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Health financing, social protection, maternal and child health, aid effectiveness, impact evaluation
Victoria Fan is a research fellow at the Center for Global Development. Her research focuses on the design and evaluation of health policies and programs as well as aid effectiveness in global health. Fan joined the Center after completing her doctorate at Harvard School of Public Health where she wrote her dissertation on health systems in India. Fan has worked at various nongovernmental organizations in Asia and different units at Harvard University and has served as a consultant for the World Bank and WHO. Fan is investigating health insurance for tertiary care in Andhra Pradesh, conditional cash transfers to improve maternal health, and the health workforce in India.
If recognized in its totality, congenital syphilis--with an annual death toll of around half a million--might rank among the top five causes of child mortality. But congenital syphilis is not widely acknowledged as a priority in global health. Despite being relatively simple to diagnose and treat, it is largely absent from high-level discussions of child health or reproductive health and is not a major programmatic focus at prominent global health organizations.
So, why does congenital syphilis remain unseen? This case adds an important new dimension to our understanding of global health politics, highlighting how problems are defined and how common metrics elevate some issues over others. The discussion of this disease allows us to reassess what counts as a significant problem in global health and suggests ways to improve how we understand and act against death and disease in developing countries.
The US has an untapped opportunity to offer global leadership against drug resistance through the major global health programs it already supports, namely PEPFAR, the Global Fund, and the Presidents Malaria Imitative. In this memo, Victoria Fan and Amanda Glassman highlight considerations for Congress with respect to oversight of these key channels of US development assistance for health that greatly affect drug resistance.
A new report by the World Health Organization argues that the poor should be prioritized under universal health coverage (UHC). To that end, the WHO makes three arguments:
(1) wider pooling to share risks and reducing fragmentation of benefit packages within countries;
(2) using health aid for domestic pooled resources rather than vertical programs (of global health funding agencies); and
(3) encouraging civil society to see efficiency improvements as a means to save more lives rather than cut budgets. (sounds familiar!)
No disagreements on points (1) and (3), but (2) seems to raise more questions than answer them.
First, does health aid, pooled or not, predominantly reach the poor? Consider health aid for AIDS, malaria, and TB, which makes up about 44% of all health aid globally. Malaria and TB generally affect people of lower socioeconomic status, so perhaps aid for these diseases does reach the poor. However, AIDS appears to exhibit a paradox, such that in wealthier regions/countries, poorer individuals are more likely to be infected with HIV, whereas in poorer regions/countries, wealthier individuals are more likely to be infected with HIV (see here). Our More Health for the Money report argues for better targeting of resources, both of interventions and key populations, but for disease control purposes. So even if AIDS is concentrated among the richest, in fact it would be necessary to target that population to control an epidemic.
But more broadly, the institutions that fund these three diseases have been slow at targeting overall. In our own work, we found it impossible to say to whom, for example, the Global Fund’s resources were targeted in country (see our paper on this). But even as the WHO (unusually) declares that vertical health aid does not reach the poor, consider the alternative: can the WHO can say whether its funding, more “pooled” and “horizontal” and aimed at “health systems,” really benefits the poor more than the Global Fund or other agencies? I would argue that we have no idea.
Second, telling vertical global health donors to pool their funds would be nice for all sorts of benefits, including perhaps better coordination of resources to target to the poor. But it’s not politically feasible, particularly since these donors have all sorts of mandates to achieve a variety of results, which could be much harder to do in a pooled fund. Further, these funders have institutions and interests to continue fundraising, for their diseases and for their institutions.
Perhaps resigned at the status quo, WHO therefore says that “donors working to support health financing should therefore be mindful of the impact their work has on the population as a whole, and particularly the poor and otherwise vulnerable, rather than being solely focused on their project’s immediate beneficiaries.” But will more mindfulness help the sprawling state of the global health family and lead to more cooperative actions among donors—and ultimately prioritize the poor or at highest risk of a disease? Our More Health for the Money report found cases where there was little knowledge by one funder of another funders’ programs—in the same country. There is very little knowledge to be mindful about.
So I’d argue that policies to coordinate and pool information are crucial—as a minimum before pooling money. So I’m encouraged by a recent glimmer of intentions to try to create a “one-country platform” for information and accountability, thereby reducing a “burden of global reporting” (see here). That in turn might lead to better coordination (and a greater focus on the poor, maybe?).
Perhaps that group will also take a clue from GAVI which is working to improve their information systems, though in a rather lonely way (see my post here). Amanda Glassman and colleagues have also been at work on Data for Development initiative, to improve all the bad and fragmented data funded by donors. And maybe then, there will be more momentum to move towards pooled funding, something previously proposed under the Health Systems Funding Platform, which admittedly has never really taken off.
Finally, missing from all three points is perhaps the most central challenge of achieving universal health coverage, which can undoubtedly help target the poor better: expansion towards UHC necessarily requires selectivity. Countries have to choose who benefits and for what, first. In other words, countries have to set priorities, and those priorities can include the poor or those at highest risk. In fact Amanda Glassman along with Kalipso Chalkidou of NICE International, has a report on priority setting. Why didn’t the WHO even mention institutions for priority setting as part of the core for ensuring that UHC benefits the poor?
Victoria Fan is a research fellow and health economist at the Center for Global Development. The authors thank Jenny Ottenhoff for helpful comments. You can follow Victoria Fan at @FanVictoria on Twitter.
