Causes, risks, and probabilities: probabilistic concepts of causation in chronic disease epidemiology

Identifying and understanding causes of disease is arguably the central aim of the discipline of epidemiology. However, while the discipline has matured over the past sixty years, developing a battery of quantitative tools and methods for data analysis, the discipline of epidemiology lacks an explicit, shared theoretical account of causation. Moreover, some epidemiologists exhibit discomfort with the concept of causation itself, concerned that it creates more confusion than clarity. This paper describes how, during the post-war period, epidemiologists began to think about causation in new ways as they encountered novel challenges in studying chronic diseases. The epidemiologic evidence linking cigarette smoking and lung cancer in the 1950s provided a focus for debates over causation. While some epidemiologists embraced probabilistic concepts of cause and effect, others maintained that causal mechanisms must ultimately be deterministic. The tension between probabilistic risk factors and deterministic causal mechanisms continues to haunt epidemiology today.     

Causal diagrams in systems epidemiology

ABSTRACT: Methods of diagrammatic modelling have been greatly developed in the past two decades. Outside the context of infectious diseases, systematic use of diagrams in epidemiology has been mainly confined to the analysis of a single link: that between a disease outcome and its proximal determinant(s). Transmitted causes ("causes of causes") tend not to be systematically analysed.The infectious disease epidemiology modelling tradition models the human population in its environment, typically with the exposure-health relationship and the determinants of exposure being considered at individual and group/ecological levels, respectively. Some properties of the resulting systems are quite general, and are seen in unrelated contexts such as biochemical pathways. Confining analysis to a single link misses the opportunity to discover such properties.The structure of a causal diagram is derived from knowledge about how the world works, as well as from statistical evidence. A single diagram can be used to characterise a whole research area, not just a single analysis - although this depends on the degree of consistency of the causal relationships between different populations - and can therefore be used to integrate multiple datasets.Additional advantages of system-wide models include: the use of instrumental variables - now emerging as an important technique in epidemiology in the context of mendelian randomisation, but under-used in the exploitation of "natural experiments"; the explicit use of change models, which have advantages with respect to inferring causation; and in the detection and elucidation of feedback.     

Teaching epidemiologic methods

Advances in causal inference, study designs, and quantitative methods have led to new challenges for structuring a cohesive epidemiology methods course. This is particularly true for courses aimed at students who are training for a research career. Such students are expected to have a strong understanding of epidemiologic inference and modeling. They should grasp the principles of study design and analysis, and be able to interpret and report results effectively. This commentary discusses ways in which courses on epidemiologic methods can be made more effective, including: (1) a definition of the scope of epidemiologic methods; (2) a framework for defining a curriculum for heterogeneous student populations; (3) the need for computing and practical exercises; and (4) a call for increased attention to epidemiology education.     

Causal thinking and causal language in epidemiology: it's in the details

Although epidemiology is necessarily involved with elucidating causal processes, we argue that there is little practical need, having described an epidemiological result, to then explicitly label it as causal (or not). Doing so is a convention which obscures the valuable core work of epidemiology as an important constituent of public health practice. We discuss another approach which emphasizes the public health "use value" of research findings in regard to prediction and intervention independent from explicit metaphysical causal claims. Examples are drawn from smoking and lung cancer, with particular focus on the original 1964 Surgeon General's report on smoking and the new version released in 2004. The intent is to help the epidemiologist focus on the pertinent implications of research, which, from a public health point of view, in large part entails the ability to predict and to intervene. Further discussion will center on the importance of differentiating between technical/practical uses of causal language, as might be used in structural equations or marginal structural modeling, and more foundational notions of cause. We show that statistical/epidemiological results, such as "smoking two packs a day increases risk of lung cancer by 10 times" are in themselves a kind of causal argument that are not in need of additional support from relatively ambiguous language such as "smoking causes lung cancer." We will show that the confusion stemming from the use of this latter statement is more than mere semantics. Our goal is to allow researchers to feel more confident in the power of their research to tell a convincing story without resorting to metaphysical/unsupportable notions of cause.     

Kindesmisshandlung und neue Morbidität in der Pädiatrie

The effects of child maltreatment on children’s chronic health conditions have become more visible during recent years. This is true for mental health problems as well as some chronic physical conditions, both summarized as new morbidity within pediatrics. As several Bradford Hill criteria (criteria from epidemiology for the determination of the causal nature of a statistical association) are met, the likely causal nature of underlying associations is discussed. Early family support may have the potential to modify such associations, although empirical evidence is lacking. At least for attachment-based interventions with foster carerers after child maltreatment, positive effects on child HPA axis dysregulation have been demonstrated.     

