The activity has been on-going since early 2009, implemented by JSI under the USAID | DELIVER PROJECT and by the Strengthening Pharmaceutical Systems project implemented by Management Sciences for Health. It is a quarterly survey administered to health facilities. Each of the countries started at a different time, so the number of times it has been implemented varies by country, with Tanzania having the most with 10 or 11 rounds. The sampling strategies vary by country, ranging from 20 health facilities per quarter, to 320.  The survey was originally intended to provide quick, actionable information on malaria medicine availability, with an emphasis on the turnaround time for the analysis. More recently, however, the sampling strategy is being adapted to provide national level representation for some of the indicators. The surveys are now administered using a cell phone-based application called EpiSurveyor (see diagram below on how it works, courtesy of JSI).

So imagine –if the sampling were done well– a Minister, a program manager, a civil society advocate or a donor representative could have robust, independent information every quarter on the availability of rapid diagnostic tests and ACT, and a read out on available human resources and appropriate clinical practices. This is in sharp contrast to existing Global Fund and PEPFAR strategies for monitoring that rely mainly on self-reported, administrative data.

However, it is not yet clear how this valuable information is being used to improve supply chain efficiency and health provision effectiveness.

I have a couple of ideas on how this effort could be built into the Global Health Initiative (GHI):

• Transparency. At minimum, PMI, USAID and the other U.S. Government programs should post the raw data and results of the end-use verification survey (although the data is really the host government’s data, so they would need to be OK with sharing the data more broadly), on their websites. Since Tanzania has implemented the most rounds, you’d expect to see those trends analyzed somewhere. (Note to USAID/Tanzania – the last publication you posted is from 2007.) With greater transparency, civil society and researchers could use this information to better understand bottlenecks, pressure politicians and generally improve accountability. It could also feed into Global Fund grant planning.
• Demand forecasting. This survey sounds like a step in the direction of a functioning logistics management information system – probably, but not necessarily electronic – that gives decision makers timely and accurate information. Collection of this information was among the recommendations made by the Center’s Demand Forecasting Working Group in 2009. Given that many health systems continue to rely on historical budget and stock allocations, these data represent an opportunity to construct more accurate demand forecasts for government and donor purchasing, which in turn could result in more secure supply and –potentially- lower prices in the medium term.
• Other disease control priorities. If you are going out to health facilities already, why not check on vaccines, anti-helminthics, oral rehydration salts, family planning and other essential commodities? This is already being done in a number of countries (such as Ghana, Malawi and Tanzania), but can this survey be consolidated and leveraged to help out the entire GHI?
• Performance-based incentives. Both USAID and its contractors –including JSI- are discussing the possibility of using financial incentives to improve the performance of the supply chain. With this real-time, independent information on supply chain performance, could a portion of support to Central Medical Stores be conditional on progress?

I’d love to know more about how countries are doing, how much these surveys cost per round and how GHI plans to use and disseminate this information more widely. It seems to me that this is the sort of thing that USAID does best – in the tradition of the Demographic and Health Surveys – undertaking independent measurement to leverage better results, without getting in the way of recipient governments’ own processes.

Thanks to Mike Frost and Jim Rosen at JSI for their factual input into this blog. All opinions and errors are mine.

Computer models are also at the heart of the active policy debate over the degree to which the AIDS community should depend on AIDS treatment as a way to prevent future infections.  The possibility that the so-called “test-and-treat” proposal, which I have blogged here,  could eliminate the AIDS epidemic is contested by other modelers using other models here.

But often these disputes occur above the heads and out of sight of the policy makers, US Congressional staffers and other consumers of these long-run projections, who have few grounds on which to judge their plausibility.  What are the questions that consumers of these projections should  (and should not) be asking?

First of all, it is clear that consumers should not be asking whether these estimates are “correct,” because no model is ever “correct.” but whether they are “plausible” and internally consistent guesses that obey some fundamental adding-up constraints.  A further criterion is whether they convey to the decision maker a realistic appreciation of the impact that policy decisions will have on the AIDS epidemic and also, importantly, the *uncertainty* around the “best guess” scenario.  Finally, one might ask whether policy makers exposed to the model results will be less likely to make decisions today that they or their successors will regret ten years down the road?

