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 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.