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 didn’t hear about this until the very last minute, but was lucky enough to get the invitation letter in time to at least make it to the last day.
The Kenya Medical Research Institute (KEMRI) has, for the past five years, held a research dissemination event intended to highlight KEMRI sponsored and Kenya based research.
Research led by Africans is sadly scarce. R&D funding in SSA is the lowest in the world. In a context where so few people are able to receive an education of sufficient quality to allow post graduate studies, African researchers are few and the resources available to them are low.
Kenya has committed 2% of GDP to R%D. Contrast this with South Korea, which at one point committed 23% of GDP to R&D efforts. While KEMRI is truly a leader in the context of African research, the low level of commitment on the part of the national government makes it tiny in the context of worldwide research.
The presentations I have seen so far have been excellent, but of course, much of this research survives on the good graces of international funding and training. Most of the research presented was performed within the CDC.
So this begs the question, when will and can African countries take ownership of their research? Is this even possible given the dysfunctional nature of politics here?
The story of Africa and African identity (in a global context) is written by the rest of the world. As a foreign researcher, I quite aware that I am part of this phenomenon.
Presenters have pointed to two main issues (which I agree with). First, African countries cannot proceed to develop their research sectors (or any other sector really) unless Africans take charge of in country and continent wide research priorities. It is important to note that foreign research often takes on issues which were of importance in the colonial period (childhood infectious diseases) despite a growing burden of chronic diseases and diseases of aging which will break the budgets and economies of African countries.
While I do not suggest that attention be diverted from the incredible burden of infectious disease in African countries, it is telling that research priorities are still driven by the international community. Central Province in Kenya is quite well developed. Even my taxi drivers ask me why we don’t do research in Central, given the incredible problems of heart disease, cancer and alcoholism up there. Unless Kenyans spearhead the main issues impacting their country, these problems will go unadressed.
Second, as noted before, governments have to make firm commitments to support domestic research. As of now, African countries wait for international funding to support their projects, which shifts the conversation away from domestic priorities to international priorities. This is a tall order here, of course.
Of interest, though, besides the macro level problems of funding and support, presenters passionately call for people with Masters and PhD to use the degrees. “Why don’t you do research? What is wrong with you?”
I can’t speak to this issue effectively. But my sense is that many capable people don’t sense the urgency of doing research and lack the personal initiative to make it happen. I’ve seen it happen that researchers wait to have foreigners write their research for them, and simply wait to have their name rubber stamped on the paper, taking credit for work that they did not do. This is an unacceptable situation that we, unfortunately, enable. Certainly there are issues of experience and capability, but we shouldn’t handle capable African researchers with kid gloves, particularly this well educated young generation.
Sadly, the history of aid and foreign involvement here has set this precedent. This is an era that needs to come to an end. In the private sector, it has. In the public sector, these problems persist. Older researchers, many of whom came of age during the beginnings of the post-independence era, here are screaming that point at the top of their lungs.
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.
Same thing. Wrong way down an unmarked one way. Cop at the end. After arguing with him for a bit, I threw 1000 schillings at him and just left.
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.