In approaching the GOALS model, or any other model of the future course of the AIDS epidemic, the consumer might be better armed for critical engagement if he or she understands that any set of model projections is dependent on both the structure of the model and the data or “parameters” that populate that structure.  As an annex to this posting, I present a few questions that the model consumer might raise, divided into issues of “structure” and issues of “parameter estimation or data”.

Having considered all the issues I raise below, should we be skeptical about the modeling results promulgated by UNAIDS, WHO and PEPFAR, which are all based on a single model?  Yes, I think we should.  First, we should ask how this particular model is constructed and how its parameters are estimated.  Were these the best choices to inform the policy questions under discussion?  And we should also ask for modeled predictions of the effect of alternative AIDS policies to be replicated by various groups of modelers.  We should ask whether each model has been validated by being subjected to a barrage of independent tests.  As is the case for projections of the future of the US economy, we should be asking for “consensus models” or  perhaps for the “consensus forecast” of a group of modelers.

To correct the market failure caused by insufficient academic rewards for impact evaluation, various public sector financiers have seen the wisdom of establishing specialized impact evaluation institutions like NICE and 3IE.  Similarly, the academic community provides few rewards for the mundane task of replicating already published, agency-supported predictions of the future course of the global HIV epidemic.  As in the case of impact evaluation, there is a strong justification for public support of an institution that would facilitate and underwrite public comparisons and even competitions among epidemiological projection models for AIDS and other long-cycle epidemics.  An important principle of such an institution would be that a model is run through its paces and evaluated on criteria like those I propose below by someone other than its author.  For example, why not turn loose squads of graduate students on each of the available models?  Do I have any volunteers?

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Questions to ask of any model:

Issues of Structure

A model’s structure, like the structure of an airplane, affects not only whether it flies at all (i.e. whether it can make plausible predictions of future trends based on past trends), but also its behavior in response to its pilot’s guidance (i.e. what it predicts will happen as a result of a policy change).  Predicting the continuation of a past rend is relatively easy.  Correctly predicting the response of a dynamic system like the AIDS epidemic in response to policy changes is a much more daunting challenge to the modelers.  And structure plays a particularly important role in the latter.

Epidemiological structure

In the structure category, I would include characteristics of the epidemiological model of HIV transmission, such as whether it is “compartment-based” or “agent-based”.  A “compartment-based” model characterizes a population of people by allocating each person to a stage or a compartment and then specifying equations to describe how people move or transition from one compartment to another.  The Wikipedia entry gives a good introduction here.  Instead of compartments containing aggregates of people, the components of an agent-based model each represents an individual person who sequentially “decides” how to “behave” in response to a sequence of situations or events to which that simulacrum is exposed.  Again Wikipedia has a detailed description here.

For some purposes, such as understanding the impact of concurrent sexual partners on the spread of HIV, an agent-based model is thought to be better suited.  Since GOALS is compartment-based, it is fair to ask whether it can successfully capture concurrency and if not, how much damage that inability does to its projections.  If one accepts using a compartment-based model, because of its relative simplicity, it then becomes relevant to know how many compartments there are, what the transition probabilities are across them, etc..  The equations that link the compartments constitute the most basic description of the structure.  But these are hard for most of us to parse, so a diagram and sensitivity analysis would be helpful.

In addition to these structural characteristics, a model has “emergent” characteristics – which are only revealed by running it many times to check its sensitivity to alternative assumptions.  For example, the figures that I used in two previous blogs (here and here) emerge from about 5000 runs each of my AIDSCost model.  The GOALS model could similarly be run multiple times in order to trace out response frontiers which would more clearly reveal the emergent properties of its underlying structure than do a handful of runs.

Similarly, for the GOALS model, which is used to construct most of the projections cited in the first paragraph of this blog, I am curious whether the relationship between various prevention interventions is additive or synergistic or possibly one of substitution.  That is, when two prevention interventions are expanded to scale jointly, does the GOALS model predict that their effects on averted HIV infections would be the simple sum of their independent effects, or more than the sum (synergy) or less than the sum (redundancy)?  While an analysis of the equations of the model would yield clues to the answer to this question, running the model for a variety of intervention combinations and tracing out response frontiers would be more informative.