Data quality and rigorous measurement is important for any funder using performance-based or results-based aid. Poorly measured or self-reported data are often subject to major biases. Indeed, recent CGD research by Justin Sandefur and Amanda Glassman found a clear increase in over-reporting of DTP3 vaccination after GAVI introduced a now defunct pay-for-performance program (its immunization services support program) in the early 2000s. Thus, strengthening systems to verify data is important and increasingly feasible; recent experience from the World Bank’s HRITF suggests that independent verification of data isn’t overly expensive.
So we’re encouraged by the GAVI Alliance’s new application guidelines, which outline strengthened requirements for data verification of the immunization outcomes used for performance payments as part of its Health System Strengthening (HSS) support. Per the new guidelines, countries must meet both performance goals and “checks and balances for data verification based on WHO/UNICEF estimates, independent assessments of the quality of administrative data, and periodic household surveys.”
Specifically, all GAVI grants will require that countries have a mechanism to independently assess the quality of administrative data and to monitor data quality over time. In addition, GAVI will require household surveys to be administered at certain frequencies (two surveys every five years). If a country doesn’t have independent data quality assessment mechanisms, parts of GAVI’s HSS grants can help cover these activities. Discrepancies between coverage estimates greater than 5% require an explanation form the recipient and a plan for improving M&E systems.
These new guidelines are an improvement on GAVI’s previous M&E requirements from 2013, which are not specific and state only general intentions, such as: “Performance payments will be based on... administrative data, with…estimates and surveys used for data verification” and “Countries with discrepancies are encouraged to invest in strengthening data quality and routine information”. By contrast, the new guidelines specify the frequency of independent surveys and thresholds for coverage estimates discrepancies. This is one promising step forward to help strengthen national administrative data systems.
However, much more work on data verification is still needed, both by GAVI and the global health aid community writ large. While GAVI appears to be moving towards increased use of survey-based estimates for future grants, it remains unclear how countries with historical grants will be transitioned to this new requirement—or whether improved data quality will estimate lower coverage values, which in turn has ramifications for GAVI’s performance payments. Further, it remains to be seen if these additional checks and balances actually lead to improved accuracy and consistency of administrative data, or if they will simply uncover over-reporting. Similar issues were faced by GAVI’s Data Quality Audit, which conducted audits in 2002-3.
While more rigorous verification is important for more effective results-based aid, additional verification alone provides only limited information on how M&E systems should be improved and the underlying drivers of discrepancies, raising broader questions about the need to strengthen statistical systems (check out CGD’s Data for Development Working Group here and Amanda Glassman’s recent blog post here). Of course, GAVI’s guidelines alone won’t be sufficient to address low national statistical capacities, which have affected quality and timely data use and production by other global health agencies as well. The Global Fund, for example, faces poor (if not worse) quality data, representing a major obstacle to improving value for money (see here). There is a mechanism called the Health Systems Funding Platform in which donors can pool funds to pay for things like verification. But without major donor participation in that platform, it remains irrelevant, unfortunately (see here), and verification is still mainly done donor-by-donor. It remains to be seen how far down the road towards improved administrative data GAVI’s valuable but lonely policy will take us.
Victoria Fan is a research fellow and health economist and Kate McQueston is a program coordinator at the Center for Global Development. The authors thank Jenny Ottenhoff for helpful comments. You can follow Victoria Fan at @FanVictoria and Kate McQueston at @kate_mcq on Twitter.
“For though change is inevitable, change for the better is a full-time job.”
When Adlai E. Stevenson made this statement in 1956 he wasn’t thinking about healthcare. But it is actually a good summary of historical trends in health spending, which we analyzed recently in “The health financing transition: A conceptual framework and empirical evidence” (also available here as a CGD Working Paper). It is almost inevitable that health spending will rise in most countries, but the character, efficiency and equity of that spending are something we can alter – if we take that full-time job seriously.
In the paper, we show that health spending in most countries is very likely to increase – and for some very good reasons. Most countries are experiencing rising incomes, people are living longer, and medical care technologies continue to expand. In other words, much of that money is buying more health. It is also likely, but hardly inevitable, that most of that increased spending will be channeled through taxes or insurance premiums rather than out-of-pocket. If countries work for that to happen, health spending will be less burdensome to the sick and the poor.
These two common trends – a rise in per capita health spending and a decline in the share out-of-pocket spending – comprise what we describe as a “Health Financing Transition.” Just like the demographic and epidemiological transitions, the health financing transition provides a conceptual framework for thinking about long term trends which are not inevitable but still remarkably widespread. Like these other two transitions, timing varies for different countries regarding when they start the transition and how quickly they move through it. They may even regress. Some of the determinants are influenced by economic or technological factors but others can be affected by public policy.
This can be illustrated by looking at a particular country. According to WHO, Myanmar is among the countries that spend the least on health – about US$18 per capita in 2011. It is also among those countries with the highest shares of out-of-pocket spending – over 80% of that money is paid by individuals when they are sick and need health care services rather than as insurance premiums or taxes when they’re feeling well.
Yet, Myanmar has demonstrated that it is like many countries in the way its health care spending is evolving. Even before it made its recent commitment to move toward Universal Health Coverage by 2030, Myanmar was increasing its per capita spending on health and reducing its reliance on out-of-pocket expenditures (see Figure 1).
Source: Author’s calculations from World Health Organization data.
