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Links I liked January 23, 2015

Measles cases by yearSome public/global health things that caught my eye today:

1. A visit to the most sickest town in America, a coal mining town in Virginia. Dear Republicans, pay for health care now and abandon “clean coal” or pay more later. It’s up to you. (The Atlantic)

2. How paid sick leave could boost American productivity. (CEPR)

3. Dealing with antibiotic resistance is going to take more than just technology. We can’t sit by and watch everything burn around us while we wait for new drugs to come down the pipe…. because they aren’t coming. (Project Syndicate)

4. I want to deny vaccine deniers. Generally speaking, I don’t like people who are willing to sacrifice kids for politics. Vaccine deniers stick together and increase risks for everyone. (WP) and this one, which puts it all into a nice picture for you. (WP)

5. Diseases without borders: ignoring the problem of piss poor health care in developing countries won’t help us from Jim Kim of the World Bank. (Project Syndicate)

What are we talking about when we discuss socio-economic position and health in developing countries?

OLYMPUS DIGITAL CAMERAA wide body of literature has found that socio-economic position (SEP) has profound impacts on the health status of individuals. Poor people are sicker than rich people. We find this relationship all over the world and in countries like the United States, it couldn’t be more apparent.

Poor people, particularly poor minorities, are more likely to see their children die, are more likely to be obese, have worse cardiac outcomes, develop cancer more often, are disproportionately afflicted by infectious diseases and die earlier than people who are not poor. There is ample evidence to support this.

However, the exact factors which lead to this disparity are up to debate. Some focus on issues of lifestyle, diet, neighborhood effects and access to health care. Poor people, particularly minorities, live hard, eat worse, live in dangerous or toxic environments and have low access to quality care all contributing to a perfect storm of dangerous health risks.

However, even when controlling for all or any of these factors, we still find that poor people, and particularly African-Americans, still get sick more often, get sicker and die earlier. This leads us to speculate that health disparities are not simply a matter of access to material goods which promote good health, but are tightly related to something less tangible, such as social marginalization and racism, which are both incredibly difficult to measure. Though difficult to quantify, however, we do have plenty of well documented qualitative and historical data which indicate that these relationships are entirely plausible.

The awful history of slavery and apartheid, however, is somewhat (but not completely) unique to the United States. Further, our ideas of class come from another Western idea, the Marxist concept of one privileged group exploiting the weak for their own financial gain, particularly in the context of manufacturing.

Yet, though these ways of conceiving of race and class are so specific to the West, they are applied liberally to analyses of developing country health, with little consideration of their validity.

It is not uncommon to see studies of socio-economic status and health. The typical method of measuring socio-economic status in developing countries is to examine the collection of household assets such as TVs, radios, bicycles, etc. and, using statistically derived weights, sum up all of the things a household owns and call that sum a total measure of wealth. The collection of total measures for each household are then divided into categories, with the implication that they roughly approximate our conception of class.

Not surprisingly, it is usually found that people who don’t own much are, compared with people who do, at higher risk for malaria, TB, diarrheal disease, infant and maternal mortality and a host of other things that one wouldn’t wish on anyone.

But this measure is problematic. First, there is often little care taken to parse out which items are related to the disease of interest. For example, we would expect that better housing conditions are associated with a decreased risk for malaria, since mosquitoes aren’t able to enter a house at night. We would also expect that people with access to clean water would be more likely to not get cholera. If we find relationships of SEP with malaria or diarrheal disease which include these items, these associations should be treated with suspicion.

Second, if we do find a relationship of “class” with health, can we view it in the same way in which we might view this relationship in the United States? A Marxist approach, with a few exploiting the many for profit, in sub-Saharan Africa doesn’t make a whole lot of sense. The manufacturing capacity of African countries is tiny, and most people are sole entrepreneurs operating in an economy that hasn’t changed appreciably from pre-colonial times. Stripping away any requirements of legal protection of property rights, Africa looks incredibly libertarian.

Further, the elite in Africa hardly profit financially from the poor, receiving their cash flows mainly from abroad in the form of foreign aid or bribery and foreign activity is mostly limited to resource exploitation, which doesn’t make a dent into Africa’s vast levels of unemployment. While the West is certainly complicit is Africa’s economic woes, post slavery, the West rarely engages Africans themselves.

