In 2012, my friend Akira and I went hiking in the mountains outside Osaka. It was a pretty easy hike, but on the way down Akira twisted his ankle and sort of lumbered down the rest of the trail. After a few days, the pain got worse and he had to cancel an upcoming research trip to Vanuatu. He asked me to go in his place and offered to pay my expenses. I was due to go on a couple of other research trips that summer so I couldn’t commit, but the only other gringo on the trip begged me and at the last minute I decided to go.
Long story short, it was a crazy set of interpersonal dynamics, we suffered bacterial infections, got stuck on an island for ten days because a plane needed to be repaired, one of us didn’t eat or drink water for ten days, much fish was eaten (but the people who ate), much kava was drank and stories were told. Our diet alternated between delicious seafood and fresh fruits to ramen noodles over rice.
It was a surreal experience. I lost ~16 pounds, down from 175 to 159, came back with numerous skin infections and was a general physical wreck for months, more so than usual. It was challenging, but an experience I am unlikely to forget. I hope to go back one day.
The paper can be found here.
Pictures from Vanuatu (back when I took pictures) are here.
Insecticide-treated nets (ITNs) are an integral piece of any malaria elimination strategy, but compliance remains a challenge and determinants of use vary by location and context. The Health Belief Model (HBM) is a tool to explore perceptions and beliefs about malaria and ITN use. Insights from the model can be used to increase coverage to control malaria transmission in island contexts.
A mixed methods study consisting of a questionnaire and interviews was carried out in July 2012 on two islands of Vanuatu: Ambae Island where malaria transmission continues to occur at low levels, and Aneityum Island, where an elimination programme initiated in 1991 has halted transmission for several years.
For most HBM constructs, no significant difference was found in the findings between the two islands: the fear of malaria (99%), severity of malaria (55%), malaria-prevention benefits of ITN use (79%) and willingness to use ITNs (93%). ITN use the previous night on Aneityum (73%) was higher than that on Ambae (68%) though not statistically significant. Results from interviews and group discussions showed that participants on Ambae tended to believe that risk was low due to the perceived absence of malaria, while participants on Aneityum believed that they were still at risk despite the long absence of malaria. On both islands, seasonal variation in perceived risk, thermal discomfort, costs of replacing nets, a lack of money, a lack of nets, nets in poor condition and the inconvenience of hanging had negative influences, while free mass distribution with awareness campaigns and the malaria-prevention benefits had positive influences on ITN use.
The results on Ambae highlight the challenges of motivating communities to engage in elimination efforts when transmission continues to occur, while the results from Aneityum suggest the possibility of continued compliance to malaria elimination efforts given the threat of resurgence. Where a high degree of community engagement is possible, malaria elimination programmes may prove successful.”
I’m reading through news about the American rights hijacking of the Ebola crisis for their own political gain. Did this outbreak have to occur right before the midterms, and right before a Senate election? The awful toll it will take on West African states aside, the virus couldn’t have picked a worse time (or a better, depending on how you look at it).
Ebola is a scary virus, assuming that one ever has the misfortune to come into contact with it. “Contact” in this case, means that you have to have direct contact with the blood, feces or vomit of a person infected and symptomatic with Ebola. Unfortunately for the virus, people don’t really live that long once they become symptomatic with the disease and the people who survive appear to be immune to it
This is a terrible model for an infectious pathogen. The symptoms are so severe that all around the person will immediately run away (except health workers, who bear the brunt of the risk) and the host doesn’t live very long providing only a short window with which to infect other hosts.
So the duration of infectiousness is short, the pathways are really awful and repeat infections are unlikely.
To put this into perspective, looks at the most successful pathogens out there, pathogens like influenza. Influenza transmits easily, nearly two thirds of those infected show no symptoms and thus can happily shed viral particles to everyone they know undetected. When symptoms do occur, they aren’t so bad as to keep every outside of a 5 miles radius of you. Influenza mutates at an incredible rate, so that a single infection doesn’t provide much protection against later infections. Even better, though its rapid mutation rate sometimes leads to horribly virulent strains like the 1918 flu pandemic which killed millions, in most cases influenza spares a healthy host.
It has developed an incredibly efficient and effective survival strategy (and for this reason is far scarier than Ebola).
So I’ve been thinking of how a virus like Ebola might persist in the wild, given it’s odd mode of transmission.
Now, we know that Ebola is a zoonotic disease, that is, it is transmitted from animal to humans. Since humans have not developed genetic resistance to the disease, we are at particular risk for its worst effects. Many of the scariest diseases out there are zoonoses. Examples would include HIV, SARS and, of course, influenza. While not always true, we tend to make peace with pathogens that are old and exclusively human. Many of the bacteria which live happily in your gut would be examples. As we haven’t had sufficient time to make peace with Ebola or HIV, the outcomes can be far worse than those seen in their normal hosts.
Thus, it is possible that Ebola is far less serious in whatever host it is adapted for. Nipa virus, which has a case fatality rate (the percentage of all infections of a pathogen which result in death) of more than 90% does nothing to the fruit bats it happily resides in. It is possible that Ebola is also harmless to whatever host it depends on.
