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.
In my seminal paper, “Distance to health services influences insecticide-treated net possession and use among six to 59 month-old children in Malawi,” I indicated that Euclidean (straight line) measures of distance were just as good as more complicated, network based measures.
I didn’t include the graph showing how correlated the two were, but I wish I had and I can’t find it here my computer.
Every time I’ve done presentations of research of the association of distances to various things and health outcomes, someone inevitably asks why I didn’t use a more complex measure of actual travel paths. The idea is that no one walks in a straight line anywhere, but rather follows a road network, or even utilizes a number of transportation options which might be lost in a simple measure.
I always respond that a straight line distance is as good as any other when investigating relationships on a coarse scale. Inevitably, audiences are never convinced.
A new paper came out today, “Methods to measure potential spatial access to delivery care in low- and middle-income countries: a case study in rural Ghana” which compared the Euclidean measure with a number of more complex measurements.
The conclusion confirmed what I already knew, that the Euclidean measure is just as good in most cases, and the pain and cost of producing sexy and complicated ways of calculating distance just isn’t worth it.
It’s a pretty decent paper, but I wish they had put some graphs in to illustrate their points. It would be good to see exactly where the measures disagree.
Access to skilled attendance at childbirth is crucial to reduce maternal and newborn mortality. Several different measures of geographic access are used concurrently in public health research, with the assumption that sophisticated methods are generally better. Most of the evidence for this assumption comes from methodological comparisons in high-income countries. We compare different measures of travel impedance in a case study in Ghana’s Brong Ahafo region to determine if straight-line distance can be an adequate proxy for access to delivery care in certain low- and middle-income country (LMIC) settings.
We created a geospatial database, mapping population location in both compounds and village centroids, service locations for all health facilities offering delivery care, land-cover and a detailed road network. Six different measures were used to calculate travel impedance to health facilities (straight-line distance, network distance, network travel time and raster travel time, the latter two both mechanized and non-mechanized). The measures were compared using Spearman rank correlation coefficients, absolute differences, and the percentage of the same facilities identified as closest. We used logistic regression with robust standard errors to model the association of the different measures with health facility use for delivery in 9,306 births.
Non-mechanized measures were highly correlated with each other, and identified the same facilities as closest for approximately 80% of villages. Measures calculated from compounds identified the same closest facility as measures from village centroids for over 85% of births. For 90% of births, the aggregation error from using village centroids instead of compound locations was less than 35 minutes and less than 1.12 km. All non-mechanized measures showed an inverse association with facility use of similar magnitude, an approximately 67% reduction in odds of facility delivery per standard deviation increase in each measure (OR = 0.33).
Different data models and population locations produced comparable results in our case study, thus demonstrating that straight-line distance can be reasonably used as a proxy for potential spatial access in certain LMIC settings. The cost of obtaining individually geocoded population location and sophisticated measures of travel impedance should be weighed against the gain in accuracy.
These words are mostly regional and the uses and nuances of calling people stupid also vary by place.
Over dinner, I was reminded of an episode of Tante Night Scoop, an investigative television program which ran throughout the 90’s. They did an exhaustive survey and mapped the locations of the common ways of calling people stupid throughout Japan.
Of interest is the centrality of the word “aho,” commonly used throughout the Kansai region of Japan (and denoted in red) and the radial spread of “baka” (denoted in blue), a word mostly associated with Tokyo and commonly found in Kanto-centric anime programs.
The map was intended as entertainment, but it has serious historical significance.
When people move, they take words with them. It would appear that people in Kansai, historically the political and economic center of Japan, had little reason to leave the region, which would explain “aho”‘s limited spread. Baka, however, can be found on both sides of Kanto, indicating that there were strong connections between the two sides, despite the distance between them.
Oddly, the other words for “stupid” occupy the same radii from Kansai indicating that certain groups of people had peculiar spatial advantages in trade, where as others did not. Though I really have no idea, I’m thinking that particular perishable products traded with Kansai might have different spoiling times necessitating particular proximities. It’s important also to note that the extreme peripheries might have been trading non-perishable resources like coal, which, though heavy, doesn’t rot.
