Causal inference and scientific paradigms in epidemiology

76 Pages·2010·0.536 MB·English

by  by Steven C. Coughlin

CAUSAL INFERENCE AND SCIENTIFIC
PARADIGMS IN EPIDEMIOLOGY

By

Steven S. Coughlin
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TABLE OF CONTENTS
About the Author i
Preface ii
PART I – FOUNDATIONS
Chapter 01 : Historical Foundations of Causal Inference in Epidemiology 1
Chapter 02 : Contemporary Epidemiologic Concepts Regarding Causality 7
PART II – CAUSAL INFERENCE IN EPIDEMIOLOGIC RESEARCH
Chapter 03 : Seeking Causal Explanations in Epidemiology Subdisciplines 19
PART III – TOWARD A NEW RESEARCH PARADIGM IN EPIDEMIOLOGY
Chapter 04 : Scientific Paradigms in Epidemiology and Professional Values 35
Chapter 05 : Emerging Paradigms in Epidemiology and Public Health:
Metaphors and Conceptual Models 42
Chapter 06 : Genetic Variants and Individual- and Societal-Level Factors 50
PART IV – RESEARCH PARADIGMS IN EPIDEMIOLOGY AND CAUSAL
INFERENCE
Chapter 07 : Research Paradigms and the Strengthening of Causal Inference
in Epidemiology 56
Index 65

Copyright and Permissions
“Scientific Paradigms in Epidemiology and Professional Values” originally appeared in Epidemiology and is used with permission of
Williams & Wilkins, Inc. “Genetic Variants and Individual- and Societal-Level Factors” originally appeared in the American Journal
of Epidemiology and is used with permission of Oxford University Press, Inc.
Disclaimer: The findings and conclusions in this book are those of the author and do not necessarily represent the official views of the
Department of Veterans Affairs.
i
ABOUT THE AUTHOR
Steven S. Coughlin received his MPH degree from San Diego State University in
1984 and his PhD from The Johns Hopkins University in 1987. Dr. Coughlin lives in the
Washington, DC metropolitan area where he is a member of the Environmental
Epidemiology Service, Office of Public Health and Environmental Hazards, at the U.S.
Department of Veterans Affairs. He is an adjunct professor of epidemiology at the
Rollins School of Public Health at Emory University in Atlanta, GA. Previously he was a
senior cancer epidemiologist at the Centers for Disease Control and Prevention, and
an associate professor of epidemiology and Director of the Program in Public Health
Ethics at the Tulane University School of Public Health and Tropical Medicine in New
Orleans, LA. Dr. Coughlin is an Associate Editor of the American Journal of
Epidemiology, Editor-in-Chief of The Open Health Services and Policy Journal
(www.bentham.org/open/tohspj/index.htm), and a member of the Honorary Editorial
Board of Risk Management and Healthcare Policy. He is the author or coauthor of more
than 190 articles and the author or co-editor of several books including Ethics and
Epidemiology (Oxford University Press, 1996, 2nd edition 2009), Case Studies in Public
Health Ethics (American Public Health Association, 1997, 2nd edition 2009), The
Principle of Equal Abundance (Xlibris, 2007), The Nature of Principles (Xlibris, 2008),
the first edition of Ethics in Epidemiology and Public Health Practice: Collected Works
(Quill Publications, 1997, www.books.google.com), and the second edition of Ethics in
Epidemiology and Public Health Practice: Collected Works (American Public Health
Association, 2009).
ii
PREFACE
This anthology of articles on causal inference and scientific paradigms in
epidemiology covers several important topics including the search for causal
explanations, the strengths and limitations of causal criteria, quantitative approaches for
assessing causal relationships that are relevant to epidemiology, and emerging paradigms
in epidemiologic research. In order to provide historical context, an overview of
philosophical and historical developments relevant to causal inference in epidemiology
and public health is also provided. Several theoretical and applied aspects of causal
inference are dealt with.
The aim of the book is not only to summarize important developments in causal
inference in epidemiology but also to identify possible ways to enhance the search for
causal explanations for diseases and injuries. Examples are provided from such fields as
chronic disease epidemiology, Veterans health, and environmental epidemiology. A
particular goal of the book is to provide ideas for strengthening causal inference in
epidemiology in the context of refined research paradigms.
These topics are important because the results of epidemiologic studies contribute
to generalizable knowledge by clarifying the causes of diseases, by combining
epidemiologic data with information from other disciplines (for example, psychology and
industrial hygiene), by evaluating the consistency of epidemiologic data with etiological
hypotheses about causation, and by providing the basis for evaluating procedures for
health promotion and prevention and public health practice. As Douglas Weed and other
writers have noted, deliberations about whether or not associations with suspected
etiologic factors are causal, and how much evidence is sufficient to warrant public health
agencies to intervene to protect public health, raise important ethical and professional
issues.
This book would not have been possible without the support and encouragement
of many friends and colleagues who share my enthusiasm for etiologic research,
epidemiology, and causal thinking. I am also indebted to anonymous reviewers who were
generous with their time and constructive critical comments. I would especially like to
thank colleagues in the Office of Public Health and Environmental Hazards at the
Department of Veterans Affairs, and faculty and students at the Rollins School of Public
Health at Emory University.