In our paper, we showed that this trajectory is apparent among wealthy countries over the past half century and discernible in a majority of countries over a 15-year period for which data is available
The first part of the Health Financing Transition – rising per capita health spending – is extremely likely in most low- and middle-income countries for perfectly sensible reasons. More people are earning more income and as their incomes rise, they will seek out medical care that was previously unaffordable or inaccessible. The literature demonstrates that health spending is substantially driven by rising income, to a lesser degree by population aging, and additionally by some combination of prices, technologies, and institutional change. Public policies can influence how that money gets spent by regulating private providers, structuring health insurance options, or providing public health care services.
The second part of the health financing transition – the declining share of out of pocket spending – is less predictable because it depends more directly on public policy. In our analysis, the out-of-pocket share of total health spending was not related to changes in income. It was, however related to changes in a country’s mobilization of public revenues which we interpret as demonstrating the importance of public policies in reducing out-of-pocket spending. We also found a small secular downward trend in the out-of-pocket share which may reflect the ubiquity of social movements – from Ghana to Myanmar – which want to protect people from having to pay for care when they are sick and most vulnerable. Historical accounts provide plenty of evidence to support such a hypothesis.
Countries progress more rapidly through the health financing transition when the pooled share of health spending (i.e. money for health care that is paid through insurance premiums or taxes) is rising faster than out-of-pocket spending. This diagram from our paper shows that countries like Colombia and Thailand moved rapidly along this trajectory between 1995 and 2011; countries like Brazil and India moved slowly; and countries like the Philippines and Uzbekistan regressed.
The last decade in Myanmar puts it firmly in the lower right quadrant of this diagram – countries that are progressing rapidly through the health financing transition. But this progress is not inevitable. While pressures for rising health spending are going to continue, progress on increasing the share of pooled spending will require public commitments and good policies. For countries like Myanmar, the commitment to universal health coverage is a commitment to continue to spend more on health and to finance it with pooled funds. Whether it stays on this trajectory or not is in their hands … and it is a full-time job.
The Global Fund’s New Funding Model (NFM) was approved by its Board more than a year ago, representing what the Fund’s Director Mark Dybul called “a new beginning” to “achieve greater impact in the lives of people affected by HIV and AIDS, TB and malaria. A key piece of the NFM is an allocation methodology that aims to inform how the Global Fund distributes funding between countries and among disease based on objective criteria of disease burden and country ability to pay . This should demonstrate a marked improvement from past allocation measures which funded proposals on a first-come, first-served basis until the money ran out .
The NFM’s allocation methodology was intended to solve mismatches between disease burden and Global Fund money, and to increase the predictability of available funding to countries. In recent work, we find that in the past most top-25 HIV grant recipients received disproportionately high funding relative to their disease burden, ability to pay, and performance. There were enormous disparities in per-capita allocation between countries that would appear similar on a variety of criteria (including disease burden, population at risk, income, and performance capacity), with a number of countries receiving too much or too little than might be expected (see illustrative figure below based on one criteria).
Figure 1. Historical Global Fund HIV/AIDS spending (%) vs global HIV cases by country (%)
The new allocation methodology was intended to fix this imbalance, which in turn might drive quicker progress against disease. At the 30th Board Meeting, the Strategy, Investment and Impact Committee (SIIC), as requested by the Board, approved the parameters to be used by the allocation methodology for the 2014-2016 period. Aidspan published a summary and a description of the new approach, and the Global Fund recently put out a Resource Book for Applicants.
Yet the justification for the choices made as part of the NFM allocation methodology remain unclear and sometimes perplexing, with uncertain effects on the money-disease mismatch. Here’s how we see it:
The new allocation approach is composed of three major steps:
First, there is a “global disease split” that is “determined by the Board,” resulting in 50% HIV, 32% malaria and 18% TB. This split is not related to the relative burden of disease or the relative cost-effectiveness of the interventions to battle each disease, and is instead the historical distribution of Global Fund monies amongst diseases. This decision implies a missed opportunity to reduce mismatch between money and burden.
Next, the allocation formula is calculated within each disease. In the diagrammed example below from Aidspan, the number of HIV cases (HIV burden) would be multiplied by the ability to pay factor (could be interpreted as the proportion of the HIV cases that will be externally funded, 95% in the case of low-incomes according to the guidance). The product generates a number of cases that is called a “country score for HIV” that is then divided by the sum of all country HIV cases. This generates some proportion of cases that is applied to total available HIV funding, termed the “notional HIV amount.” Basically, this is just taking HIV cases that will (might?) be externally funded in full or in part and dividing up the money among them. This approach makes sense if the idea is to pay only for HIV treatment, if the share of externally funded cases is approximately accurate and if the unit cost of treatment is reasonably ascertained in low-income countries, none of which may be valid assumptions.
The measures of disease burden to be used in the formula are inconsistent across diseases (see table below), and for TB and malaria, would lack the rationale that makes the HIV formula reasonable if treatment were our only objective. The disease burden indicators were based on recommendations from technical partners (WHO, STOP TB, RBM, UNAIDS).
Finally, after several other steps, there is a provision for “graduated reductions,” meaning that countries that are supposed to receive less given the new allocation formula will not actually receive less for some period of time, “2017-1019 and beyond” according to the Aidspan document, with the rationale of maintaining the gains made and the need to responsibly transfer programs to host country governments or other donors.
