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Improving health care outcomes through reallocation of health care expenditures
CLINICAL TIIERAPEUTICSVVOL. 19, NO. 5, 1997
Improving Health Care Outcomes Through Reallocation of Health Care Expenditures Paul C. Langley, PhD School of Pharmacy, University of Colorado Health Sciences Centel; Denvel; Colorado
ABSTRACT
INTRODUCTION
The use of incremental cost-outcomes ratios as decision variables by those working in the area of pharmacoeconomics is commonplace. Unfortunately, few question the assumptions implicit in the use of such ratios. In this paper, the author argues that under certain reasonable assumptions regarding the production of health care and alternative therapy options, if partial substitution or switching between therapy options occurs, it may be possible not only to stay within an initial health care budget but also to yield higher total outcomes for the treating population. This argument is presented by developing a model of health care costs and outcomes in a dualtherapy environment. Key words: outcomes, incremental ratios, decreasing returns, partial substitution.
The purpose of this paper is to examine, through the development of a model of health care production, the relationship between health care outcomes and the resources allocated to the provision of health care when a new product is introduced to therapy and the production of health care involves decreasing returns. The analysis takes into account the achievement of enhanced target outcomes for the treating population within budgetary constraints. The question of partial substitution between therapies is considered, and its implications for patient outcomes are examined. The analysis contrasts a partial therapy switching process with the usual approach of complete switching and shows that once increasing costs are assumed, a higher outcome level can be achieved within a fixed budget environment than would be
1092
PC. LANGLEY
achieved by a constant-cost, incremental cost-outcomes model. It is suggested that it may be misleading for decision makers to report incremental cost-outcomes ratios under constant returns conditions and base decisions on these ratios, which is the approach typically advocated in the pharmacoeconomic literature. l This paper builds on previous work by the author in which the nature of health care production and the role of cost-effectiveness ratios as decision variables have been considered.* It differs from the author’s previous work in its analysis of the interaction between health care outcomes and the resources allocated to treatment, considering this interaction within an explicit budgetconstraint model. As in the previous work, however, a partial therapy switching is modeled in which the production of health care with single therapies is chatacterized by decreasing returns. The analysis presented here takes into account the underlying process of health care production, the therapy switching process, and the final cost-outcome equilibrium for the treating population. THE HEALTH CARE PRODUCTION FUNCTION The model of health care production considered in this paper takes the following form:
0 = (b (N, I-‘,M) where 0 is total outcomes (product), + describes the technical options for combining the inputs, N is the treating population, P is pharmacy (drug) inputs, and M is medical inputs. The production function is assumed to be convex to the origin (ie, to exhibit a diminishing marginal rate of substitution),
continuous, and twice differentiable. Where N is fixed, the production function can be represented by a series or family of isoquants, which are equal-output relationships showing how medical and drug inputs can be combined to produce target levels of outcomes (0, c 0, < O,, etc). Within any disease or therapy area, therefore, it is always possible to increase the total outcomes produced for the fixed treating population by using additional pharmacy or medical inputs. These inputs can be improved monitoring or screening services, greater access to medical care, programs to improve drug compliance, or training to reduce the incidence of drug misadventures or inappropriate prescribing by physicians. The outcome that is actually achieved for the treating population will be a function of the budget determined for the disease area. Given the relative unit prices of the pharmacy and medical inputs, the actual production point is determined by the point of tangency between the budget line and the highest possible isoquant (Figure 1). Points A, and A, represent points of economic efficiency at which, for the population being treated, health care outcomes are maximized on the expansion path OE. There are two aspects to be considered in the interaction between total outcomes and total cost or the budget allocated to a disease area. The first is the relation between total costs and outcomes when the target outcome per patient is set and the number of patients increases from zero to the total treating population, which is represented by a movement along a total product or outcomes function. The second is the impact on total outcomes when a new perpatient outcome tatget is set with the number of patients treated held constant_This is represented by a movement from one out1093
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Medical Inputs Figure 1. Health care production isoquants. A, and A, = points of economic efficiency at which health care outcomes are maximized, OE = expansion path.
