2.1. Definition

Broadbent defines epidemiology as “the study of the distribution and determinants of disease and other health states in human populations by means of group comparisons for the purpose of improving population health” (1). It would be a mistake to interpret Broadbent’s definition of epidemiology as excluding veterinary epidemiology, but it would be nice to consider animal health explicitly. It would also be wrong to think of it as including public health, which is the field whose purpose is to improve population health. Strictly speaking, epidemiology is just one of the basic sciences of public health. The short-term goal of epidemiology is not to improve health but to generate data for decision making in medicine and public health. For instance, David Savitz writes that “the goal for epidemiologic research (is) the quantification of the causal relation between exposure and disease” (Reference Savitz2003, 9). This more focused definition ascribes epidemiology a realm of study (health- and illness-related phenomena), restricts its activities to the collection and analysis of data in the context of various study designs, and deliberately includes causal inference. This is where philosophy comes in—at the point of transition from observed data to causal inferences needed to justify medical or public health action.

2.2. Epidemiology’s Features

I fully agree with four of six “striking features” Broadbent describes to distinguish epidemiology from other forms of research: the virtual absence of experimental wet lab work (although molecular, genetic, and infectious disease epidemiologists might disagree), population thinking, the lack of theory, and the high stakes associated with the health issues studied.

Another feature is “domain insensitivity” of its methods, because the main technique for gathering data is simple counting, which obviously can (and frequently does) include phenomena and characteristics outside the medical realm. In this section, Broadbent seems to restrict epidemiology’s realm of influence somewhat artificially to medical science. However, most of observational epidemiology is designed to inform those who work in public health, not medicine, where nonmedical risk factors (e.g., poverty, environment, access to health care) and nonmedical interventions are often of highest importance. Only one epidemiologic study design, the interventional randomized controlled trial (RCT), often has direct influence on medical decision making. Observational studies, on the other hand, generate information about the relationships between exposures and outcome, thereby hinting at issues related to disease causation.

Broadbent also points out that epidemiology and philosophy of science share a vested interest in causation and causal inference. I certainly agree that epidemiologists doing this kind of work will indeed benefit from philosophers’ attention; what will be needed is that epidemiologists realize the importance of such interdisciplinary work. The book’s emphasis on how useful epidemiologic concepts, techniques, and results are in improving population health will be particularly helpful in achieving this goal.

3.1. Translation

In light of his emphasis on the usefulness of epidemiologic results, Broadbent rejects the notion of “translation” as “what happens between a piece of scientific research being done and being used” as a myth (57). In particular, he suggests that the term is used out of frustration with the fact that the results of epidemiologic research have yet to yield the expected impact on population health, which would be “another way of expressing the results in question” (58). He rejects the idea that “the fact that smoking causes lung cancer can be translated into a reduction in smoking prevalence” (58), as if the term “translation” is used by epidemiologists as an indication that the epidemiologic claim (smoking causes lung cancer) is necessary and sufficient to initiate a public health intervention (smoking cessation), which is in turn necessary and sufficient to produce the intended population health effect (reduced incidence of lung cancer).

I think that this is not what most biomedical researchers refer to when using the term “translational research.” Indeed, it is not frequently used to describe epidemiologic research at all. In an oft-cited commentary, medical doctor and public health researcher David Woolf suggests that the term “translational research” has two meanings: “harnessing knowledge from basic sciences to produce new drugs, devices, and treatment options for patients,” and “ensuring that new treatments and research knowledge actually reach the patients or populations for whom they are intended and are implemented correctly” (Reference Woolf2008, 211). The first meaning refers to bench (basic) science results being “translated” into useful medical interventions, mainly by feeding basic science results into drug and device development. As such, the first meaning of “translation” is mainly confined to the medical realm. The second refers to activities that ensure access to health care and a healthy environment, thereby focusing on issues usually covered by public health. Note that neither meaning explicitly includes observational (risk factor) epidemiologic research, although the first can be interpreted as including results produced by RCTs, the main form of interventional epidemiologic research.

Broadbent appears to think of translation as a transformation of the results of a given study into more clinically relevant post hoc interpretation of those very results. I agree with one of my students, who recently wrote,

In other words, it is not the case that results of translational work are the translation, but they can be used to design interventions intended to improve the health of populations, which in turn is the job not of those working in epidemiology but of those working in public health (see Savitz’s definition of the goal of epidemiology above). Broadbent correctly interprets “translation” as a metaphor for “the path from the production of knowledge to its use” (57). Some might view this path as a straight, direct, and reliable road, leading safely and somewhat automatically (without further ado) to guaranteed success (improved health) even if you are asleep at the wheel. Thus interpreted, I agree that such “translation” is a myth. However, I am quite certain that most epidemiologists think of “translation” as a winding, rocky road through unknown territory, sometimes leading nowhere, but almost always to new hypotheses, more research, and novel ideas.