Economics, trade and language have deep links. English wouldn’t exist without it, and the many forms of English spoken throughout the world have been influenced by the multitude of groups of people who chose to speak it to facilitate trade.
OK, enough for now and back to Kenya.
Actually, I was an infant, but as an adult, I wrote a blog post and made a cool video of the locations and magnitude of bomb drops in Laos from 1965-1973.
Now, Jerry Redfern & Karen Coates have written a great (I assume) book “Eternal Harvest”on the United States’ unbelievably devastating bombing campaign of neighboring Laos during the Vietnam War. I suggest that everyone go out and read this book immediately.
However, they created an accompanying video, which is eerily similar to a video I created, though theirs is embellished with narration and bookend explanations. I want to think that I helped inspire such a cool video. Or maybe this is wishful thinking. I don’t know. But it’s reassuring to know that this blog might have contributing something to the world.
And here’s mine:
A new study which just appeared in Malaria Journal, however, calls this optimism into question.
This review presents two central arguments: (i) that empirical studies measuring change are biased towards low transmission settings and not necessarily representative of high-endemic Africa where declines will be hardest-won; and (ii) that current modelled estimates of broad scale intervention impact are inadequate and now need to be augmented by detailed measurements of change across the diversity of African transmission settings.
So, our ability to accurately determine whether transmission intensity has declined is hampered by the fact that most studies of the disease occur in areas of low transmission. This would make sense. It is much easier for us to evaluate the malaria situation in Kenyan context than in the Democratic Republic of Congo due to availability of surveillance infrastructure, official mechanisms which allow research projects to move forward, and security issues.
The obvious problem with this, is the relationship of governance, economy an instability to malaria itself. People in the poorest countries are at the highest risk for malaria and people in the poorest parts of the poorest countries are at the highest risk of all. The trouble is, despite being the populations we are most concerned about, they are the hardest to reach, and the hardest to help.
Worse yet, the estimates of malaria prevalence found in a number of studies were considerably lower than estimates for the entire African continent.
The combined study area represented by measurements of change was 3.6 million km2 (Figure 1), approximately 16% of the area of Africa at any risk of malaria . The level of endemicity within these studied areas (mean PfPR2-10 = 16%) was systematically lower than across the continent as a whole (mean PfPR2-10 = 31%) (Figure 2). While 40% of endemic Africa experienced ‘high-endemic’ transmission in 2010 (PfPR2-10 in excess of 40%) , only 9% of the studied areas were from these high transmission settings.
This is a huge issue and one that shouldn’t be limited to malaria. While it is helpful to hear good news of malaria declines in formerly afflicted areas, we need to be careful about overstating the impact of interventions. Funding for malaria projects such as the distribution of insecticide treated bed nets was incredibly high throughout the 00’s but it is unlikely that trend will continue. Offering an positive picture can show that our efforts are valuable, but might also lead policy makers and donors to suggest that money be put toward other goals. If Sri Lanka is any indication, where malaria was nearly eliminated at one time but experienced a rapid and devastating resurgence, even a brief relaxation of malaria control efforts could erase current gains completely.
This map (from “Mapping of poverty and likely zoonoses hotspots”) is pretty eye-opening. Looking at this, I’m thinking that the next big disease event will most certainly come out of India.
Note that the most virulent of infectious diseases in humans are often associated with animals. India’s high density, close contact with animals and poor regulatory environment make for a frightening mix.
Rising fuel prices increase the price of delivery. Rising demand for food from an increasing world population, and demand for protein rich foods from a rapidly urbanizing world, specifically from the emerging economies of China, India and Brazil increase demand for grains. A turn toward using biofuels increases competition for grains that would normally go to feed humans and livestock. Climate change and extreme weather events create a volatile agricultural market.
However, despite all these very obvious players, the amount of volatility seen in the food commodity markets is unprecedented. Agricultural production, though regionally volatile, has not experienced the same level of fluctuation as that of prices in the food commodities markets. Energy demand and production, though increasing, also do not exhibit the same behavior. Conflicts have negatively affected market prices in certain commodities, most notably that of cocoa due to recent political conflict in the Ivory Coast, but, the large ag-producing countries of the United States, China, India and Brazil have experienced no such disruptions. In fact, China and Brazil, despite a growing population and experiencing an expansion of the middle class, are still largely able to maintain food security.