comes function to another. These outcomes functions for different per-patient target outcomes (0,, O,, etc) are assumed to exhibit decreasing returns (Figure 2). On the assumption that a health care system intends to treat all patients in a disease area, it can further be assumed that the system will select an output level that is consistent with the budget allocated to that area. This is shown in Figure 2, where, with a budget set at TC,, the maximum attainable outcome level per patient is represented by outcomes function TC, (0,). If a higher outcome level is sought, then either fewer patients can be treated (eg, OAN2)or a decision can be made to increase the budget allocated to that area. Clearly, although one can conceive of a family of such outcomes func1094
tions, minimum standards of patient care would determine a floor to expenditures; beyond this, however, health care systems could consider more targeted strategies and could accept a trade-off of increased costs for increased outcomes. The shape of the total-cost function reflects a patient selection process by physicians. In cases in which less than the prospective population is being treated, patients are selected on the basis of the costs involved in meeting the target outcome. If physicians are under a capitated provider contract and are cost minimizers, patients are selected on the basis of the costs involved in meeting the indicated target outcome. As the number of patients treated increases, patients who are expected to be less responsive to a particular
PC.
LANGLEY
Population Figure 2. Total cost per unit of outcome function. TC, = total-cost function for therapy A; 0, and 0, = target outcome levels per patient; OA= point of origin; TC,(O,) and TC,(O,) = maximum attainable outcome levels for different per-patient target outcomes; NA = total number of patients treated if 0, is the target outcome; N, = number of patients treated if 0, is the target outcome. therapy are introduced to that therapy, with the result that the marginal cost of treatment increases. This would appear to be a reasonable assumption; after all, unless the patient population is homogeneous, patients are not normally assigned to therapies at random, which is what constant marginal costs would imply. A TWO-THERAPY MODEL This paper has so far considered a single therapy in a disease or treatment area. When two therapies are available (eg, following the formulary listing of a new product), rather than a matter of one therapy supplanting another (which is typi-
cally assumed in a cost-effectiveness analysis and the estimation of incremental cost-outcomes ratios), the issue becomes one of partial substitution, in which certain patients are switched from an existing product to a new product. This is represented in Figure 3, using an Eklgeworth box for a process involving two therapies. The axes of the box are total costs and numbers of patients; the box is symmetrical, in that the numbers of patients must remain the same regardless of their distribution between the two therapies. With a fixed budget allocated to the disease area, total costs are distributed between the two therapies once a new equilibrium has been achieved. The new equi1095
CLINICAL THERAPEUTICS’
.- %I
-J
NM NA,
NA3
x
N;
Population Figure 3. Equilibrium patient distribution under a two-therapy model. 0, = point of origin of total costs and number of patients for therapy A; TC,(O) = total-cost function for therapy A, TC,(O,), TC,(O,), and TCA(03) = maximum attainable per-patient tatget outcomes for therapy A; TC, = budget for therapy A; N = total number of patients; NA, NA1, NM, and N,, = number of patients treated with therapy A; On = point of origin of total costs and number of patients for therapy B; TC,&OJ and TC,(O,) = maximum attainable per-patient target outcomes for therapy B; TCu = budget for therapy B; Na, Nut, Nsz, and Nus = number of patients treated with therapy B; F = point at which the two cost functions for a per-patient outcome level of 0, intersect; E = point at which total-cost functions for the two therapies for a per-patient outcome level of 0, are at a tangent to each other. librium is defined in terms of the health care system’s maximizing the outcomes of therapy for the treating population. The total-cost curves proposed in the two-therapy model are to be interpreted as residual total-cost curves. That is, they represent the total cost of treating patients with a given therapy, with the total costs of treating those patients increasing at an increasing rate as patients are switched to that therapy from another therapy. Obviously,
1096
the total-cost curves are anchored at zero and maximum total costs, with maximum total costs incurred when all patients have been switched from one therapy to another at an average output level consistent with the budgetary constraint. When only one therapy is available, the function represents the cost of treating an increasing number or proportion of patients (the costs of not treating these patients is zero). When two therapies (or any number of therapies) are
P.C. LANGLEY