3.2. Stability

As an alternative focus “for frustrated scientists hoping to have more impact on population health” (59), Broadbent suggests “stability.” The main idea put forward in chapters 4 and 5 is that the stability of an exposure/outcome relationship renders it worthy of consideration for intervention design. Here, stability of an epidemiologic finding can be thought of as its robustness—a finding that is not easily contradicted by subsequent results generated by well-performed research.

This version of “stability” is reminiscent of one of Hill’s classic viewpoints on causation in epidemiology: consistency (Hill Reference Hill1965), which refers to the persistence of an epidemiologic finding across multiple different study designs and populations. One single study can yield one particular result that would be considered stable without being confirmed, for example, because no additional studies on the same subject have ever been conducted or accepted for publication due to a lack of novelty. I think that stability implicitly requires multiple attempts to show that the initial, single finding is due to chance, bias, or confounding, the three traditional ways in which a spurious finding is explained in epidemiology. There is no question, from the epidemiologic perspective, that stability lends explanatory value to the interpretation of an epidemiologic finding.

3.3. Prediction

In chapters 6 and 7, Broadbent outlines a theory of prediction in epidemiology. He states that a causal inference is not a prediction of successful action: “just because X causes Y, that does not mean that removing X is a sufficient means to removing Y” (83). After surveying the admittedly sparse epidemiological and philosophical literature on prediction and finding that both leave much to be desired, Broadbent answers the question, What is a good prediction? He offers a detailed discussion of why we need “an understanding which shows how good prediction activities lead to good (true) prediction claims” (91) and concludes that epidemiological prediction activities cannot be assessed solely based on comparison with the truth on certain occasions or in the past and that prediction activities need to justify the prediction claims they yield. In chapter 7, Broadbent shows that this can be achieved by requiring prediction activities to yield stable results. He rejects the idea that stable predictions can be arrived at by extrapolating (e.g., measures of association from one population to another), by making inferences from laws of nature, or by referring to knowledge of underlying mechanisms. To make a long and very interesting story short, Broadbent suggests, in keeping with his contrastive approach, that a prediction activity is good only if it explains why the prediction claim is true rather than another scientifically possible outcome (111). As a pithy criterion for a good prediction claim, Broadbent offers, “What could possibly go wrong?”

Apparently, Broadbent takes the distinction between causal and predictive claims much more seriously than most epidemiologists. For example, I have previously argued from a neopragmatic perspective that “it simply does not matter whether you say that ‘smoking is associated with lung cancer’ or ‘smoking predicts lung cancer.’ What does matter is that identifying, and then removing or reducing predictors from populations can improve the human condition” (Dammann Reference Dammann2009). The first statement (“it does not matter”) reflects the long-standing tradition among epidemiologists of calling risk factors “predictors.” Yes, there are measures of association (relative risk estimates) and measures of prediction (positive and negative predictive values), although the latter are used almost exclusively by clinical epidemiologists interested in screening and diagnostic testing. Particularly in the branch of epidemiology that deals with the identification of risk factors, few epidemiologists would have a problem with using the terms “predictor,” “antecedent,” and “risk factor” synonymously.

The second statement above (“what does matter”) suggests that we should de-emphasize causal talk (and maybe even predictive talk) and emphasize what is actually the goal of epidemiology, namely, to help reduce the individual and societal burden associated with illness. This, in turn, motivates us to suggest adding “explanation by intervention” to Broadbent’s shortlist of explanatory foci, explanation by stability and prediction.

3.4. Explanation by Intervention

Broadbent holds that “good causal inference in epidemiology must deliver a piece of causal knowledge that can be used to improve population health” (56). This notion echoes Nancy Cartwright’s focus on usage in causal inference (Cartwright Reference Cartwright2007). It is also “a functional approach to causation . . . that takes as its point of departure the idea that causal information and reasoning are sometimes useful or functional in the sense of serving various goals and purposes that we have” (Woodward Reference Woodward2014, 693). Finally, it is a somewhat neopragmatic account in that it shifts the attention away from talk about causation and toward talk about health improvement (Dammann Reference Dammann2009).

