Document(s) 7 of 21

Even more so than rich economies, developing countries can ill-afford to waste their scarce resources. Yet, developing countries are the most hard pressed to demonstrate good returns on current R&D expenditures (see Box 3.1). We are also seeing a growing recognition that the conduct of science does not have neutral socioeconomic outcomes, and we need to scrutinize more carefully who benefits from “cutting-edge research” and whose interests are largely ignored in the dominant directions of science. The preeminent challenge for low-income countries will be to ensure that health research improves the health of the neediest people.

Conceptualizing the management of public resources for health research as a diversified investment portfolio is a useful way of thinking through the potential benefits and risks of every project. It encourages managers to work constantly toward better returns by comparing the expected benefits of diverse investment options (Eyzaguirre 1996), and it enables them to deal with the inevitable uncertainty of research outcomes by selecting a “risk profile” appropriate to their own countries. For example, an island economy like Mauritius or a small country such as Namibia (with a population of 1.4 million) would realize little benefit from R&D investments requiring large economies of scale. In this regard, Namibia’s solid emphasis on problem-solving, particularly in trying to improve efficiency and equity in resource use, seems most appropriate (Katjiunanjo 2000[7]). In contrast, the wealth of Canada and the relative health of its population enable it to allocate more funds to exploratory research and less to trying to solve pressing problems. Yet, it can still have a portfolio designed to maximize expected social benefit (CIHR 2000).

Maximizing the value of health research means allocating resources to projects with the greatest expected benefit, as defined in the following formula:

Returns to each project under ideal conditions * probability of successfully implementing each study

Thus, even if resources go to the highest-ranked, “implementation-perfect” projects, one can expect benefits to materialize only with efficient implementation of research. In essence, then, maximizing the value of health research involves two iterative steps: (1) defining an investment portfolio of research to produce the greatest benefit possible within budget constraints; and (2) implementing the research efficiently.

When designing an R&D investment portfolio, a developing country would have to determine three issues:

• Where to make investments;

• What type of investment “instruments” (for R&D) to use; and

• How much public money to put into each R&D instrument.

A number of countries have used priority-setting processes to determine where they should make investments, thus effectively defining the scope of research. For example, the Health Research Plan of Uganda (1997–2001) stipulates a clear list of topics as most important. These reflect the prominence of Uganda’s health problems in the areas of

• Maternal and child health and nutrition;

• Water, sanitation, and the environment; and

• The communicable diseases of sexually transmitted infection, HIV–AIDS, and tuberculosis.

In addition, the plan circumscribes priorities for noncommunicable diseases and identifies priorities for improving health-service delivery (Okello and Emegu 2000).

The advantage of broad-based priority-setting over determining agendas exclusively on the basis of the prevailing incentives of science is that it better expresses aggregate levels of social demand (Dasgupta and David 1994). It also reflects the urgency of a society’s need to address its health problems. In low-income countries, efforts to achieve immediate improvements in health care and other social services generally have high rates of return at the margin. To allocate resources efficiently, the public sector needs to use a high discount rate in estimating the present value of long-term projects. The implication is that projects with large short-term benefits are preferable to those with equally high long-term benefits. To some extent, broadly representative processes of priority-setting can reveal these preferences (“social discount rates”).

Yet, even with valid priorities for disease research and appropriately allocated investments, research with a low expected social benefit may still receive the lion’s share of funding. For instance, an esoteric piece of work on some biochemical change resulting from malaria may address a national priority, but it would surely fail a benefit–cost test. Even highly relevant research does not automatically pass muster. One should, for example, heavily discount the present value of future health benefits from long-term commitments to new-product development. In a country carrying a huge burden of preventable disease, failure to apply high discount rates to expected future benefits will result in an inefficient allocation of resources. However, the global investment in R&D directed to the diseases of the poor is pitifully low — about 4.5% of total public spending on health research (Ad Hoc Committee 1996). Even if every effort is made to use existing tools efficiently, without a stream of new interventions in the pipeline the future burden of disease may still be higher than expected. Malaria is a good example. Because drug sensitivities constantly change, new-product development remains a high priority.

So the next step will be to determine the profile of research expected to give the most benefit to each country. WHO’s Ad Hoc Committee (1996) argued that disease persists for one or more of three reasons:

• Knowledge of disease processes and causes is inadequate;

• Existing “tools,” or interventions, are inadequate; or

• Use of existing tools is inefficient.