Steven S. Coughlin
Causal Inference & Scientific Paradigms In Epidemiology, 2010, 01-06 1
CHAPTER 1
Historical Foundations of Causal Inference in Epidemiology
Abstract. Empiricist philosophers such as Francis Bacon, John Locke, and David Hume
believed that knowledge is gained through observations of natural phenomena. In contrast to
deductive logic, inductive logic is not self-contained and therefore is open to error. On the
other hand, deductive logic cannot by itself establish a theory of prediction since it has no
connection to the natural world. Hume observed that inductive inference does not carry a
logical necessity. He challenged the notion that causality could be proved, while highlighting
the subjectivity of knowledge and the fallibility of inductive reasoning. In the nineteenth
century, Robert Koch provided a framework for identifying acute diseases associated with
microorganisms. In the twentieth century, following World War II, efforts were made by Sir
Austin Bradford Hill and others to systematize and justify causal inference in observational
research. More recent authors, including Mervyn Susser, have offered refined accounts of
causal criteria. The Bradford Hill criteria for causal inference or subsets of the criteria are
still widely used as a heuristic aid for assessing whether associations observed in
epidemiologic research are causal. The model of sufficient component causes proposed by
Kenneth Rothman is widely used in epidemiology as a framework for teaching and
understanding multicausality.
INTRODUCTION
Fundamental concepts of causation have been the subject of philosophical inquiry
since antiquity. Early thinkers were predominantly rationalists in that they sought
scientific knowledge through reason and intuition rather than empirical observations [1].
Aristotle, for example, emphasized syllogisms, a form of deductive logical argument
consisting of a major premise, a minor premise, and a conclusion [2]. Major figures in
philosophy in the Medieval era were also rationalists. In contrast to rationalist
philosophers, empiricists such as Francis Bacon, John Locke, and David Hume believed
that knowledge is gained through observations of natural phenomena. In contrast to
deductive logic, inductive logic is not self-contained and therefore is open to error. On
the other hand, deductive logic cannot by itself establish a theory of prediction since it
has no connection to the natural world [1].
Francis Bacon (1561-1626) is well-known for his empiricist natural philosophy
and published works [2]. He viewed theories of science advanced by Aristotle and his
followers as obsolete. Bacon saw that prediction can be achieved through a process of
Steven S. Coughlin
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2 Causal Inference & Scientific Paradigms In Epidemiology Steven S. Coughlin
inductive logic. Bacon formalized the process of inductive inference and demonstrated
that deductive logic could never be predictive without the fruits of inductive inference.
Bacon’s treatise, The Great Instauration, was published in 1620, although not all parts of
it were complete. Part two outlines Bacon’s new inductivist approach for scientific
investigations, the Novum Organum. The method starts from sensible experience to lower
axioms or propositions, which are derived from tabulated data and from abstractions [2].
More general axioms are then derived from lower ones. As Klein [2] put it, Bacon’s
“induction, founded on collection, comparison, and exclusion of factual qualities in
things and their interior structure, proved to be a revolutionary achievement within
natural philosophy…” His inductive method for scientific investigations implied the need
for negation or refuting experiments.
The 18th-century empiricist John Locke (1632-1704) popularized the inductive
methods that Bacon formulated and helped establish empiricism as the major school of
scientific philosophy [1]. Locke noted that knowledge is rooted in the subjective sense
data that experience gives rise to [3]. Hume defined “a cause to be an object followed by
another and where all the objects similar to the first are followed by objects similar to the
second” [4, 5]. In other words, “where the first object had not been the second would
never exist” [5]. Hume observed that inductive inference does not carry a logical
necessity. Hume’s point, as noted by contemporary authors [1], was that induction does
not carry the logical force of a deductive argument. Thus, Hume challenged the notion
that causality could be proved, while highlighting the subjectivity of knowledge and the
fallibility of inductive reasoning [3]. He made it clear that inductive logic cannot
establish a fundamental connection between cause and effect [1, 6]. In the nineteenth
century, John Stuart Mill (1843) offered a further account of the logic of causation [7].
