African countries are blessed with ample cropland and resources, but suffer from crippling and unforgivable levels of poverty, have some of the shortest lifespans on the planet and the highest rates of infant mortality in the world. Meanwhile, Japan, Korea, Sweden, Switzerland and Singapore are wholly the opposite, yet mostly lacking in everything that Africa has. Clearly, the picture is more complicated than merely having access to a natural resources.
However, within countries, the picture might be different. African countries are complex and diverse places. Poverty is often confined to the most unproductive regions, areas with poor soils, poor rainfalls or dangerous terrains.
I was just working with some socio-economic data from one of our field sites, and noticed some interesting patterns (note the map up top). In Kwale, a small area along the Coast, socio-economic levels vary widely, but neighbors tend to be like neighbors and patterns of socio-economic clustering emerge.
Note that the poorest of the poor are concentrated to an area in the middle, which I know to be extremely dry, difficult to get to, difficult to farm and generally tough to live in.
I tried to see if socio-economic status (as measured through a composite material wealth index a la Filmer and Pritchett but using multiple correspondence analysis rather than PCA) was related to any environmental variables that I might have data for.
I fit a generalized additive model using the continuous measure of of wealth from the MCA as an outcome. Knowing that very few things in nature or human societies are linear, I also applied smoothing to the predictors to relax these assumptions. The results can be seen in the plot at the bottom.
A few interesting things came out. While it is hard to tell much about the poorest of the poor, we can tell something about the most wealthy. The richest in this poor area, tend to live in areas with the richest vegetation (possibly representing water), a high altitude (low temperature), high relief (no standing water) and in locations distant from a wildlife reserve (far from annoying and dangerous wildlife).
I’m not sure the wildlife reserve is meaningful (unless the reserve was an area undesirable for human habitation to begin with), but the others might be and represent a trend seen in other Sub-Saharan contexts. Areas without malarious swamps and ample farm land tend to do the best. Central Province, one of the most developed areas of Kenya, would be an example.
But the question has to be, does a harsh environment doom people to poverty, or do people self shuffle into and compete for access to more favorable areas? Is environmentally determined poverty (or wealth) an accident of birth, or the result of competitive selection?
Alright, back to work. Oh wait, this is my work. Well….
New Publication (from me): “Insecticide-treated net use before and after mass distribution in a fishing community along Lake Victoria, Kenya: successes and unavoidable pitfalls”
This was was years in the making but it is finally out in Malaria Journal and ready for the world’s perusal. Done.
Insecticide-treated net use before and after mass distribution in a fishing community along Lake Victoria, Kenya: successes and unavoidable pitfalls
Peter S Larson, Noboru Minakawa, Gabriel O Dida, Sammy M Njenga, Edward L Ionides and Mark L Wilson
Insecticide-treated nets (ITNs) have proven instrumental in the successful reduction of malaria incidence in holoendemic regions during the past decade. As distribution of ITNs throughout sub-Saharan Africa (SSA) is being scaled up, maintaining maximal levels of coverage will be necessary to sustain current gains. The effectiveness of mass distribution of ITNs, requires careful analysis of successes and failures if impacts are to be sustained over the long term.
Mass distribution of ITNs to a rural Kenyan community along Lake Victoria was performed in early 2011. Surveyors collected data on ITN use both before and one year following this distribution. At both times, household representatives were asked to provide a complete accounting of ITNs within the dwelling, the location of each net, and the ages and genders of each person who slept under that net the previous night. Other data on household material possessions, education levels and occupations were recorded. Information on malaria preventative factors such as ceiling nets and indoor residual spraying was noted. Basic information on malaria knowledge and health-seeking behaviours was also collected. Patterns of ITN use before and one year following net distribution were compared using spatial and multi-variable statistical methods. Associations of ITN use with various individual, household, demographic and malaria related factors were tested using logistic regression.
After infancy (<1 year), ITN use sharply declined until the late teenage years then began to rise again, plateauing at 30 years of age. Males were less likely to use ITNs than females. Prior to distribution, socio-economic factors such as parental education and occupation were associated with ITN use. Following distribution, ITN use was similar across social groups. Household factors such as availability of nets and sleeping arrangements still reduced consistent net use, however.
Comprehensive, direct-to-household, mass distribution of ITNs was effective in rapidly scaling up coverage, with use being maintained at a high level at least one year following the intervention. Free distribution of ITNs through direct-to-household distribution method can eliminate important constraints in determining consistent ITN use, thus enhancing the sustainability of effective intervention campaigns.
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.
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