available, these curves represent the total cost of treating patients who are still receiving a particular therapy after other patients have been switched to another therapy. There is, therefore, no double counting of patients who, depending on the distribution, are receiving either one therapy or the other. There is a family of residual total-cost curves, each curve corresponding to a target output level. It is important to remember that these are total-cost curves, and the reduction in total cost associated with reducing the number (or proportion) of patients treated does not mean that it is necessarily the high-cost patients who are switched from one therapy to another. The criterion for switching is the cost of achieving a particular outcome. Thus, physicians could decide to switch low-cost patients who are on therapy A because the cost of achieving the same outcome with therapy B is less. Switching patients, therefore, involves a perceived ranking by physicians of the cost savings associated with switching patients from therapy A to therapy B to achieve a given outcome. This emphasizes the point that a common outcomes target must be set for each patient for both therapy interventions. Each point on a totalcost curve represents a point of economic efficiency at which a given number of patients are allocated to a given therapy to achieve a target outcome at the lowest cost possible. Starting at the origin point 0,, the representation (for therapy A) is the same as that given in Figure 2, with total costs measured on the y-axis and the number of patients in the treating population measured on the x-axis. The total-cost function TC, (0,) describes the maximum perpatient outcome attainable with a budget of TC, with NA patients being treated and, for that outcome level, how total costs
vary with the number of patients treated. Total costs and outcomes for therapy B are represented from origin point On. In the example given here, treatment with therapy B is not only more effective than treatment with therapy A (outcome level per patient of O,), but it is also more costly. If a target outcome level of 0, were to be attempted with therapy A with a new total-cost function TC,(O,), then only O,N,, patients would be treated within the fixed budget for that disease area (which is fewer patients than would be treated to achieve the same outcome with therapy B). If all patients were to be switched from therapy A to therapy B, the required budget for a minimum acceptable standard of patient care (if 0, were now the minimum standard) would be greater than that for therapy A-based treatment. Thus within the budgetary constraints, given a total-cost function of TC,(O,), in which 0, > 0,, only OnN,, patients can be treated. This does not mean, however, that the health care system would opt for the exclusive use of therapy B and either meet the additional budgetary requirements or treat fewer patients. If treatment is intended to remain within the initial budget and all patients are to achieve the same outcome level, then the solution is to switch only some patients from therapy A to therapy B. However, “switching” is a misnomer. This is not a discussion of an omnipresent health planning authority allocating patients to particular therapies but of a “market” solution in which the distribution of patients between altemative therapies is achieved through the costminimizing (or profit-maximizing) behavior of the treating physicians. Consider point F, at which the two cost functions for an outcome level of 0, in1097
CLINICALTHERAPEUTICS@
tersect (Figure 3). At this point, the outcome level per patient could be achieved by allocating 0ANA3patients to therapy A and O,N,, patients to therapy B, while staying within the initial budget. However, although point F represents a combination of technically efficient outcomes, it is not economically efficient with regard to maximizing outcomes for the treating population with a fixed budget allocation to that disease area. The shaded area in Figure 3 represents the potential for outcomes gains for the treating population while remaining within the constraints of the original budget and continuing to treat all patients in the target population. The criterion for maximizing the outcome gains (within the cost constraint) is to reallocate patients until the marginal cost of achieving a given outcome is the same for both therapies. This is achieved when the total-cost functions for the two therapies (to produce the same outcome target) are at a tangent to each other. This is represented by point E in Figure 3. At point E, OANti patients are assigned to therapy A, with the balance, OBNBZpatients, assigned to therapy B. Total outcomes are maximized (at outcome 0,) for the treating population, and these outcomes are delivered within the constraints of the original budget (O,TC,, + O,TC,,). This may be described as an equilibrium solution in which the distribution of patients is such that benefits are maximized within a cost constraint (or, alternatively, the production of target health benefits is at least cost for a treating population). The introduction of therapy B allows the average outcome per patient to increase within the constraints of the original budget. This final or equilibrium distribution of patients is, therefore, a result of the 1098
cost-minimizing behavior of physicians, which allows them to allocate patients to different therapies given a budget constraint and target outcomes that may be determined contractually. INCREMENTAL COST-OUTCOMES RATIOS It is of interest to contrast the above analysis with the use of incremental costoutcomes ratios as decision variables. Incmmental cost-outcomes ratios are intended to summarize, for an intended health care purchaser, the additional benefits expected to arise from the introduction of a new therapy and the additional costs associated with these benefits. Because incremental cost-outcomes ratios are typically considered only when a new therapy is both more effective and more costly than an existing therapy, these ratios are usually represented by a four-quadrant diagram in which the crossover (total costs, total outcomes) point (A) is the existing therapy for the treating population. It is assumed that the entire treating population can be switched from one therapy to another, and that the only relevant quantities are estimated total costs and outcomes. Figure 4 contrasts the two therapies analyzed above, therapy A and therapy B, for the fixed treating population N. Total costs are represented on the y-axis and total outcomes on the x-axis. The total costs and outcomes for therapy A (when all patients are allocated to therapy A) are represented by point A (total costs = OTC,; total outcomes = 00,); for therapy B, costs and outcomes are represented by point B (total costs = OTC,; total outcomes = 00,). Thus the incremental costoutcomes ratio is represented by the slope of the line joining points A and B. Note