In the absence of an official explanation of the decisions that underlie this new approach, we have many unanswered questions:
Why aren’t consistent measures of disease burden used to allocate amongst diseases in a transparent way? Doing so might move more money towards malaria, since mortality from malaria is concentrated among children under 5 – wouldn’t this be a good thing?
Why should MDR-TB cases be prioritized at 8 times the number of drug-susceptible TB or HIV/AIDS cases?
What kinds of incentives exist to improve performance on disease reduction in the new allocation approach?
What explains the “kinky” sliding scale of national incomes that groups countries into bands for purposes of ability to pay, not distinguishing between the ability to pay of a country at $4,000 GDP per capita and one at $10,000 GDP per capita?
Why are the base years for measuring the disease indicators different, such that malaria allocations depend on what was happening with the disease 13 years ago?
Why put countries in “bands” adding another layer of complexity (as raised by some Board members in the 29th Board Meeting)?
How exactly should the allocation methodology based on objective criteria be reconciled with historical commitments for treatment? Or as implied by the Global Fund, how will it “ensure sufficient indicative funding to satisfy existing grant commitments”? Is this the same or different than the “graduated reduction” strategy?
All of this adds up to an allocation approach that has not yet answered how the new method - in combination with graduated reductions - actually fixes the old problem of funding imbalances, or better value for money using the allocation of resources.
In our analyses, we find that the NFM allocation methodology could –if implemented without graduated reductions – reduce discretionary or “suboptimally” allocated funding by about 40%. Importantly, we also find that a clearer, more theoretically justifiable allocation formula could reduce “suboptimally” allocated funding by 20-50% more than the NFM method. But our analyses are based on guess work since we don’t really know how the allocation process works in detail.
The new allocation methodology is a step forward. But the Global Fund Board and its partners can and should better explain the rationale for choices taken, and better document the impact of the new formula on recipients and money-burden mismatches. Most importantly, we hope that the Global Fund Board will revisit the allocation methodology in the coming years and adjust as needed – we’ve offered some options for different approaches to allocation in a draft paper.
After years of growing concern that the extensive use of antibiotics in animals was leading to the spread of drug-resistant infections, the US Food and Drug Administration (FDA) has issued a final guidance document that seeks to eliminate the use of critical antibiotics to promote growth in animals. This is an important but modest step forward for the FDA. In 2011 the FDA reported that 29.9 million pounds of antibiotics were sold for use in livestock – this represents 80 percent of the total volume of antibiotics sold in the US. The FDA is hoping that by limiting the use of antibiotics for growth promotion it can slow the emergence of drug resistant bacteria. But experience suggests the new FDA rules may contain a fat loophole.
A number of key reports highlight drug resistance as a major challenge of our time. The Center for Disease Control and Prevention’s Antibiotic Resistance Threats report estimated that in the past year, approximately 2 million people were infected with bacteria that were resistant to antibiotics and that at least 23,000 deaths could be attributed to antibiotic-resistant infections each year. A CGD report warned in 2006 that the useful life of antibiotics has been getting shorter and shorter, as resistance appears more and more rapidly.
Members of Congress have proposed legislation that would require the FDA to take steps toward withdrawing approval for all nontherapeutic use of drugs in animals (the use of drugs for a purpose other than treatment of disease, e.g. growth promotion and disease prevention). Until FDA’s new plan, the United States had resisted undertaking even modest policy changes in this area. By comparison, the European Union banned the use of antibiotics for growth promotion in 2006.
While the FDA’s new plan offers a ray of hope to those concerned about the widespread use of antibiotics, it is a basic first step. First, the plan is voluntary. Drug companies are encouraged, not required, to revise antibiotic labels to clarify that drug use is allowed only when medically necessary and not for growth promotion, but drugs can still be used to prevent (rather than just treat) infections. While several pharmaceutical companies have agreed to change their drug labels, the disease prevention loophole is a large one.
In the Netherlands, a ban on antibiotic use for growth promotion alone had little impact on farmer behavior and the volume of antibiotic use remained fairly constant during the initial years. Later, stricter regulations that instituted limits on total use and imposed fines to penalize noncompliance brought about small decreases in antibiotic use. By contrast, Denmark, a pork production powerhouse, banned antibiotic use in animals for all nontherapeutic purposes in 1999; including growth promotion and disease prevention. Result: use of antibiotics per pound of Danish meat dropped by half. The Danish government also collects extensive data on antibiotic sales, so the use of antibiotics can be traced back to individual livestock producers.
We are encouraged that the FDA is finally moving to combat the looming threat of drug resistance, but these examples suggest that this initial response is unlikely to be enough to bring about large reductions in unwarranted antibiotic use. To take it a step further, the United States should, like the Danish government, prohibit all non-therapeutic use, for growth promotion and disease prevention, and systematically track antibiotic sales and use.
Antibiotic use in livestock is not unique to Europe and the United States. For example, China is believed to use four times more antibiotics on animals than the United States. Unfortunately, data in China is even harder to find. This is a global challenge. All countries need to come together to reach an international agreement on principles for responsible livestock production, including rigorous monitoring of antibiotic use, so that the effectiveness of the world’s antibiotics are preserved and a post-antibiotic world is postponed. The US FDA move is welcome, but falls short of what is needed to protect Americans and to provide global leadership.