The Ad Hoc Committee suggested R&D instruments to respond to these inadequacies:

• Develop new health products or interventions (discovery and invention);

• Adapt efficacious but unaffordable interventions to make them cost-effective (innovation); and

• Achieve greater efficiency in the use of existing interventions (implementation R&D).

In the Ad Hoc Committee’s view, the concepts of both technical and allocative efficiency are implicit in the third instrument. It may be helpful, however, to more clearly distinguish between technical efficiency (putting inputs to best use, regardless of allocation) and allocative efficiency, defined by the Ad Hoc Committee as focusing resources on areas of greatest need. In conventional economic terms, we achieve allocative efficiency through market incentives based on people’s willingness to pay. Using this term to refer to allocations targeting the greatest burden of disease may therefore lead to misunderstanding. But more importantly, strident and politically connected interest groups and a health-information system with a focus on wealthier areas are likely to distract attention from the very sector of the population most in need of resources. These factors make it very difficult to reveal the true distribution of societal demand, leading both national and global health-research agendas to substantially neglect the problems of the poor. Unless a research portfolio has an explicit redistributive component, the current trends will prevail. And so we need to add a fourth type of R&D instrument to the three outlined above, namely, achieving greater equity in resource allocation. For low-income countries, strategies to achieve greater efficiency and strategies to achieve greater equity in resource allocation will almost inevitably be one and the same. Without an explicit agenda for equity, however, inefficient resource allocation will continue.

It is important not to define “type of R&D” in traditional disciplinary terms, such as biomedical research, clinical research, epidemiology, or social science. To develop new drugs requires laboratory research, but new service interventions may also require health policy or systems research. Similarly, improving cost-effectiveness may, for example, require biotechnological innovations in existing diagnostic tools. Thinking in terms of disciplines rather than in terms of the purpose of research projects may lead to turf protection among researchers or a deviation from an R&D trajectory with the highest returns.

The final step is to decide how much to spend on each project to maximize social benefit. In other words, the research portfolio needs to become a diversified investment portfolio, one that responds to new opportunities as they emerge while taking account of budget constraints and existing funding commitments. Having estimated the direct costs of individual projects, one can then more or less circumscribe the range of feasible research options. Costing various research options may seem like a theoretical exercise to countries heavily dependent on donor funding, as their national research management bodies do not have control over most financing for direct expenses. Nevertheless, a portfolio provides a basic framework for financial management of R&D, which should help low-income countries assume greater fiduciary control over public funds. Without costing, the research agenda remains a “wish list.” Without understanding financial flows, a country has no way of determining whether it is aligning its resources with national priorities.

The importance of this step is illustrated in the priorities chosen in Hungary and Uganda. Despite the value of their priorities, they were too broad for these countries to really direct their resources to greatest benefit. Uganda chose reproductive and sexual health in adolescents as a priority, and Hungary chose public-health research and epidemiology (Makara 2000;[8] Okello and Emegu 2000). These choices reflect the first step in clearly defining a scope of research, yet they were too ill-defined to ensure that resources would eventually go to projects with high expected returns.

A legitimate concern for researchers is that an overly prescriptive agenda may undermine the typical incentives for science, such as autonomy and curiosity. One option would be to specify a detailed research agenda but retain the possibility of funding for researcher-initiated projects. But these projects should still have to meet the criterion of high expected social benefit.

Before allocating funds to specific research projects, a country may set aside a portion of its total health-research budget for baseline institutional funding. Although competitive funding encourages diversity and discovery and is a mechanism for aligning research with national priorities, 100% competition is probably not a good option for most countries. A fragile R&D infrastructure will be intolerant to dramatic fluctuations in funding across institutions from year to year. National research capacity, already weak in certain disciplines, may be severely jeopardized if even one institution fails to secure adequate funding for a relatively short time. Although baseline funding for institutions may act as a drag on incentives to do good research, a degree of stability and security may also encourage researchers to take risks (Dasgupta and David 1994), and it may reduce destructive rivalry between organizations. A feasible alternative would be to have a mix of baseline funding for indirect costs of maintaining a research institution and a competitive system for direct investments in research. Research leadership requires considerable creativity and skill to manage the interface between competition and collaboration to ensure that scientists have incentives for both individual and collective innovation.