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.:’
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Total Outcomes Figure 4. Incremental cost outcomes with partial therapy switching. A = total costs and outcomes for therapy A within a given treating population; TC, = total costs for therapy A, TC, = total costs for therapy B; B = total costs and outcomes for therapy B; A* = total cost of treating patients with therapy A to achieve the same perpatient outcome level as with therapy B; C = point of technical efficiency when therapies are combined, D = point of economic efficiency when therapies are combined; O,, On, and 0, = total outcomes achieved for the treating population. also that in applying an incremental costoutcomes approach, there is typically an implicit assumption of constant returns to scales (or constant costs per unit of outcome), so that average costs equal marginal costs irrespective of the number (or proportion) of patients treated. In this case, decreasing returns (increasing costs) are being assumed, and only the total costs and outcomes for a fixed population being treated with each therapy are reported. If the health care system attempted to achieve the outcome level associated with therapy B by allocating more resources to
treating patients with therapy A, the new total cost might be represented by point A*, which lies directly above point B. However, if there is only a partial substitution of patients between therapies and the health care system maintains the initial budgetary constraint (OTC,), then it would be possible to move to point C, which gives the same average per-patient outcomes as therapy B but with all patients being treated. This is, however, not the final outcome. Point C represents the point of technical but not economic efficiency (ie, point F in Figure 3). If patients 1099
CLINICAL THERAPEUTICS”
are now reallocated to maximize average outcomes achieved, the final outcomes level would correspond to point D. Within the initial budgetary constraint, therefore, outcomes have increased substantially to 0, for the treating population. Thus using an incremental outcomes ratio to contrast patients being switched completely from therapy A to therapy B has the potential to be highly misleading as a decision variable. Although leaving it to the health care system to decide whether to incur the additional costs is the traditional approach to pharmacoeconomic evaluations, the situation is quite different if we allow for partial substitutions. With the only option being a complete switch, the health care system may decide that the new therapy is not worth the additional costs (particularly if resources must be switched from other areas and patient outcomes are reduced, which should be factored into the decision). If partial switching is allowed under conditions of increasing cost, then the appropriate comparisons are between A and D, not A and B. CONCLUSIONS The purpose of this paper has been to demonstrate that within a simple therapyswitching model of health care delivery, the role of incremental cost-outcomes ratios as decision variables is potentially misleading. When used to justify therapy switching by treating populations, incremental cost-outcomes ratios may understate the benefits (in both budgetary and outcomes terms) that result from partial switching between therapy options. They may also present an oversimplified picture of the health care production process and the role of intervention strategies in achiev1100
ing outcomes that are more substantial than the incremental approach would suggest. The implications for pharmacy care are also worth noting. Without additional resources being allocated to a treatment area, it may be possible to increase the average outcomes reported for the treating population (with outcomes targets assigned) by a redistribution of patients between treatment options. Rather than assuming that a particular efficiency or output productivity level is the best that can be attained (which is what the constant-cost, incremental cost-outcomes approach is assuming), this approach views the production of health care as a function of the distribution of resources within treatment areas by therapy intervention pathways. An incremental cost-outcomes calculation, which may be based on average costs and outcomes generated by or extrapolated from a clinical trial, may, therefore, underestimate the gains from introducing a new product under conditions of increasing cost. Address
correspondence to: Paul C. Langley, PhD, Department of Pharmacy Practice, School of Pharmacy, University of Colorado Health Sciences Center, 4200 East Ninth Avenue, Campus Box C238, Denver, CO 80262. REFERENCES Drummond MF, Stoddart L, Torrance GW. Methods for the Economic Evaluation of Health Care Programmes. New York: Oxford University Press; 1987:102-104. Langley PC. Therapy evaluation, patient distribution, and cost-outcomes ratios. Clin Ther. 1995;17:341-347.