Through our Value for Money working group, we’ve spent much of the past year immersed in the world of global health funding agencies. With so many new agencies, particularly in the last quarter century (Figure 1), understanding the intricacies of the global health family can be daunting, even for the most devoted observers.
Figure 1: Timeline of Selected Entrants to the Global Health Family, 1902 – 2006
For our own reference (and yours), we thought it would be useful to compile a “cheat sheet” on global health funding agencies. We used the public websites of global health funders shown in Figure 1 (supplemented by IHME’s Financing Global Health) to compile key “stats” for large global health players. Our compilation is available online as a background brief. We include:
Table 1: The basics: who, what, when, where, how
Table 2: Who gives, and how much (contributions)?
Table 3: Who’s in charge (governance)?
Table 4: The ABCs of global health agencies
We hope that this resource provides a useful overview for novices and veterans alike who are trying to make sense of the complicated global health landscape and architecture. Let us know if you have any feedback or suggestions – either below as a comment or by email – to make this resource more useful or accurate!
Victoria Fan (@fanvictoria) is a research fellow and Rachel Silverman (@rasiiii) is a research assistant at the Center for Global Development.
In her classic 2011 anthem, Beyonce posited that it was girls “who run the world.” Yet in the world of global health, we worry that Beyonce may be mistaken – from our observations, it appears that women remain severely underrepresented in top leadership positions.
It may seem counterintuitive that the world’s top advocates for women’s health and equity would be missing women leaders within their own ranks. Women’s welfare is perhaps more prominent than ever before in global health circles, as partially evidenced by this week’s massive Women Deliver conference and the Lancet’s corresponding thematic issue on gender. But gender equality issues have been our mind lately, with the new book published by Sheryl Sandberg (a member of the CGD’s Board of Directors) on women in work and leadership and Anne-Marie Slaughter’s article on women’s roles last year. And Foreign Policy published a list of “the 500 most powerful people on the planet” of which a measly ten percent were female, along with another piece noting how few think tanks are run by women. From our own experience within the global health ecosystem, it’s hard not to notice the relative paucity of women at the top ranks of academia and global health institutions, despite obvious female majorities in global health student bodies and among junior researchers.
Many global health funding agencies (namely, the Global Fund, GAVI, UNITAID, PEPFAR, PMI, the World Bank, and UNAIDS) have never in their histories had a top-executive who is female. (Granted, UNICEF, UNFPA, PAHO, and WHO are exceptions and are also older institutions.) Given the important role of academia in shaping global health, it’s also notable that only five of the top 20 schools of public health in America (per the US News Ranking) are led by female deans, and that the vast majority of their global health departments are chaired by male professors (see addendum table). Deanships and chairs aside, as one looks at the names in top-ranked faculty lists, it is clear that even tenure-track faculty within global health departments are largely men (see here and here for example).
And what’s remarkable – and well known at least to public health students and professionals – is that despite the differential in sex ratios in global health leadership, the student body in many public health graduate programs and mid-level staff in most policy/advocacy groups have a large female majority. At Harvard and Johns Hopkins, for example, female students account for 71% and 67%, respectively.
It may be that this imbalance needs time; that if we simply wait then today’s female students and junior staff will naturally evolve into tomorrow’s tenured faculty, deans, and global health leaders. But exactly how long before such a transition will occur? Given current trends at the bottom, we might expect such a “demographic transition” towards gender equity to happen quite quickly – say 5 to 8 years – unless there are other factors besides time hindering the gender balance. Notably, in several global health departments, non-tenure track research scientists and associates appear to be majority female, while tenured faculty skew male. It could be that hiring and promotion within tenure-track positions is biased against women, either explicitly or implicitly (i.e. women are pursuing tenure at the same time they are starting families), or that it is a legacy phenomenon from male-dominated times of old, one that will abate as the older cohort gradually retires.
But rather than wait passively for this desired “cohort effect” to gradually improve gender balance, the world of global health should take this issue head-on to determine what the root causes are behind this imbalance, and what needs to be changed within each individual institution.
As a start, global health agencies along with universities, departments of global health, and associated consortia should consider commissioning a report to rigorously examine whether gender imbalances are occurring – what percentage of staff at different levels are female and how many women are there are in top leadership positions? If imbalances are observed – and particularly if the gender balance of leadership does not match the composition of more junior staff or student bodies – institutions would be well served to investigate the factors underlying those disparities and take practical steps to address it. Not simply as a token effort, but because we genuinely believe it to be a problem when the viewpoints of educators and leaders lean heavily towards one gender, particularly when so much of global health focuses on the wellbeing of women and girls. These institutions could take a page from MIT, a school focused on science and engineering, which pioneered a breakthrough study on gender inequality in 1999; more than a decade later, significant progress was reported.
While women may never “run” the global health world (and nor should they, as men offer equally important and valuable voices), equitable and balanced global health leadership is itself a noble goal – one that is feasible within our lifetimes if key institutions demonstrate thoughtful and genuine leadership in this space.
The authors thank different administrators at various schools of public health for advance comments on a previous version of this piece. Victoria Fan (@fanvictoria) is a research fellow and Rachel Silverman (@rasiiii) is a research assistant at the Center for Global Development.
The New England Journal of Medicine recently published the results of “the Oregon experiment” based on the 2008 US Medicaid program expansion in Oregon. The study is one of very few randomized control trials on publicly-subsidized health insurance that exists to guide health policy, and found what some commentators considered a disappointing result: while health care utilization increased and households were protected from financial hardship, expanding Medicaid coverage had “no significant impact on measured physical health outcomes over a 2-year period.”