However, it is possible that Ebola might be harmless in some hosts, while deadly in others, and this difference might be the result of a successful evolutionary adaptation.
Ebola has been pegged as residing in bats possibly explaining its wide range over central Africa. [1-6] Bats are a pathogens dream. They multiply quickly, providing ample opportunities for transmission and for evolutionary adaptations to the pathogen which might insure its long term survival. Better yet, they fly so that pathogens can disperse themselves quickly over a large geographic space. This is particularly useful if the pathogens wants to maintain healthy genetic diversity (though the creation of multiple sub-populations) and if it can infect multiple hosts which may or may not be all that mobile.
Apes would be a good example of the latter. Apes, being fairly sensitive to environmental changes, don’t like to move around a whole lot (unlike humans which are highly adaptable to just about any environment on the planet) but still might be important to the survival of the pathogen.
Ebola has been found in apes and the disease is currently devastating local populations.[4, 7-10]
And this is where I get stuck. In nature, plenty of things happen for no reason at all, but with pathogens, even accidental occurrences can have implications for survival and are often part of the tool box with which diseases evolve and persist.
A bleeding ape on a forest floor will likely kill all of its relatives in quick fashion, assuming its family doesn’t just hightail it out in which case transmission is over anyway. But the dead ape might serve an important purpose. Predators and scavengers will quickly arrive to feast on the infected corpse, transmitting the virus to carnivorous animals all around the forest. This could provide ample opportunities for transmission to other species. Even though many of these species could be poor hosts for the disease, they could also represent new opportunities for survival.
HIV would be an example of this. From HIV’s standpoint (assuming a collective viral consciousness), the jump to humans was extremely fortuitous. Humans love to have sex with multiple people, often even after having already reproduced, and physiologically they proved resistant enough to allow the virus to hang out for a few years before dying, allowing for years of transmission possibilities.
Thus, while on the surface, blood based modes of transmission seem pretty useless, they could serve a larger purpose of insuring a pathogens survival on a macro-level. In the case of HIV, humans didn’t turn into a dead end host (as they are with diseases like Brucella) but rather a new opportunity for survival.
The deadly nature of the virus in apes and humans, then might be like an insurance policy. Like a retirement portfolio, a diversified package of stocks will keep you alive in retirement much better than a portfolio with a single stock. Work has been done on pathogens which infect multiple species, and, depending on the nature of the pathogen, species diversity can either work for or against the survival of the pathogen.[11-13]
In the case of Ebola, there is no real evidence that humans play a role in sustaining transmission, but blood and predation could be sustaining something like Brucella or Q Fever in the wild.
Now, in this article, I have rambled on and bored you to death (and bless you if you made it this far) but I have to point out that I am under no illusions that pathogens act consciously, though I have like many of my colleagues present it as such. Actually, no living thing really does have a long term plan outside of its narrow goals of producing offspring. But new opportunities for transmission do present new opportunities for the long term evolutionary survival or a biological entity. These lucky occurrences are not consciously sought out, but rather enable the pathogen to do what it does successfully.
It must be said that the ecology of Ebola is somewhat of a mystery. Not much work has been done on the subject, as the pathogen hides out in some of the most inaccessible areas of the planet, and conflict and political instability in places like the Central African Republic and Northern Uganda prevent researchers from doing extensive work on the pathogen.
1. Stan D: Ebola and Fruit Bats. Clinical Infectious Diseases 2006, 42(5):V.
2. Olival KJ, Islam A, Yu M, Anthony SJ, Epstein JH, Khan SA, Khan SU, Crameri G, Wang L-F, Lipkin WI et al: Ebola virus antibodies in fruit bats, bangladesh. Emerging infectious diseases 2013, 19(2):270.
3. Hayman DTS, Yu M, Crameri G, Wang L-F, Suu-Ire R, Wood JLN, Cunningham AA: Ebola virus antibodies in fruit bats, Ghana, West Africa. Emerging infectious diseases 2012, 18(7):1207.
4. Kumulungui B, Leroy EM, Swanepoel R, Gonzalez J-P, Pourrut X, Rouquet P, Yaba P, Paweska JT, Délicat A, Hassanin A: Fruit bats as reservoirs of Ebola virus. Nature 2005, 438(7068):575.
5. Vogel G: Infectious disease. Are bats spreading Ebola across sub-Saharan Africa? Science (New York, NY) 2014, 344(6180):140.
6. Hayman DTS, Emmerich P, Yu M, Wang L-F, Suu-Ire R, Fooks AR, Cunningham AA, Wood JLN: Long-term survival of an urban fruit bat seropositive for Ebola and Lagos bat viruses. PloS one 2010, 5(8):e11978.
7. Groseth A, Feldmann H, Strong JE: The ecology of Ebola virus. Trends in microbiology 2007, 15(9):408.
8. Vogel G: Ecology. Tracking Ebola’s deadly march among wild apes. Science (New York, NY) 2006, 314(5805):1522.
9. Leroy EM, Rouquet P, Formenty P, Souquière S, Kilbourne A, Froment J-M, Bermejo M, Smit S, Karesh W, Swanepoel R et al: Multiple Ebola Virus Transmission Events and Rapid Decline of Central African Wildlife. Science 2004, 303(5656):387.