Improving Health Care Outcomes Through Reallocation of Health Care Expenditures Paul C. Langley, PhD School of Pharmacy, University of Colorado Health Sciences Centel; Denvel; Colorado
ABSTRACT
INTRODUCTION
The use of incremental cost-outcomes ratios as decision variables by those working in the area of pharmacoeconomics is commonplace. Unfortunately, few question the assumptions implicit in the use of such ratios. In this paper, the author argues that under certain reasonable assumptions regarding the production of health care and alternative therapy options, if partial substitution or switching between therapy options occurs, it may be possible not only to stay within an initial health care budget but also to yield higher total outcomes for the treating population. This argument is presented by developing a model of health care costs and outcomes in a dualtherapy environment. Key words: outcomes, incremental ratios, decreasing returns, partial substitution.
The purpose of this paper is to examine, through the development of a model of health care production, the relationship between health care outcomes and the resources allocated to the provision of health care when a new product is introduced to therapy and the production of health care involves decreasing returns. The analysis takes into account the achievement of enhanced target outcomes for the treating population within budgetary constraints. The question of partial substitution between therapies is considered, and its implications for patient outcomes are examined. The analysis contrasts a partial therapy switching process with the usual approach of complete switching and shows that once increasing costs are assumed, a higher outcome level can be achieved within a fixed budget environment than would be
1092
PC. LANGLEY
achieved by a constant-cost, incremental cost-outcomes model. It is suggested that it may be misleading for decision makers to report incremental cost-outcomes ratios under constant returns conditions and base decisions on these ratios, which is the approach typically advocated in the pharmacoeconomic literature. l This paper builds on previous work by the author in which the nature of health care production and the role of cost-effectiveness ratios as decision variables have been considered.* It differs from the author’s previous work in its analysis of the interaction between health care outcomes and the resources allocated to treatment, considering this interaction within an explicit budgetconstraint model. As in the previous work, however, a partial therapy switching is modeled in which the production of health care with single therapies is chatacterized by decreasing returns. The analysis presented here takes into account the underlying process of health care production, the therapy switching process, and the final cost-outcome equilibrium for the treating population. THE HEALTH CARE PRODUCTION FUNCTION The model of health care production considered in this paper takes the following form:
0 = (b (N, I-‘,M) where 0 is total outcomes (product), + describes the technical options for combining the inputs, N is the treating population, P is pharmacy (drug) inputs, and M is medical inputs. The production function is assumed to be convex to the origin (ie, to exhibit a diminishing marginal rate of substitution),
continuous, and twice differentiable. Where N is fixed, the production function can be represented by a series or family of isoquants, which are equal-output relationships showing how medical and drug inputs can be combined to produce target levels of outcomes (0, c 0, < O,, etc). Within any disease or therapy area, therefore, it is always possible to increase the total outcomes produced for the fixed treating population by using additional pharmacy or medical inputs. These inputs can be improved monitoring or screening services, greater access to medical care, programs to improve drug compliance, or training to reduce the incidence of drug misadventures or inappropriate prescribing by physicians. The outcome that is actually achieved for the treating population will be a function of the budget determined for the disease area. Given the relative unit prices of the pharmacy and medical inputs, the actual production point is determined by the point of tangency between the budget line and the highest possible isoquant (Figure 1). Points A, and A, represent points of economic efficiency at which, for the population being treated, health care outcomes are maximized on the expansion path OE. There are two aspects to be considered in the interaction between total outcomes and total cost or the budget allocated to a disease area. The first is the relation between total costs and outcomes when the target outcome per patient is set and the number of patients increases from zero to the total treating population, which is represented by a movement along a total product or outcomes function. The second is the impact on total outcomes when a new perpatient outcome tatget is set with the number of patients treated held constant_This is represented by a movement from one out1093
CLINICALTHEFUPEUTICS”
Medical Inputs Figure 1. Health care production isoquants. A, and A, = points of economic efficiency at which health care outcomes are maximized, OE = expansion path.
