Towards an analytical approach to health sector reform

hdni policy Health Policy32 (1995)93-109

Towards an analytical approach to health sector reform Christopher J.L. Murray Burden

of Disease

Unit, Harvard Center for Population and Development 22 Plympton Street, Cambridge, MA 02138, USA

Studies,

Revisionreceived20 January1995;accepted24 January1995

Abstract

An analytical approach to health sector reform requires definition of the objectives of the health sector. Although a multiplicity of objectives may be appealing, there are compelling analytical reasons for simplicity. It is argued that a health status maximization objective is a widely acceptable choice, which captures most of the important aspects of utility maximization of relevance for health sector reform. Equity can be addressed in the process of aggregating individual outcomes. The widely recognized need for health sector reform in developing countries constitutes, in itself, evidence of the market failures in the health sector. If the market worked, why would we need reform? Given an objective and a justification for reform, analysis should proceed to address measurement questions and intervention strategies. The disability-adjusted life year lost is proposed as a consistent and feasible measure of health. Based on an optimization model of health sector performance in Africa, the paper discusses interventions to improve allocative and technical efficiency. These are broadly characterized as interventions dealing with lack of knowledge and interventions dealing with institutional shortcomings. They lead to different approaches to health sector reform. The paper concludes that we now have a systematic analytical approach to reform, in which tools and methods for addressing information gaps are well developed and need to be more widely applied, while those addressing institutional failures still need further development and application. Key words:

Health sector reform; Equity; Developing countries; Analyses

016%8510/95/$09.50 0 1995ElsevierScienceIreland Ltd. AU rights reserved. 0168-8510(95)00729-C

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1. Introduction A number of political and intellectual initiatives are bringing new insights into the process of health reform. In the United States, health sector reform has become a major political issue forcing broad evaluation of alternative visions of the US health system. While most attention has focused on various institutional arrangements and their effect on cost and access, the Oregon Health Services Commission [l], introduced an approach based on cost-effectiveness and allocative efficiency. Nearly simultaneously, at the international level, the World Bank has made a major contribution to the debate on health sector reform with the World Development Report 1993 [2], which also draws heavily on considerations of cost-effectiveness and allocative efficiency. The World Bank and Oregon reports bring to the fore different diagnoses and prescriptions to cure ailing health systems as compared to the prior work on health sector reform. Given the rapidly evolving array of analytical approaches and ultimately policy prescriptions, the purpose of this paper is to explore the available analytical approaches to health sector reform. Specifically, the goals of this paper are several: first, to review the range of objectives for the health sector; second, to review the theoretical reasons why health sector reform may be necessary; third, to explore the analytical tools available to assist in understanding the health sector if health maximization is the goal of the health sector, and fourth, to speculate on the role of these tools in applied analysis. 2. Objectives of the health system Analysis of health sector reform will remain vague and difficult to quantify unless the objectives of the health sector can be clearly articulated. Defining a single overarching goal for the health sector is not as straightforward as one might assume. At one level, there is a lively debate on the meaning of health and the boundaries of the health sector. Positive definitions of health such as the World Health Organization’s complete state of physical, mental and social well-being, may be so broad as to be indistinguishable from welfare [3]. On the other hand, negative definitions, such as the absence of disease, impairment or disability, are seen by some as too restrictive [4]. For the purposes of this paper, we will not explore the far-ranging literature on the meaning of health. Rather, the rest of the paper presumes we are operating with some consensus negative definition of health and operationally meaningful boundaries to the health sector. Most public health practitioners are comfortable stating that the objective of the health sector is improving the health of the population or maximizing an appropriate measure of health status. For example, during the Health Leadership Forum at Harvard in June 1993, more than a dozen ministers of health were asked about their objective for their health systems, and they all responded that it was improving population health status to the greatest extent possible with their budgets. This widely held view is the population equivalent of the individual physician’s responsibility to improve the health of a patient.