Should we be surprised? To date, there are few countries that have demonstrated a causal link between health insurance/coverage and physical health status using a randomized trial, and many of the studies that find a linkage used observational, or non-experimental, methods (see here and here for comprehensive reviews of the literature). For example, a 2012 observational study found that the Thai health insurance program reduced infant mortality by 6.5 per 1000 live births.
But in many studies there are no detected associations between expanded health insurance or risk pooling with population health outcomes. For example, an update on an experiment in Mexico on the Seguro Popular public insurance program finds out-of-pocket spending dramatically reduced, but impact on health status remains elusive.
For studies with null results, the absence of evidence does not mean an evidence of absence. There are many reasons why detecting a causal effect between insurance and health outcomes is complex and challenging. Methodologically, detecting changes in health status in a short time period provided from an academic study is very difficult. Mortality is a rare event, requiring large sample sizes to detect change (which is probably why the Oregon experiment did not measure it), while morbidity is still relatively infrequent (which the study did measure) – but also not so easy to change even with health insurance (just think about the last time you tried to lose weight, stop smoking, or reduce your blood pressure!)
In addition to methodological difficulties, the mechanism between health insurance and health status operates through increasing the utilization of health care, holding individual, household, and social and community factors constant. If insurance does indeed increase utilization, but does not have an impact on health outcomes amenable to efficacious clinical interventions, then there are many other factors that could be at play: poor clinical quality on the provider side; poor adherence or compliance of medicines by patients of treatment; or other unobserved behavioral variables on both sides.
Moreover, while this may seem obvious, changes in utilization and associated health outcomes need to be directly related to the benefits offered under an insurance scheme. In other words, changes in physical health may have more to do with the scope and content of benefits plans, the structure of and incentives associated with provider payment and quality oversight mechanisms, and the amounts of premiums and co-pays – and much less to do with the mere accessibility and availability of services through insurance. Hence it holds that insurance may be a necessary but not a sufficient condition for improving health outcomes.
Indeed, these various possibilities are mentioned by the Oregon experiment authors. Compliance and treatment-seeking behaviors were likely critical; for example, the study found that there was no effect on the use of hypertension medications and hence, not surprisingly, no statistically significant change in blood pressure in the experimental group.
Physical health measures aside, one particularly encouraging finding in the Oregon experiment is that within just two short years there was a detected impact on mental health and several measures of self-reported health. While it is easy to disregard self-reported health measures, there are many studies out there that show a strong correlation between self-reported health and long-run mortality (see here, here, and here for example) – and these studies are the reason why such mental health measures are often used when it is impossible to detect mortality changes.
So what are the lessons for global health? In the drive to universal health coverage, as championed by the UN and others, we should remember the evidence base is still developing, and that key interventions delivered with quality as well as people’s own behavior and incentives are important factors that will drive health status improvements in the short-term. Countries should not shy away from embarking on robust experimental and quasi-experimental studies (e.g. through a lottery) as they expand pooled funding for health including through insurance – and in doing so they can learn a great deal to improve their health programs.
On World Toilet Day, we’d like to take a moment to celebrate the toilet for not only saving lives – by reducing the risk of deadly diarrhea – but also for helping people to grow taller, a key measure of childhood malnutrition. Indicators of overall childhood well-being, height and weight tell us about the critical period of life when bodies and brains are developing. New studies emphasize the relationship between toilets and height, suggesting that a large share of childhood stunting in India can be attributed to the high rates of open defecation. Worse, the effects are likely larger if you live in an area where many others defecate in the open.
“Stuff from your body I'll happily take,
Liquid or solid, whatever you make.
When you think of all the things that I do,
It's time to say thanks to your loo.”
In developing countries, one in five people defecate in the open for lack of access to a functioning toilet. Sixty per cent of the world’s population that openly defecates lives in India, with economic costs on the order of US$50 billion each year.
These levels of open defecation (and low levels of toilet use) persist in India, even as different ventures of varying scale and impact to improve sanitation have been pursued – latrines have been built for free or provided subsidies, the country introduced open defecation patrols and even tried to scare bachelor men through the “No Toilet No Bride” campaign. Worse, the problem persists even though hygiene and sanitation promotion are ranked as two highly cost-effective public health interventions by the World Bank – $3 per averted Disability-Adjusted Life Year (DALY) for improved hygiene and $11 per DALY averted for improved sanitation. These figures demonstrate that toilets are incredibly affordable and life-saving.
New research shows that toilets reduce malnutrition and help children grow taller. This paper by Dean Spears attempts to unpack the paradox of why children in India are shorter on average than children in Africa, who are poorer on average. Spears found that much of the cross-country variation in height can be explained by the differing levels of open defecation. After controlling for GDP, the paper suggests that the difference in Nigeria and India’s open defecation rate is associated with an increase in child height that is equivalent to the effect of quadrupling GDP per capita. Crucially, the paper suggests that there are large negative externalities associated with open defecation: an increase in the number of people engaging in open defecation per square kilometer is associated with a decrease in child height. So your neighbor’s behavior matters greatly for your own child.
