I’m only a middle author, but I have a new publication out. After being involved in this, I will never eat mukimo (Kenyan mashed potato dish) ever again. Ever again.
First Report of a Foodborne Providencia alcalifaciens Outbreak in Kenya.
Shah MM, Odoyo E, Larson PS, Apondi E, Kathiiko C, Miringu G, Nakashima M, Ichinose Y.
Am J Trop Med Hyg. 2015 Jun 29. pii: 15-0126.
Providencia alcalifaciens is an emerging bacterial pathogen known to cause acute gastroenteritis in children and travelers. In July 2013, P. alcalifaciens was isolated from four children appearing for diarrhea at Kiambu District Hospital (KDH) in Kenya. This study describes the outbreak investigation, which aimed to identify the source and mechanisms of infection. We identified seven primary and four secondary cases. Among primary cases were four mothers who had children and experienced mild diarrhea after eating mashed potatoes. The mothers reported feeding children after visiting the toilet and washing their hands without soap. P. alcalifaciens was detected from all secondary cases, and the isolates were found to be clonal by random amplified polymorphic DNA (RAPD) fingerprinting. Our study suggests that the outbreak was caused by P. alcalifaciens, although no fluid accumulation was observed in rabbit ileal loops. The vehicle of the outbreak was believed to be the mashed potato dish, but the source of P. alcalifaciens could not be confirmed. We found that lack of hygiene, inadequate food storage, and improper hand washing before food preparation was the likely cause of the current outbreak. This is the first report of a foodborne infection caused by P. alcalifaciens in Kenya.
© The American Society of Tropical Medicine and Hygiene.
PMID: 26123962 [PubMed – as supplied by publisher]
Infectious disease transmission dynamics and the ethics of intervention based public health research
I think a lot about ethics and ethical issues. Research in Sub-Saharan Africa presents unique risks for ethical breaches. Given income and power disparities between individuals and foreign researchers and even between individuals and local political leaders the possibility of coercive research is ever present. Pressure to produce can lead to unrealistic assumptions of risks and benefits to very poor individuals. Inadequate knowledge or willful ignorance of local political issues can compromise future research activities, both by international and domestic groups.
Recently, though, an interesting situation came across my desk that included an intersection of ethics and the dynamics of infectious disease transmission.
As everyone knows, not all infectious diseases are the same. Some, like measles, impart full immunity upon exposure, whereas diseases such as malaria impart only partial immunity, requiring repeated exposures to acquire full or adequate immunity to prevent death or serious injury. Moreover, as immunity and immune reactions change over the life course, the time (age) of exposure are sometimes crucial to prevent serious disease. Polio is a great example. Exposure in infancy leads merely to diarrhea, where exposure at older ages can lead to debilitating paralysis.
I was thinking of an population based intervention study which provides some sort of malaria medication to a small population in a holo-endemic area. Given the year round nature of malaria transmission in this area, we would expect that even with a depression in symptomatic and asymptomatic cases, active transmission in the surrounding areas would lead to recrudescence within a very short time. Given the short time frame, we would assume very little interruption in the development of immunity in small children and might even see a short term reduction of childhood mortality. Assuming that this medication presented little or no risk of serious side effects, I believe that there is little reason to assume an ethical breach. A short term reduction in malaria would suggest that the benefits far outweigh the risks.
However, conducting the same study on a very large population in the same area might have very different outcomes. Delivering a malaria medication to, say, an entire county surrounded by other areas of extremely high transmission would indicate that recrudescence is also inevitable but that the time required to return to pre-intervention levels is extended. Infectious disease transmission requires a chain of hosts. The longer that chain, the longer it will take for new hosts to become newly infected.
Theoretically, this could delay infections in small children and it is theoretically possible that we might see a spike in childhood mortality, since the timing of initial malaria infection and frequency of infections are crucial to preventing the worst outcomes.
Of course, I’m not suggesting that people should just get infected to induce immunity, but I am suggesting that a study which seeks to reduce transmission through pharmaceuticals given only intermittently (as opposed to prophylactically) consider all possible implications. Insecticide treated nets (ITNs) provide protection over time and are a form of vector control. A medication given at a single time point merely clears the parasite, but does nothing to prevent bites or kill mosquitoes.
Though I could be overthinking the issue, my worry is that ethical approvals approach the issue of mass distributions of pharmaceuticals as a one size fits all issue without taking other factors such as population size and acquired immunity into account. Malaria, as a complex vector borne disease introduces complexities that, say, measles does not. Researchers, IRBs and ethics board would do well to consider this complexity.
I just found this short article on the LSE blog from Professor Sylvia Chant, who does work on female genital mutilation in Sub-Saharan Africa:
“Opportunities for taking one’s research beyond textbooks and journal articles are critical for teaching at LSE, where students at all levels and from an extensive range of geographical and disciplinary backgrounds are eager to see theory translated into practice, and to engage with impact. From my experience, it is the anecdotes about the lives of people who have formed part of one’s research which help to make ideas and arguments more accessible; how one went about fieldwork in different localities, or the stories of what you, as lecturer, have done in the public and policy domain (whether acting as an expert witness in court cases for asylum seekers, or playing an advisory or consultant role for international agencies). These really grab students’ attention, with photographs and video clips adding more value still!”
