Published last week in The Lancet, a new study by the Institute for Health Metrics and Evaluation (IHME) finds that there were 1.2 million deaths from malaria in 2010, not 655 thousand as estimated by the WHO. Following its release, headlines began splashing uncritically: “Malaria kills twice as many as previously thought, study finds” (The Guardian) “Malaria deaths hugely underestimated” (BBC). Which set of estimates is correct? Or at least which is less biased? Given the 1.2 billion dollars by donors to malaria in 2010, is it unreasonable to demand to know with more certainty, how many people are dying from malaria?

In an effort to gain clarity on these questions, we’ve ventured into the sausage-making of both malaria estimates (see below, if you dare venture into the methods of the sausage machine). In essence, both the IHME and WHO start with ‘raw ingredients’ (some data), then apply different ‘cooking’ techniques (some methods). From both of their ‘recipes’, it is not obvious to us that IHME’s estimates are necessarily less biased (or more correct) than WHO’s. Both IHME and WHO are doing the best they can, given shockingly unavailable and bad data. In Africa where 90% of malaria deaths occur, almost all mortality data come from verbal autopsy (VA) studies, where family are asked to report symptoms and diagnose the cause of a person’s death well after the death occurred. Malaria mortality estimates are taken from sub-national samples of this VA data and then extrapolated to the broader population. For example, one study in a Nigerian village with 227 malaria deaths was used as the main basis to extrapolate all malaria deaths in all of Nigeria.

One distinguishing feature of the IHME study is that it nobly tries to correct these VA studies, but even the validity of these corrections for estimates of Africa is questionable (see below). The other main factor contributing to the differences between IHME and WHO estimates is the extent to which the burden of malaria falls on children under age 5 compared to those older (see below). These two differences probably explain much of the discrepancy between IHME and WHO in counting malaria deaths in Africa. Beyond that, both IHME and WHO take fairly similar methods in Africa to extrapolating and modeling bad and non-existent data (although we’re not too certain since it’s a bit harder to find out what WHO did).

It would help if both IHME and WHO (WHO probably more so) were more transparent and made their data and methods available for interested parties to replicate their results. In any case, if we want to say anything reasonably straightforward without having to make sausages every year from VA data, better quality vital registration systems and data are needed. Data-production and sausage-making enterprises, fancier or simpler, are ultimately temporary coping mechanisms until countries themselves have better information and can correct it on their own. The long-term goal should be to not rely on people sitting in Seattle and Geneva for the ‘correct’ numbers. To realize this goal, we will need to value this investment of knowledge and country capacity as essential to public health. But until then, will donors continue to spend million or billions of dollars on malaria without really knowing how many people malaria kills each year?

The Method of the Sausage Machine: How to Count Malaria Deaths

IHME’s sausage recipe: what did IHME do?

IHME researchers compiled vital registration (VR) and verbal autopsy (VA) studies on malaria deaths of all ages globally. In Africa alone, the researchers found 106 VR and VA studies since 1980, which were almost entirely subnational VA studies. As an example of such ‘subnational’ studies, look at Nigeria (which according to IHME had 380,632 malaria deaths and the highest in Africa). For all of Nigeria the IHME paper used 1 subnational VA study in 1 Nigerian village which had 227 malaria/fever deaths among infants in 1995 (see Appendix, p. 100).

Using this VR/VA data as the main ‘ingredient’, IHME processed the data in two main steps. First, they attempted to correct the VA estimates given that malaria was likely misclassified. Why correct for misclassification? Malaria is notoriously known for being incorrectly diagnosed: a fever may be incorrectly classified as malaria when it is simply a fever, for example. Here is where the sausage-making starts. In the VA studies, IHME used a “validation” study from a “multi-site” study – and by multi-site they mean 6 sites in Mexico, Tanzania, India, and the Philippines which had a total of only 217 malaria deaths – to correct their VR/VA database to correct the proportion of malaria deaths. From this exercise, they shift estimates up by 21%. At this point you may be asking, “So, for all of Africa which accounted 90% of all malaria deaths – 1.1 million (IHME) or 596 thousand (WHO), they used 1 validation study in Tanzania (and perhaps the other regions) to correct the array of VA/VR data points?” It is tough to imagine that these sites can be considered representative of all Africa.

