A historical overview
11.3 Levels of causation
We now turn to introducing the concept of levels of causation, which is coming to the fore in epi- demiology. Before doing so, we should note that exceptional, forward-thinking individuals have sys- tematically considered causation at multiple levels throughout the history of epidemiology [2]. Histor- ical eras are demarcated by prevailing paradigms but these are not the exclusive method in any given era. Nonetheless, it is only recently that this kind of thinking has received sustained attention from the field and has been used as a foundation for training in epidemiology.
The idea of expanding the scope of psychiatric epidemiology ‘up’ to social contexts and ‘down’
to biological mechanisms is immediately appealing for several reasons. It allows the possibility of integrating disparate orientations into an organic whole. A combined undertaking takes greater advantage of advances in understanding across levels of research and disciplines. In addition it releases us from prejudice that the ‘real causes’
reside at any one level, to conceive disease causation as occurring at many levels. Once we are able to specify the potential relevance of any particular level
of analysis the idea of excluding that level raises the spectre of incompleteness, missed opportunity, model misspecification and confounding.
Conceptualising disease causation in this way does not mean that every study or even any study has to include many levels. Integrated understanding may be achieved through a series of studies with a much more limited purview. It does mean that every study has to begin by asking the question: what level/s of organisation are most relevant to the question at hand? Then the research is designed accordingly.
11.3.1 Individual level
Why are some people within a population more likely to develop disease than others? This is the question posed by risk factor investigations, which are conducted at the individual level. An individual level observational study, whether it is cohort or case–control, is designed to see whether variation in the disorder among individuals within the population reflects variation in their exposure histories. It does not require venturing down to the level of the cell, where we might ask which cells are affected by the exposure and in what ways, nor up to the level of the society, where we might ask which societies are organised in such a way that their members are exposed.
Imagine that you posit a relation between exposure to sunlight and the risk of seasonal affective disor- der. This model is appropriately conceptualised and investigated at the individual level. Individuals with more exposure to sunlight are hypothesised to be less vulnerable to this disorder, within the population of interest. To examine this hypothesis, it is sufficient to collect data on sunlight exposure and seasonal affec- tive disorder for individuals within the population.
The effects of sunlight exposure on cells, and the effects of social organisation on sunlight exposure, are related topics but are not directly addressed by either the hypothesis or the study design.
Thus, the risk factor investigation is at once impor- tant, useful and incomplete. We will mention three important limitations on what can be revealed about determinants of disease using individual level designs.
This discussion will provide a link to the next section on the contextual level, where we will see that these limitations can be partially overcome by research on other levels of causation.
First, not all risk factors of interest will vary between individuals within the study population.
A factor, that is universal in the study population, even if it participates in causing disease, cannot be readily examined in this framework. This can arise for exposures that are effectively mandated by gov- ernment policy (e.g. vaccines) or by cultural norms (e.g. circumcision) in a given society. A small num- ber of people may not follow the mandate, however;
they tend to differ from the rest of the population in important ways making them unsuitable as an unexposed comparison group.
A second limitation is that individual level risk fac- tor designs are not well suited to discover the causes of an increase (or a decrease) in disease incidence in a population. A noticeable increase in the incidence of a disease is often what motivates an investigation.
Generally the most parsimonious and useful explana- tion for a change in incidence is found at the societal level, albeit a societal change that brought about an increase (or decrease) in the population prevalence of an individual risk factor. An individual level study is ill equipped to identify the pivotal event, soci- etal change. Consider the example of autism. Studies suggest that the prevalence of autism has increased markedly in developed societies over the last two decades. Hypothesised explanations include environ- mental exposures, potential toxins we encounter in our environment that are a byproduct of modern living (e.g. air and water pollution, plastics, food additives, products made from synthetic materials).
This latent variable – an increasing multiplex expo- sure consisting of>80 000 synthetic chemicals in the environment and counting – is ubiquitous, and could contribute to the time trend. Beyond the measure- ment and identifiability challenge, isolating a subset of exposures causing an individual case may not explain the trend if each subset of component causes is individually rare.