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An alternative objective for the health system is to provide interventions and services that maximize welfare or, in other words, some aggregation of individual utilities. Most economists are more comfortable defining health sector objectives in terms of welfare maximization rather than in terms of health status maximization. Both health maximization and welfare maximization objectives, when clearly stated, require some explicit method of aggregating individual health status or utility to derive population health status or welfare. Because population health and welfare are functions of individual health and utilities, respectively, equity considerations must be captured in this aggregation process’. Ultimately, a social value for equity which can be formalized in various ways must be incorporated when real application of either approach is attempted. Other objectives are at times proposed for the health sector. There is an extensive literature where the stated goal is cost minimization, but as Barr [7] states, cost containment is not, per se, a sensible objective. It becomes one if health sector expenditure is larger than it should be, implying some normative judgment which can only be made with reference to some objective function such as welfare. Similar claims for other health sector objectives can nearly always be subsumed into the form of individual’s utility functions which can and must be functions of income and many other things, or into the form of the social welfare function that aggregates individual utilities to calculate social welfare. Detailed lists of objectives may have practical uses, but it is important to recognize that the underlying goal is usually welfare maximization or health maximization. A digression into welfare economics is warranted to highlight the variant of welfare maximization that underlies much of economic analyses of the health sector. Following Robbins [8], most economists believe that interpersonal utility comparisons should not be attempted2. Vilfredo Pareto proposed a much weaker criterion for evaluating social options that does not require comparisons of interpersonal utility. He proposed that a change where no-one is worse-off and someone gains can still be judged desirable. If in State A someone is better off and no-one is worse off than in State B, State A is said to be Pareto preferred to State B. A state in which no-one can be made better off without making someone worse off is called a Pareto optimal state. When interpersonal utility comparisons are not possible, the best that can be achieved is thus a Pareto optimal state. It is critical to

‘Bergson [5] followed by Samuelson [6] and many others have formulated rules for aggregating individual utilities to derive social welfare which are generally called social welfare functions. Clearly, the contentious part of social welfare functions is not the need for them but their exact form. For example, how much importance should the utility of the poor have as compared to the rich in calculating social welfare. ‘The argument is simply that we can have no way of knowing if individual A is in a higher utility state than individual B regardless of ancillary information. Some economists, such as Harsyani [9] argue in effect that interpersonal utility comparisons can and are often made. More recent discussion of the information requirements to make interpersonal utility comparisons can be found in Elster and Roemer [lo] and Sen [ll].

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recognize that there are nearly an infinite set of possible Pareto optimal states in any given society. The Pareto criterion is not very useful in any applied welfare analysis because nearly every social choice involves winners and losers. This is particularly true when evaluating major changes such as health sector reform. In order that the welfare economist can analyze changes in which there are losers as well as winners, Hicks and Kaldor proposed that if the gains to the winners were large enough that they could compensate the losers for their losses and still be better off, the change proposed was a potential Pareto improvement 112,131. By inference, a ‘potential’ Pareto improvement is held to be a desirable thing. Clearly, whether a changes is desirable depends on whether the losers are in fact compensated. To call a potential Pareto improvement a desirable state is in effect to make interpersonal utility comparisons between the winners and losers. Despite their obvious conceptual difficulties [14,15], compensation tests, the approach proposed by Kaldor and Hicks, are at the heart of cost-benefit analysis and most welfare economics analyses applied to the health sector [12,13]. Using a compensation test to identify welfare-enhancing social changes is tantamount to stating that the marginal welfare of an extra dollar of consumption across individuals is equal. If everyone in society had a similar utility function but different incomes, it would also imply that the marginal utility of increased consumption does not decline with increased income. Taking a dollar from a poor person and giving it to a rich person would not decrease societal welfare according to the compensation test criterion. This highly counter-intuitive premise lies at the heart of many welfare economics analyses of the health sector. Not surprisingly, many of the debates between public health practitioners and health economists stem from the strong value choice implied by the use of compensation tests and the resulting policy implications. Even if one is comfortable with using the compensation test approach, which the author is not, in order to identify welfare enhancing social changes, the problem in the health sector is that there is no market for health and thus no market value of health gain, only a market for health care. The benefits in dollars of improvements in health must, therefore, be estimated if this variant of welfare analysis is to be undertaken, A variety of methods have been developed to estimate the value to different individuals of health improvement. These are broadly subsumed under the rubric of willingness-to-pay (WTP). Estimates of individuals willingness-to-pay for health improvement can be derived from examining how much workers must be compensated to take on risky jobs [16], consumers willingness-to-pay for increased safety [17,18] and direct elicitation of valuations for hypothetical situations using standard gamble methods3 [191. Not surprisingly,

‘In a standard gamble, an individual is offered the choice between two options. In option one, they are offered a state J of duration 1 with certainty. In option two, they are offered a state K with probability P and a state L with probability 1 - P. The probability at which they are indifferent between the two options is then a measure of the utility of J on a scale from the utility state K to state L. Applied to health, a standard gamble might set state J equal to blindness, state K to perfect health and state L to death.