In short, demand is rising, though not volatile. Supply also, is rising, though not volatile. One can make the argument that volatility in the oil markets is spilling over into grain commodity markets, but biofuels still only account for a small percentage of energy use. These factors do little to explain the large fluctation of the of the food commodity markets that we are experiencing today.
According to a UN Conference on Trade and Development report
• “these factors (rising food demand, biofuels, climate change) alone are not sufficient to explain recent commodity price developments; another major factor is the financialization of commodity markets. Its importance has increased significantly since about 2004, as reflected in rising volumes of financial investments in commodity derivatives markets – both at exchanges and over the counter (OTC). This phenomenon is a serious concern, because the activities of financial participants tend to drive commodity prices away from levels justified by market fundamentals, with negative effects both on producers and consumers.”-UNCTAD, 2011
I am not an economist. Through my limited understanding of futures markets, I think that what I understand is this: Prices of commodities are usually set on a supply and demand basis, with considerable elasticity. If one wants to buy gold, for example, demand and supply work to set prices. If one wants to take advantage of cheap gold now, all one has to do is buy and store to sell it later. The storage costs must be rolled into the final resale price. With oil and agriculture, the model is similar, but these commodities can only be stored temporarily, before they are unsalable (rot). Thus, as there is little to hedge against future risk, speculators will buy contracts for future, as yet unproduced, goods at set prices. This practice is not uncommon and was originally conceived to protect American and European farmers from risk and to insure consistent supply and price in the market.
What is different now is that interest prices are rolled in based on the length of contract, linking worldwide financial markets with the prices of commodities and distorting the true supply and demand relationship. As futures, by definition, are conceived to protect investors from risk, they are a perfect target for large hedge funds, which protect large investors from long term risk. The tying of interest rates into commodity prices means that end prices will fluctuate wildly with the market, while protecting investors from losing their shirts.
One important way that hedge funds minimize long term risk, is through machine trading. Computers and mathematical models available market information, predict future fluctuations and sell when necessary. What this does is insert an even greater level of volatility into the market. Sudden sales of commodity futures will induce other funds to sell as well, creating a herd effect of commodity sales that has little to do with true supply and demand models. Imagine how flocks of birds or schools of fish move in response to one change in direction by a member of the group and you can get an appreciation for how machine trading works.
We are already seeing the effects of the financialization of food commodities. There was an unprecedented rise in food prices during the period of 2000-2007. The financial crash of 2007, brought in part by the activities of the very same financial players that are driving food prices up, saw a drop in prices, but as the market rebounds, prices are increasing once again, now higher on average than they were in 2007.
Der Spiegel recently penned an excellent article on the rising price of food. In it, they spotlighted scumbag of the week, Alan Knuckman. Mr. Knuckman and a host of other US and worldwide speculators are unconcerned as long as the money is flowing to them. To Mr. Knuckman, he could be investing in GM, a new chain of box hardware stores, big pharma, copper, oil or food for kids, it’s all the same as long as it brings him a profit. In fact, he is quoted as saying, without a hint of irony, “the age of cheap food is over. Most Americans eat too much, anyway.” Yep, these dirtbags are just like the rest of America, blind to whatever happens outside their own gated communities.
Rising food prices are not a problem for Americans. In fact, we only use, on average, 13% of our income on food. In places like Kenya, however, food can consume nearly 100% of a household’s monthly income. In Kenya, food must be imported to account for shortages due to underdeveloped ag and transportation infrastructure, which prevents Kenyans from protecting themselves against extreme weather events and disruptions in supply. Even a 1% shift in the worldwide price of food can spell death for an Kenyan infant. What we have seen, however, is not a 1% shift, but rather a 71% increase in the worldwide price of grains since 2007. In Kenya, the price of corn meal has shot up 100% in the past five months. To Knuckman, this is just “an undesirable side effect of the market,” kind of like having to drink coffee that sat in the pot too long and turned bitter.