With further refinement, the investment portfolio may enable planners to anticipate future health problems, exploit transient opportunities with high expected benefits, or “sunset” less relevant projects. Revenue centres should be in place to channel funds for direct costs and allow monitoring of financial flows.

Each country would have to decide on its own strategic emphasis, and the resultant R&D profile would have to include ballpark estimates for the allocation of public funds across investment strategies. For example, warning bells should sound if there is overwhelming agreement that the country’s priority is to improve the efficiency of existing interventions while most resources are going to developing new ones. Although practical considerations would shape the final investment portfolio, it should bear a close resemblance — both in scope and in strategic emphasis — to priorities expected to maximize social benefit. Even if the research agenda aims at the greatest social returns, poor implementation of the research program would eliminate much of its potential gain.

The efficient implementation of the R&D portfolio means

• Enhancing research outputs; and

• Reducing the costs of research.

For the purpose of this discussion, research outputs are enhanced if they lead to greater social benefit (holding costs constant). The gist of the following argument is that far greater effort should be made to stimulate the demand for research. Considerable potential also exists to bolster supply, simply by reallocating and leveraging existing resources.

Gibbons et al. (1994) remarked that research has, if not a standard, then nevertheless a “social” market, in which various types of consumers use the outputs of researchers. A supply-driven model lies behind much of the research in low-income countries. Using this model, these countries’ policymakers assume that if they can train enough researchers and build enough institutional capacity, outputs will be put to good use. An implicit assumption of supply-side strategies is that market-driven (economic) incentives will provide the impetus for innovation once critical research mass has been achieved.

This approach draws on the conventional economic wisdom that the main market failure in R&D is underinvestment in basic research, because basic research has no obvious commercial application and therefore requires public financing (Pavitt 1991). Yet, in low-income countries, underinvestment in “upstream” research is not the only “market failure” — the demand for research expected to meet an enhanced supply often fails to materialize (Alvendia 1985; Bhagavan 1992). Public officials, the media, industry, community groups, and other potential users rarely seize opportunities to capitalize on new knowledge, and this weak demand is reflected in low national investments in R&D, low salaries for researchers, and limited use of research findings. Newly trained researchers find little incentive to remain in universities or other public research centres. Those who do remain find it difficult to sustain their enthusiasm for life-long learning and innovation, and many settle into a bureaucratic mode of work, with little potential for new discovery, and this further suppresses the aggregate demand for research (Acemoglu 1997). Supply-side capacity-building strategies that do nothing to stimulate the demand for research are unlikely to achieve expected results and may actually further distort allocations by creating incentives among scientists for private gain. Without public demand for useful research, efforts to strengthen institutions may only help to create personal empires, and funds to foster individual incentive may only lead to the self-aggrandizement of researchers.

Bowles and Gintis (1996) referred to this mismatch between supply and demand as “coordination failure.” Innovation theorists echo this charge of disequilibrium, describing inefficient research as uncoordinated “pushing and pulling” — being tugged in different directions by the respective motivations of researchers and users. Researchers “push” R&D in the direction of their own interests and scientific incentives. Market-oriented users “pull” research in the direction of applications they expect to yield the highest returns. In this situation, research leadership can be instrumental in efficiently integrating push and pull (Baskerville and Pries-Heje 1997).

What does it mean, in practice, to stimulate demand for research? Science and technology (S&T) managers have traditionally focused on detailed financial, physical, and human-resource planning: How many researchers do we need? What institutional capacity is required? What level of investment in R&D is sufficient? Now we are realizing that the main purpose of research leadership is to stimulate interaction among researchers and between researchers and users (Segal 1987; Neufeld et al. 1995). For a small island like Mauritius, the need to engineer interaction between researchers and users is not a great issue: scarcity of human resources leads to the multitasking of individuals, who often wear the hats of researcher and service manager interchangeably (Mohabeer 2000[9]). But bigger countries need to make a deliberate effort to forge links between people traditionally working in separate spheres. Lithuania, for example, explicitly designed its National Health Program to integrate health care, research, and teaching. Synthesized research findings are a regular input into the design and monitoring of the National Health Program, and these findings are presented to plenary sessions of parliament (Grabauskas 2000).