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these methods all show that the rich are willing to pay more for the same health gain than the poor are willing to pay. When combined with the compensation test principle in cost-benefit analysis, WTP implies society should prefer ceteris paribus to improve the health of the rich as compared to the poor. While it is my experience that very few policy makers would agree with this evaluation of comparative welfare change, uninformed users of cost-benefit analyses may not recognize the distributional values buried in the analysis. If population health maximization was welfare maximizing then the analytical distinction between these two health sector objectives would disappear. Garber and Phelps [20], propose a model of individual utility to argue that purchasing units of health care up to the point that the cost per Quality Adjusted Life Year4 equals the marginal utility of income is utility maximizing for an individua15. Their result depends on an individual’s consumption remaining constant over time and the effect of health states worse than perfect health is to multiply the utility function by a constant value, the quality weight for the health state. While their result for an individual is interesting, when applied across individuals it raises a number of problems. If welfare were simply the arithmetic aggregation of different individuals’ utilities, the social goal according to their formulation that would maximize welfare would be to maximize some form of income-weighted QALY@. Other strange properties would follow: ceteris paribus, taking money from a sick person and giving to a healthy person of the same income would enhance welfare. The work of Garber and Phelps, however, opens up the prospect of defining under what assumption population health maximization may be welfare maximizing [21]. While work on this subject has not defined these assumptions, we can highlight key properties that such a set of utility functions and a social welfare function that aggregates these individual utilities should have to be intuitively appealing. First, most of us believe that consumption and income growth are good so that the marginal social welfare of consumption of any individual in society should always be greater than zero 7. Second, the egalitarian tradition of public health leads many in the field to claim that the marginal social welfare of consumption for the sick should be equal to or greater than the healthy ceteris paribus; otherwise, taking money from the sick and giving it to the healthy would be welfare enhancing. Third, the amount a sick person is willing to pay to improve

4Following Zeckhauser and Shepard [21], a QALY is an indicator of health outcome in which time lived in a particular health state is multiplied by a utility weight for that health state. One QALY is equivalent to a year of life lived in perfect health. ‘Garber and Phelps developed a model where expected utility equals the sum of over years of the utility of consumption multiplied by a scaler for the quality of life in a particular health state multiplied by the probability of surviving to that year. The expression is also multiplied by an exponential decay to allow for discounting. 6The exact form of income weighing would depend on the shape of the utility function as income or more accurately consumption changes. 7Not all would agree. Someone very concerned with the effect of envy on individual’s utility may argue that at some point giving more money to the richest person in society may actually decrease social welfare.

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his health by a fixed amount should rise with income. Note this does not mean that the increment in social welfare of improving a rich person’s health is greater than improving a poor person’s health. While there may be a broad consensus on many or all of these points, it proves rather difficult to formulate individual utility functions and a social welfare function that will have all these properties*. It is interesting, however, to speculate that many individuals will hold much more clearly articulated distributional values when considering health. Perhaps, work on measuring a community’s distributional values and thus a social welfare function will be more likely to succeed if attention is focused on the distribution of health benefits. Pending further theoretical work in this area, we are forced to conclude that there is likely a difference between maximizing health status as measured using an appropriate indicator and maximizing welfare except perhaps for a specific set of social welfare functions. In summary, health maximization and welfare maximization as goals for the health sector each have constituencies. The goal of health maximization is likely to be only consistent with welfare maximization for very particular social welfare functions. Given the incipient state of theoretical work in this area, there appears to be three options for specifying goals for the health sector in a way that can be quantified and can facilitate analytical approaches. l

l

l

Use the standard willingness-to-pay approach to measure the monetary costs and benefits of different social options. To get around the obviously counter-intuitive premise underlying compensation tests, post-hoc adjustments for equity must be introduced. Unfortunately, the results of the willingness-to-pay analysis is frequently seen as positive and thus scientific whereas the post-hoc treatment of equity is seen as soft and normative. Moreover, even if there is a consensus to modify the basic compensation test results for distributional considerations, there is no consensus of how this should be done. Most often decision-makers are not willing to introduce in a defensible fashion the necessary consideration of equity. The distribution of benefits and losses thus falls out of the debate. Define a social welfare function that incorporates explicitly the value of benefits to different groups and calculates the total welfare gains from each option directly. As with the first option, it is difficult to convince sceptical audiences that the exact choice of a social welfare function is correct. Experience in institutions like the World Bank has shown that this is not a popular approach. Use health maximization as a second best solution to welfare maximization. It may well be much easier to come to a consensus on how to measure health than how to define a social welfare function. There appears to be a broad natural consensus that the equivalent health event in any individual should count