So, is it valid to attempt to apply the same ideas of class to African health problems? Is there a way to attribute health disparities to class in societies with limited economic capacity and where the “citizenry” is only marginally engaged and groups suffer mainly from a reluctance to cooperate and engage people of other tribes or neighboring countries?

Certainly, the causes of poverty and marginalization in Africa need to be examined, but I don’t think that we can approach them in the same way we do in the States.

Links I liked, November 18, 2014

I liked so many things I read today that, rather than clutter social media, I’ll make note of them right here:

“Falling” by William McPherson – By far, the most depressing thing I have read in a while. McPherson is a Pulitzer winning writer and former editor at the Washington Post who chose a life of curiosity and is now paying the ultimate price. It’s awful that the brightest people have to be punished for thoroughly embracing life. So many people I know are going to go this way, it is possible that I might, too.

In India, Growth Breeds Waste NYT – Documenting India’s mounting problem of what to do with its waste. Europe went through their urbanization pains centuries ago. Unfortunately, developing countries are rising to the challenge fast enough. The problem, of course, is that elites are sheltered from the problems of waste and weak and corrupt government structures disallow people from demanding that their countries clean up. International environmentalists need to focus less on screaming about corporate polluting (though it is important) and need to start making demands for more boring things, like managing waste on a local level.

Stop calling me ‘the Ebola nurse’ – Kaci Hickox – This lady was a hero. She never had ebola, but was still illegally interned for having it because a few Americans don’t understand science. Anybody who supported her detainment should just stop speaking to me now. It was shocking how readily Americans were willing to lock people up simply because they were scared and even more shocking where the calls for her “arrest” came from. I give up. People like Hickox put their money where their mouths are. She did what most humans wouldn’t do and she was vilified for it. Unforgivable.

Ten Things that Anthropologists Can Do to Fight the West African Ebola Epidemic I think it should be required that every field research project include an anthropologist.

Q Fever Is Underestimated in the United States: A Comparison of Fatal Q Fever Cases from Two National Reporting Systems People are dying of Q, but much of it isn’t recorded.

Ebola: we don’t have time to waste

Ebola is a cool disease. It transmits among fruit bats in the area in and around the Central African Republic. Apes live in and under the trees the bats live in and ingest their feces. Humans who ingest the apes pick up the virus when slaughtering the animal, or so some think. The truth is that no one really knows for sure.

Contacts between humans is increasing as settlements expand and a demand for meat increases. Lacking access to formal methods of employment, individual sellers happily take advantage of market demand and a thinly profitable trade in bushmeat profulgates. Meat equals success and in the place of professionally or pastorally raised beef, which is mostly unavailable to poor people in countries like Liberia and Sierre Leone, people eat the monkeys, chimps and many other of our cousins which are able to harbor the many of the same pathogens we do.

One person gets sick. He or she has no access to formal care because his or her government can’t or won’t provide it so he remains at home. The family consults the local herbalist who provides some medications which offer temporary psychological relief but nothing more. As time ticks on, the victim becomes even sicker until the situation becomes so serious that the family has no choice but to carry their dying loved one to a health clinic 20 km away from their house. Along the way, everyone carrying him or her touches infected feces and vomit and three weeks later the process is repeated.

This could have all been avoided if rural economies were developed enough so that a mass migration to urban areas wasn’t necessary, had there been safer sources of meat available for an affordable price, were there sufficient jobs which wouldn’t necessitate the bushmeat trade, were the governments of Liberia and Sierre Leone effective enough to place a proper health facility close by to patient 0’s house and if health care was dependable enough to be able to spot and deal with an Ebola case.

Ebola is a conflation of ecology, economics, sociology, culture and politics, all mixed together to create conditions for one of the worst health crises the African continent has seen since HIV. It’s going to erase any of the gains of the past decade and collapse the already struggling health systems of some of the poorest places on the planet.