For a dynamic model, the mathematics suggests that emergent properties are particularly likely to be surprising and unpredictable from structure alone when the model is non-linear.  Nonlinearities can occur in the epidemiology (e.g. from a standard SIS model) or from any of the areas of structure I list below.

Structure of the cost of supplying services

For models like my AIDSCost model and the Futures Institute’s Model, which project the the cost of the supply of delivered HIV treatment or prevention services, it becomes relevant to ask whether the structure of the cost model is linear (i.e. constant unit costs) or more realistic.  For example, can the cost structure of the model capture economies of scale (at the national or the facility level), economies of scope (ditto), economies of integration with the health system (ditto), economies that accrue to competitive service delivery as opposed to hierarchically controlled monopolistic service delivery (whether public or private).  All of these nonlinearities are relevant to projecting the future costs of HIV service delivery, but their relative magnitudes and the degree they are amenable to policy manipulation are empirical questions that have not yet been answered – or in some cases even addressed.

Structure of the determinants of service uptake (i.e. of the “demand” for services)

In order to make plausible predictions of the cost of achieving any given degree of future service uptake or utilization, it is necessary to model not only the cost of supplying services but also the demand for those services.  (Econ 101: Utilization is the intersection of supply and demand.)  Thus one must ask how any projection model captures the demand for services.  It is well known that demand for any service is elastic to varying degrees with respect to price, distance, convenience, attractiveness, and the price distance, convenience and attractiveness of substitute and complement goods and services.  What assumptions does the GOALS model make about these elasticities?

(To my knowledge the only AIDS cost projection model that incorporates demand elasticities is that done for Thailand by Tim Brown, Wiwat Peerapatanapokin, myself and co-authors here.  For example, demand elasticities do not appear in my AIDSCost model.)

Stochastic structure

Uncertainty can influence a model’s predictions either as part of a model’s structure or by way of its data.  Some models embody the view that all human and natural phenomenon are fundamentally stochastic and therefore make a random draw from a probability distribution at every point that an arithmetic computation is performed.  Other models are a mix of deterministic computations and a few stochastic components.  Still others are fundamentally deterministic, but could be run many times with randomly distributed parameter values.  I believe that GOALS (like my AIDSCost model and many others) is in this latter category.   Imagine two distributions of projected HIV prevalence in the year 2020.  The two are produced from:
(1) a stochastic model run 1000 times with the same mean values for every parameter, yielding 1000 predictions for HIV prevalence in the year 2020,
(2) a deterministic model run with 1000 randomly chosen values of those same parameters, again yielding 1000 predictions for HIV prevalence in the year 2020.
Other things equal, which of these depictions of the uncertainty in future HIV prevalence is more plausible?  This is a deep question, to which I don’t have an answer.  I suspect a case could be made for the stochastic model, provided the details of its stochastic specifications are themselves plausible.  But ultimately one would have to compare actual models.

Given the stochastic structure of any specific model, the question then arises how to convey truthfully to policy makers the uncertainty contained in model predictions.  This is a difficult communication challenge.  Although the UNAIDS modelers wanted for years to release upper and lower bounds for their estimates of AIDS prevalence, UNAIDS only began to publish ranges rather than single estimates after data accumulated that they had badly over-estimated the worldwide total number of HIV infections for decades.  See my discussion of the revision here.

Parsimony

“A model should be as simple as possible, but no simpler.” (Einstein said this but so did the Lord of Occam before him.)  Of course, parsimony, like beauty, is in the eye of the beholder, so one person’s “beautifully parsimonious” model is another’s “overly reductionist caricature” of reality.  While model builders often like to add bells and whistles to their models, there is a serious danger that the fillips and adumbrations they add to a model’s basic structure will, like the  epicycles added to the Ptolemaic model of the solar system, lead the model farther and farther away from reality.  (The Ptolemeic model, with the earth at the center instead of the sun, predicted the motions of the planets across the sky pretty well but would have done a really bad job of predicting the impact of a policy intervention – such as  blasting a rocket towards Mars.)  Thus all of the complexity that I suggest above should be introduced only to the degree that it improves the plausibility of the model’s predictions and the usability of the model for policy analysis.   Anybody like me who would like to see some additions to a model must make a convincing case that greater complexity would be worth the loss of parsimony.