‘While there is not the space in this brief paper to present the subject formally, one can achieve the desired properties if each individual’s utility function is structured to give property 1 and 4 while the social welfare function weights each individual’s utility as an inverse function of utility in the absence of ill health.

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equally, providing an easy starting point on the distributional considerations that plague defining social welfare functions. As a second best solution, health maximization may actually be close to welfare maximization for a reasonable range of social welfare functions. This claim is speculative and further studies on how much health maximization deviates from welfare maximization need to be undertaken. In its favour, health maximization already enjoys widespread appeal with many governments and those working in the health sector.

3. Measures of health If we choose to specify the goal of the health sector as health maximization, how should health be measured? Any population measure of health status incorporates a series of social preferences. The closer the value choices incorporated into an indicator match social preferences, the closer the indicator is likely to be to measuring the health dimension of welfare. There are at least four critical preferences in any measure of population health status. In the following, these choices are briefly mentioned; readers are directed to Murray [22] for a detailed treatment of the issues. First, what is the duration of life lost due to a death at each age. Alternatives include the period expectation of life at that age, the estimated cohort expectation of life at each age, a standardized expectation of life at that age and a potential limit to life. Second, what is the value of a year of life lived at different ages? This could be equal across all age-groups or could reflect social roles that vary with age giving higher values to young and middle aged adults on whom children and the elderly depend. Third, how is time spent living with a disability compared to time lost due to premature mortality? A set of disability states and weights for each state must be defined and the basis for choosing weights established. Fourth, should a population health measure incorporated time preference? Discount rates in use for health sector assessments range from zero to 10% or more [23-271. One variant of a health measure is the Disability Adjusted Life Year developed for the Global Burden of Disease study [28-301. The social preferences included in this indicator are described fully in Murray [22]. One of the major advantages of this indicator is that there are now estimates by age and sex for 100 conditions in eight regions. In addition, Jamison et al. [31] have presented cost-effectiveness results in terms of cost per DALY for nearly 50 of the most important health interventions in developing countries. The Global Burden of Disease Study provided the first comprehensive picture of health sector outcome by etiology using standard measures. Much of the following discussion uses DALYs as a suitable measure of population health status and intervention outcome; nevertheless, the discussion would also apply to other measures of population health status. 4. Why is health sector reform analysis necessary? If the objective of health sector reform was welfare maximization, why is explicit analysis of objectives, interventions and institutions necessary? Again, a brief

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digression into welfare economics is critical to answering this question. The first fundamental theorem of welfare economics states that given any initial distribution of resources between individuals, a perfectly competitive market with perfect information will lead to a Pareto optimal state. In other words, accepting the distribution of resources in society as given the best outcome society can achieve will be reached through the market. If markets worked in health and we accepted the current distribution of resources, we would not need any reform of the health sector nor any analysis in support of reform. A market will not lead to a Pareto optimal state if any of the assumptions about perfect competition, perfect information and no externalities are violated. Likewise, we may not accept the Pareto optimal state achieved if we do not accept the initial distribution of resources. From a welfare economists point of view, the mandate to analyze the objectives, interventions and institutions of the health sector is derived from the extent of market failure and/or distributional concerns captured in a social welfare function. Three types of market failures that suggest the health market does not lead to a Pareto optimal state are worth reviewing. First, there is a widespread consensus that health insurance markets do not work because of the problems of moral hazard and adverse selection’. Second, there is also a consensus that there are externalities and public goods in the health sector which cause market failure and justify government actions. Externalities are situations where the consumer is not the sole beneficiary of something he/she purchases. Treating many infectious diseases involves an externality because everyone in the community may benefit when someone that is infectious is treated and reduces the risk of transmission. Public goods are goods that are not affected by the number of consumers such as national defence. Examples of public goods in the health sector include environmental sanitation or vector control programs. Moral hazard, adverse selection, externalities and public goods form a minimal set of market failures in the health sector. This minimal set justifies at least a focused range of health sector analysis but these alone do not justify more detailed studies of the content of interventions in the health sector. A more controversial area with some economists, although not with most physicians, is the extent of market failure in the health care market beyond that caused by externalities and public goods. Unlike many other sectors of the economy, consumers are in a difficult position to purchase the utility maximizing quantities of health care for a number of reasons. First, individuals are not always able to evaluate their own health status or their risk of future ill health warranting current intervention. For example, most national health examination surveys in high income countries find that a large proportion of hypertensives and diabetics are undiagnosed and clearly cannot demand appropriate therapy for problems that are not even perceived. In developing countries, expected norms of health may be

