I routinely ask people who have degrees the following question: "So what did you do your thesis on?" Since I routinely encounter problems outside my domain, I like to be aware of the resources around me. I have been reminded that a student doesn't necessarily have to complete a major research paper to earn a degree. A student can just "do the program." As a person who has always chosen to do the research paper, I can say that this normally takes a fair amount of collaboration. There is a team. I sometimes think I never gave the people in my team enough credit. I don't necessarily "take the degree and run." But I do leave the gate running. For me, academia has been about doing something I am passionate about in a protected and supportive setting. I have never put much weight on the degree itself. I realize that the clock is ticking as soon as I leave the building with my degree. However, I consider it important to interact with those that help me achieve my goals. In this blog, I will be considering an important concept in my mentor's research: social determinants have a role in human disease. Apart from the general idea of social circumstances such as poverty contributing to disease, I have come to recognize how this conversation can have the effect of defining the parameters of data science: how far data science can go and who can go there in an area of study that seems to belong to domain specialists.
I have noticed, after listening to questions that others have posed, there is some interest on "how" social determinants can be associated with disease - for example, poverty and diabetes. The link doesn't seem obvious. I understand that my mentor came to his conclusions by crunching numbers. It is an approach that I think that many in the data science community would appreciate. Other researchers less socially inclined might provide a coherent physiological explanation such as how sugar consumption contributes to diabetes. A peculiarity that researchers sometimes exhibit when considering the factors contributing to disease is asserting and then fighting for exclusivity. Sugar and poverty can both be connected to diabetes. The question really for me is whether a physiological explanation is the only one that should receive recognition. In my own research in relation to workplace stress, the literature has pointed out how professionals might propose therapy even in situations where the work environment is toxic. "Feeling stressed? Take a pill. Your fear has nothing to do with the company being in constant violation of workplace safety standards." "Still feeling stressed? Learn to control these feelings. It has nothing to do with your manager assaulting you." Although it is true the disease is something embodied - something realized by the body - the actual source might be external and possibly persistent in the environment.
Similarly, it is possible for a condition to be regarded as a disease in relation to a particular setting. For example, being in a wheelchair is not necessarily a problem in an environment that is optimized for wheelchair use. But in a world without ramps or elevators, being mobility impaired in a wheelchair might have the same impact as having a disease. Being a black person is a problem in a racist world; it makes being black almost seem like a disease. In fact, I found references to published materials describing black people as diseased; they were described as so incapable of caring for themselves that they need white people to guide them. Disease can most certainly be the product of social construction. A genetic predisposition to alcohol addiction becomes realized in a society whose daily social interactions involve alcohol, a substance that is toxic although not necessarily lethal to the human body. I am not saying that diabetes is not a disease. However, I do suggest that disease is not an entirely bodily concern; that social circumstances can sometimes matter a great deal. The disease might not truly exist except as a result of socially deterministic conditions. As such, the focus on medical data can needlessly and unproductively impair the expansion of knowledge, limiting authority to a privileged few. For data scientists advising policy- and decision-makers, it might be difficult to offer a multifaceted assessment when reality is portrayed in a highly medical light. Entrenched social problems contributing to disease might never be addressed or even recognized.
Consider the case of aging, which is associated with many diseases. Government agencies having a purely medical perspective might attempt to "treat" aging by addressing all of associated diseases. Doing so can bankrupt a nation. Why? There is no "cure" for aging. People will die of old age; this will happen regardless of how much money is spent dealing with all of its related ailments. An unwillingness to consider the role of social constructs and circumstances in age-related disease is extremely unenlightened; because the best way to deal with age-related disease might be through social action. Social action would be difficult to formulate without social data. Not always considered an aspect of society is the environment. Because humans so often occupy built environments, our habitats are strongly influenced by social processes. Many older people will have special housing requirements. Poor indoor air quality, lack of care and maintenance, inadequate sanitation, absence of retrofitting for accessibility, and high levels of vandalism can adversely affect the health of older people especially. Suffice it to say, addressing emerging challenges posed by an aging population might be less about medical research and more about analyzing multifarious data resources. I believe that this is the age of big data for a reason: as problems have become more complex, we face massive amounts of data to overcome the hurdles.
In closing, although there might be domain specialists and experts clinging to their niches, many health concerns extend well beyond the body. Attempting to internalize issues as if they exist in a purely medical sense is quite different from dealing with these problems as they present themselves internally. In the latter, a person is being a professional; in the former, although I am not a lawyer, I feel that a person is being negligent. Dealing with a simple case of cuts and bruises on an infant is not the same as addressing a case of child abuse or child molestation. The medical concern is important. The social concerns surrounding the medical consequences are at times more important and a great deal more relevant in order to halt the progression of the "disease." Data scientists are not necessarily medical professionals. I recognize that the discoveries made by data scientists might lack the convincing explanations that come with domain expertise. However, when a problem exists outside a particular realm, or if it overlaps many others, I believe that a data scientist is sometimes in a better position to offer constructive insights than the individual silo lords. Expertise dealing with large amounts of different kinds of data is itself a kind of domain. For those responsible for public policy, problem assessments based on multifarious data resources seem inherently more sensitive to the true nature of disease than medical testing alone. Moreover, I believe that the cost of a purely medical approach to disease will ultimately present itself as unsustainable; and some government agencies might already be coming to this conclusion.