Issues of Parameter “guesstimation” and Data

A model’s structure, with the components described above, is just a set of equations with unknown parameters.  In order to make predictions, we must of course attach values to those parameters.   For a simple model of demand, when one has observations of 1000 individuals choosing how much detergent to buy at 1000 different combinations of price, distance, characteristics and the price distance and characteristics of substitute products, one has enough degrees of freedom to formally estimate the dozen or so parameters of the structure of detergent demand.  Unfortunately for these epidemiological-economic projection models, we are in the opposite situation, with perhaps ten times as many parameters as we have data points.  So instead of estimating those parameters, we have to “guestimate” them.  The French call it “la pifometrie”.   And that’s appropriate, because the process of attaching values to these parameters, we would all agree, requires one to hold one’s nose.

Perhaps it is useful to distinguish among: Epidemiological parameters, biological or medical parameters, efficiency parameters, effectiveness parameters and demand parameters.

Epidemiological parameters.  These include the shares of the various risk groups in the population and the baseline rates of activity and the rate of sexual mixing between the various groups.  Depending on the structure of the model, a measure of concurrency or a measure of mean partnership duration is also required, for each compartment in the model.   Important theoretical work by Anderson and May has shown that accurate prediction requires information about not only the mean but also the variance of each of these numbers, but whether it would be useful to know the variance within each separate compartment or only the variance across all compartments (which could be deduced from the distribution of their mean values) is unclear to me.

Biological or medical parameters.  These include features of the natural history of HIV and the degree of infectiousness of an infected person in the various compartments through which s/he passes and the susceptibility of the uninfected individual.  If acquired and used, condoms and microbicides intervene at this point to reduce an individual’s infectiousness and susceptibility.

Efficiency parameters.  These include the parameters of the structure of the cost of production and distribution of HIV services.

Effectiveness parameters.  These include the effectiveness of a service at preventing an HIV infection and/or prolonging the life of an infected person – assuming that the service is used.  But this is a big assumption.  To relax this assumption, a model would need to have a demand structure and …

Demand parameters.  These include the responsiveness of utilization to changes in the policy instruments that governments and donors can manipulate, such as the price, distance, convenience attractiveness of a service and of its substitutes and complements.

With respect to each parameter within each class of parameters, we can ask whether sufficient data exists to rigorously construct estimates of a mean and a confidence interval.  Where insufficient data exists, we can ask whose subjective Bayesian priors are represented in the model, what those priors are and how sensitive the model’s predictions would be to the choice of alternative priors.

If you have gotten this far, you must be either really interested in modeling per se or really interested in whether HIV models are producing reliable predictions about the magnitude of the future health and fiscal burden of AIDS.  If you are a model “consumer,” rather than a modeler, I’m curious whether this checklist seems helpful.  Or would you rather just accept the model predictions from someone else – and then take them on faith?

Success does depend on “crowding in” other funders’ investments, but under some scenarios the $10 billion could actually “crowd out.” Bilateral donor agencies, who themselves are fighting a tough budget climate, might breathe a sigh of relief thinking that they can hold at historical spending levels or even pull back from GAVI, and Daddy Healthbucks will save the day. Governments in GAVI-eligible countries, which under current rules now provide a co-pay for every dose of vaccine, might drag their heels a bit on moving toward greater levels of financial self-sufficiency. “Why are we chipping in 10 cents for every jab,” a Minister might ask, “when Bill Gates can afford $10 billion?” Even industry might think that they can cut a more favorable deal on both prices and support for R&D through product development partnerships.

