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.
Does sampling design impact socio-economic classification?
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.
New professional titles? A confusing (but exciting) mess….
But all part of my grand plan.
I now hold two titles, one as an Assistant Professor of Epidemiology at the Nagasaki Institute of Tropical Medicine and another as an adjunct Assistant Professor in the School of Natural Resources and Environment at the University of Michigan.
However, despite having positions in both America and Japan, I am based in Kenya.
This is pretty exciting, but I guess this means I have to do something now!
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