‘See the paper in this volume by Hsiao (pp. 125-139) for a careful discussion of these types of market failure.

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so low that even individuals with health problems that should be easily perceived do not perceive themselves as ill and thus do not demand health care. Secondly, individuals are uniquely unable to judge ex ante the quality of health care and frequently cannot judge the quality of health care ex post. The near inability of consumers to assess the quality of care available stems from many factors. For a large subset of common conditions such as influenza, the common cold, travellers diarrhea and many others, the condition is self-limited. Despite having many opportunities to experiment with different therapies, consumers are at huge risk of ascribing their recovery to whatever therapy was used. The proliferation of ineffective but commonly purchased remedies for these conditions is evidence of the difficulty of judging quality in a self-limiting condition. For a smaller but more important subset of conditions, medical care may be critical to survival. Such situations are rare, so that if an individual mistakenly judges the quality of care they probably do not have the opportunity to make a better judgment in the future. The cost of making a mistaken judgment may be very high including disability or even death. For a third subset, such as many serious chronic diseases the costs in terms of time and training of acquiring information may be prohibitively high. To independently read and use the published medical literature may often require a degree of sophistication that the vast majority of consumers can never afford to acquire. Frequently, consumers will turn to health care providers both for information on what they require as well as its supply. The health care provider is then in a position to manipulate demand which may also contribute to market failure. Even after receiving an intervention, many consumers are not in a position to judge ex post the quality of care they have received because they do not have sufficient knowledge about outcomes with alternative interventions. This cluster of market failures that turn on the costs of acquiring information, the huge costs of making a mistake, the inability of many consumers to make independent judgments on quality and the agency relationship between health care provider and consumer may be even more important that the more traditional sources of market failure cited above. The extent of these new market failures dictates how much analysis of the mix of services delivered in the health sector is necessary [7]. Most of the rest of this paper proceeds under the assumption that there is substantial health care market failure. 5. Analyzing the performance

of the health sector

If we accept that an unregulated market in health care will not lead to a welfare maximizing or even Pareto optimal state, we are left with the complex task of proposing a mechanism by which such a state can be approximated. Once again, if we state the goal of the health sector is to maximize population health status, we are in a position to try and analyze what the mix of services and even the distribution of services are that should be provided. The challenge to the health sector becomes how to organize the delivery of health services including financing method, provider structure and the mix of interventions so as to maximize the gain in health status measure using some appropriate indicator. Recent analytical

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advances in measuring the burden of disease, cost-effectiveness of interventions and modeling optimal resource allocation allow one to provide a partial answer to this challenge. The answer, as the following discussion will highlight, is partial because the analytical tools to give direct answers to critical questions of finance and provision are poorly developed. Fig. 1 illustrates a health sector production function for a hypothetical country in sub-Saharan Africa. On the y-axis is Disability Adjusted Life Years averted through the delivery of health interventions and on the x-axis is total resources spent on health. At each budget level a higher number of DALYs can be averted. The production function represents the largest number of DALYs that can be averted at any given budget level given complete technical and allocative efficiency bearing in mind the constraints created by the existing human and physical infrastructure on the delivery of interventions. The curve also takes into consideration the existing profile of burden of sub-Saharan Africa. The analysis underlying this health sector production function also provides information on not only the combination of services delivered or interventions purchased at each budget level but also investments in human and physical infrastructure that will maximize DALYs. This health system production function has been constructed using three critical inputs. First, estimates of the regional burden of disease in terms of DALYs or some other measure of population health status are required. Second, estimates of the cost-effectiveness functions for preventive and curative health interventions are required. These functions must define the relationship between expenditure and burden from a particular disease including the increase in burden that would occur if expenditure is decreased from current levels in a particular country. It must be stressed that cost-effectiveness estimates are not numbers; they are functions that include changing marginal costs with increasing output. Third, the available human

