Epidemiology is the study of patterns of disease in populations -- who's at risk, what's the cause, how to prevent it. For more than 100 years after John Snow epidemiologists concentrated on proximate single causes or specific exposures -- cigarettes and lung cancer, Legionnaire's disease and Legionella bacteria, HIV and AIDS -- but recently the field of "social epidemiology" has gained some traction. This is the study of the social determinants of health and disease. The proximate cause of AIDS is HIV, but often, HIV is contracted through drug use and shared needles, or patterns of multiple sex partners. And, in most places, those at highest risk are poor, so that it's perfectly legitimate to say that poverty causes AIDS. So, what's the actual 'cause' of the disease? And, where does Public Health intervene to control it?
There has long been debate over whether epidemiology has a theoretical framework, or whether it is just a set of established statistical methods. Social epidemiologist Nancy Krieger, in her seminal paper in 1994 called "Epidemiology and the web of causation: has anyone seen the spider?" discussed just this, writing that epidemiology at the time, yes, was interested in the 'web of disease causation' -- what causes disease in populations? -- but was neglecting the search for the spider, the maker of the web. This paper kick-started the field of social epidemiology.
[This paper] emphasizes why epidemiologists must look first and foremost to the link between social divisions and disease to understand etiology and to improve the public’s health, and in doing so exposes the incomplete and biased slant of epidemiologic theories reliant upon a biomedical and individualistic world-view.She's just published a new paper in the American Journal of Public Health, "History, Biology and Health Inequities: Emergent Embodied Phenotypes and the Illustrative Case of the Breast Cancer Estrogen Receptor" in which she argues that health inequities can only be reduced if diseases are considered not just as static biological entities, but within their social and evolutionary contexts, and as individual histories (she calls this broad view of, in this case the estrogen receptor, the "emergent embodied phenotype" -- we can ignore the jargonizing of what is a rather obvious idea). The paper was brought to our attention by Susan Oyama, who has done some very good work on developmental systems, broadening the understanding of genes in context, among other things.
There's a new field in biology, too; 'systems biology'. This is the study of interconnected biological systems -- metabolic pathways, gene interactions, cell signaling networks. This is intended to be a more holistic approach to biology rather than a reductionist one. The idea is that describing these interactions will help us to understand the 'emergent' traits that are complex diseases. It is hoped that there will be practical applications -- understanding networks will, e.g., elucidate 'druggable' pathways that pharmaceutical companies can then intervene on, to prevent or cure disease.
Ken and I have often criticized the idea that the new field of 'systems biology' is going to be all it promises. Like social epidemiology, systems biology is a somewhat self-congratulatory jargonized term whose proponents suggest the very reasonable idea of taking a broader view of causation, in an attempt to take into account as many factors as possible that might explain how genes function or how traits are made or the causes of a disease. It's basically a way of enumerating identifiable interactions among components.
So, rather than being satisfied with knowing that mutations in a specific gene on their own cause a particular disease, the idea is to take a larger view: Enumerate the regulatory pathways or gene interactions (that is, between proteins) so that there are more targets for pharmaceutical intervention.
|Seoul from space|
Metabolism is an example in which chains or cycles or hierarchies of interactions pass molecules from one stage to the next. Even if the interaction networks are the mechanism of action, there is no reason to expect that the effects of variation in the components will lead to simple or additive variation in the result.
However, the temptation (whether explicit or implicit) is to try to rescue complexity by treating networks as self-contained causal units. Take sets of tens or hundreds of genes and treat each as a single causal unit, and magically you've reduced the causal dimensions you need to consider by an order of magnitude! But this is wishful thinking, and doesn't really simplify causation or the hunt to understanding, because we know that genes contribute to multiple networks, that vary, overlap, and have alternative pathways that are used under different circumstances.
Social epidemiology faces similar complexity.
Broad causal networks may seem to smooth out causal relationships until they look generalizable, but they aren't necessarily any smoother in social than molecular life. There's a common issue in population sciences called the "ecological fallacy." This is when an association that is true on the population level is inferred to be true on the individual level. The county may always vote Republican, or this neighborhood may be a wealthy one, but it can't be assumed that everyone in the county votes Republican, or that everyone in the neighborhood has an income above the average. Or that the neighborhood causes political preference or wealth in a given individual.