In the second main ‘processing’ step IHME ran statistical models to determine associations on malaria death rate and cause of death fraction and an array of predictor variables including malaria transmission rates, rainfall, bed net coverage, etc. Running literally hundreds of these models, they test the “predictive validity” in order to choose the best-fitting model and extrapolated in time and within region where they did not have data.

WHO’s sausage recipe: what did WHO do?

The WHO relies on two main sources of data. The first set are administrative data on the number of cases multiplied by a case fatality rate, estimated from previous studies, for each country and voila, the numbers of malaria deaths (p. 73 in WMR 2011); WHO used this data for low-transmission countries in Africa plus countries outside of Africa.

The second set of data involved compiling vital registration (VR), verbal autopsy (VA), and other mortality studies to estimate the percentage of deaths under age 5 attributable to malaria; WHO used this data for the remaining countries in Africa plus Somalia and Sudan. Sound familiar? Like IHME, WHO had no other choice but to mainly rely on VA data because of the lack of VR data in Africa. In addition, it’s not clear whether, like IHME, WHO also adjusted for VA misclassification (we suspect not). Next using the VR/VA data, WHO processed them by using statistical models to find associations between the fraction of child deaths from malaria and various country predictors (not clear what variables they use). As for those aged 5 and older, it seems that WHO deduced mortality rates using previous studies on age-specific malaria mortality rates and the intensity of malaria transmission (p. 140 in WMR 2008).

How do the results between WHO and IHME differ?

We compared the numbers by IHME and WHO by WHO Region (see Table 1 below). Both IHME and WHO agree that Africa accounts for 90% of all malaria deaths. There is agreement between IHME and WHO in the numbers for the Americas, Western Pacific, and Europe (regions which have better vital registration data). However, the numbers differ greatly for Africa, Eastern Mediterranean, and South-East Asia regions. This difference in South-East Asia is likely explained by WHO non-use of the latest VA estimates for the South-East Asia region (and similarly for the Eastern Mediterranean). But why do estimates in Africa vary so much if both IHME and WHO compiled and processed VA/VR data?

Source: Compiled by authors from IHME and WHO

One distinguishing feature is that IHME seemed to correct for misclassification. The other major question is how much of the malaria burden falls on children under 5 compared to people aged 5 and older. Will the IHME study overturn years of clinical observation that, by far, most of malaria’s victims are children? IHME says that in 2010 children under 5 accounted for 58% of the world’s malaria deaths (our calculations from IHME data), whereas WHO says it is 86% (p. 74). Yet when we looked by region at the percentage of malaria deaths aged 5 and older (see Table 2), we found that indeed both IHME and WHO agree that age distribution of malaria deaths differ by region and that at least in the Americas, South-East Asia, and Western Pacific, most malaria deaths occur in people older than age 5.

Source: Compiled by authors from IHME and WHO

Where IHME and WHO differ the most, in both the numbers and the age profile of death, is for Africa. How valid is either group’s estimates in Africa? It would depend on how much you believe each stage of the sausage-making process:

• Are WHO’s VA/VR/mortality study for child deaths estimates combined with review of other mortality studies for adult deaths sufficient compared to IHME’s VA/VR studies of deaths for all ages? (And how exactly did WHO calculate its adult deaths? Why is it hard to figure out what WHO did?)
• How valid is IHME’s “multi-site” validation study (with its 214 malaria deaths in 4 countries, of which only 1 is in Africa) to “correct” the VA data? Did WHO correct for misclassification?
• How sensitive are results to the choice of predictors in the models by both WHO and IHME?
• More importantly, how much does each stage of the sausage-making process contribute to the estimates?

Which donor provides the “best” health aid, and why is it a relevant question? We attempted to answer these questions by adapting the Quality of Official Development Assistance (QuODA) methodology to health aid. To be honest, one working paper later, we still do not have a definite answer to either question.