But I expect that the Gates Foundation will use the resources in ways specifically designed to leverage others’ investments, and to lower the costs of getting vaccines to market and then to kids and teens. Accomplishing this will require a combination of incentives and institutional improvements to make all the dollars for vaccines and immunization work as hard as possible. What follows is pure speculation, but maybe they will:

• Put up all new contributions to GAVI in the form of a match: For every dollar GAVI raises from other sources, Gates could match it 1:1. (Interesting research shows that matching strategies are effective, but there’s no benefit from higher matching levels.) If particular types of contributions – say, support from private individuals – merit greater emphasis by GAVI’s resource mobilization team, the match could be higher as a special motivator to the organization.
• Invest in strengthening and streamlining the regulatory infrastructure, both globally and – very importantly – within the countries that are home to emerging manufacturers (Indonesia, India, Brazil). Ditto for clinical trials capacity and platforms that can be used for the development of multiple vaccines as time goes by. Bringing down the costs of developing these products, which is not as significant an issue in the price-insensitive markets, is high priority.
• Create the Maurice Hilleman Global Vaccine Prize, named after the remarkable microbiologist who developed more than three dozen vaccines. The prize could recognize achievements of scientists who have made major contributions to the development of vaccines that specifically benefit low-income countries.
• Explore whether and how strategic investments or incentives can hasten the development of joint ventures and other collaborations between multinational research-based firms and capable emerging manufacturers. Over the long term, moving to a high-scale/low-cost model of production is the route to vaccine affordability, regardless of whether the payers are national governments or donors.
• Recognizing that the health of the vaccine market globally is essential to the continued and diversified supply of vaccines for the poorest, make a one-time contribution to the Pan American Health Organization’s Revolving Fund to manage the introduction of higher-priced vaccines to the middle-income countries of the region.
• Create a 10-year Global Health Policy Fellows program, modeled on the Robert Wood Johnson Foundation Health Policy Fellows, to place mid-career global health professionals within Congressional and Executive branch offices. No amount of advocacy from the outside for health aid can replace dedicated, value-adding expertise on the inside. And the eventual network of Policy Fellows would be the next generation of policy movers and shakers.
• Endow immunization advocacy organizations in key countries, like Nigeria, where voices outside of the government are essential to keep the pressure on for immunization performance, and to counter the proliferation of negative messages about vaccines. An endowment rather than a grant is particularly important, so that the organizations can credibly say they are independent of a particular outside agenda.
• Work with the leadership at the World Health Organization and UNICEF to reinforce capacity for processes like developing evidence-based recommendations about vaccination schedules, prequalifying vaccines, issuing tenders, forecasting demand and more. Look at all the bottlenecks and focus resources on eliminating them.
• Create an innovation prize not for a vaccine but for a technology that will make many vaccines more usable in developing country contexts, such as for needle-free administration.

Oops, I think I just spent $10 billion!

These are just a few of the possible ways that the generosity of a lead donor can be extended and amplified. I suspect that cleverer ideas are being cooked up out there on the shores of Lake Union – and by some of our blog readers. Please use our comments feature to offer up your own thoughts.

In mid-2006, as the Global Health Forecasting Working Group was underway, my co-chair Neelam Sekhri and I were feeling stuck. With working group members from a range of global health organizations, who brought perspectives from industry and international public health, we had been able to describe the magnitude of the challenge of forecasting the demand for global health products, particularly new ones like the rotavirus vaccine and artimesenin-based anti-malarials. We’d also developed a good understanding of how inadequate information about effective demand – how much money would be available to buy what, and at what pace countries would be likely to introduce – constrained the ability of firms to make the business case for investment in manufacturing capacity, let alone new R&D. What we were missing, though, was the deeper understanding about why the demand forecasting problem persisted, despite reasonably wide recognition that it caused shortfalls in supply, wasted of products, time and money. It’s often in answering the question, “So why hasn’t someone solved that yet?” that you discover the most interesting new ways to approach a problem.

Looking for information on demand forecasting for health care and other products in industrialized markets, Jessica Pickett, then a Program Coordinator at CGD, came across a fascinating article about difficulties in forecasting demand for seasonal flu vaccine. Intrigued, we called the author, Prashant Yadav, and had the first of what was to be many, many conversations. Prashant, a faculty member at the MIT Center for Transportation and Logistics in Zaragoza, Spain, not only had a special way of analyzing the way the different actors along the supply chain relate to one another, and can be incentived to work more efficiently, he also brought a passion for using business expertise and creativity to contribute to a better world. Although we didn’t have the budget to pay anything close to corporate rates for his consulting services, Prashant cheerfully devoted an uncountable number of hours to analyzing incentives along the supply chain for global health products, and ultimately sowing the seeds of the recommendations that the group adopted.