While such cross-country analyses are not causal, other research by Dean Spears and Jeff Hammer try to estimate the causal effect of sanitation on height by evaluating the implementation of a Total Sanitation Campaign in one district of a large state in India (Ahmednagar, Maharashtra). They estimate that the campaign was associated with an increase of 1.3 cm in the height of a four-year-old (specifically, a 0.3 to 0.4 standard deviation increase in children’s height-for-age z-scores). Granted, this estimate should not be taken as the last word, given a number of the study’s methodological qualifications and challenges.
Nevertheless these studies on sanitation’s role in malnutrition, as measured by height, helps to quell a mischievous notion that Indians are somehow genetically predisposed to being shorter. Indeed, in a recent series in the Economic & Political Weekly, a number of outstanding researchers, including Angus Deaton, Jean Drèze, Seema Jayachandran and Rohini Pande show that the broader determinants of malnutrition in India (such as sanitation) continue to win over the (fairly 19th century) genetic arguments.
There are still few research studies testing whether toilets help one and others grow taller, as shown in a recent Cochrane systematic review. The review also found that, among studies on the effects of interventions that improve access to water, sanitation and hygiene on nutritional status, the study period was very short (9-12 months), suggesting that there is still room for better evidence and the usual research caution.
The relationship between malnutrition and diarrheal disease (as distinct from the interventions of sanitation and hygiene) has arguably stronger evidence. One study on 2-year-olds found that five or more episodes of diarrhea is associated with a 25% probability of stunting. Additionally, poor sanitation and water access may lead to environmental enteropathy, an inflammatory response in the gut that makes one’s gut less able to absorb nutrients from an early period.
New longitudinal studies on this relationship continue to be published, including the GEMS study which found that children with diarrhea who were shorter than average were also more likely to die within the study period and that child height is a serious risk factor for death from diarrheal disease. And the MAL-ED study found that intestinal inflammation due to diarrhea was associated with stunting among infants.
Bottom line: Toilets, diarrhea, height and weight, and mortality are interrelated, though scientific evidence of the precise direction and mechanism of these causal pathways will not be easy to disentangle. So on World Toilet Day, like Matt Damon, let’s give a sh*t about toilets. They not only save millions of children, but they make them healthier and taller too.
Victoria Fan is a research fellow and health economist and Rifaiyat Mahbub is a research assistant at the Center for Global Development. The authors thank Dean Spears, Claire Chase, Stephanie Psaki, and Jenny Ottenhoff for helpful comments. You can follow Victoria Fan at @FanVictoria and Rifaiyat Mahbub at @rifaiyat_mahbub on Twitter.
Mosquito-zapping lasers, refrigerators for vaccines, and disease modeling: these are the three impressive technologies featured in a TED talk by Nathan Myhrvold. On the first two technologies, I responded in a piece “When engineering met public health” in the Huffington Post. What I didn’t get a chance to talk about was the importance of his third technology – disease modeling:
“One of the problems that you have if you're trying to eradicate malaria or reduce it is you don't know what's the most effective thing to do. Okay, we heard about bed nets earlier. You spend a certain amount per bed net. Or you could spray. You can give drug administration. There's all these different interventions but they have different kinds of effectiveness. How can you tell? So we've created, using our supercomputer, the world's best computer model of malaria, which we'll show you now…”
“By doing these kinds of simulations, we want to eradicate or control malaria thousands of times in software before we actually have to do it in real life; to be able to simulate both the economic trade-offs -- how many bed nets versus how much spraying? -- or the social trade-offs -- what happens if unrest breaks out?
Without a doubt, this software may well be a more powerful technology than a laser zapper or a vaccine refrigerator. Myhrvold notes that the disease modeling software developed by Global Good now informs eradication strategies for polio, HIV/AIDS, tuberculosis and malaria.
We can’t be more supportive of this work, an issue we’ve supported in our CGD Working Group on Value for Money in Global Health. In the Planning Allocation chapters in our More Health for the Money report, we recommend the Global Fund “optimize investments for greatest health impact.” True, the Global Fund is beginning to move in this direction, with its greater focus on targeting programs and grant-funded interventions to key populations. But the Global Fund can do much more by requiring that its recipients systematically consider intervention effectiveness, costs, environment, and other factors into a model, however simple or complex, to maximize health impact.
Detractors will say that these models are too hard, the data aren’t available, and recipients don’t have the technical capacity. But the data requirements for these models are not onerous or burdensome, nor do they need to involve Myhrvold’s supercomputers. Dr Tim Hallett, a reader at Imperial College London, found that impact could increase by 20%, simply by redirecting the same resources to the populations at greatest risk of infection and transmission. Simpler modeling tools producing “good-enough” decisions on how to allocate national resources are needed and are being developed by various groups – and the Global Fund should tap into these activities.
In the meantime, the Global Fund can encourage applicants to talk with networks and organizations such as Myhrvold’s and Hallett’s as well as the various disease modeling consortia (see here, here, and here) to make sure that proposed plans are good value for money. That can start a conversation where ultimately more lives will be saved and more people will benefit. When done appropriately, countries using more information and data can better target their interventions to the right populations and ultimately control, if not eliminate, their epidemics.
Victoria Fan is a research fellow and health economist at the Center for Global Development. She thanks Amanda Glassman, Jenny Ottenhoff, Justin Sandefur, and Huffington Post for their inputs. You can follow Victoria Fan on Twitter at @FanVictoria.
“Human development is at the core of development. We hope this forum will substantially push forward health cooperation between China and Africa.”