What we do know is that health aid is relevant: effective health aid has saved lives, and technologies like oral rehydration salts and vaccination are among the most efficient development interventions money can buy. Determining the quality of health aid is also relevant because Western donors spent $28 billion on global health aid in 2011: such a large amount, equaling almost one-fifth of all development aid spending, merits analysis. Finally, donors and recipients themselves seem to think aid effectiveness is important. Donors gathered in Busan in November 2011 to discuss and act on aid effectiveness; in the health sector, the International Health Partnership and the Health Systems Funding Platform are multi-donor/recipient efforts to improve aid effectiveness.

However, despite health aid’s scale and scope, the literature on aid effectiveness in health is inconclusive. As we discuss in our paper, existing studies correlate “good” aid practices to better health outcomes, but cannot quantify how improved aid effectiveness -measured using the Paris Declaration indicators or others- translates into better health results. In an ideal world, we would be able to say “a decrease in donor fragmentation increases coverage of vaccination by X%,” but given data constraints, we can’t go beyond correlations (see here).

If we can’t make this link, should we bother with measuring progress on aid effectiveness? Even conceptually, some aid effectiveness measures are not clearly linked with greater development effectiveness. For example, many authors (including myself) have been critical of the volatility of health aid disbursements, arguing that because health aid finances the recurrent costs of providing basic health care, smooth and predictable disbursements are necessary. Yet, procurement of medicines or vaccines can be lumpy over time, with big disbursements one year and even none the next, without necessarily implying that this health aid is “bad.”

Nevertheless, a subset of aid effectiveness indicators can –at least conceptually- be good proxies for development effectiveness: giving to countries with higher disease burdens and higher poverty, untying aid, channeling more aid through multilaterals, reducing transaction costs to alleviate the burden on recipients and adopting transparency measures have all consistently related to development effectiveness in the literature, as we cite in the methodology of our paper.

Calculating such indicators (see here) forms the basis of our index. We address issues that can be quantified, and leave out those that cannot be. Those we omit, such as volatility, innovative financing, harmonization, ownership and mutual accountability, may very well be as important as the included indicators but can simply not be measured for the whole sample of donors. We rank donors across four dimensions of aid effectiveness: maximizing efficiency, fostering institutions, reducing burden and transparency and learning.

Rankings within each dimension differ: The Netherlands ranks best in the maximizing efficiency dimension, but fares worse in giving to countries with national health plans. Some donors, such as the United Kingdom, fare above average in every dimension. By presenting rankings across different dimensions, we seek to nudge the donors in the right way: Australia, for example, could increase its overall aid effectiveness tremendously if it improved in allocating its aid to countries with higher disease burdens, as well as increasing the share of its aid that makes it on to recipient budgets (see figure below). Similarly, the United States, the largest health donor, can improve tremendously if it focuses on reducing the burden on recipients through reducing transaction costs; as we also recommend here. We hope donors take note of their relative rankings and capitalize on the results; we also hope recipients hold their donors accountable and report best (or worst) practices and have included analysis of the a sub-set of indicators in the aid-dependent countries.

Tracking progress from 2008 to 2009, we find that most donors have regressed in terms of allocative efficiency: donors actually reduced giving to countries with higher disease burdens. Health aid also became more fragmented, which increased the burden on recipients. Comparing health aid to overall aid, we find that health aid is more focused geographically and less tied, but fares worse in going to poor or well-governed countries.

In the coming days, I’ll blog on DAC aid purpose codes and donor reporting practices, and connect aid quality with the emerging value-for-money agenda in health aid. Meanwhile, we invite you to explore our data to see how donors do on different indicators and dimensions.

For the most part, the development community is largely in the dark when it comes to knowing who lives and dies, where, and why. The methods of this new study (as with many studies that estimate disease burden) relied on data based from vital statistics registries and verbal autopsy results. But in many malaria endemic countries, these systems offer low coverage and poor quality—leaving the vast majority of deaths poorly-coded or never documented in the first place.

For instance, from 1995-2004 the proportion of the population living in countries with complete death registrations in Africa, South-East Asia, and the Western Pacific was 7%, 1%, and 13% respectively (see here) and only half of the countries in Africa and South Asia even record cause of death data. In Africa, only the islands of Mauritius and the Seychelles currently have complete registration of births, deaths, and cause of death.