comes function to another. These outcomes functions for different per-patient target outcomes (0,, O,, etc) are assumed to exhibit decreasing returns (Figure 2). On the assumption that a health care system intends to treat all patients in a disease area, it can further be assumed that the system will select an output level that is consistent with the budget allocated to that area. This is shown in Figure 2, where, with a budget set at TC,, the maximum attainable outcome level per patient is represented by outcomes function TC, (0,). If a higher outcome level is sought, then either fewer patients can be treated (eg, OAN2)or a decision can be made to increase the budget allocated to that area. Clearly, although one can conceive of a family of such outcomes func1094
tions, minimum standards of patient care would determine a floor to expenditures; beyond this, however, health care systems could consider more targeted strategies and could accept a trade-off of increased costs for increased outcomes. The shape of the total-cost function reflects a patient selection process by physicians. In cases in which less than the prospective population is being treated, patients are selected on the basis of the costs involved in meeting the target outcome. If physicians are under a capitated provider contract and are cost minimizers, patients are selected on the basis of the costs involved in meeting the indicated target outcome. As the number of patients treated increases, patients who are expected to be less responsive to a particular
PC.
LANGLEY
Population Figure 2. Total cost per unit of outcome function. TC, = total-cost function for therapy A; 0, and 0, = target outcome levels per patient; OA= point of origin; TC,(O,) and TC,(O,) = maximum attainable outcome levels for different per-patient target outcomes; NA = total number of patients treated if 0, is the target outcome; N, = number of patients treated if 0, is the target outcome. therapy are introduced to that therapy, with the result that the marginal cost of treatment increases. This would appear to be a reasonable assumption; after all, unless the patient population is homogeneous, patients are not normally assigned to therapies at random, which is what constant marginal costs would imply. A TWO-THERAPY MODEL This paper has so far considered a single therapy in a disease or treatment area. When two therapies are available (eg, following the formulary listing of a new product), rather than a matter of one therapy supplanting another (which is typi-
cally assumed in a cost-effectiveness analysis and the estimation of incremental cost-outcomes ratios), the issue becomes one of partial substitution, in which certain patients are switched from an existing product to a new product. This is represented in Figure 3, using an Eklgeworth box for a process involving two therapies. The axes of the box are total costs and numbers of patients; the box is symmetrical, in that the numbers of patients must remain the same regardless of their distribution between the two therapies. With a fixed budget allocated to the disease area, total costs are distributed between the two therapies once a new equilibrium has been achieved. The new equi1095
CLINICAL THERAPEUTICS’
.- %I
-J
NM NA,
NA3
x
N;
Population Figure 3. Equilibrium patient distribution under a two-therapy model. 0, = point of origin of total costs and number of patients for therapy A; TC,(O) = total-cost function for therapy A, TC,(O,), TC,(O,), and TCA(03) = maximum attainable per-patient tatget outcomes for therapy A; TC, = budget for therapy A; N = total number of patients; NA, NA1, NM, and N,, = number of patients treated with therapy A; On = point of origin of total costs and number of patients for therapy B; TC,&OJ and TC,(O,) = maximum attainable per-patient target outcomes for therapy B; TCu = budget for therapy B; Na, Nut, Nsz, and Nus = number of patients treated with therapy B; F = point at which the two cost functions for a per-patient outcome level of 0, intersect; E = point at which total-cost functions for the two therapies for a per-patient outcome level of 0, are at a tangent to each other. librium is defined in terms of the health care system’s maximizing the outcomes of therapy for the treating population. The total-cost curves proposed in the two-therapy model are to be interpreted as residual total-cost curves. That is, they represent the total cost of treating patients with a given therapy, with the total costs of treating those patients increasing at an increasing rate as patients are switched to that therapy from another therapy. Obviously,
1096
the total-cost curves are anchored at zero and maximum total costs, with maximum total costs incurred when all patients have been switched from one therapy to another at an average output level consistent with the budgetary constraint. When only one therapy is available, the function represents the cost of treating an increasing number or proportion of patients (the costs of not treating these patients is zero). When two therapies (or any number of therapies) are
P.C. LANGLEY