Social epidemiology by its nature and objectives searches for population level associations and attempts to infer from those associations causal relationships that apply at the level of the individual. Poverty causes AIDS. Racism causes stress which causes high blood pressure. In a sense, as reasonable and plausible as these ideas, and as much as they must reflect causal processes in some way, the ecological fallacy is always lurking, because not everyone with AIDS is poor, and not everyone who is poor gets AIDS, not everyone who has experienced racism has high blood pressure, and so on. And, even eliminating poverty won't prevent further cases of AIDS.
Unlike systems biology, where the idea is to intervene in the networks with targeted pharmacological agents, it's hard to know what to do with the information that social epidemiology is producing. Public Health is, ultimately, an applied science. It has very little that could be called real theory, beyond the use of statistical methods to design and evaluate studies -- that is, assuming the kinds of repeatabliity that any statistical sampling requires, similar to saying that every roll of dice has the same probability of coming up 6. Dice rolls may be repeatable events, but to a great extent, humans aren't. And as with systems biology, the idea is not just to enumerate interactions but to identify targets of intervention. But, when the field is identifying "poverty" or "racism" as causal factors, what can be done to intervene?
Indeed, one might say that at least simple biological systems like the krebs energy cycle or photosynthesis require networks of interactions among a step-wise hierarchy of truly enumerable molecular components in quantitative relationships, and a very high degree of repeatability so they can be studied experimentally and evaluated with standard statistical methods. But are 'poverty' and 'racism' even serious concepts of similar type? If not, then current statistical or other study-design methods may simply be inappropriate, too vague, or unable to provide the type of answers we would like to get, especially if we desire to understand causation at the individual level.
Public Health, and epidemiology in particular, has a legacy of successful research and intervention in infectious diseases -- these are 'point source' diseases, with largely replicable causality, and if you cut the problem off at the source, you prevent the disease. This has its parallels with human genetics, where clearly Mendelian, single-gene disorders are relatively easy to explain. It's when causation gets complex that genetics -- and Public Health -- get into difficulties.
But, ok, granted, Public Health is a population-level field, and eliminating all cases of a disease has never been asked of it. But it's curious to see the field seeming to homogenize causation at a time when complexity is a buzzword in other fields. Though, again, this is quite in line with much of genetics, which has its own legacy of successes with point causation (Mendelian disease), just not so much with complex diseases. And which also mixes population-level effects of particular variants with the ability of those variants to 'personalize' medical care.
Have you seen the photos that Col. Chris Hadfield is posting on his Facebook page? He's in the Space Station, taking pictures of Earth in his free time and sharing them with the world. They are stunning. He photographs cities, regions, large chunks of continents; they are lit up at night or green, or snow-covered, or cloud-covered, or dry-as-a-bone desert, or stretches of ocean during the day. When he wonders what something is in one of his pictures, he crowdsources the answer. The other day it was a spot in Tehran that looked curious to him. It turned out it had once been an airport, and is now a playground, which Iranians told him.
If a region is experiencing drought, that's easy to see from these photos. But, if there are cracks in the macadam on those long stretches of highway, or a bridge is unstable, or the rivers are polluted, you can't see it from space. At least, not from these pictures. You do get the big picture, but you have to zoom in to discover where to intervene to prevent a catastrophe due to crumbling infrastructure.
Social epidemiology may be a lot like Col. Hadfield's photographs -- beautiful descriptions, but so far removed from the individual that it's impossible to actually predict who's going to get sick and why, never mind useful for figuring out where to intervene. Even identifying the risk factors themselves is notoriously difficult. And one need not give it a special name, which is often a way we academics have of making their ideas seem new or particularly insightful. Just 'epidemiology' will do very well--and the point is to include social as well as other potentially causal factors (and, as we hope is clear, we are not just picking on public health rhetoric, because genetics is just as much affected by unnecessary jargonizing).
Public Health is by goals and design a population-based field, and it works well when the message is that cholera is waterborne, so we need to keep our water sources clean, or that vaccination can protect entire populations against infectious disease. Its methods have saved countless lives. But, like trying to make risk predictions from genetic data from whole populations, it's hard to know what to do with these descriptions of the causes of complex diseases from such a distance.
And ultimately, of course, if we go out enough concentric circles of causation, it's life that is the cause of disease, and the only prevention is death.