A third limitation is that the effect of an individual level determinant on the risk of disease is context dependent – even at the purely individual level of analysis. Under the paradigm of risk factor epidemi- ology, disease causation requires the participation of multiple risk factors, and individual cases may result from different constellations of risk factors, so that many different constellations may be ‘sufficient’ to cause disease. For the risk factors comprising any one
sufficient constellation, the impact of each risk factor upon the disease risk will vary, depending upon the relative frequency of the other risk factors within the constellation, in the population being investigated.
Generally, in studies within a given population, the common risk factors of a sufficient constellation tend to appear less ‘influential’ in disease causation than the rare risk factors of the same constellation [1].
This occurs in spite of their joint contribution to disease occurrence in a given case.
Suppose that congenital neural tube defects (spina bifida, anencephaly) are caused by a combination of two risk factors: a genetic defect that increases the need for folate, and low folate in the maternal diet.
(This causal model is realistic albeit simplified for exposition.) When the genetic defect iscommonand a low folate maternal diet is uncommon, in a crude analysis, the effect of the genetic defect on the risk of disease will appear to be muchless than that of low folate diet. On the other hand, when the genetic defect isuncommonand a low folate maternal diet is common, the effect of the genetic defect will appear to begreaterthan that of low folate diet. The more common risk factor thus carries a lower relative risk, and will be more difficult to detect.
Yet, it may be precisely the common risk factors that carry the most implications for disease preven- tion. Thus it is in part for this reason that some of the causes that would be important for prevention are common, and will be of small effect when evaluated in an individual level analysis. Effects of common risk factors tend to be among the most controversial of epidemiological findings.
A corollary result is that the magnitude of effect attached to a given risk factor can be expected to vary across populations due to variation in the prevalence of causal cofactors. Hence, we should not expect identical findings when we conduct the same study in two populations with somewhat different constel- lations of risk factors. The findings may be similar in populations with similar risk factors, supporting the pursuit of ‘replication’ of findings, but thereshould be some variation.
11.3.2 Contextual level
Why do some populations have higher rates of disease than others? To identify determinants that
explain differences in rates between populations, or in the same population over time, we often turn to studies at the level of the social context. A social con- text may be any combination of individuals who are connected in some meaningful way, such as a fam- ily, a community or a society. Thus, we move ‘up’
from the individual level to higher levels, in order to gain access to causal determinants that may not be identifiable in individual level studies. As implied earlier, these include determinants that are invariant within a population and therefore obscured or even invisible at the individual level, as well as those deter- minants that are not defined in individuals but in the relationships and contexts that surround them.
The core idea in reasoning about contexts is that properties emerge as we move up from the individual to these higher levels of organisation. For example, most of us are accustomed to thinking about the emergent properties of neighbourhoods, and intuitively understand their meaning. In New York City, Harlem, Greenwich Village and Chinatown are examples of neighbourhoods with particular attributes, although the individuals living in each of them are by no means homogeneous. Living in one or another of these neighbourhoods will have a large influence on many dimensions of life, for example the cost and quality of housing, the type of recreation available (e.g. parks, gymnasiums, cinemas, museums), the presence of noxious facilities (e.g. sewage treatment plants, power plants), the quality of schooling for children and the amount and type of police surveillance. Residents will also be affected by the perceptions of other people about these neighbourhoods. In these and other ways, the emergent properties of the three neighbourhoods will shape the experiences of people who live there. The same can be said of emergent properties of nations, regions of the country, cities, schools, work places, families and dyadic relationships. The critical issue for epidemiologists is to identify which are most cen- tral to health and then to measure those properties so as to test causal explanations that involve them.
The societal determinants of health may appear remote from the occurrence of a specific disease in an individual, and yet be of great consequence as a causal determinant. Consider the hypothetical example of sunlight and seasonal affective disorder, which we previously used to illustrate the individual
level of investigation. We could now elaborate our causal model by positing a relation between rates of seasonal affective disorder among women and societal determinants of women’s work and leisure activities. Let us propose that societies which severely restrict women’s access to outdoor occupations and recreations will have higher rates of seasonal affective disorder among women. This model is appropriately conceptualised at the societal level because the cru- cial determinant of health is societal constraints on women, and the outcome is the rate of disorder in the population. To examine the hypothesis, we might choose to compare several populations with different societal constraints on women, but similar geographic and climatic conditions, with respect to both pattern of sunlight exposure and rate of sea- sonal affective disorder among women. Note that while the risk factor investigation would provide the more ‘proximal’ causal mechanism, the societal level investigation might be more likely to indicate an effective intervention. Unless the societal barriers can be reduced, individual women may find it difficult to change their work and leisure patterns.