In the absence of complete information, researchers must model their estimates based on what data they have access to, even if poorly coded cause-of-death data and low sample sizes result in high levels of uncertainty.  Accurate information on cause of death—and other vital events—is essential for understanding disease burden, influencing policy, and determining resource allocation. In the case of malaria, knowing how people are dying can save lives by allowing funders and governments to understand the true burden of disease and enabling them to realign their priorities and scale up programs. Still, an empirical evidence base for global health resource allocation will be absent as long as the vast majority of causes of deaths go unrecorded or miscoded.

Yet the Fund -like other global health funders- faces a final frontier: how can the organization invest its resources to obtain the highest health return possible?

Answering this question requires a fundamental rethink of the organization’s role as a commissioner of or payer for health services and, ultimately, health outcomes. Instead of a passive cashier, the Fund can become an active and strategic investor in the shared enterprise of producing health results. And that is a banker’s business.

To move from cashier to investor, the Fund needs new information, competencies and organizational arrangements that allow it to commission or purchase care, outputs and outcomes effectively and responsibly.

The Fund has led on these issues in the past, and is a leader in transparency and providing information to the public, as well as collecting prices and procurement information. Yet more can be done.

An agency that plans to commission needs a clear definition of what interventions are to be purchased -at what minimum quality standard, on both the supply- and demand- sides. Ideally, interventions and products to be financed will be identified through a country-run process that rigorously considers economic evaluation and affordability, as well as social preferences and ethical considerations. Once a country has determined the appropriate mix of interventions that maximizes health impact within a given priority, countries -with Fund or partner support if needed- will need to track how much it costs to provide, how much is spent, and how results can be measured and attributed at baseline and follow-up.

This is not research or academic nicety, but rather the necessary intelligence that will allow for effective policy in a recipient country and a return on investment by the Fund. With this information, the Fund and its recipient partners can implement genuine performance-based contracts based on mutual accountability with a quantifiable, credible return on investment.

In the absence of such intelligence, analysis and support ex ante and during implementation, proposals are filled with hundreds of non-standardized performance indicators, budgets are submitted with no financial or operational connection to performance indicators and targets, and the focus is on producing receipts instead of results.

As Gabriel Jaramillo begins his work, his banking background should serve him well.

Latent demand is thought to be expansive: Despite vast science demonstrating the impact of contraceptive use on fertility, there is still a significant unmet need. Dr. Fred Sai, former Presidential Adviser on Population Issues, Reproductive Health, HIV and AIDS in Ghana showed that modern contraceptive use is still less than 20 percent in Ghana, with usage rate ranges from 50-10 percent throughout the region. Improved access and uptake of contraceptives would help reduce maternal mortality (Africa currently accounts for more than 40 percent of the global burden) and have positive effects on child and economic outcomes (See more on Dr. Sai’s keynote here). While reduced fertility and increased contraceptive use has been correlated with increasing levels of development, there has been debate as to the true source of the “demand” for reproductive health services. CGD non-resident fellow Lant Prichett has suggested that unmet need for contraceptives is virtually non-existent (perhaps with the exception of family planning advocates), as the majority of the differences in fertility are due to individual preferences rather than access to contraceptives.

Studying the effects of existing polices can have surprising results: It was repeatedly stressed during the PopPov conference that analyzing the results of existing policies is often just as important as conducting research to inform the creation of new policies. For instance a presentation by Kelly Jones of IFPRI showed that polices intended to reduce the use of abortion, in this case the Mexico City Policy, actually increased total abortions in Ghana. Similarly, a study by Pörtner et al demonstrated that there was no apparent benefit of family planning programs in Ethiopia for women with formal education—suggesting that improving education may be a viable alternate to family planning. As such, research that evaluates the efficacy of policies can ensure the best use of scare resources.

Context matters: “There are very few Bangladeshi’s in Navrongo.” This was a response from Dr. James Phillips from Columbia University, following a question about why the results of the Matlab project in Bangladesh differed from the impact of the Navrongo project in Ghana. In population policy (and likely all areas of policy), fitting interventions to local context is integral to ensuring the success of programs. Interventions tailored for insular and diffused societies, such as Bangladesh, need to be designed differently from the community based systems that are successful in Ghana. Operational models need to be adjusted for specific cultural and geographic changes. Program designers can’t mechanically assume that one intervention will effectively translate from one side of the world to the other.