available, these curves represent the total cost of treating patients who are still receiving a particular therapy after other patients have been switched to another therapy. There is, therefore, no double counting of patients who, depending on the distribution, are receiving either one therapy or the other. There is a family of residual total-cost curves, each curve corresponding to a target output level. It is important to remember that these are total-cost curves, and the reduction in total cost associated with reducing the number (or proportion) of patients treated does not mean that it is necessarily the high-cost patients who are switched from one therapy to another. The criterion for switching is the cost of achieving a particular outcome. Thus, physicians could decide to switch low-cost patients who are on therapy A because the cost of achieving the same outcome with therapy B is less. Switching patients, therefore, involves a perceived ranking by physicians of the cost savings associated with switching patients from therapy A to therapy B to achieve a given outcome. This emphasizes the point that a common outcomes target must be set for each patient for both therapy interventions. Each point on a totalcost curve represents a point of economic efficiency at which a given number of patients are allocated to a given therapy to achieve a target outcome at the lowest cost possible. Starting at the origin point 0,, the representation (for therapy A) is the same as that given in Figure 2, with total costs measured on the y-axis and the number of patients in the treating population measured on the x-axis. The total-cost function TC, (0,) describes the maximum perpatient outcome attainable with a budget of TC, with NA patients being treated and, for that outcome level, how total costs
vary with the number of patients treated. Total costs and outcomes for therapy B are represented from origin point On. In the example given here, treatment with therapy B is not only more effective than treatment with therapy A (outcome level per patient of O,), but it is also more costly. If a target outcome level of 0, were to be attempted with therapy A with a new total-cost function TC,(O,), then only O,N,, patients would be treated within the fixed budget for that disease area (which is fewer patients than would be treated to achieve the same outcome with therapy B). If all patients were to be switched from therapy A to therapy B, the required budget for a minimum acceptable standard of patient care (if 0, were now the minimum standard) would be greater than that for therapy A-based treatment. Thus within the budgetary constraints, given a total-cost function of TC,(O,), in which 0, > 0,, only OnN,, patients can be treated. This does not mean, however, that the health care system would opt for the exclusive use of therapy B and either meet the additional budgetary requirements or treat fewer patients. If treatment is intended to remain within the initial budget and all patients are to achieve the same outcome level, then the solution is to switch only some patients from therapy A to therapy B. However, “switching” is a misnomer. This is not a discussion of an omnipresent health planning authority allocating patients to particular therapies but of a “market” solution in which the distribution of patients between altemative therapies is achieved through the costminimizing (or profit-maximizing) behavior of the treating physicians. Consider point F, at which the two cost functions for an outcome level of 0, in1097
CLINICALTHERAPEUTICS@
tersect (Figure 3). At this point, the outcome level per patient could be achieved by allocating 0ANA3patients to therapy A and O,N,, patients to therapy B, while staying within the initial budget. However, although point F represents a combination of technically efficient outcomes, it is not economically efficient with regard to maximizing outcomes for the treating population with a fixed budget allocation to that disease area. The shaded area in Figure 3 represents the potential for outcomes gains for the treating population while remaining within the constraints of the original budget and continuing to treat all patients in the target population. The criterion for maximizing the outcome gains (within the cost constraint) is to reallocate patients until the marginal cost of achieving a given outcome is the same for both therapies. This is achieved when the total-cost functions for the two therapies (to produce the same outcome target) are at a tangent to each other. This is represented by point E in Figure 3. At point E, OANti patients are assigned to therapy A, with the balance, OBNBZpatients, assigned to therapy B. Total outcomes are maximized (at outcome 0,) for the treating population, and these outcomes are delivered within the constraints of the original budget (O,TC,, + O,TC,,). This may be described as an equilibrium solution in which the distribution of patients is such that benefits are maximized within a cost constraint (or, alternatively, the production of target health benefits is at least cost for a treating population). The introduction of therapy B allows the average outcome per patient to increase within the constraints of the original budget. This final or equilibrium distribution of patients is, therefore, a result of the 1098
cost-minimizing behavior of physicians, which allows them to allocate patients to different therapies given a budget constraint and target outcomes that may be determined contractually. INCREMENTAL COST-OUTCOMES RATIOS It is of interest to contrast the above analysis with the use of incremental costoutcomes ratios as decision variables. Incmmental cost-outcomes ratios are intended to summarize, for an intended health care purchaser, the additional benefits expected to arise from the introduction of a new therapy and the additional costs associated with these benefits. Because incremental cost-outcomes ratios are typically considered only when a new therapy is both more effective and more costly than an existing therapy, these ratios are usually represented by a four-quadrant diagram in which the crossover (total costs, total outcomes) point (A) is the existing therapy for the treating population. It is assumed that the entire treating population can be switched from one therapy to another, and that the only relevant quantities are estimated total costs and outcomes. Figure 4 contrasts the two therapies analyzed above, therapy A and therapy B, for the fixed treating population N. Total costs are represented on the y-axis and total outcomes on the x-axis. The total costs and outcomes for therapy A (when all patients are allocated to therapy A) are represented by point A (total costs = OTC,; total outcomes = 00,); for therapy B, costs and outcomes are represented by point B (total costs = OTC,; total outcomes = 00,). Thus the incremental costoutcomes ratio is represented by the slope of the line joining points A and B. Note
