Does international aid “crowd out” government funding for healthcare?

A striking article published in The Lancet in April 2010 concluded that when governments receive international aid for healthcare projects, their own spending gets “crowded out”, or displaced. That is, when $1 of money gets delivered from the US to help build a hospital in Ethiopia, the Ethiopian government often takes away $0.43 of its own spending on the hospital and puts that money into something else like the military. The article also indicated that this “displacement” doesn’t happen when international aid is given to non-government organizations like charities.

The implications are obvious–is aid making governments less responsible for their own healthcare systems, and should aid be redirected away from governments to private charities? This week, a new article published in PLoS Medicine questioned those conclusions–and the very premise of the original Lancet article. In today’s blog post, we take a look at the data underlying these two articles and the debate they have generated, and ask whether we should be concerned about “crowding out” issues in international health aid.

Are we effectively controlling tobacco? A look at the industry’s data

If BMW made a car that was sold to one billion people worldwide, and had a fatal mechanical flaw–it locked passengers into their seatbelts and suddenly accelerated uncontrollably, crashing and killing half of its owners–surely the car would be pulled immediately off the road with great scandal, and probably tarnish the company’s reputation for decades. But today, tobacco is sold to about one billion people worldwide and kills almost half of them; it requires about five to seven attempts on average to quit smoking because of addictive materials in tobacco products; and while sales have diminished in the United States, they are accelerating and even being sponsored by governments in some low- and middle-income countries, where 80% of smokers live. In 2003, the World Health Organization signed the first global health treaty, the Framework Convention on Tobacco Control (FCTC), legally binding 174 signing countries to minimum standards that govern the production, sale, distribution, advertisement, and taxation of tobacco. In this week’s post, we look at data from the tobacco industry itself to understand how well efforts like the FCTC are working, and to determine where greater efforts in tobacco control may be needed in the future.

Should doctors make “house calls” again? Preventive hot spotting and early active intervention

In the olden days, doctors would travel from house to house when community members fell ill. Now, we usually expect patients to come to our office-based clinics. The modern model of care is certainly more efficient for us as physicians. But it’s also a barrier for patients to receive medicine; the highest-risk people usually make it to our clinics after being discharged from their first or second hospitalization, well after high blood pressure or diabetes has already taken its toll on their bodies. Our latest research suggests that we can statistically predict which people are most likely to end up having chronic diseases five or ten years from now. We can pinpoint these people right down to which house they live in. Such predictive models present a new opportunity to prevent disease before it becomes costly or deadly. In this week’s post, we look at a new idea for community-based disease prevention in medicine: the geographical mapping of chronic disease risks, and preemptive visits of healthcare workers to households where people are likely to become ill in the future.

The production of consumption: addressing the impact of mining on tuberculosis

Mining for coal, diamond, gold and other minerals has been associated with some of the greatest disasters in occupational history, with recent mine collapses in Chile, West Virginia, and China capturing headlines. The environmental impact of mining, along with the use of gold and “conflict diamonds” to fund proxy armies, has also been the subject of novels and documentaries.

But from a public health perspective, the dangers of mines are not isolated to dramatic explosions, trapped miners and guerilla wars; the epidemiology of mining, in fact, suggests that the occupation carries with it a unique set of secondary effects on the rest of the population. In this week’s post, we look at the evidence suggesting that mining amplifies infectious disease epidemics (especially tuberculosis, TB) on a regional and worldwide scale. We look at how the use of statistics and simulation models can enable us to investigate the broad effects of mining on a wider population, as well as compare alternative policy options to control the epidemic effects of mining on TB.

How many Americans die from racial segregation? About 176,000 a year.

We often hear that poverty and inequality contribute to poor health, but how much difference do they actually make? More than smoking? Less than fast food?

Over the past few weeks, three landmark papers have emerged that actually quantify how much social factors affect the health of Americans. One study manages to put numbers on the “upstream social determinants” of ill health–from racial segregation to low education; another compares the costs and benefits of neighborhood health improvements with expanded health insurance or more preventive medical care; and the third finally answers the question of how much health insurance coverage actually makes a difference to the lives of the poor. In this week’s blog, we look at how researchers attach numbers to “social factors” behind ill health, and quantify how community-based public health efforts stack-up to the latest drug or medical invention.

Where to get your data (on)

After blogging for weeks about various trends in mortality, aid and disease risk, we received a simple but critical question in our email inbox this week: where do you get your data?

The message wasn’t from someone desperate to finish their term paper; it’s a valid question about how we make conclusions about what problems need attention, what the underlying causes might be, and what new issues are arising in public health practice.

In this week’s blog, we attempt to summarize some of the most useful—and occasionally off-the-wall—resources we use to gather and analyze data on public health—from a website that let’s you directly compare the strength of evidence behind different health interventions, to a map that compares the number of liquor stores to the number of healthy food outlets in your neighborhood.

Comparing global health claims to data: a look at the 2011 Snooze Report

Global health reports have a propensity to include dramatic pleas for attention. Here’s a formula: if you want to define yourself in global health politics, pick a disease or population, present yourself as the world’s leading advocate for that topic or group, and write reports that declare your issue neglected or misunderstood by ignorant masses paying “too much attention” to “popular” causes of death (“you don’t listen to those mainstream bands, do you?,” says the hipster). Better yet, include striking statistics about your issue that no one can verify, with accompanying photographs of pregnant women or cute children (darker skins preferred).

How do we verify claims made about global health? In this blog post, we put popular claims to the test–by explaining (in plain English) what we call the “Snooze Report”, the annual report revealing actual statistics behind global mortality.

Displacement and discrepancies: the data on global health aid

Donations for global health programs have risen from $5.6 billion in 1990 to more than $21 billion by 2007. Most of this global health aid comes from our contributions as taxpayers to government-based aid agencies like the US Agency for International Development (USAID) and the UK Department for International Development (DFID):

Suppose we donate $1 of our taxes to a global health program. How much should we expect to actually make it into public health or healthcare services in the recipient country? If we look at the financial reports of leading non-profits like Doctors Without Borders, we see that about 85% of each donation is ultimately spent on healthcare (the rest goes to management and fundraising). But if we look at the OECD dataset describing government-based foreign aid, we find that only about 37 cents of every aid $1 given through government agencies actually makes it into the health budgets of recipient countries.

Where does the rest go? Corruption? Bureaucracy? Actually, when we look at the data, we find a strikingly different answer…