By that time, Prashant had been seriously bitten by the “global health” bug, and many individuals who were struggling with supply chain issues had seen the contribution he and his colleagues at Zaragoza could make. So for the past couple of years, Prashant has been spending a very large portion of his time responding to requests to working on that dimension of the “access to medicines” problem.

But, as he told me in a recent e-mail, the story doesn’t stop there. He writes:

Ruth and Neelam, Since you were the ones who created the motivation for this, I wanted to send you an update.

1. We have started a work-study program for African pharmacists who work on supply chain and logistics in Ministries of Health. We bring them in for two years to our regular Masters in Supply Chain Management Program, we give them a tuition waiver and a small stipend and they work on small projects in return. We wanted to select a good group of 2-3 for our first crop, so we interviewed many during my travels and carefully picked the highly motivated and those who had the potential to become change agents when they go back.This year the students we have in our Masters program include: the distribution manager of the Central Medical Stores of Ghana; the Logistics and Pharmaceutical advisor for USAID in Sudan; the distribution manager of Mission and Essential Drug Supplies unit in Kenya; and the pharmacist consultant for MoH, East Timor.
2. The ‘MIT-Zaragoza Africa Health and Humanitarian Supply Chain Scholarship’ is in its third year now. One of the two past recipients has obtained a position as a deputy minister in his home country. The other African recipient went to work for a private company. But if I believe a recent paper by Michael Clemens at CGD on skilled migration, even if they work for a for-profit company, the incentives will lead to more people train in supply chain management in Africa and then some of them will stay to work at MOHs due to social, family and other factors.
3. Our graduates have shown keen interest in working for global health organizations over our typical recruiters, i.e. US /EU based large corporations. We have two students who have picked to work for small NGOs in Mozambique, Malawi, and Tanzania over very well paying jobs from pharma companies and others. We have one student who has gone to work for Medicines Sans Frontiers as their supply chain champion and has a great story to tell (a 24-year-old woman from Ohio who goes to a different war zone in the world every day to help create more efficient drug supply systems). Another former student has moved to Tanzania to work on an innovative pilot project we are doing there.
4. Our graduates who go and work for big pharma are acting as change agents within big pharma about the developing world. A student from last year who works for big pharma has convinced his emerging markets group to spend more time understanding their supply chains in Africa instead of the product hand-off model they currently use. The company is starting a small project to look at this. Another student in big pharma is showing keen interest in his emerging markets supply division instead of North American and European market supply chains.

Does this remarkable cascade of social value – the goodness multiplier – always happen when those in business are brought into the conversation about development challenges? No. But my recent experience – with Prashant, with the incredible dedication of Covington & Burling’s John Hurvitz to development of the Advance Market Commitment, with the commitment of Eli Lilly’s Gail Casell around drug resistance – has convinced me of the value of meaningful involvement of the business sector in advancing global health.

Thanks for the update, Prashant. Looking forward to seeing what you (and your students) do next!

WHO estimates often rely on ‘need,’ a normative concept of how many people should be treated, rather than on demand, a positive concept of what can and will be bought. In 2004, the WHO projected that the global need for ACTs in 2005 would be over 130 million treatments. This projection proved to be way too high; in 2005, maximum demand was only 25 million treatments. Major suppliers such as Novartis and Sanofi-Aventis relied on WHO estimates and, as a result, were forced to either destroy unused products or declare substantial losses when the anticipated demand never materialized. In December 2006, Novartis temporarily shut down its production facility in Suffern, New York, to prevent the production of too much medicine with a short shelf life; Chinese farmers had begun to complain that they had no buyers for their Artemisia annua. With an excess of supply, prices of Artemisia annua have plummeted, and now the WHO fears that farmers and artemisinin producers may withdraw from the market, reducing the overall supply of drugs and creating a risk of future shortages.

In the short run, unrealistically high demand estimates are costly for companies. In the long run, they are costly for the millions of people afflicted by malaria. If drug companies must weather too many losses as a result of misjudging malaria demand, they may decide to invest in drug development for other diseases. The WHO argues that its forecasts are better today. But to be useful to companies, they have to be provided at least 12 months in advance, and the WHO forecasts are not.