Regression models for twin studies: a critical review

Twin studies have long been recognized for their value in learning about the aetiology of disease and specifically for their potential for separating genetic effects from environmental effects. The recent upsurge of interest in life-course epidemiology and the study of developmental influences on later health has provided a new impetus to study twins as a source of unique insights. Twins are of special interest because they provide naturally matched pairs where the confounding effects of a large number of potentially causal factors (such as maternal nutrition or gestation length) may be removed by comparisons between twins who share them. The traditional tool of epidemiological 'risk factor analysis' is the regression model, but it is not straightforward to transfer standard regression methods to twin data, because the analysis needs to reflect the paired structure of the data, which induces correlation between twins. This paper reviews the use of more specialized regression methods for twin data, based on generalized least squares or linear mixed models, and explains the relationship between these methods and the commonly used approach of analysing within-twin-pair difference values. Methods and issues of interpretation are illustrated using an example from a recent study of the association between birth weight and cord blood erythropoietin. We focus on the analysis of continuous outcome measures but review additional complexities that arise with binary outcomes. We recommend the use of a general model that includes separate regression coefficients for within-twin-pair and between-pair effects, and provide guidelines for the interpretation of estimates obtained under this model.     

Causal inference from randomized trials in social epidemiology

Social epidemiology is the study of relations between social factors and health status in populations. Although recent decades have witnessed a rapid development of this research program in scope and sophistication, causal inference has proven to be a persistent dilemma due to the natural assignment of exposure level based on unmeasured attributes of individuals, which may lead to substantial confounding. Some optimism has been expressed about randomized social interventions as a solution to this long-standing inferential problem. We review the causal inference problem in social epidemiology, and the potential for causal inference in randomized social interventions. Using the example of a currently on-going intervention that randomly assigns families to non-poverty housing, we review the limitations to causal inference even under experimental conditions and explain which causal effects become identifiable. We note the benefit of using the randomized trial as a conceptual model, even for design and interpretation of observational studies in social epidemiology.     

Approches d’analyse causale en épidémiologie

Epidemiological research is mostly based on observational studies. Whether such studies can provide evidence of causation remains discussed. Several causal analysis methods have been developed in epidemiology. This paper aims at presenting an overview of these methods: graphical models, path analysis and its extensions, and models based on the counterfactual approach, with a special emphasis on marginal structural models. Graphical approaches have been developed to allow synthetic representations of supposed causal relationships in a given problem. They serve as qualitative support in the study of causal relationships. The sufficient-component cause model has been developed to deal with the issue of multicausality raised by the emergence of chronic multifactorial diseases. Directed acyclic graphs are mostly used as a visual tool to identify possible confounding sources in a study. Structural equations models, the main extension of path analysis, combine a system of equations and a path diagram, representing a set of possible causal relationships. They allow quantifying direct and indirect effects in a general model in which several relationships can be tested simultaneously. Dynamic path analysis further takes into account the role of time. The counterfactual approach defines causality by comparing the observed event and the counterfactual event (the event that would have been observed if, contrary to the fact, the subject had received a different exposure than the one he actually received). This theoretical approach has shown limits of traditional methods to address some causality questions. In particular, in longitudinal studies, when there is time-varying confounding, classical methods (regressions) may be biased. Marginal structural models have been developed to address this issue. In conclusion, “causal models”, though they were developed partly independently, are based on equivalent logical foundations. A crucial step in the application of these models is the formulation of causal hypotheses, which will be a basis for all methodological choices. Beyond this step, statistical analysis tools recently developed offer new possibilities to delineate complex relationships, in particular in life course epidemiology.     

Climate change adaptation: Where does global health fit in the agenda?

ABSTRACT: Human-induced climate change will affect the lives of most populations in the next decade and beyond. It will have greatest, and generally earliest, impact on the poorest and most disadvantaged populations on the planet. Changes in climatic conditions and increases in weather variability affect human wellbeing, safety, health and survival in many ways. Some impacts are direct-acting and immediate, such as impaired food yields and storm surges. Other health effects are less immediate and typically occur via more complex causal pathways that involve a range of underlying social conditions and sectors such as water and sanitation, agriculture and urban planning. Climate change adaptation is receiving much attention given the inevitability of climate change and its effects, particularly in developing contexts, where the effects of climate change will be experienced most strongly and the response mechanisms are weakest. Financial support towards adaptation activities from various actors including the World Bank, the European Union and the United Nations is increasing substantially. With this new global impetus and funding for adaptation action come challenges such as the importance of developing adaptation activities on a sound understanding of baseline community needs and vulnerabilities, and how these may alter with changes in climate. The global health community is paying heed to the strengthening focus on adaptation, albeit in a slow and unstructured manner. The aim of this paper is to provide an overview of adaptation and its relevance to global health, and highlight the opportunities to improve health and reduce health inequities via the new and additional funding that is available for climate change adaptation activities.     

