I sent our DSS out looking for snake bites, and they found them! We simply asked households if anyone in the household had been bitten by a snake in the past five years. More then 5% of them said “yes” and since we know the locations of the households, we were able to make this cool map. Note that the snake bites tend to cluster around two large areas and are noticeably absent from others.
Kenya hosts many varieties of extremely poisonous snakes included the Puff Adder, the Black and Green Mamba and the spitting Cobra. Snakes are universally feared here and often killed on sight by the locals.
Because we shouldn’t deceive ourselves. The digital age has provided too many opportunities for people who shouldn’t necessarily be putting out records and flooded whatever market may exist, reducing opportunities for everyone.
It’s like the famous tragedy of the commons, “an economic theory of a situation within a shared-resource system where individual users acting independently and rationally according to their own self-interest behave contrary to the common good of all users by depleting that resource.” So, I just quoted Wikipedia. Strike two, maybe.
So here I am, acting in my own self-interest and behaving contrary to the common good, through my second collection of songs for Mark Maynard’s Saturday Six Pack Radio show. For those who don’t know, I wake up every Saturday, improvise a song and send it to him for airplay later than evening. It requires little work from me, no financial investment, and gives me something to do besides mope in my Nairobi apartment about what my life has become.
Enjoy (if you can). You can find it here on Bandcamp and even purchase it if you are feeling particularly sorry for me.
Here’s the video for the lead track.
Looking at some data on socio-economic status (SES) from two regions of Kenya, I was able to compare current levels of household wealth with those of 2007 in the same households.
We measured SES using a method common to studies of developing countries. An accounting of specific material goods including ownership of radios, TVs and bikes along with type of water source, toilet is performed. We then use multiple correspondence analysis to assign weights to each item as they appear in the data set and a total score is calculated for each household (Filmer and Pritchett, 2001 though they use PCA). Each score (ideally) represents the relative level of wealth of each household.
Kenya’s GDP has been increasing rapidly since 2001. During my five years of travelling to this country, I’ve seen the place transform itself. There are more goods on the shelves, people look better, kids die less and women have fewer children. HIV and malaria are down and people are busier. It’s worth noting that Kenya has no real natural resources; its economy is mostly based on a well developed domestic market economy and agricultural exports.
The question, however, is whether these economic gains are being felt by everyone equally. To test this, I compared data from 2007 and 2015 to see if all households experienced an increase in wealth during this period.
I made the graph above. Assuming I’m interpreting the graph correctly, this would suggest that while wealthier households in 2007 consistently continue to be wealthy in 2015, the relationship for poor households is scattered. Some households are doing better, while other may have experience no change, while others may be poorer in 2015.
Clearly, no matter how one interprets these results, we should be explore what types of households might be falling behind, or experience no gains at all.
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]
I traveled out to Kwale on the Kenyan Coast and celebrated Eid (the end of Ramadan) with my friend Juma and his family in Mwachinga. It was a great time for everyone (except the goat). It rained on and off, but the ladies made up some great Pilau and the men had some conversations about Islam and witch doctor Senators. Juma even gave us a detailed family history. Finally, everyone had a serious discussion on the impacts of social media on family relations.
After a grueling 12 hour bus ride from Nairobi to Mombasa, the highlight of the trip was a two hour tuk tuk ride across the ferry and all the way out to Kwale Town. I’m still not sure how it made it up all the hills.
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.