In other countries, pressing problems have provided the impetus for demand-driven research. For example, Thailand’s concern over high concentrations of iodine in salt led to a new low-cost technique to measure iodine levels, and government efforts to reduce the purchasing cost of medicine resulted in an effective new scheme for drug procurement (Sitti-Amorn 2000[10]). In Bangladesh, research is an important part of the Integrated Nutrition Program, accounting for 28% of the total public expenditure on health R&D. Its clear goal is to improve nutrition (Bhuiya 2000[11]).

In time, demand-induced research should translate into a greater benefit to society, as well as to researchers. As researchers’ salaries increase, so will the cost to society of their research. But additional public benefits outweigh these costs — a win–win situation.

Nurturing the supply of health research is an important way to enhance research outputs, but the focus of supply-side strategies is often too narrow. For instance, they often emphasize building up resources for R&D at the expense of allocating them most efficiently; alliances with international partners, to the detriment of national consortia; and leveraging resources only by gaining access to donor funds, without giving adequate attention to creating a synergy of national efforts. A different, entrepreneurial mind-set opens up new possibilities for low-income countries. This new approach views health-research leaders, not as information bankers, but as “knowledge entrepreneurs” who aim to squeeze as much social benefit as possible out of every rupee or shilling. We can think of these research leaders as investment-portfolio managers, whose tasks are to

• Constantly redirect resources to options expected to give the highest returns;

• Seize on new opportunities for unusually high expected benefits; and

• Achieve economies of scale and risk-sharing through innovative partnerships.

By executing these tasks, research managers can add substantially to the value of current investments in R&D.

A recent report on health research in Kazakhstan illustrates some of the challenges in developing an entrepreneurial approach to managing resources for R&D. This country has built up an extensive and well-organized scientific infrastructure in 14 research centres and 6 medical universities. Akanov, the author of the report (Akanov 2000[12]), noted that despite sufficient scientific potential, Kazakhstan’s research capacity fails to address the main needs of the population, and investments in underresourced areas of R&D could dramatically improve the efficiency of allocation. These would include investments in strategy development, health promotion, and a clearer analysis of the specific determinants of ill-health in Kazakstan.

One effective strategy for reallocating resources would be to design appropriate incentives for researchers to work on neglected topics. Although some researchers in low-income countries can compete internationally, most have low opportunity costs and lower salaries than other professionals in their countries. Given the poverty of these economies, the countries have little prospect of increasing researchers’ salaries, so they need to improve the “psychic rewards” of being a researcher. However, individual rewards and incentives are difficult to institute and, in any case, incompatible with a team-based approach. Where interaction and collaboration among researchers are the driving forces for innovation, personal financial incentives may well be counterproductive, and team incentives would be more efficient (Gibbons et al. 1994).

Amabik (1999) suggested that the strength of team incentives depends on the

• Amount of challenge they give;

• Degree of freedom researchers have in the R&D process;

• Design of the teams;

• Level of encouragement; and

• Nature of organizational support.

Carefully designed team incentives can enhance outputs by redirecting efforts to create the greatest social benefit and improve the quality of R&D. In sum, stimulating demand for research and reallocating resources to maximize expected social benefit should increase the returns on investment in R&D. Another way of increasing returns would be to hold outputs constant but reduce the cost of doing research.

However, in real terms, researchers in low-income countries face higher costs than their counterparts in wealthier countries. These differentials may result from the various types of higher costs encountered in low-income countries:

• Financial costs (almost all financial transactions are more expensive);

• Economic costs (transaction costs are greater, particularly in communication and collegial interaction); and

• Political costs (researchers may incur personal and professional costs in societies that repress free speech).

Dasgupta and David (1994) argued that the main transaction costs in research are those incurred in communicating information, which is a point borne out in many developing countries. Their communication infrastructure is poor, and their researchers find it difficult to tap into global R&D networks (which have little interest in low-income countries) (Gibbs 1995). Furthermore, the shift away from the conception of knowledge as a public good to that of a sellable commodity is pushing up the cost of information. In real terms, developing countries pay far more than wealthier ones for the same information. Some communication costs are internal, however, and stronger interaction among researchers and between researchers and users would reduce many of the costs resulting from inefficiencies.