10. Walsh PD, Biek R, Real LA: Wave-like spread of Ebola Zaire. PLoS biology 2005, 3(11):e371.
11. Renwick AR, White PCL, Bengis RG: Bovine tuberculosis in southern African wildlife: a multi-species host–pathogen system. Epidemiology and Infection 2007, 135(4):529.
12. Dobson A, Meagher M: The population dynamics of brucellosis in the Yellowstone National Park. Ecology 1996, 77(4):1026.
13. Dobson A: Population Dynamics of Pathogens with Multiple Host Species. The American Naturalist 2004, 164(S5):S64.
Tunga penetrans is native to South America, was brought to West Africa through the slave trade. In the mid 19th century it was brought on an English shipping vessel and made its way through trade routes and is now found everywhere throughout the continent.
Bacteria opportunistically invades the site and super-infections (multiple pathogens) are common. Victims suffer from itching and pain and multiple fleas are common. Due to the location of the bite, people often have trouble walking and due to the disgusting nature of the infection, victims are stigmatized and marginalized. Worse yet, the site can becomes gangrenous and auto-amputations of digits and feet and eventually death are not uncommon.
The Parliaments of both Kenya and Uganda have introduced bills in the past calling for the arrest of people suffering from jiggers. Of course, these ridiculous bills don’t come with public health actions to control the disease.
Jiggers are entirely preventable, treatable through either surgical excision or through various medications but risk factors for it are mostly unknown and the data contradictory and mostly inconclusive.
It sometimes occurs in travelers and is easily treated in a clinic on an outpatient basis but is a debilitating infection for poor communities. Thus, it is not taken seriously by international public health groups who choose to focus on big issues like HIV and malaria.
Jiggers are a classic example of the neglected tropical disease: it devastates the poorest of the poor but gets almost no attention from donors or the international press.
We gathered some data on jiggers back in 2011 along the coast of Kenya. Without presenting these results as official, I was drawn to the attached map.
Animals of various species have been implicated as reservoirs for the disease, most notably pigs and dogs. Less understood is the role of wildlife in maintaining transmission. On the map below, the large yellow dots represent cases. Note that they are nearly all located along the Shimba Hills Wildlife Reserve. I calculated the distance of each household to the park’s border (see the funny graph at the bottom), and found a graded relationship between distance and jiggers infections. Past 5km away from the park, the risk of jiggers is nearly zero.
What does this mean? I have ruled out domesticated animals, at least as a primary reservoir. People in this area tend to all own the same types and numbers of animals. Being Islamic, there are no pigs here, but dogs are found everywhere. Despite this, there are distinct spatial patterns which are associated with the park. Note that all of the cases are found between the parks border and a set of lakes, perhaps implying that certain wild animals are traveling there for water and food.
The ecology of jiggers is very poorly understood and, like many pathogens (like Ebola, for example), wildlife probably play an important role.
It’s worth paying me a lot of money to study it.
I’m reflecting on an interesting discussion that some of my colleagues and I had over dinner last night. We were discussing the Demographic Surveillance Program (DSS) that I am currently managing.
For those not familiar, DSS programs, generally speaking, do what you local city hall and health department do. They track births, deaths and migration and keep track of specific health outcomes such as cause of death and incidence of particular diseases. It’s pretty basic stuff, but outside the capacity of many developing countries, and the focus on health issues allows for deeper investigation of public health outcomes.
One of us, who is Kenyan, remarked that the DSS program needs to coordinate with the Kenyan government to satisfy specific data needs. I took issue with this idea.
First, as a program sponsored by a research institution, our number one priority should be the accommodation of research projects. A university receives public money to perform research projects which benefit and are directed by the scientists who run them.
Second, a research institution is not a development NGO that seeks to provide public services that countries are unwilling or unable to provide themselves. It is a fact that the Kenyan Government, like many African governments, has a poor record of providing public services. I would argue that while we should certainly offer results of our activities to the government and local stakeholders, that a university sponsored DSS should not be considered a substitute for that which governments should be doing themselves. The assumption is often that African government are incapable of doing things like tracking births and deaths, but the reality is that they are simply unwilling and the activities of NGOs often exacerbate this problem.
Third, government involvement in foreign sponsored research is a slippery slope. Many African governments are wholly or partially autocratic. They do not exist or function to benefit their people, but rather exist only to enrich themselves and maintain power. Academic research should, in no way, contribute to keeping autocrats in power and should operate independently from any political body. In fact, academia has a duty to question power both domestically and internationally. Unnecessarily submitting to government demands opens up a number of potentially problematic issues, including the suppression of results which are inconvenient to government bodies.
While research projects require government approval to function, the power of government to influence the course and outcomes of research should be limited. While many bureaucrats would take issue with this, the reality is we can always just go somewhere else.
I 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.
We 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.