven- tion under consideration. First, they will need to ascertain the budgetary implica- tions of such a decision. Recall that two interventions can have the same ICER although one’s incremental costs and effects are small and the others are large. Decision makers will need to know the intervention’s cost as well as its cost effectiveness.
In addition, and related to the above, decision makers will likely want to know
where the resources allocated to this decision are coming from. That is, are other
programs being cut to fund the new allocation? Are taxes being raised or bonds being generated? If so, are the losses that some individuals will incur offset by the gains in the new program?
9.4. How Can Other Considerations, Particularly Those Relating to Health Equity, Be Combined with Cost Effectiveness Analysis by Decision Makers?
Most applications of cost effectiveness analysis focus only on efficiency and neglect equity implications associated with the distribution of changes in health and wealth.
That is, there is no attention paid to how widely the health effects are distributed.
While “what gain?” matters, “who gains?” may matter as much or more (Culyer, 2001; Ubel, et al., 1996; Holm, 1998; Culyer and Wagstaff, 1993).
To illustrate, consider three interventions (A, B, and C), each associated with an increase of 200 QALYs (Table). Intervention A yields an average of 1 QALY gain for 200 individuals, whereas intervention B yields 2 QALYs for 100 individuals and intervention C yields 0.5 QALYs for 400 individuals. A simple application of the QALY model, without consideration of the number of people who are benefiting, may lead decision makers to conclude that these interventions are equivalent.
However, empirical evidence shows that members of the general public, ethicists, and decision scientists place a value on how wide the benefits are shared. For example, individuals who prefer intervention C over A would be willing to trade off smaller individual gains (0.5 vs. 1 QALY) to gain a wider distribution of benefits (400 people helped instead of 200).
In addition to considerations about how gains are distributed across individu- als, reporting outcomes as incremental QALY gains can obscure two other factors that may be important for decision makers making resource allocation decisions.
First, focusing on QALYs may obscure the fact that some interventions extend (or save) more lives than others. For example, our hypothetical intervention C is associated with no increase in survival, although it does increase quality of life. In contrast, interventions A and B are both associated with an increased survival of two years. Decision makers may wish to place a higher priority on life-saving interventions.
A second feature that is not apparent when focusing on QALYs arises from the focus of cost utility analysis on incremental QALY gains. In our example, a patient
Table 1. Gains with Three Hypothetical Interventions
A B C
Gains per individual
Life expectancy before intervention (years) 10 20 20
Life expectancy after intervention (years) 12 22 20
Gain in life years 2 2 0
QALYs before intervention
*5 16 19
QALYs after intervention 6 18 19.5
Gain in QALYs 1 2 0.5
Gains across individuals
Number of people affected 200 100 400
Total QALY gain 200 200 200
*