In Uganda, the Ministry of Health has recognized the need for a dynamic facilitator to bring together researchers, policymakers, and the public to improve health and development. The Uganda National Health Research Organization has responsibility for forging networks and effectively breaking down the barriers that push up the costs of research and place the country at an unnecessary disadvantage (Okello and Emegu 2000).

The Rural Advancement Committee in Bangladesh has recognized the potential of information-sharing to improve returns on investment in research. Its Research and Evaluation Division places increasing emphasis on information-sharing at field, program, and policy levels (BRAC 2000). It has tried and had success with new avenues for disseminating information, including field-level workshops, prominent bulletin boards, and the popular press. Although such strategies require more money, they reduce the real costs of research and enhance outputs.

This review of insights from the 1990s supports what antipoverty activists and community organizers have been saying for decades. We need to invest in the health of all people and reduce inequities that constrain economic growth and human development. In addressing these objectives, health research holds even greater promise than it did a decade ago, as the application of knowledge increasingly underpins global development.

The experience of the 1990s, however, suggests that this potential is going to waste in a world dominated by the interests of the rich. Prevailing incentives for S&T reinforce these interests and do little to improve the health of the poor. The task, for both national governments and the international community, will be to create incentives for more R&D to improve equity in resource allocation and efficiency in use. And as traditional distinctions between science and technology are increasingly blurred, it becomes more critically important to design a different type of international research architecture — one that combines public, private, and nongovernmental efforts for the sake of a common global good. Given the striking opportunities to attain higher returns on current investments in health research, there is no reason why other incentives should jeopardize the existing capacity of any research discipline. On the contrary, better alignment of R&D with expected social benefit should, in time, lead to stronger demand for every type of research.

Public investment in health research should aim to maximize social benefit, defined as better health for those who need it most. Developing countries cannot justify health R&D on the basis of its contributions to educational and scientific capacity alone, despite the benefits of basic research for the educational system (Garrett and Gransquist 1998); at the margin, investments in primary and secondary education will produce higher returns (Psacharopoulos 1994). Nor can developing countries justify health research on the basis of its contribution to economic productivity, despite the effects of R&D on social welfare (Temple 1999). R&D only becomes a major factor in economic growth once a country has reached a certain threshold level of productivity (Birdsall and Rhee 1993). Low-income countries can only justify health research if it efficiently and equitably improves the health of their people.

Figure A3.1 is an attempt to “map” the relationship between health research and development. If health research is to be an effective instrument for development, we need to understand the mechanisms mediating its effects. We can then strengthen linkages that lead to development based on equity and counter the tendency of health research to favour the rich. Trying to depict the linkages between health research and development is ambitious and, some would say, naive. Trying to suggest causal linkages is fraught with problems! But we need to start somewhere and, rather like doing a jigsaw puzzle, begin to fill in the pieces as best we can. I have tried to summarize current knowledge (as I understand it) and give one or two references for each piece of current knowledge that I find particularly useful.

The first problem is defining development. Current approaches to this task come under four headings:

• Economic growth;

• Reduction of inequality (defined later);

• Reduction of poverty; and

• Maximization of individual capability.

The second problem is dealing with the complexity and iteration of relationships, which I have tried to simplify without being overly simplistic.

Figure A3.1. Health research and development.

Notes:

1. Neoclassical approaches to development have stressed the fundamental importance of economic growth. This argument is well established and needs no elaboration.

2. For those who view development as economic growth, inequality usually means income inequality. But it also refers to inequality in political participation, economic assets (land, human capital, and communal resources), and social conditions (housing, education, health). A distinction is often made between inequality of opportunity (lack of access to essential inputs for productivity) and inequality of outcome (Tobin 1970). Others, such as Amartya Sen, see development as the maximization of individual capabilities. I have separated these two approaches, because they present different theories of how reducing inequality fosters development.

3. Poverty may be defined in absolute terms (for example, the World Bank defines extreme poverty as income of less than 1 USD/day, adjusted for purchasing-power parity) or relative terms (for example, as a proportion of the median income). Relative poverty is a measure of income inequality, and the prevalence of absolute and relative poverty can move in opposite directions.

4. Amartya Sen defined development as the maximization of individual capability through strategies to achieve equality and efficiency (defined in terms of “capability space”) (Sen 1992).

