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Association or causation: evaluating links between "environment and disease".

Article Excerpt
Epidemiological studies typically examine associations between an exposure variable and a health outcome. In assessing the causal nature of an observed association, the "Bradford Hill criteria" have long provided a background framework--in the words of one of Bradford Hill's closest colleagues, an "aid to thought" (1). First published exactly 40 years ago, these criteria also provided biomedical relevance to epidemiological research and quickly became a mainstay of epidemiological textbooks and data interpretation (2). Their checklist nature suited the study of simple, direct causation by disciplines characterized by classic scientific and mathematical training.

Most diseases have a multifactorial pathogenesis, but the conceptualization of their causation varies by discipline. While it is scientifically satisfying to elucidate the many component causes of an illness, in public health research the more important emphasis is on the discovery of necessary or sufficient causes that are amenable to intervention. Even so, over the four decades since Bradford Hill's paper appeared, the range of multivariate, multistage and multi-level research questions tackled by epidemiologists has evolved, as have their statistical methods and their engagement in wider-ranging interdisciplinary research. Within that context it is often not appropriate to seek the discrete cause or causes of a disease, but rather to identify a complex of interrelated and often interacting factors that influence the risk of disease (1). This complicates the assessment of causality.

The general context within which Bradford Hill developed his ideas about causal inference warrants brief review here. Most epidemiological research is non-experimental, being conducted in an inherently "noisy" environment in free-living populations. For example, the quality of the measurement of exposure and of health status is usually less than in controlled clinical trials or laboratory-based studies (measurement error); there are potential confounding variables that are statistically associated with the exposure variable of interest while also predictive of the health outcome in their own right, and these covariates must be controlled for; the sample of persons studied may not provide true information about the relationship between exposure and outcome in the source population, with respect either to the relationship that the sample actually displays (selection bias) or apparently displays (classification bias). Epidemiologists therefore seek research settings and study designs that maximize the signal-to-noise ratio.

These sources of noise, intrinsic to much epidemiological research, require one to proceed cautiously in making causal inference. Once sufficient studies have been done, in diverse settings, and adequately limiting random error (an intrinsic property of a stochastic universe), systematic error (bias) and logical error (confounding), then the causal nature of observed associations can reasonably be assessed.

Note, though, that particular phrase: "causal nature". Causation is an interpretation, not an entity; it should not be reified. The 18th-century Scottish philosopher David Hume pointed out that causation is induced logically, not observed empirically (3). Therefore we can never know absolutely that exposure X causes disease Y. There is no final proof of causation: it is merely an inference based on an observed conjunction of two variables (exposure and health status) in time and space. This limitation of inductive logic applies, of course, to both experimental and non-experimental research.

Around the mid-20th century, the philosopher Karl Popper offered a solution to this problem of reliance on induction. He stressed that science progresses by rejecting or modifying causal hypotheses, not by actually proving causation. While flirting briefly with Popper's ideas in the 1970s (4), epidemiologists have generally taken a practical data-based approach to the notion of causation, comfortably embracing Bradford Hill's criteria of causality. In general, these seem well suited to the mostly non-experimental, bias-prone, confounding-rich nature of epidemiological research. These nine criteria, or guidelines, lay particular emphasis upon the temporality of the relationship, its strength, the presence of a plausible dose--response relationship, the consistency of findings in diverse studies, and coherence with other disciplinary findings and biomedical theory. Rather than proposing absolute criteria, Bradford Hill considered these as aspects of the association between an exposure and an outcome that "we especially consider before deciding that the most likely interpretation of it is causation".

Bradford Hill's ideas about causal inference were formulated in the heady early years of the rise of noncommunicable disease epidemiology, which was essentially a post-Second World War phenomenon. His own experience included, in particular, the first definitive controlled clinical trial--of streptomycin in the treatment of tuberculosis, in the late 1940s (5)--and the early studies of cigarette smoking and lung cancer, principally the British doctors cohort study (6). Other early successes in non-experimental epidemiological studies of noncommunicable diseases included those that entailed substantial, quantifiable occupational exposures, for example to ionizing radiation (7), asbestos (8) and nickel (9). It is not surprising that, against that background, the challenge seemed not so much that of elucidating and apportioning complex causality but, more fundamentally, of inferring simple, relatively direct-acting causality.

Bradford Hill recognized the importance of moving from association to causation as a necessary step for taking preventive action against environmental causes of disease. But there are questions about the universal applicability of his classic criteria. How valid are they in the assessment of multifactorial causality? Are they useful in a widening research agenda within which, for example, we try to identify and quantify the effects of more distal, often indirectly acting determinants of health such as factors related to socioeconomic status, the effects of urban design on physical activity levels and the incidence of obesity, or the effects of ongoing climate change on risk of death from flooding? More subtly, does our reliance on causal criteria as an intellectual framework shape and direct our research questions and funding opportunities?

Ten years after Bradford Hill's classic paper, Rothman presented a model of causation that stressed the multifactorial pathogenesis of disease, with multiple component causes or factors that increase risk, and diverse causal pathways (10). He identified necessary elements and combinations of exposures sufficient to result in disease development. Causal inference, then, would focus more on how well the results of epidemiological studies fit with such a model. Rothman and Greenland note that none of Bradford Hill's criteria alone is sufficient to establish causality--for each criterion there are situations in which both lack of satisfaction of the criterion may be causal and satisfaction of the criterion may be non-causal. Temporality, the requirement that the exposure must precede the effect, is the only necessary criterion for a causal relationship between an exposure and an outcome (11).

In the following section we briefly review the Bradford Hill criteria and their contemporary use in epidemiology.

Strength. Bradford Hill suggested that strong associations were more likely to be causal than weak associations. The strong associations he cites (a 200-fold increase in mortality from scrotal cancer in chimney sweeps exposed to tar or mineral oils, and a 20-fold increased risk of lung cancer in smokers compared with non-smokers) have more credence, being less likely to be attributable solely to uncontrolled residual confounding. Relatively weak associations are common in contemporary epidemiology, so that we are reliant on strong study design and methodology, with minimization of bias, evaluation of the role of chance and comprehensive measurement of possible confounders for a valid measure of association. This is often difficult in the study of complex environmental influences on human health.

Consistency. Bradford Hill also...