Meanwhile, the United States is having another 9/11 moment and this is where I’m starting to get quite concerned. Panic is about to become policy. Fears of global terrorism prompted our entry into Afghanistan, which might have been justified. But it also paved the way for the invasion of Iraq, which, from the beginning, was a disaster waiting to happen. Out of 9/11, we got the Patriot Act, a massive expansion in government powers to search, seize and detain and America stood by and allowed it to happen with little debate.

I am not a Libertarian, though keep getting accused of being one. I believe in public schools, public health care and government oversight of dangerous industries. So there. John Galt wouldn’t be much into me (but I guess from the far, far left anyone looks like a Libertarian).

I am, however, despite my leftist pedigree, quite concerned with the rights of individuals and the potential for panic and ignorance to lead to a rhetoric that can quickly spiral out of control and veer seemingly caring people away from the direction that the moral compass would normally point us in. I am remembering how many Americans supported torture during Bush II and wondered how many of them would support torture were it to be practiced on their own children. Though seemingly alarmist, I think that we need to be extremely careful.

Enough about me. The reality of Ebola is that it is a man-made crisis. Forest dwelling locals have eaten bushmeat for as long as humans have lived there but there is little evidence that there has ever been a large scale outbreak like the one we are currently experiencing (though history in Africa is often obscure). As I noted earlier, many forces are at play, all of which are associated with the rapid social change that Sub-Saharan African states are currently experiencing.

Some of these forces are inevitable. Population growth, as it did in Europe and Asia before, has led to the creation of mega-cities. The connections, however, between the rural and the urban, however have not been severed. People are going to do what they do, regardless of risk, particularly if they can make a buck meeting some market demand.

Some forces, though, are avoidable. While health care did not initiate the crisis, it helped drag it along. Liberia and Sierre Leone can boast to have two of the worst health systems in the world, but their poor capabilities are hardly unique in Sub-Saharan Africa. NGOs and missionary groups work to plug some of the gaps, but the reality is that without a concerted and proactive effort from the governments of those countries, the system will never improve. International funding is too poor and weak national economies and top heavy tax structures can’t adequately fund these systems domestically. Poor funding leaves many clinics, particularly those in rural areas where these outbreaks begin, without supplies, trained staff and diagnostic equipment. In Kenya, Malawi and Tanzania, I’ve seen more than one rural clinic without power or clean water. Worse yet, Ebola outbreaks, though devastating, are infrequent so that more pressing needs like malaria, diarrheal disease and HIV eat up the brunt of the already scarce funds clinics receive. Pathogens not only compete in the wild, but also for funding and support. This leaves many rural health workers without the protective gear they need, so that they work are the highest risk for death from diseases like Ebola.

What can we do? First, we can calm down. In the United States, the reality is that one of far more likely to be killed by an oncoming car than from Ebola and the probability of sustained transmission extremely low. Though people like to view domestic transmission events such as the one in Texas as failure, the reality is that public health and medical resources move much more quickly and effectively in Texas than in troubled Liberia. Much is made over Ebola’s lethality, but a patient who is found to be infected in the United States has a vastly higher likelihood of surviving than one in Liberia.

Second, leaders can stop spreading and capitalizing on misinformation. While attractive, promoting hysteria only leads to bad policy. The tendency in America is to view as some kind of apocalyptic movie scenario. While fun (not to me), the reality is that there are people in the world who are dying who shouldn’t be. Moreover, closing schools because someone knows someone who knows a Liberian is just simply unwise and counterproductive in the long term.

Third, the international community needs to engage the governments of Liberia and Sierre Leone to improve their public health infrastructure. This is not an easy task. The histories of working relationships of international health bodies and developing countries governments are fraught with failure. Mutual distrust, corruption and indifference of political leaders to the plight of their constituencies has created a mostly untenable system. However, providing supplies and training come at little cost is a mostly uncontroversial affair.

How long will this last? No one knows but it is inevitable that, even if this epidemic is brought under control, it certainly won’t be the last of its kind. We don’t have time to waste.

Measuring socio-economic status in Kenya

Rplot04I was just screwing around with some data we collected a bit ago. In a nutshell, I’m working to try to improve the way we measure household wealth in developing countries. For the past 15 years, researchers have relied on a composite index based up easily observable household assets (a la Filmer/Pritchett, 2001).