5. Econometric studies have found almost zero correlation between economic growth and subsequent levels of income inequality (contrary to the Kuznets hypothesis [Bruno et al. 1998]). In some countries, economic growth worsened inequality (many Latin American countries that undertook structural adjustment, for example). In others, income inequality decreased with economic growth. The implication is that national policies determine inequality outcomes, and governments can design strategies consistent with both economic growth and reduction of inequality.

6. Econometric analyses, as well as regional and country case studies, strongly support the assertion that high levels of initial income inequality constrain economic growth. Possible channels for mediation include inefficient access to capital markets leading to underinvestment in human-capital development (Persson and Tabellini 1994) and sociopolitical instability limiting investment and saving (Alesina and Rodrik 1994). An additional channel is a disproportionately high burden of disease in a sector of the population leading to inadequate human-capital formation in this sector and decreasing aggregate productivity.

7. Poverty reduction can also reduce inequality if targeted strategies reach the poorest groups, or if untargeted strategies benefit the poor more than they do the rich (Subbarao et al. 1997).

8. Lower levels of income inequality occur with faster rates of poverty reduction (Ravallion 1997), and high levels of inequality can partly explain the persistence of global poverty (Londoño and Széleky 1997).

9. The elimination of poverty is the central strategy for maximizing individual capability, by enabling individuals to reach their educational, health, and social potentials (Sen 1992).

10. For established market economies, economic growth has been a major factor in poverty reduction. But the emerging economies of East Asia have helped refine our understanding of the relationship, and even organizations like the IMF now call for equity-directed economic policies (subject to “nondistortional” conditions) (Tanzi et al. 1999).

11. Human-capital development, through education, and greater worker productivity are well-established factors for economic growth.

12. The connection between better health and nutrition and economic growth is difficult to pin down (and is now the subject of a new WHO Commission). However, the World Development Report 1993 and subsequent publications have argued that better health and nutrition improve educational and employment outcomes, leading to greater total-factor productivity (World Bank 1993; Temple 1999).

13. Strategies that make the biggest inroads into global and national burdens of disease will almost inevitably reduce inequality, because they will focus on those people who bear a disproportionate burden of disease. However, global and national strategies aimed principally at marginal improvements in the health of wealthier sectors will worsen inequality.

14. Conversely, strategies that reduce inequality (of opportunity or outcome) will almost invariably improve health status (through improved access to health care, higher income, less risk-taking behaviour, etc.).

15. Better health and nutrition can lead to better educational outcomes for individuals and communities, which will improve their prospects of obtaining higher earnings. It should be noted that persistent social stratification can inhibit this outcome, by trapping poorer families in a steady state of low human capital and income (Bénabou 1996), even after health status has improved. Once again, this illustrates the need for equity-oriented development.

16. The relationship between poverty reduction and better health is well established. Poverty is associated with higher rates of morbidity and mortality (Ad Hoc Committee 1996; Gwatkin et al. 1999).

17. Good health and nutrition are prerequisites for the attainment of maximal individual capability (by definition).

18. R&D drives technological progress, which in turn drives economic growth. Many studies have found high social and private rates of return on investments in R&D. Some have pointed out, however, that growth rates in OECD countries have not shown a persistent upward trend, despite accelerating R&D (Jones 1995). This is possibly explained by the growing importance of knowledge-capital development relative to physical-capital investments in established market economies. It is clear that R&D has substantial large-level effects on economic growth in established market economies, even though the relationship between levels of R&D investment and total-factor productivity is difficult to interpret (Temple 1999). There is no clear evidence that greater investment in R&D leads to higher economic growth in developing countries, and technology transfer from developed countries seems to be the principal determinant of economic growth (Birdsall and Rhee 1993). (Note that technology transfer is not a passive process but requires R&D [inventive] capacity [Helpman 1997]. The point is that targeted investments in R&D [as a way to achieve economic growth] are, of themselves, of little value in low-income countries.)

19. New technologies and rapid knowledge development and diffusion create new possibilities for more equitable human development. They may create opportunities for individuals, communities, and countries to “leapfrog” many of the barriers to human progress traditionally associated with industrialization. But at the moment, the trend is in the opposite direction. Gaps between rich and poor are widening as a result of global patterns of investment. There is an urgent need to shape knowledge production and dissemination strategies to reduce inequalities (UNDP 1999).

20. Discovery or invention of new health interventions arises from new knowledge of health and disease processes, and it establishes a dominant paradigm or course for further product development, process innovation, and implementation (Teece 1987). For example, the discovery of DNA in 1956 established a new paradigm for diagnosis and therapeutics.