Policy communication is integral to research: How can a researcher make sure her findings get into the hands of relevant policy makers?  Most agreed that the key to successful policy communication is to keep the message short and simple using issue briefs to describe larger bodies or work.  And of course, make sure the research answers relevant and timely policy questions!

Making development sustainable: The need for effective population policy is not embraced by all policy makers in Africa. Some governments feel that increased fertility rates will increase market share and strengthen national economies, despite research that shows decreased fertility can lead to higher educational attainment, better health outcomes, and greater labor force participation (i.e. alternatives to increasing market share through income growth). Similarly, it has been argued that decreased fertility is beneficial from the environmental perspective. David Wheeler suggests that population policy is one of the most cost-effective methods to reduce carbon emissions. Of course, like any good research, Wheeler’s work has been debated—since decreasing fertility is often associated with increased income and industrial growth which typically also increases emissions. It’s not clear yet if the issue of sustainable development will become a larger aspect of the population and development debate, but based on the conversations at PopPov, it’s a theme to keep on your radar.

It was clear from the conference that population issues can’t be addressed by the Minster of Health alone. They require cross-disciplinary approaches from education and gender equality, to food security and social insurance. The growing research base on population and development is beneficial in so far as it provides indication for areas of policy changes and innovations, but overall the path ahead is far from clear. What’s great about PopPov is the ubiquitous understanding that the process is a learning experience—and that it takes trial and error sometimes to really figure out ‘what works.’

If you want to read more about this year’s PopPov Conference, I suggest you check out the Population Reference Bureau’s blog for some great posts by Jay Gribble and Eric Zuehlke.

The value of disease eradication is very high because VPD elimination is a global public good: once a VPD is eliminated, everyone benefits from it. This also brings in the incentives to free ride, given the nonrivalrous and nonexclusive nature of global public goods. The economic and humanitarian benefits of vaccination are evident; but so are the economic limits of achieving elimination.

Thus, the discussion shifts from elimination to control, or achieving herd immunity: the threshold for vaccination rates above which disease cases become highly infrequent. Herd immunity levels for several diseases are achieved when vaccination rate goes up to 85%; yet vaccination rates in most low- and middle-income countries are significantly below that, according to household survey data. Herd immunity against most vaccine-preventable diseases benefits all countries by reducing the frequency of imported outbreaks.

What would be the best way to go to achieve herd immunity, given the resource constraints? A study by Klepac et al, recently featured in The Economist, finds that the optimal vaccination rate depends on the relative costs of vaccination, and not on the level of contagiousness. Coupled with porous borders and immigration, the burden of each additional infection exceeds the cost, pointing out to the need for sustained investment. This inter-connectedness also promotes free-riding in vaccination efforts between populations and results in lower levels of vaccination in each subpopulation relative to the global optimum. Given this, it is important to pool resources together, especially for neighboring communities, to ensure herd immunity: India, for example, would benefit from supporting vaccination efforts in Bangladesh as much as it would benefit from increased uptake within its own borders. (Granted, India performs worse than Bangladesh on vaccination, so India probably should be thanking Bangladesh for doing the right thing.) The same is true for many countries in South and Southeast Asia, where neighboring countries have low vaccination rates.

This is why vaccination should be perceived as a global public good across the world: achieving herd immunity requires coordinated action to increase vaccination levels.

Vaccine uptake in several countries is stagnating or even declining (see here and here for example). What explains this poor uptake and coverage? Public health researchers have recently begun to apply the concept of  ‘vaccine hesitancy’ and ‘vaccine refusal’, largely focusing on individual knowledge, attitudes, and practices (KAP). But in a new blog post Robert Steinglass of JSI has argued that, while communications and advocacy interventions to change individual KAP are important, this person-centric view will fail to consider the context and the role of quality on the supply-side in determining uptake. He writes:

For example, when I brought my child to the vaccination session:
- was the health worker present at the appointed time?
- was one or more of the required vaccines or syringes absent?
-was I yelled at for not having “retained” a vaccination card which I might never have been given in the first place or that was damaged in the rain on the long walk home or that I perhaps did lose?
- was I reprimanded publicly for not having returned exactly four weeks after the previous dose?
- was I ridiculed for my child’s threadbare or unclean clothing?
- was I informed in my own language what the health worker was trying to say to me?
- was I made to feel ignorant for asking the health worker to explain the purpose of the vaccination or why my child needed to return yet again for another dose?
- was I told when to return for subsequent doses?
- was I requested to make unofficial payments that I could not afford?
- was I expected to wait in the hot sun without any explanation, without seats, without water?