P.C. LANGLEY
TC,’
>A* ..:
l-r ‘“B
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Total Outcomes Figure 4. Incremental cost outcomes with partial therapy switching. A = total costs and outcomes for therapy A within a given treating population; TC, = total costs for therapy A, TC, = total costs for therapy B; B = total costs and outcomes for therapy B; A* = total cost of treating patients with therapy A to achieve the same perpatient outcome level as with therapy B; C = point of technical efficiency when therapies are combined, D = point of economic efficiency when therapies are combined; O,, On, and 0, = total outcomes achieved for the treating population. also that in applying an incremental costoutcomes approach, there is typically an implicit assumption of constant returns to scales (or constant costs per unit of outcome), so that average costs equal marginal costs irrespective of the number (or proportion) of patients treated. In this case, decreasing returns (increasing costs) are being assumed, and only the total costs and outcomes for a fixed population being treated with each therapy are reported. If the health care system attempted to achieve the outcome level associated with therapy B by allocating more resources to
treating patients with therapy A, the new total cost might be represented by point A*, which lies directly above point B. However, if there is only a partial substitution of patients between therapies and the health care system maintains the initial budgetary constraint (OTC,), then it would be possible to move to point C, which gives the same average per-patient outcomes as therapy B but with all patients being treated. This is, however, not the final outcome. Point C represents the point of technical but not economic efficiency (ie, point F in Figure 3). If patients 1099
CLINICAL THERAPEUTICS”
are now reallocated to maximize average outcomes achieved, the final outcomes level would correspond to point D. Within the initial budgetary constraint, therefore, outcomes have increased substantially to 0, for the treating population. Thus using an incremental outcomes ratio to contrast patients being switched completely from therapy A to therapy B has the potential to be highly misleading as a decision variable. Although leaving it to the health care system to decide whether to incur the additional costs is the traditional approach to pharmacoeconomic evaluations, the situation is quite different if we allow for partial substitutions. With the only option being a complete switch, the health care system may decide that the new therapy is not worth the additional costs (particularly if resources must be switched from other areas and patient outcomes are reduced, which should be factored into the decision). If partial switching is allowed under conditions of increasing cost, then the appropriate comparisons are between A and D, not A and B. CONCLUSIONS The purpose of this paper has been to demonstrate that within a simple therapyswitching model of health care delivery, the role of incremental cost-outcomes ratios as decision variables is potentially misleading. When used to justify therapy switching by treating populations, incremental cost-outcomes ratios may understate the benefits (in both budgetary and outcomes terms) that result from partial switching between therapy options. They may also present an oversimplified picture of the health care production process and the role of intervention strategies in achiev1100
ing outcomes that are more substantial than the incremental approach would suggest. The implications for pharmacy care are also worth noting. Without additional resources being allocated to a treatment area, it may be possible to increase the average outcomes reported for the treating population (with outcomes targets assigned) by a redistribution of patients between treatment options. Rather than assuming that a particular efficiency or output productivity level is the best that can be attained (which is what the constant-cost, incremental cost-outcomes approach is assuming), this approach views the production of health care as a function of the distribution of resources within treatment areas by therapy intervention pathways. An incremental cost-outcomes calculation, which may be based on average costs and outcomes generated by or extrapolated from a clinical trial, may, therefore, underestimate the gains from introducing a new product under conditions of increasing cost. Address
correspondence to: Paul C. Langley, PhD, Department of Pharmacy Practice, School of Pharmacy, University of Colorado Health Sciences Center, 4200 East Ninth Avenue, Campus Box C238, Denver, CO 80262. REFERENCES Drummond MF, Stoddart L, Torrance GW. Methods for the Economic Evaluation of Health Care Programmes. New York: Oxford University Press; 1987:102-104. Langley PC. Therapy evaluation, patient distribution, and cost-outcomes ratios. Clin Ther. 1995;17:341-347.