When we turn attention from the individual to the contextual level we encounter great opportunities and enormous challenges. The opportunities arise from the fact that the full scope of contextual level influence (family, neighbourhood, school, work group, country) has barely been explored. Our fundamental understanding of context is also con- stantly evolving. Social contexts entirely supported by virtual medium mean that physical contact may be optional or entirely nonexistent, geographic proximity not always relevant. Both relational and physical-distances and boundaries are important in defining the ‘level’ affecting health [21]. While there are exemplary studies that indicate the importance of contexts for health outcomes [22–26], we are still in the early stages of development in putting together social, physical, cultural and other contexts with health outcomes.
These opportunities exist in part because the con- ceptual and measurement work needed to capture variation in contexts like these is still early in its development (e.g. [27, 28]). Current practice in col- lecting data for epidemiologic research has, perhaps, slowed our progress. The standard approach is to sample and collect data on individuals; data are
provided either through self reports or lab based measures. As useful as this approach is, it does not give us direct access to information about contexts.
Often, we only learn about context indirectly through what people tell us about contexts, or what their biological measurements may reveal about contexts;
however, few fine examples of direct measurements of social context exist (e.g. [24]). Our attention is drawn towards individual level processes, and away from the potential importance of processes at the contextual level. Consequently concepts and mea- surements at the contextual level do not come into the purview of the scientist on a regular basis when this approach is used.
The best way to think about conceptual level cau- sation is not yet entirely clear, and competing pro- posals have generated some excitement. Link and Phelan [29] propose thinking of contexts as units that vary in the power they possess to secure health enhancing living conditions – the capacity to secure good things for health and avoid bad things. The example of neighbourhood suggests some possibili- ties along these lines in that well-heeled neighbour- hoods can resist noise, pollution and crime in ways that neighbourhoods that possess less social and polit- ical power cannot. Similarly, in a unionised work- place the union can negotiate for safe work conditions and better health care opportunities. Social capital (e.g. [30]), social stratification (e.g. [31]), social cohe- sion (e.g. [32]), social fragmentation (e.g. [33]), ethnic density (e.g. [34–37]), inequality (e.g. [38]) may be the most commonly investigated contextual features in relation to health – the literature for the first two being the most extensive. We are reminded, however, that careful measurement of context is as crucial as careful measurement of disease outcomes [39].
11.3.3 Combining individual and contextual levels
Thinking about both individual and contextual levels at the same time frees us to ask different questions than we would thinking at either level alone. Previously, we were limited to two essential questions: Why do some people in a population develop disease and not others? Why are the rates of disease higher/lower in some populations than others? We can now ask about the interplay between determinants at different levels.
Studies of neighbourhood social isolation and schizophrenia provide an example from contempo- rary research. Following on early findings from the landmark ecologic studies of Faris and Dunham [40], Hare [41] demonstrated that in the city of Bristol, the incidence rate of schizophrenia was associated with neighbourhood social isolation, measured by the proportion of people living alone. He proposed two explanations (not mutually exclusive): individ- uals might migrate to these neighbourhoods, or, the social context of these neighbourhood might foster the development of schizophrenia.
van Oset al. [42] took up this line of enquiry, in a study in Holland, using a multilevel analysis that well reflects the emerging era of epidemiology. They too found an effect of neighbourhood social isolation, measured by the proportion single and the propor- tion divorced, on the risk of schizophrenia. They also found an effect of marital status at the individual level. The neighbourhood effects were not explained, however, by the individual effects of marital sta- tus, indicating that the measure of neighbourhood social isolation tapped some emergent property of the neighbourhood. Furthermore, in their study neigh- bourhood interacted with individual risk factors in the following manner: being single and living in a neighbourhood with a lower proportion of single persons more than doubled the risk of schizophre- nia over being single and living in a neighbourhood with a higher proportion single persons. A plausible interpretation is that one is more at risk – perhaps one feels more alone – as a single person when living in a neighbourhood comprised of married people.