Put differently, if donors and governments push for improved communication and advocacy in order to influence knowledge and practice of patients and people, this is likely to be an insufficient remedy if the supply of health-care is of poor quality or lacking. When will behavioral scientists start accounting for health systems and supply-side factors of health-care?

Yesterday the global health community celebrated a much anticipated anniversary: one year has passed since India’s last reported case of polio. While still tenuous, this achievement is an important milestone for the international effort to attain polio eradication. If India can maintain this progress, then only three countries – Afghanistan, Nigeria, and Pakistan – will remain polio-endemic, down from 125+ countries worldwide in 1988. (As an aside, the WHO describes India as “one of the largest donors to polio eradication being largely self financed.” Are donations to oneself – or “unilateral” donors, if you will – the way of the future?)

While we applaud India for its commitment to reaching this milestone, let us not allow this recent success obscure the sorry state of vaccination in India. In 1985 the Indian government launched its Universal Immunization Programme (UIP), an effort to protect infants from six serious diseases including diphtheria, measles, pertussis, and polio. The chart below shows vaccination coverage from 1980 to 2010 based on DHS and UNICEF data. Vaccination coverage rose rapidly between 1985 and 1990. Unfortunately progress stopped around 1990, and coverage rates remain essentially unchanged since then. Over a quarter of all Indian children still do not receive basic immunization against diphtheria, measles, and pertussis, leaving them vulnerable to potentially deadly but preventable diseases. Household surveys from DHS paint an even more dismal picture – just 34% of Indian children under age 5 are fully immunized.

Figure 1. Vaccination coverage in India, 1980-2008 Source: UNICEF and DHS

India’s lack of universal vaccination has had predictable consequences. The first years of UIP coincided with a steep drop in the prevalence of corresponding diseases. Since about 1995, however, the reported cases of measles and pertussis have stagnated, even as polio cases approached zero.

Figure 2. Reported Cases of Diptheria, Measles, Polio and Pertussis: India 1980-2010 Source: WHO

We wonder whether India’s focus on polio may have come at the expense of other diseases such as diphtheria. Until 2000 polio and diphtheria followed roughly similar trends. Since 2000, however, diphtheria rates have been consistently higher. While India should be applauded for its contribution to global eradication, we urge India to consider the trade-offs in focusing on any one disease at the expense of another and, as much as possible, to try to piggy-back one effort to another. And most importantly — India, please don’t slack now on both polio and immunization. The game is not yet over!

Figure 3. Diphtheria and polio cases in India, 1980-2000 Source: WHO

Since October 2010, Haiti has struggled to control a deadly cholera outbreak—on top of ongoing recovery efforts from the devastating earthquake in January 2010. To date 7000 Haitians have died from cholera and more than half a million have been infected; PAHO recently called it the largest cholera outbreak in modern history.  So last month, a group of lawyers in Haiti, on behalf of some 15,000 victims of cholera, sued the United Nations for $50,000 for each victim and double that for families of those who died.

While Time magazine asks, “Can Haitians sue the UN for the (cholera) epidemic?”, it may be useful to ask instead, “When can people sue others for spreading a disease in general?” The problem with infectious diseases, of course, is that they usually come from somebody or something other than you. The tendency to blame the immediate originator, often suspected and often incorrectly so, is a recurring theme. For example, in the 2009 H1N1 flu pandemic blame was laid on pigs (by calling it ‘swine flu’) and on Mexico (where it was first detected). Both attributions were incorrect but still led to the effect of temporarily cut pork prices, the unnecessary slaughter of pigs in Egypt and the reduction of tourism in Mexico. Similarly, polio has been reintroduced into Europe from India and conversely measles has been reintroduced from Europe into the Americas. The list of these examples in public health is truly endless—disease X spreads from country A to country B, so country A is blamed as responsible.