.>

119

L ~~ _~

L.-_--L~

136 153 170 187 Total Cost (millions of dollars)

Fig. 1. Sub-Saharan Mica

~~~_-~

204

i---l

221

health sector production function.

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Table 1 Sub-Saharan Africa optimal infrastructure expansion Percent of GDP

New clinics New district hospitals New referal hospitals

2

3

4

5

6

215 17 0

411 41 0

523 47 0

578 51 0

597 54 0

and physical infrastructure in different regions of the country and the population with access to the system must be estimated. Given such information, one can use an optimization model to choose the allocation of resources across different interventions and infrastructure investments that will maximize health status for any given budget level. Elsewhere, Murray and coworkers [29,32] report on the development of a resource allocation model, the Health Resource Allocation Model (HRAM) written in GAMS. In HRAM, the inputs to the exercise are the burden of each disease or health problem in DALYs, the fixed cost of each intervention, the marginal cost per DALY function of each intervention, the total portion of the burden of each problem that can be alleviated if 100% of each intervention is purchased, the current referral hospital capacity, the current district hospital capacity, the current clinic capacity and the proportion of the population with access to clinics. Using this information, the program searches for the combination of interventions and infrastructure investments which maximize the number of DALYs that can be averted. Expanding the infrastructure has an indirect effect on DALYs by increasing the capacity of the system to deliver cost-effective interventions at the relevant level. Tables 1 and 2 provide illustrative results of this model for a hypothetical country in sub-Saharan Africa with a population of 10 million. Fig. 2 shows both the optimal expansion path for this country in sub-Saharan Africa and the current level of production for sub-Saharan Africa. The gap between the current level of production and the best that can be achieved can be measured on the x-axis in dollar terms and on the y-axis in DALY terms. The distance is a combined measure of technical and allocative inefficiency”. Through simple examination, one cannot desegregate the dollar or DALY costs of inefficiency into technical or allocative shares. That requires analyzing detailed information on the current pattern of expenditure and service provision as compared to the estimated optimal pattern and exploring in a case-by-case manner the reasons for the discrepancy. The full analysis of the mix of technical and allocative inefficiency that explains the

“Here allocative efficiency is taken to mean the efficient mix of health interventions while technical efficiency refers to the efficiency of producing any one intervention.

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Africa

Intervention

3.W

of GDP

Spending Low cost management of acute MI AIDS treatment with medicine and surgery AR1 screening and referral BCG added to DPT for tuberculosis Breast-feeding promotion Breast cancer treatment Annual breast exams Angioplasty or by-pass graft surgery Cataract surgery Cervical cancer referral Short course chemotherapy for sputum negative patients Short course chemotherapy for sputum positive patients Chlamydia treatment with antibiotics Colon and rectum cancer treatment CVD preventive program Gonorrhea treatment with antibiotics School-based anthelminthic chemoprophylaxis Hepatitis B immunization HIV blood screening Improved domestic and personal hygiene Management of hypertension Injected insulin and health education for IDDM Iodization of salt or water Sugar or salt fortified with iron Leprosy multi-drug clinic Referral leukemia Liver cancer treatment Lung cancer treatment Vector control for malaria Measles Oral iron supplementation for duration of pregnancy Mouth and pharynx cancer treatment Oral rehydrdtion treatment for diarrhea1 disease Papsmear at 5-year intervals Pneumococcal vaccine Poliomyelitis immunization Antibiotics for rheumatic heart disease RHD open heart surgery referral Schizophrenia Stomach cancer treatment Syphilis treatment with antibiotics Tetanus immunization Tetanus referral case management Semiannual vitamin A dose for children O-5 Improved weaning practices from education Infrastructure

0 0 6874 970 2564 0 0 0 860 0 6688 340X I07 0 u Ill 5Y7 3Yl 911 0 0 0 24Y 1’4 537 0 0 0 12 123 40X6 70 0 78’) 246 8X4 YO7 0 0 ‘4X 0 147 1651 0 577 1526 20713

J.U% of GDP

S.Oc; of GDP

DALYs

Spending

DALYs

Spending

DALYs

0 0 233 71 74 0 0 0 Y u 372 453 6 0 0 Y I? x 39 u 0 0 32 IY 6 0 U 0 304 277 1 0 12 1 16 30 0 0 3 0 156 213 0 3s 36

0 0 1 I 107 1783 2755 0 0 0 Y35 0 10208 501x 107 0 0 111 597 SOS Y67 5306 0 0 24s I24 541 0 0 0 16 5x7 7686 70 0 3x3 I 227 2543 I737 -388 0 331 II I47 3042 0 8X1 1526 34 502

0 0 277 SO 77 0 0 0 10 0 415 484 6 0 0 Y 12 Y 40 37 0 0 32 I0 7 0 0 0 34s 2’)s I 0 53 2 32 33 3 0 3 0 156 7.77 ---

‘77 s.l 77 0 1 0 IO 0 443 JS7 h Cl 7 Y I’ Y 10 7,/‘_ 0 0 32 IY 7 0 0 0 3wi 32-I I 0 123 2 36 3-l 5 0 3 0 1% 17, ---

0 -II 30

3 Jl 46

3 0

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DALYs (thousands) -

3000 2500 2000 1500 1000 500 0 L.--51

102

119

1 136 153 170 187 Total Cost (millions of dollars)

204

221

238

Fig. 2. Sub-Saharan Africa inefficiency.