Why does this matter? Since 2000, China has hosted six ministerial Fora on China-Africa Cooperation (FOCAC), held every three years, in which health is but one of many areas of attention. In the last FOCAC, the accompanying Beijing Action Plan for 2013-15 listed cooperation in many areas – 6 in political, 9 in economic, 6 in cultural, and 6 in development – of which ‘medical aid and public health’ is one. And, while official figures are hard to come by (see paper by my colleague Vij Ramachandran), it is likely that health has played only a minor role in China’s development assistance to Africa. This inaugural forum on health and Beijing declaration may well mark a turning point in the history of Chinese development and health cooperation to Africa. China’s top-level leadership clearly sees the political, economic, and perhaps health importance of global engagement especially in Africa.
What did I like about the Declaration? I liked the shout-out to “universal coverage of health services”, “sustainable, long-term health solutions”, “support strengthening of health information systems”, and the focus on vaccine-preventable diseases. I also like how “universal coverage of health services” in Chinese translates to an explicit focus on covering all people of some set of health services (卫生服务的全民覆盖). Universal health coverage has been a recurring theme both in China’s recent major health reforms and at the WHO led by Margaret Chan (see picture). This focus on universal health coverage and, to some extent, on health systems appears to be a marked distinction from the previous Beijing Action Plan.
Chinese president Xi Jinping (left) meets with the director general of the World Health Organization (WHO) Margaret Chan (right) on 20 August 2013 in the People’s Great Hall in Beijing, China. Source
And there were other things in Chinese, with its typical metaphorical style, that were endearing, like “中非友好源远流长，历久弥新” which was generically translated to “China and Africa enjoy a sustainable friendship for a long time”, but it conveys a feeling of a long flowing river of history, old yet constantly new. In the related news, there was repeated emphasis on mutual benefit, win-win, and equal partnership (平等互利、合作共赢), all echoing Mr Deng Xiaoping’s early principles of foreign engagement.
Second, even with the Declaration’s greater attention to universal health coverage, the Declaration and the Action Plan still risk focusing excessively on providing ‘things’ – sending medical teams, drugs, prefabricated clinics (yep, that’s there too). There’s nothing wrong with focusing on these inputs per se, but there are risks. First, temporary inputs from China, particularly health workers, are just that – temporary. Once they depart or stop flowing in, the system reverts back to what it was previously. Even if the Chinese medical team can build capacity of local physicians or nurses through their visits, such training may tend to focus on tertiary care, which will not reduce major causes of disease in many African countries.
More critical, however, is that a focus on inputs does not necessarily consider the incentives, the systems, and the health outcomes. Are these particular inputs, eg, medical teams, what is needed to improve health status in a particular African country? It’s not obvious. The Chinese should remember and learn the difficult lessons from their own history of health care, recognizing that their “barefoot doctors” provided services in a system with different incentives – through its Cooperative Medical System. In order to realize its aspiration of “sustainable, long-term solutions,” Chinese officials need to put more attention on the incentives within an African health system, the local governance structures, and local health information systems. Without that, all these things may at best be one-off tokens of goodwill, or worse, it may leave a trail of Chinese products and buildings that are ultimately unused and empty. China should consider other options besides input-based aid and instead look to cutting-edge aid tools such as performance-based financing or Cash on Delivery Aid.
In addition, the Declaration had zero mention of China’s complex aid architecture. China, like the United States, already has a hodgepodge of national-level agencies involved in international cooperation including its health ministry (now called the National Commission on Health and Family Planning). But unlike the United States, the Chinese government has increasingly decentralized foreign affairs to the province level. My coauthor Gordon Shen presented our working paper on China’s provincial diplomacy for health, during the China-Africa Health Young Leaders Roundtable, which coincided with last week’s events. The paper describes the role of Chinese province to African country relationships in health cooperation (for example, Fujian-Botswana, Henan-Ethiopia, etc.), the implications of locked pairings, and how to improve the allocation of such pairings. While lacking data, our paper overall suggests a historical role of provinces in China’s global health diplomacy, which adds another layer of complexity in an already complex aid architecture.
Finally, readers may naturally ask – what about the financial commitments with this Beijing Declaration? This is important. But the problem is that, because of China’s multi-actor and decentralized aid architecture, it is not surprising that even the Chinese national government hardly has a clue of the size of resources being invested to Africa for health. I wrote a blog on this, and the problem still persists. It’s hard to make pledges to increase aggregate commitments if one doesn’t even know what one’s baseline or historical disbursements.
Bottom line: wait and see. To where will the long and flowing river of China-Africa cooperation lead?
Dr Victoria Fanis a research fellow and health economist at the Center for Global Development. The author thanks Lincoln Chen, Yanzhong Huang, LIU Peilong, Jenny Ottenhoff, Gordon Shen, and TANG Shenglan for helpful comments. You can follow her on twitter at @FanVictoria
Little is known about the President’s Emergency Plan for AIDS Relief (PEPFAR) financial flows within the United States (US) government, to its contractors, and to countries. We track the financial flows of PEPFAR – from donor agencies via intermediaries and finally to prime partners. We reviewed and analyzed publicly available government documents; a Center for Global Development dataset on 477 prime partners receiving PEPFAR funding in FY2008; and a cross-country dataset to predict PEPFAR outlays at the country level. We present patterns in Congressional appropriations to US government implementing agencies; the landscape of prime partners and contractors; and the allocation of PEPFAR funding by disease burden as a measure of country need.