This analysis underscores many of our own findings in the CGD working group report A Risky Business: Saving Money and Improving Global Health through Better Demand Forecasts, which looks beyond malaria to expore the full impact of demand forecasting across diseases and stakeholders. Our research points towards three mutually reinforcing solutions:

• Improving the capacity to develop credible forecasts by taking forecasting seriously
• Mobilizing and sharing information about product demand in a coordinated way through the establishment of an infomediary
• Adopting a broad range of contractual arrangements

AEI’s recommendation that companies agree to supply a certain amount of drugs in exchange for a contractual commitment from donors to purchase them at an agreed price would be a big step in the right direction; another variant on this approach, known as a “rolling horizon forecast commitment,” is detailed in a background paper by our colleague Prashant Yadav. As the price of Coartem continues to drop and new manufacturers enter the market, the stakes will only become higher. With AEI and others now lending their voice to the call, hopefully the global community will begin to take action.

For those from outside the health sector (is anyone like that reading this blog?), a horizontal program is one which attempts to provide the population with access to generalist health care practitioners who can attempt to diagnose any patient, to treat some and refer others to more highly trained or specialized providers at “higher levels” of a health care referral structure. In contrast, a vertical program is one which is designed to deliver a single package of interventions, often aimed at a single disease or at a group of diseases that can all be addressed by that package.

In order to dramatize the distinction between these two extreme types of support to a health care system, Ooms et al. offer novel cartoons to show the advantages and disadvantages of each system. The horizontal philosophy of health system support is illustrated by the following picture, in which the support is shown as a layer of orange sand labeled “aditional [sic] health expenditure” which only succeeds in elevating total public health care spending from US$10 to US$20, not high enough to reach the US$40 target set by the commission on macroeconomic and health. Since no part of the system extends above water, the implication is that, in a “horizontal” health system, fish may swim, but patients will drown.

In contrast, the vertical system is shown as a column of sand all piled precariously in one spot of the system. By depicting the vertical program as extending above the “water line” of US$40, the authors are suggesting that, at least on this “island of sufficiency,” patients can receive effective care or public health programs, at least for this one set of problems, because public expenditure per capita (however defined) is greater than the “water line.” The problem depicted by the cartoon is that the structure supporting the island is undermined by erosion from the surrounding underfunded “swamp.” The authors imply that a vertical program can survive for a short time in the “swamp” of an inadequately funded health care system, but forces that arise from the contrast between the inadequate funding for the rest of the system and the relatively luxurious funding of the vertical program lead to the eventual destruction of the vertical program.

From these diagrams it is clear that the authors’ sympathies lie more with vertical than with horizontal programs. According to the cartoons, a vertical program temporarily creates an island of sufficiency, whereas the horizontal program is a total failure.

While vertical programs certainly have their advantages, to suggest that they prevent drowning while horizontal programs never do is unfair to horizontal programs. A better model is one in which both horizontal and vertical programs produce benefits, but using different technologies. Years ago I was caught in a battle between an ardent advocate of each type of health care during a frustrating mission to Mauritania. Inspired by that experience, I subsequently wrote a paper arguing that the advantage of a horizontal program should be in the possibility of technical complementarity, both for the producer and for the consumer, between different health care services located in the same building. In economics terms, a horizontal program might be a more cost-effective intervention than a vertical one if it achieves sufficient “economies of scope” by offering multiple health care services from the same location. These cartoons are unable to capture the benefits of economies of scope.

The diagrams are biased in favor of vertical programs in another hidden way. The orange sand crumbling from the vertical sides of the island in panel (b) and sifting down to the ocean floor only seems to elevate the level of funding of the surrounding horizontal program – not to actively harm it. In fact, the open question that needs to be resolved is whether AIDS treatment programs and other vertical programs, by paying higher salaries than the surrounding system, actually undermine the surrounding system. Perhaps a reader of this blog can suggest an alternative cartoon that could depict this possibility.

The innovation that Ooms et al are introducing is the idea of a so-called “diagonal” support program, which they illustrate with a third clever diagram shown in Panel (c). Their cartoon suggests that vertical programs can only be sustained if they have a broad supporting structure which funds enough of the surrounding structure of the health system to allow the vertical program to function indefinitely. If orange sand is equated to financing, the picture suggests that a diagonal program will require a lot more funding than a vertical program. But how much more?