The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology

The resurgence of interest in the effect of neighborhood contexts on health outcomes, motivated by advances in social epidemiology, multilevel theories and sophisticated statistical models, too often fails to confront the enormous methodological problems associated with causal inference. This paper employs the counterfactual causal framework to illuminate fundamental obstacles in the identification, explanation, and usefulness of multilevel neighborhood effect studies. We show that identifying useful independent neighborhood effect parameters, as currently conceptualized with observational data, to be impossible. Along with the development of a dependency-based methodology and theories of social interaction, randomized community trials are advocated as a superior research strategy, one that may help social epidemiology answer the causal questions necessary for remediating disparities and otherwise improving the public's health.     

Epidemiology 101: toward an educated citizenry

Epidemiology, as the core science underpinning public health, encompasses methods and concepts that are fundamental to understanding health-related information and health policy. Thus, understanding these concepts would enhance the lay public's ability to make informed decisions with respect to health and prevention, and teaching epidemiology at the undergraduate level would be consistent with the goal of creating an educated citizenry. While epidemiology has traditionally been taught largely within graduate schools, there has been experience at the undergraduate level as well. This experience has demonstrated that such courses are popular and effective. While there may be some challenges inherent to teaching Epidemiology 101 at every college and university, this is a worthy and important goal, and most challenges can be successfully overcome with creativity and effort. Perhaps the greatest barrier is instinctive resistance to this idea, since most faculty with epidemiology training received such training in graduate schools. It is up to us to cast off those preconceptions; if one explores the notion of undergraduate epidemiology teaching with an open and unbiased mind, the logic, feasibility, and importance of this effort becomes clear.     

A sufficient cause based approach to the assessment of mediation

The minimal sufficient cause (MSC) model, also known as the sufficient component cause model, has been used to facilitate understanding of several key concepts in epidemiology. To improve the understanding of mediation, we introduce a causal model for mediation that is grounded in the MSC approach. First, we describe an unbiased model for mediation, to clarify the causal meaning of previously described indirect effects. Through the use of potential outcomes and response types, we express each indirect (and direct) effect in terms of component causes within the MSC model. Second, we use an MSC-based model to illustrate a common cause of the mediator and outcome, i.e. a confounder of the mediator–outcome relationship. By describing this potential source of bias within the MSC-based model, important complexities are noted that impact the magnitude of plausible confounding. In conclusion, an MSC-based approach leads to several important insights concerning the interpretation of indirect and direct effects, as well as the potential sources of bias in mediation analysis.     

Causation and causal inference in epidemiology

Concepts of cause and causal inference are largely self-taught from early learning experiences. A model of causation that describes causes in terms of sufficient causes and their component causes illuminates important principles such as multi-causality, the dependence of the strength of component causes on the prevalence of complementary component causes, and interaction between component causes. Philosophers agree that causal propositions cannot be proved, and find flaws or practical limitations in all philosophies of causal inference. Hence, the role of logic, belief, and observation in evaluating causal propositions is not settled. Causal inference in epidemiology is better viewed as an exercise in measurement of an effect rather than as a criterion-guided process for deciding whether an effect is present or not.     

Causal analysis of individual change using the difference score

Causal analysis of change in time-related characteristics such as health or disease is an increasingly important area of epidemiology. Change is often analyzed using data from 2 waves of a longitudinal study, using the difference score--the difference between the scores at the 2 waves--as the outcome in a regression model. In this article, I show how and when causal analysis of change can be performed using simple linear regression models of continuous difference scores. Not only do causal analyses require making adjustments for confounding bias, but also for the shape of individual "growth curves"--the way in which each individual's score changes over time. In practice, the type of growth curve is critical to determining whether age or start score or neither is included in the regression model. For valid analyses, both sets of adjustments require assumptions based on prior theory that cannot be tested using the study data; choosing to make adjustments using variables based solely on observed associations with the difference score can give misleading results. However, analysts can state their assumptions clearly using this framework and put them up for rigorous scientific scrutiny. The approach is illustrated by an application to data from the Whitehall II study of British civil servants.     