Yet the thought of suing the ‘sending’ government—Mexico for H1N1, India for polio, etc.—for the spread of these diseases seems absurd because it does not recognize the dynamics of infectious diseases. The spread of disease depends crucially on the conditions and context of the spread. In the case of cholera, the disease is preventable in a setting with protected and improved water and sanitation facilities and very treatable with functioning health-care facilities and providers who know how to treat cholera using IV and ORT (cholera mortality is as low as 0.1% at the ICDDR, B in Dhaka, Bangladesh). In Haiti, access to safe drinking water and water treatment is not widely available, good sanitation and hygiene are lacking, and proper treatment of diarrhea is not available to most. Just 17% of Haitians had access to proper sanitation. Who exactly in Haiti is responsible for water and sanitation – the government, the aid agencies, or the NGOs? In the case of the reintroduction of polio into Europe, the spread relied on the presence of ‘susceptible’ individuals, that is, people who were not properly immunized. As Pogo, the famous cartoon character put it, “We have seen the enemy and it is us.”

Let us assume that the person who spread a disease from one place to another could be traced. Do we focus on that person or on the environment that contributed to its spread? Should we direct our attention to the person’s country (e.g. Nepal) or his/her organization? Focusing on these immediate objects of blame are of epidemiologic interest, but in fact deflect attention away from the country experiencing the disease, and in this case, unable to control the spread. In a country where aid agencies and NGOs play major roles relative to the government, this outbreak should draw attention not only to immediate causes but more importantly to the long-term failure by every involved party and to the urgency of improving Haiti’s water and sanitation as soon as possible. An event at PAHO today – with the presidents of Haiti and the Dominican Republic along with CDC, UNICEF, and PAHO – may mark the beginning to a “Cholera-Free Hispanola”.

How do we get to universal health coverage? This was the focus of a panel with William Hsiao, David de Ferranti and Yanzhong Huang at the Council for Foreign Relations in Washington yesterday. Of the many salient points discussed, including defining “universal health coverage”, Hsiao emphasized the importance of improving efficiency. He noted that 20-40% of money in health-care is “wasted” due to inefficient processes, as cited in the World Health Report 2010 (see p. 79) and Hsiao’s own research in China.  Other studies have found similar results.  In the Philippines, Paul Gertler found that providers (i.e. hospitals and doctors) capture rents from social insurance. And, in an evaluation of Aarogyasri health insurance in the Indian state of Andhra Pradesh, my own work (Fan) calculated very roughly that for every dollar reportedly spent, only 40 cents went to actual household savings, with the remainder likely going to one-time costs and rents by hospitals and doctors.

But why does efficiency matter for achieving universal health coverage?

• Ideally, increasing efficiency makes more resources available to the health sector, which is especially important in today’s constrained economic environment.
• Universal health coverage is an ideal – a “true north” for which health systems should aim. But in practical terms, as a country pushes to “universalize” health care access, coverage and service, it will inevitably face financial constraints and need to prioritize. In many developing countries this priority-setting and allocation process is often conducted on an ad-hoc basis, rather than being based on explicit principles and trade-offs between equity and efficiency (see the CGD working groups on priority-setting institutions and value for money).
• As economies continue to grow and transform, health spending is rising. In terms of financing systems aimed towards universal health coverage, some policymakers believe that “If you build it, the money will come”. Yet one participant noted, “The money is coming, so you better build it right”. One way to help get it “right” is by getting “value for money” and increasing efficiency, by being explicit about who will benefit from any expansion of health coverage and by how much they will each benefit. Or as another participant put it, “getting to” universal health coverage in a country without careful design, though socially popular, may end up as a substantial cash transfer to health-care providers, more so than the intended beneficiaries – the patients and the people.

These technical questions can be esoteric to politicians and bureaucrats who push for health-care reform through political processes. Instead of focusing on health financing reform, will the concept of ‘universal health coverage’ help to overcome such political challenges? This will be the topic of the annual Prince Mahidol Award Conference in Bangkok at the end of January.