I completely agree. Graphs and tables are great for making specific points of interest to researchers, but photos and videos humanize the results and make our research accessible to regular folks and policy makers. People have a real hard time with numbers, which are essentially about communities, countries and institutions, but are used to listening to stories of the struggles and challenges of individuals. Providing plenty of interesting visuals and stories is essential to what we do.
Public health work is about people. Our mission is to be an advocate for the sick and those at risk of becoming sick, who are often marginalized, poor or lack a political voice. Telling their stories simply in a way that non-experts can understand helps us to draw support for what we do.
I have long taken the position that we are essentially journalists. Though we, as scientists, follow a strict set of protocols and rules, our job is to tell stories of particular groups of people and provide information which is often difficult to obtain.
Doing research in developing countries is not easy. However, with a bit of care and planning, one can do quality work which can have an impact on how much we know about the public health in poor countries and provide quality data where data is sadly scarce.
The root of a survey, however, is sampling. A good sample does its best to successfully represent a population of interest and can at least qualify all of the ways in which it does not. A bad sample either 1) does not represent the population (bias) and no way to account for it or 2) has no idea what it represents.
Without being a hater, my least favorite study design is the “school based survey.” Researchers like this design for a number of reasons.
First, it is logistically simple to conduct. If one is interested in kids, it helps to have a large number of them in one place. Visiting households individually is time consuming, expensive and one only has a small window of opportunity to catch kids at home since they are probably at school!
Second, since the time required to conduct a school based survey is short, researchers aren’t required to make extensive time commitments in developing countries. They can simply helicopter in for a couple of days and run away to the safety of wherever. Also, there is no need to manage large teams of survey workers over the long term. Data can be collected within a few days under the supervision of foreign researchers.
Third, school based surveys don’t require teams to lug around large diagnostic or sampling supplies (e.g. coolers for serum samples).
However, from a sampling perspective, assuming that one wishes to say something about the greater community, the “school based survey” is a TERRIBLE design.
The biases should be obvious. Schools tend to concentrate students which are similar to one another. Students are of similar socio-economic backgrounds, ethnicity or religion. Given the fee based structure of most schools in most African countries, sampling from schools will necessarily exclude the absolute poorest of the poor. Moreover, if one does not go out of the way to select more privileged private schools, one will exclude the wealthy, an important control if one wants to draw conclusions about socio-economic status and health.
Further, schools based surveys are terrible for studies of health since the sickest kids won’t attend school. School based surveys are biased in favor of healthy children.
So, after this long intro (assuming anyone has read this far) how does this work in practice?
I have a full dataset of socio-econonomic indicators for approximately 17,000 households in an area of western Kenya. We collect information on basic household assets such as possession of TVs, cars, radios and type of house construction (a la DHS). I boiled these down into a single continuous measure, where each households gets a wealth “score” so that we can compare one or more households to others in the community ( a la Filmer & Pritchett).
We also have a data set of school based samples from a malaria survey which comprises ~800 primary school kids. I compared the SES scores for the school based survey to the entire data set to see if the distribution of wealth for the school based sample compared with the distribution of wealth for the entire community. If they are the same, we have no problems of socio-economic bias.
We can see, however, from the above plot that the distributions differ. The distribution of SES scores for the school based survey is far more bottom heavy than that of the great community; the school based survey excludes wealthier households. The mean wealth score for the school based survey is well under that of the community as a whole (-.025 vs. -.004, t=-19.32, p<.0001).
Just from this, we can see that the school based survey is likely NOT representative of the community and that the school based sample is far more homogeneous than the community from which the kids are drawn.
Researchers find working with continuous measure of SES unwieldy and difficult to present. To solve this problem, they will often place households into socio-economic "classes" by dividing the data set up into . quantiles. These will represent households which range from "ultra poor" to "wealthy." A problem with samples is that these classifications may not be the same over the range of samples, and only some of them will accurately reflect the true population level classification.
In this case, when looking at a table of how these classes correspond to one another, we find the following:
Assuming that these SES “classes” are at all meaningful (another discussion) We can see that for all but the wealthiest households more than 80% of households have been misclassified! Further, due to the sensitivity of the method (multiple correspondence analysis) used to create the composite, 17 of households classified as “ultra poor” in the full survey have suddenly become “wealthy.”
Now, whether these misclassifications impact the results of the study remains to be seen. It may be that they do not. It also may be the case that investigators may not be interested in drawing conclusions about the community and may only want to say something about children who attend particular types of schools (though this distinction is often vague in practice). Regardless, sampling matters. A properly designed survey can improve data quality vastly.