gap between the current level of health achievement and the level that can be achieved given existing resources has not been completed in any country. Nevertheless, with existing tools, it is likely that such a composite health sector appraisal of performance and inefficiency will be performed in the near future. If the contribution of technical and allocative inefficiency to the performance of the health sector can be measured, it is interesting to speculate on the likely determinants of these two important aspects of health system performance. If the prior comments on market failure are correct, there may be a paradoxical relationship between technical and allocative efficiency. Exposing providers to market signals or in other words consumer demand will naturally tend towards increasing the technical efficiency of service production in a competitive economy. On the other hand, if the mix of interventions delivered is driven by the market, this may well increase allocative inefficiency as consumers and/or providers combine to deliver cost-ineffective interventions and ignore cost-effective interventions. The appropriate regulatory strategy to enhance technical and allocative efficiency in the setting of substantial health care market failure may well be considerably different and complicated by this inverse relationship. The heavy emphasis on either technical or allocative efficiency characterizes much of the work on health sector reform. Most of the analysis of the United States health system is predicated on the assumption that the primary source of the gap between reality and the optimal production function is technical inefficiency. Researchers working on technical efficiency often search for variance in unit costs of production. Once variance is identified in for example hospital unit costs; investigators attempt to find the determinants of this variance and propose changes to raise the level of the inefficient. Mills and coworkers provide examples of such an approach in developing countries [33,34].

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While the existence of substantial technical inefficiency in nearly all health systems seems beyond doubt, the underlying cause for technical inefficiency and thus the general solution is not as clear-cut. Two basic explanations can be offered: deficient knowledge and deficient institutions. First, lack of knowledge on the best way to produce services may mean that some producers by chance are more efficient than others. An example of attempts to increase technical efficiency of health production from a given intervention through the provision of knowledge would be clinical decision guidelines for the management of acute chest pain in emergency rooms. The second broad class of explanation for technical inefficiency is institutional arrangements including provider incentives, reimbursement mechanisms, peer review, regulation, etc. Steps to introduce competition through privatization or internal markets are examples of reforms oriented to decreasing technical inefficiency though institutional change. The major purpose of these types of analyses are to try and structure provision and financing mechanisms to use incentives of price signal to encourage technical efficiency. With the development of cost-effectiveness as a more general tool, the analysis of allocative efficiency has now become feasible. Ten years ago, before the World Bank Health Sector Priorities Review [31] and the Oregon experiment [l], there was not a sufficient database on which to base an objective analysis of allocative efficiency of the entire health sector. With increasing work on cost-effectiveness, it now appears that there is quite substantial allocative inefficiency in developing countries especially in low-income countries. While it has long been recognized that there is an under-investment in preventive care and over-investment in curative care [35], detailed work on cost-effectiveness shows that such crude generalizations are not always true. Some of the most cost-effective interventions are curative while some cost-effective interventions are preventive or public health programs. As with technical efficiency, we can ascribe allocative inefficiency to two types of explanation: knowledge and institutions. The case for lack of knowledge as a key factor explaining allocative inefficiency is stronger than for technical inefficiency. If one examines the 10 most cost-effective interventions in developing countries [31], a number of them such as short-course chemotherapy for tuberculosis using hospitalization during the first 60 days of therapy would not have been considered cost-effective 20 years ago. Many other interventions such as oral rehydration therapy for diarrhea or food supplementation for children under five which are perceived as highly cost-effective do not appear in this short-list. Cost-effectiveness analyses to date have yielded many surprises: interventions thought to be cost-ineffective can in fact turn out to be excellent investments and interventions thought to be the best investment may be cost-ineffective. Jamison et al. [31] conclude, as one of the major findings of the World Banks Health Sector Priority Review, that not all prevention is cost-effective and not all curative care at clinics or hospitals is cost-ineffective. Allocative inefficiency may not be due to a lack of knowledge but to poor institutional arrangements so that available knowledge on the cost-effectiveness of health interventions is not used. Proponents of this viewpoint concede that costeffectiveness analysis may give more precise cardinal estimates of cost-effectiveness