What is a cause and how do we know one? A grammar for pragmatic epidemiology

In this paper, criteria used by many epidemiologists as aids in causal inference are reviewed and revised. The revised scheme emphasizes the distinction between essential properties of a cause and criteria useful for deciding on the presence of these properties in a given case. A systematic procedure for causal inference tests each essential causal property in turn against appropriate criteria. For a pragmatic epidemiology in which all determinants serve as causes, their essential properties are held to be association, time order, and direction, in an ascending hierarchy. Criteria for association are probabilistic and can be enhanced by strength and consistency. Given association, criteria for time order of the relevant variables follow from access to observation, which is dependent on design. Given association and time order, causal direction (or consequential change) calls on an array of criteria, namely, consistency and survivability, strength, specificity in cause and in effect, predictive performance, and coherence in all its forms (e.g., theoretical, factual, biologic, and statistical). The evolution of such criteria is traced through the epidemiologic literature in the light of historical context. Although Popper's philosophy cannot directly serve an inherently inductive judgmental process, his notion of survivability has here been added, alongside replicability, as a subclass of consistency. This criterion is proposed to bridge the gap between the particularity of designs and the generality required of causal relations. Designs are ordered and described in the framework of testing survivability. Finally, definitions are offered for the list of criteria deployed.     

Compound treatments and transportability of causal inference

Ill-defined causal questions present serious problems for observational studies-problems that are largely unappreciated. This paper extends the usual counterfactual framework to consider causal questions about compound treatments for which there are many possible implementations (for example, "prevention of obesity"). We describe the causal effect of compound treatments and their identifiability conditions, with a special emphasis on the consistency condition. We then discuss the challenges of using the estimated effect of a compound treatment in one study population to inform decisions in the same population and in other populations. These challenges arise because the causal effect of compound treatments depends on the distribution of the versions of treatment in the population. Such causal effects can be unpredictable when the versions of treatment are unknown. We discuss how such issues of "transportability" are related to the consistency condition in causal inference. With more carefully framed questions, the results of epidemiologic studies can be of greater value to decision-makers.     

Causal inference in epidemiological studies with strong confounding

One of the identifiability assumptions of causal effects defined by marginal structural model (MSM) parameters is the experimental treatment assignment (ETA) assumption. Practical violations of this assumption frequently occur in data analysis when certain exposures are rarely observed within some strata of the population. The inverse probability of treatment weighted (IPTW) estimator is particularly sensitive to violations of this assumption; however, we demonstrate that this is a problem for all estimators of causal effects. This is due to the fact that the ETA assumption is about information (or lack thereof) in the data. A new class of causal models, causal models for realistic individualized exposure rules (CMRIER), is based on dynamic interventions. CMRIER generalize MSM, and their parameters remain fully identifiable from the observed data, even when the ETA assumption is violated, if the dynamic interventions are set to be realistic. Examples of such realistic interventions are provided. We argue that causal effects defined by CMRIER may be more appropriate in many situations, particularly those with policy considerations. Through simulation studies, we examine the performance of the IPTW estimator of the CMRIER parameters in contrast to that of the MSM parameters. We also apply the methodology to a real data analysis in air pollution epidemiology to illustrate the interpretation of the causal effects defined by CMRIER.     

精准医疗:分子流行病学的机遇与挑战

自2003年人类基因组计划完成,到2015年1月美国总统奥巴马宣布启动精准医疗计划,人类初步完成基因组到生物学、生物学到健康、健康到社会的三级跳。作为新型交叉领域,精准医疗的产生和发展离不开生命科学、基础医学、临床医学、流行病学、统计学、社会学和信息学等多学科的共同支撑和促进。分子流行病学作为流行病学与分子生物学的交叉学科,被认为是促进精准医疗的核心力量。本文将基于精准医疗和分子流行病学各自的特点和研究进展,着重探讨分子流行病学对精准医疗的贡献和意义,并展望精准医疗未来可能的机遇与挑战。     

Challenges for epidemiologic research on the verge of a new era

Although risk factor epidemiology has achieved much, it has its limitations (e.g., a failure to reveal causal mechanisms at multiple levels). To illustrate contemporary challenges for epidemiological research, we present a dialog with examples and argue for incorporating a “systems thinking through a life course” paradigm in epidemiological research. There is an increasing interest in moving part of public health from a discipline concerned primarily with risk factors at the individual level toward one concerned with complex causal patterns which often operate across different levels in time and space (e.g., from the molecular to the population, from the past to the future, and from the distal to the proximal). However, the methodology for discovering these complex and dynamic relationships remains to be improved. We propose strategies for taking up this challenge.