Engineers refer to the angle between the ground and the stable slope of a pile of sand as the “angle of repose.” The smaller the angle of repose of the diagonal health program, the more sand will be required to sustain the “island of sufficiency” and the more the program will cost. An advocate of horizontal programs might argue that the only stable angle of repose is zero, with public health system spending topped up until the whole health care system is equally funded at a level which removes the special nature of the vertical programs. Palm trees everywhere. Thus a challenge to those who would propose a “diagonal” health program is to describe which systems should be strengthened to support their favorite vertical program – and by implication which systems can be excluded.

I highly recommend a thorough read of the most recent draft, which paints a truly comprehensive picture of the critical linkages between agricultural and medical market dynamics. As this is still a work in progress, Dr. Dalrymple warmly invites comments and suggestions from the broader global health community. Feedback should be sent to ddalrymple@usaid.gov.

Please consult Dr. Dalrymple before quoting, citing or reproducing any part of the paper.

• In August, the New York Times described the extent to which pharmaceutical companies are banking on profits from their vaccine businesses, rather than from their therapeutic sides. This is a dramatic turnabout, given that the vaccine industry used to be the poor stepsister to the drug business. (We blogged about this phenomenon earlier.) Could it be that Big Pharma doesn’t see many blockbuster drugs on the horizon and, after the Vioxx debacle, sees fewer and fewer products that will sell in such large volumes? Perhaps vaccines are among the only remaining products for the masses.
• The October 1st issue of Newsweek features an article on the science and financing of vaccines, with a helpful focus on the often-overlooked challenge of conducting large clinical trials, which are more difficult for vaccines than for most other products. (If you’re injecting healthy people with biologically active material, you’ve got to pay extra-special attention to safety. And in looking for evidence about effectiveness in protecting against infectious disease in a population, the sample size typically has to be much larger than if you’re measuring effectiveness of a product to treat people who have already been identified as being sick.) The pilot advance market commitment (AMC) for pneumo vaccine makes a cameo appearance in the article.
• At the Clinton Global Initiative this week, a high-profile panel explored vaccine development, with particular attention to how to expand coverage while at the same time providing incentives for R&D. Of particular note: Suresh Jadhav of the Serum Institute of India was on the panel and brought a powerful message to the assembled members of the global health gang: its not just the multinationals who see the promise of vaccines both for health and for the bottom line – emerging manufacturers are seeing a bright future in this growing market, too. (You can access the full webcast via the Kaiser Family Foundation.)
• In a remarkable display of (mostly) “girl-power,” luminaries (including at least two princesses) gathered in Brussels this week to talk about cervical cancer and HPV, with attention to when, where and how to introduce the new vaccine; the HPV vaccine could prevent the deaths of something like 200,000 women in developing countries each year. It’s pretty clear that the roll-out of the Merck and/or GSK products will depend on serious commitments from governments and donors, combined with – you guessed it – good demand forecasting. PATH already has a major project underway looking at precisely these issues, and at the CGI Merck just committed to contributing 3 million doses of their vaccine, Gardasil, to developing countries over the next five years.

All this is some combination of well choreographed public relations (of course) and something quite real: To achieve the broadly shared ambitions of better health in low-income countries, there’s a growing recognition among donors and developing country governments of the potential of immunization. At the same time, the commercial interests are intensifying, both among Big Pharma and emerging suppliers – and they are carefully watching for potential opportunities to reach both rich and not-so-rich markets. Meaning that maybe there’s a chance for genuine win-win solutions (the AMCs and more) to be developed.

So, the International AIDS Vaccine Initiative has made a wise choice to focus some recent forward-looking analytic work on AIDS vaccine policy issues in India. Three publications were just released including:

• Accelerating AIDS Vaccine R&D in India: An Assessment of Obstacles and Possible Solutions, which identifies possible ways to enhance the role and efficiency of the public and private sectors in vaccine development.
• Forecasting Demand for Preventive HIV Vaccines in India, assessing the demand for a first-generation HIV vaccine.
• The Introduction of New Health Technologies in India, which takes a close look at what it would take to adopt and implement AIDS vaccines in India.

Interesting reading for all those who are thinking about future health technologies.