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of interventions by arguing that the results will not change the estimated ordinal rankings to any significant degree. The major thrust for health sector reform must in this view be changing institutions. This raises the question: why are some country’s institutions better than others? If markets worked it might be simply the degree, then it would be useful to not only identify institutional arrangements that work well but the reasons that they have evolved in certain location and not others. While one can over emphasize the relative contribution of deficient knowledge and deficient institutions in causing allocative and technical efficiency, the balance of the two views leads to different approaches to health sector reform. Most public health practitioners would consider that both are present but emphasis varies. For example, consider the emphasis in the World Bank health financing paper [361 versus the World Development Report 1993 [2]. Even if both knowledge and institutions contribute to the explanation of inefficiency, an independent question is the extent to which each can be changed. An example of major institutional reform, the attempt to institute cost recovery in developing countries has not been a success to date. Despite some theoretical arguments for the reform largely founded on the notion that health care markets should work well, the results do not appear to have improved health sector performance [37]. Because the allocative efficiency approach based on cost-effectiveness is relatively new, there are no sectoral applications of the process that can be evaluated for impact. We must conclude that there is insufficient evidence to judge if the yield from a knowledge based approach is greater or less than an institutional strategy. 6. Conclusions

Before one can respond to the analytical challenge of health sector reform, the objectives of the health sector must be clearly defined. If the objectives are defined in such a way that progress towards these objectives can be quantified, an array of new tools can be used to analyze some important dimensions of poor performance. The need for such analyses, however, is predicated on the belief that there is substantial market failure in the health care market. Otherwise, the best reform strategy would be to reduce government intervention in this market. If the goals of the health sector are health maximization then using burden of disease methods, cost-effectiveness analysis, and health resource assessment, we are now in a position to analyze quantitatively the extent of inefficiency in a health system. From this analysis of inefficiency, one hopes that the main causes of both technical and allocative inefficiency can be identified and thus reform strategies proposed. The following steps are a speculative strategy for providing an analytical basis for debate on health reform when the objective of the health sector is population health maximization. . . .

1. Assess the burden of disease. 2. Assess the cost-effectiveness of both preventive and curative health interventions. 3. Assess the human, physical and financial resources used in the health sector and the set of activities they currently support.

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4. Define the health maximizing package of services and infrastructure investments at each budget level. 5. Analyze the gap between currently financed interventions and the intervention mix that would maximize health status in terms of allocative and technical inefficiency. 6. Analyze the political, institutional and financial determinants of the observed technical and allocative inefficiencies. 7. Determine the combination of financing, provision and regulatory strategies that can best address the political, institutional and financial constraints on technical and allocative efficiency.

l

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This list is not meant to be a chronological sequence, many of the steps should proceed simultaneously such as the first three. Some like steps 4 and 5 depend on prior results and thus must come in sequence. We are now in a position to provide concrete advice and tools to facilitate steps 1 through 4 and most likely step 5. The analytical methods to achieve steps 6 and 7 are not as clear. Clearly, successful health sector reform depends absolutely on getting steps 6 and 7 right. Accumulated wisdom based on previous anecdotal experience with different financing and provision arrangements is available. One limitation of this past body of experience is that its interpretation is clouded by strong ideological beliefs - for example, some believe that health care markets except for some notable externalities and public goods work well while other analysts believe there is extensive market failure. To fortify our abilities to provide analytical support to health sector reform, new tools and quantifiable experience with different institutional arrangements must urgently be acquired. In the meantime, powerful diagnoses of the ails of health sectors can be made with only uncertain but hopefully beneficial prescriptions provided. References [ll

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