21. Once a dominant paradigm has taken root, product innovation tends to follow. The invention of monoclonal antibody (in 1975) and recombinant DNA techniques (in 1980) provided the impetus for further biotechnological innovation. New disease-specific diagnostic probes and therapies are appearing at a phenomenal rate.

22. Process innovation concerns the refinement of the application of new tools (product innovation may be thought of as a refinement of the tool itself). Together, these processes improve the efficacy of interventions. An important implication is that learning (knowledge-sharing and assimilation) is a crucial component of R&D. Knowledge management strategies focusing almost exclusively on production will tend to have much lower expected benefits.

23. The existence of efficacious interventions does not necessarily lead to effective outcomes. The report of the WHO Ad Hoc Committee (1996) showed that known interventions can avert a large proportion of the global burden of disease, but it persists because of technical and allocative inefficiency, or because known interventions are still too expensive. Country-specific political, social, cultural, economic, and other factors affect outcomes.

24. Research that identifies inequities in health status and public-resource allocation and proposes and monitors strategies to reduce inequity can assist in improving the health of those who bear a disproportionate burden of disease (CHRD 1990).

25. Some discoveries lead rapidly and quite directly to improvements in health, such as the discovery of penicillin.

26. The persistence of communicable diseases like tuberculosis and malaria illustrates the importance of continuing product innovation in health research. New drugs are constantly needed to respond to changing patterns of resistance.

27. A major concern at present is that the R&D “pipeline” of drugs, such as those for malaria, is almost empty (Silverstein 1999). Global repercussions are potentially considerable: reemergence of resistant strains of diseases in areas formally free of them and reversals in gains made in disease eradication.

28. Biotechnology holds significant potential for process innovation. For example, diagnostic-probe techniques for a microbial infection like salmonella can provide a basic template for priority diseases in developing countries (Clark 1990).

29. In developing countries, process innovation is especially important in achieving cost-effectiveness. Differences in the budget constraints of developed and developing countries mean that a group of interventions considered cost-effective in developed countries may be cost-ineffective in developing ones.

30. Technologies are embedded in specific economic, organizational, and cultural situations. The real value of technology diffusion comes from using and combining technologies in various ways, not merely the passive transfer of information. Identifying the barriers to technology diffusion (often structural or organizational) in various settings is critical to improving efficiency (Watkins 1991). (IMB’s “knowledge triad” is based on the integration of people, process, and technology [Cohen 1998].)

31. Improved efficiency leads to better health by focusing public resources on people who bear the greatest burden of disease (improved efficiency of allocation) and investing in interventions with the greatest returns (improved technical efficiency). In developing countries, these are predominantly interventions to improve child and maternal health to reduce the current burden of disease, along with health-promotion strategies, like tobacco control, to reduce the future burden of disease.

32. Equity- and efficiency-enhancing strategies for health in developing countries are consistent and usually reinforce each other. For example, allocating public resources for health care evenly across income quintiles (in other words, trying to achieve equity of access, let alone equity of outcome) would improve allocative efficiency. This would happen through a substantial reduction in the burden of disease on the poor, with marginal declines in the well-being of the rich. Health outcomes would be more equitable.

33. Equity-oriented strategies enhance aggregate national well-being (reduced mortality and morbidity).

[7] Katjiunanjo, P. 2000. Health research profile: Namibia (case study). Council on Health Research for Development, Geneva, Switzerland. (In draft.)

[8] Makara, P. 2000. Health research profile: Hungary (case study). Council on Health Research for Development, Geneva, Switzerland. (In draft.)

[9] Mohabeer, R. 2000. Health research profile project: Mauritius (case study). Council on Health Research for Development, Geneva, Switzerland. (In draft.)

[10] Sitti-Amorn, C. 2000. Health research profile project: Thailand. Council on Health Research for Development, Geneva, Switzerland. (In draft.)

[11] Bhuiya, A. 2000. Health research profile project: Bangladesh (case study). Council on Health Research for Development, Geneva, Switzerland. (In draft.)

[12] Akanov, A. 2000. Health research profile project: Kazakhstan. Council on Health Research for Development, Geneva, Switzerland. (In draft.)

Document(s) 7 of 21