Enumerators enter a households and quickly note the type of house construction, toilet facilities and the presence of things like radios, TVs, cars, bicycles, etc. Principal Components Analysis (PCA) is then used to create a single continuous measure of household wealth, which is then often broken into quantiles to somewhat appeal to our sense of class and privilege or lack thereof.

It’s a quick and dirty measure that’s almost universally used in large surveys in developing countries. It is the standard for quantifying wealth for Measure DHS, a USAID funded group which does large surveys in developing countries everywhere.

First, I take major issue with the use of PCA to create the composite. PCA assumes that inputs are continuous and normally distributed but the elements of the asset index are often dichotomous (yes/no) or categorical. Further, PCA is extremely sensitive to variations in the level of normality of the elements used, so that results will vary wildly depending on whether you induce normality in your variables or not.

It’s silly to use PCA on this kind of data, but people do it anyway and feel good about it. I’m sure that some of the reason for this is the inclusion of PCA in SPSS (why would anyone ever use SPSS (or PASW or whatever it is now)? a question for another day…)

So… we collected some data. I created a 220 question survey which asked questions typical of the DHS surveys, in addition to non-sensitive questions on household expenditures, income sources, non-observable assets like land and access to banking services and financial activity.

The DHS focuses exclusive on material assets mainly out of convenience, but also of the assumption that assets held today represent purchases in the past, which can act as pretty rough indicators of household income. So I started there and collected what they collect in addition to all my other stuff.

This time, however, I abandoned PCA and opted for Multiple Correspondence Analysis, a technique similar to PCA but intended for categorical data. The end result is similar. You get a set of weights for each item, which (in this case) are then tallied up to create a single continuous measure of wealth (or something like it) for each household in the data set.

Like PCA, the results are somewhat weak. The method only captured about 12% of the variation in the data set, which sort of begs the question as to what is happening with the other 88%. However, we got a cool graph which you can see up on the left. If you look closely, you can see that the variables used tend to follow an intuitive gradient of wealth, running from people who don’t have anything at all and shit in the shrubs to people who have cars and flush toilets.

HistogramsWe surveyed three areas, representing differing levels of development. Looking at how wealth varies by area. we can see that there is one very poor area, which very little variation to the others which have somewhat more spread, and a mean level of wealth that is considerably higher. All of this agreed with intuition.

“Area A” is known to be very rural, isolated and quite poor. Areas B and C are somewhat better though they are somewhat different contextually.

My biggest question, though, was whether a purely asset based index can truly represent a household’s financial status. I wondered if whether large expenditures on things like school fees and health care might actually depress the amount of money available to buy material items.

Thus, we also collected data on common expenditures such schools fees and health care, but also on weekly purchases of cell phone airtime. Interetinngly we found that over all the two were positively correlated with one another, suggesting that higher expenses do not depress the ability for households to make purchases, but found that this relationship does not hold among very poor households.

There is nothing to suggest that high expenses are having a negative effect on material assets among extremely poor households located in Area A at all. It might be the case that there is no relationship at all. This could indicate something else. Though overall there might not be a depressive effect of health care and school costs on material purchases, they might be preventing households from improving their situation. It might only be after a certain point that the two diverge from one another and households are then able to handle paying for both effectively.

Also of interest were the similar patterns found in the three areas.

Expenses

Who pays for development?

I was just reading this on the Guardian’s Poverty Matters blog:

First, identify the most important issues. One of the main problems of the MDGs, as noted in countless analyses, was their failure to bring the major structural issues to the table. I know of no one who thinks that aid is the most important contribution that wealthier countries can make to development, but the vague terms of MDG eight allowed politicians to get away with aid promises (which in some cases they didn’t keep) rather than setting a bold agenda for transformational change in global financial governance, dealing with illicit financial flows, for example, taking bold steps towards international tax reform, and introducing fairer mechanisms for working out debt repayments.

Well, yeah, very true, but again this type of reporting skirts the issue of where those illicit flows are coming from and who took out the loans. The problem with the MDGs was that it failed to put any pressure on leaders of developing countries to stop being parasites. Worse yet, they didn’t allow for the provision and protection of basic individual rights to free expression, judicial rights and economic freedom, instead opting for a few vague and unverifiable targets which failed to address structural problems WITHIN developing countries.