I wanted to go and see what this jigger thing was really about so I had my guys rent a car and we drove into Mtsangatamu town. Mtsangatamu (I still can’t pronounce it properly) lies along the edge of the Shimba Hills Wildlife Reserve and, according to my data, is a hot spot for tungiasis, or infections from the so called “jigger flea.”
It is a beautiful area. Filled with tropical trees and overgrowth, the landscape looks almost uncontrollable, despite the soil being so sandy that not a drop of water stands anywhere. The air is blistering hot.
People don’t get out here much, though the packed buses that pass by every few minutes indicate that the area isn’t entirely isolated. We drop off some gas for one of our drivers, who has to slowly fill his tank, drop by drop, with the tiniest of plastic funnels. Some development project should provide proper plastic funnels to these guys.
For some reason, we drive into the bush along a foot path, until we find ourselves wedged between a number of small pine trees. “We have to walk now,” I am told while I wonder why we drove this far anyway. Walking would have been easier.
We exit the car, walk through what a patch of neatly arranged trees. A tiny tree farm. I never see this in Western, ever. Coming out, we walk into a compound laid out in a manner wholly uncharacteristic of Kenya. A two story building sporting an upstairs patio complete with a winding staircase to the top, the place looked like the type of patchwork architecture that you associate with off-gridders in the US rather than Kenyan peasants.
The Mighty Paraffee turns out to be a kid of about 24, chilling out in the shade. He built this place himself, installed power, has a guest room and an upstairs shower and toilet. His room is decorated with reggae stars and pictures of the saints. Indian music is blaring out of the building. I’ve seen creative interiors from reggae fans in Kenya, but this is something else. This kid should be in architectural school. He even made sure to place the building under a giant tree to keep it cool.
I never figure out what the family does for money and no one can tell me, but the mother is exceedingly proud.
No jiggers here. We walk on. After about a kilometer, we find a poor family sitting outside their house. Children aren’t in school and no one speaks any English indicating that none of them go.
Hassan (one or our workers) brings over a little girl and tells me to look at her feet. Fatuma is 10 years old and her feet are infested with jiggers. She says the don’t hurt much in the day, but they itch at night. Her brother apparently has them, too. Her mother and her aunt do not.
Everyone is barefoot and they all sleep in the same house. I’m wondering if there might be something about the skin which makes kids susceptible while adults are spared.
I notice a group of goats in a pen and start asking questions about animals.
Tungiasis is a zoonotic disease. It is passed from wildlife to domesticated animals to people who bring it into the household and infect their other family members. Or so it is though. Not many people have really explored the question sufficiently. Of course, this is why I’m here.
They have about 15 goats, a few chickens and I notice a young dog and a cat walking around. I ask if they ever notice whether the dog ever has jiggers. They say no.
“What kinds of wildlife do you see around here?” One of the kids was killed by an elephant last year. There are wild dogs and hyenas which come and try to get the goats. Wild pigs dig up the cassava at night.
Pigs. That has to be it. A big mystery has been why there is such a tight relationship between distance to the park and jiggers infections. Wild pigs come out of the forest, raid the fields of the locals and get water from the river, and then recede back into the darkness before morning. 5km is approximately the distance that a pig could feasibly travel and return home in one night.
Pigs travel through and around the compound, dropping eggs, they mature and are probably picked up by dogs, but are most likely picked up by kids walking in the bush. They then bring them back home and pass them on to their family members.
Hassan associates jiggers with mango flowers, but I probe him further and find that the flowers coincide with the very dry season, which could explain why pigs are making the trek to the river and why they prefer the fields since both water and food are probably scarce in the forest.
I have to send a student out to investigate this further.
An old man comes out. He looks nearly 90, but is mostly likely on 60 at most. He has arthritis in his back. He shows me his feet which are moderately infected, mostly only between the toes. He asks for medicine. I tell him I’ll send some along. He offers me some boiled cassava which I graciously take. My colleague refuses because there are no cashew nuts with it, but I suspect that he’s worried about getting sick. I become concerned.
We take some pictures and go.
On the way back, we run into an elderly lady. She’s sitting next to her husband, who is busy getting lit on homemade beer at 11 in the morning. She shows me her feet. The spaces around her feet are infested with jiggers. It must be horribly painful.
She points out that she doesn’t have a whole lot of feeling in her left foot. I notice that her skin in this area is clear; the bone is visible through her skin. I ask what happened. She says that she got bitten by a snake 40 years ago. She was pregnant. I ask her if the baby was ok. “The baby is standing there!”
I consider making a joke about a snake baby, but think better of it. I’m just amazed that both of them survived. The wound was horrible looking.
Somehow, we manage to pull ourselves out of the trees and move on. There are some baboons removing mites from one another on the road on the way back, and I take some pictures. My colleague is about to pass out from the heat. I offer to drive.
What are we talking about when we discuss socio-economic position and health in developing countries?
A 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.