In recognition of the fact that epidemiologic results are generated with the goal of designing medical and/or public health interventions, I suggest that stability and prediction should be supplemented by an interventionist account of explanation. In fact, I think that Broadbent is actually doing this implicitly. However, I suggest that the interventionist stance should be considered explicitly in a theory of explanation in epidemiology because it is the main underlying idea of epidemiology in the setting of RCTs and public health interventions.

I think of successful intervention at the clinical or population level as the litmus test of causal inference by explanation in epidemiologic contexts, broadly construed as including both observational and intervention studies. Causal claims in epidemiology (broadly construed) are best supported by explanations that include evidence that interventions on the exposure variable result in the expected change of the outcome variable (Dammann and Leviton Reference Dammann and Leviton2007). Of course, findings produced by clinical or population trials should themselves be stable and allow for good predictions. Successful intervention should perhaps be thought of as a post hoc confirmation by intervention based on observational data rather than an additional way to support the conclusion that observational results themselves might be useful. On the other hand, even in observational settings, intervention on the exposure (e.g., variable manipulation such as stratification) during data analysis might be associated with changes in the outcome that qualify as useful observational results (quasi-intervention).

4.1. Attributability

In chapter 8, the limelight is on measures of attributability (as in attributable [aka excess] risk). The attributable/excess risk is calculated as the difference between risk among the exposed and risk among the unexposed individuals in an epidemiologic study. First, Broadbent describes the attributable risk (AR) as being prone to two fallacies: exclusive cause fallacy, that is, the erroneous belief that the cause of interest is the only and unconfounded cause of the outcome, and the counterfactual fallacy, that is, the erroneous belief that “magicking the exposure away” would reduce the number of cases of the disease in this population by exactly the number indicated by the AR. He holds that the AR does not accurately reflect the amount the risk would drop if you removed the exposure, because in practice other unaccounted-for exposures might replace the exposure of interest and would cause the outcome even in the absence of the original exposure. This is an interesting, possible, and perhaps even likely scenario. However, this caveat does not apply so much to what is measured by the AR as to how we frequently interpret it, that is, as the fraction of total risk we would get rid of if we got rid of the exposure for which the AR was calculated. Broadbent discusses issues related to the causal interpretation of the term “attributable to” as “caused by” and suggests, again, to transition from causation to explanation by stating that “a fraction of a risk is attributable to an exposure if and only if the exposure explains why the corresponding net difference between exposed and unexposed risks arises” (127).

4.2. Risk Relativism

Estimating the relative risk (RR) of developing the outcome among the exposed compared to the risk among the unexposed is at the core of epidemiologic inquiry. Broadbent discusses the mistake of interpreting RR as an expression of causal strength. He reviews the recent epidemiologic literature and reminds us that epidemiology is a context-sensitive toolbox rather than a natural science that is supposed to describe natural phenomena independent of context.

4.3. Multifactorialism

In his tenth chapter, Broadbent posits that epidemiology’s lack of domain specificity (v.s.) broadens the scope of medicine by identifying new kinds of causes and new kinds of diseases. Rather provocatively, he states that “merely by studying the common and distinctive elements in the histories of persons who meet an unhappy fate, epidemiologists exert pressure on health professions to countenance both those elements and that unhappy fate as part of their concern” (147). After having offered an interesting discussion of whether those who work in medicine and public health ought to respond to such pressures, Broadbent transitions into multivariability, mainly thought of as multicausality. He contrasts it with monocausality as conceptualized in the Henle/Koch postulates (one bug, one disease), which he calls an unhappy implication. Indeed, as an instructor of introductory epidemiology, I confirm that, unfortunately, this assumption prevails among medical and public health students, although “to call a disease multifactorial . . . hardly says anything at all, because every kind of event we ever encounter is multifactorial (149). He ends his chapter with yet another provocative suggestion, that is, to consider the contrastive model a better way to classify states of ill health than monocausal and multicausal models.

4.4. Epidemiology and the Law

In his final chapter, Broadbent surveys “existing legal positions on the use of epidemiologic evidence” and finds them confused (162). The main point here is the tension between general and specific causation, as well as the related question whether epidemiologic evidence can be applied at all in individual cases. The perception of what qualifies as good epidemiologic evidence in the courtroom in support of specific causal claims appears not to go far beyond the RR > 2/AF > 50% rule that gives rise to the question, Is it more likely than not that the exposure caused the outcome in the particular case at hand? Broadbent debunks such a simplistic notion as fallacious, based on the exclusive cause fallacy offered in his previous discussion of the AF. Still, he holds that epidemiologic evidence should be able to “help prove” under the right circumstances, and he offers a very interesting discussion about what exactly these circumstances might be.