In Kenya, at least, the government is bleeding the populace dry. Evidence from countries such as Botswana and Korea has shown that countries who want to develop can. The biggest obstacle (among all the other obstacles) to development is a lack of political will to do it.

To its credit, the article goes on to point out that domestic ag subsidies in wealthy countries are distorting the world market and preventing developing countries from being competitive on the world market. Eliminating these subsidies will be a real challenge, at least in the US. First, subsidies control price and market volatilities. The US electorate would go bonkers if the price of food went up and down like the price of corn does in developing countries. Second, Americans simply like subsidies and enjoy protecting agricultural interests at all levels. The right likes to pander to farmers for the rural vote while the left is somewhat bummed out because their favorite organic farms don’t have access to them. Though the left loves to pay lip service to ending ag subsidies, I can’t imagine they’d be all that sad if they were offered to their local hippie farmers. That’s speculation for another day, however, and I’m no expert on ag matters.

I hate to be pessimistic about development, but the barriers to progress are hobbled by forces both within and without developing countries and no one seems to be tackling the right issues to improve matters.

The need to look for more than just malaria

I was just reading a comment in the new Journal of the American Society of Tropical Medicine and Hygiene “After Malaria is controlled, what next?”

Fortunately for all of our jobs, there is little to worry about. Malaria, as a complex environmental/political/economic public health problem, won’t be controlled anytime soon. As there’s no indication that many sub-Saharan countries will effectively ameliorate their political problems and also no sign that, despite the “Rising Africa” narrative, African countries will develop in such a way that economic rewards will trickle down to the poorest of the poor, malaria transmission will continue unabated. This is a horribly unfortunate outcome for the people, particularly small children, who have to live with malaria in their daily lives.

In all of the places it occurs, malaria is merely a symptom of a greater political and economic failure.

Indeed, we really know less about the causes of suffering and death in the tropics than many believe. Even vital statistics of birth and death are unrecorded in many areas of the world, much less the accurate causes of disease and death. Some diagnoses, such as malaria, dengue fever, and typhoid fever, are often ascribed to patients’ illnesses without laboratory confirmation. Under the shadow of the umbrella of these diagnoses, other diseases are lurking. I have found significant incidences of spotted fever and typhus group rickettsioses and ehrlichiosis among series of diagnostic samples of patients suspected to have malaria, typhoid, and dengue in tropical geographic locations, where these rickettsial and ehrlichial diseases were previously not even considered by physicians to exist.4–8 Control of malaria or dengue would reveal the presence and magnitude of other currently hidden diseases and stimulate studies to identify the etiologic agents.

This is the problem with our public health fascination with malaria. We are missing all of the other pathogens and conditions which case untold suffering in the poorest and most isolated communities. It can’t be the case that malaria acts in a box. In fact, it could be the case, that multiple pathogens coordinate their efforts to extract as many human biological and behavioral resources as possible to obtain maximum opportunities for reproduction and sustenance. A public health system only designed to look for and treat a limited window of diseases misses the opportunity to disrupt what is probably a vast ecological complex.

First, we have a problem of poor diagnostics. Facilities traditionally treat most fevers presumptively as malaria, dispensing drugs appropriate to that condition. However, conditions like dengue fever exhibit similar symptoms. While is it extremely likely that dengue is all over the African continent, particularly in urban areas, there is little ability to identify true dengue cases in the public health sector, and thus, in addition to mistreating patients, the extent of the disease burden is unknown. We cannot tackle large public health issues without proper data.

Second, we have the problem of all of the “known unknowns,” that is, we know for a fact that there’s more out there than we have data for but we also know (or at least I do) that there is a greater disease ecology out there. We know that many pathogens interact with one another for their mutual advantage or to haplessly effect significantly worse outcomes. The awful synergy of HIV and TB is just one example.

OK, I’m going to go and deal with my own pathogenic tenant which I think I’ve identified as an enteric pathogen of the genus Pseudomonas, which might have taken hold opportunistically through an influenza infection. This is complete speculation, however. Data quality issues prevent a reliable diagnosis!

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