NINETEENTH & EARLY TWENTIETH-CENTURY DEVELOPMENTS
IN CAUSAL INFERENCE IN MEDICINE AND PUBLIC HEALTH
Notable developments in the nineteenth century included empirical studies that
were guided by the miasma theory and, later, the germ theory of disease causation [8, 9].
Other nineteenth-century developments related to applications of causal inference to
health problems included work by the German physicians Friedrich Gustav Jakob Henle
(1809-1885) and Robert Koch (1843-1910) on microbial causes of disease, and the
articulation of social causation of disease by Virchow [10-12]. Kosch’s postulates and
later refinements provided a framework to readily identify acute diseases associated with
Historical Foundations Of Causal Inference Causal Inference & Scientific Paradigms In Epidemiology 3
microorganisms. The deterministic criteria for causality proposed by Koch applied tests
to identify causative agents of infectious disease. For example, the agent must always be
found with the disease, the agent must be shown by isolation and culture to be a living
organism and distinct from any other that might be found with the disease, and the agent,
isolated from the body in pure culture, must induce the disease in susceptible
experimental animals. The important bacteriological discoveries by Koch and others led
to the increasing acceptance of the germ theory of disease causation [13, 14].
In the early twentieth century, attribution of causality in biomedical research was
greatly influenced by the development of statistical inference by Ronald Aylmer Fisher
(1890-1962), Jerzy Neyman (1894-1981), and other leading statisticians [10, 15]. The
roots of statistical methods such as path analysis and structural equations modeling also
date to the early twentieth century [16, 17].
THE EMERGENCE OF CONTEMPORARY CONCEPTS OF CAUSAL
INFERENCE IN EPIDEMIOLOGY
Following World War II, in parallel with key developments in the evolution of the
randomized trial as the methodologic standard for gathering evidence of causal attribution
in clinical medicine, efforts were made by Sir Austin Bradford Hill and others to
systematize and justify causal inference in observational research. These early writings
stemmed from the debate over the nature of the association between cigarette smoking
and lung cancer [10, 18, 19].
The 1964 Report of the Advisory Committee to the U.S. Surgeon General on
“Smoking and Health” listed five criteria for evaluating the causality of an association:
time order, strength, specificity, consistency, and coherence. In a summary of a lecture
given to the Section of Occupational Medicine at the Royal Society of Medicine, Sir
Austin Bradford Hill [19] expanded this list of criteria to include analogy,
experimentation, and biologic gradient or dose-response curve. Bradford Hill also
separated biologic plausibility from coherence. More recent authors have offered an
account of what Bradford Hill meant by criteria such as analogy (which is infrequently
invoked in practice) and experimentation and how sets of causal criteria have evolved
over the half-century since Hill’s now famous article was published [3]. Causal criteria
emphasized by Susser [3] include the strength of an association (the size of estimated
risk), specificity (the precision with which one variable, to the exclusion of others, will
4 Causal Inference & Scientific Paradigms In Epidemiology Steven S. Coughlin
predict the occurrence of another), consistency (the persistence of an association upon
repeated test), predictive performance (the ability of a causal hypothesis drawn from an
observed association to predict an unknown fact that is consequent on the initial
association), and coherence (the extent to which a hypothesized causal association is
compatible with preexisting theory and knowledge) [3]. In Susser’s account, the presence
of an association, time order (a suspected causal factor must precede the effect), and
direction (change in an outcome is a consequence of change in an antecedent factor) are
essential properties of causes rather than criteria for identifying causal associations like
strength and consistency. The directionality of an association between exposure and a
disease or other adverse health outcome is often an essential point in deliberations about
the causality of an association. For example, diabetes mellitus may increase risk of
pancreatic cancer and the onset and growth of pancreatic cancer may also lead to
symptoms of diabetes.
The Bradford Hill criteria for causal criteria or subsets of the criteria are still
widely used as a heuristic aid for assessing whether associations observed in
epidemiologic research are causal. For example, criteria or guidelines such as
consistency, strength of the association, specificity, temporal relation, and biological
plausibility were mentioned by the Global Advisory Committee on Vaccine Safety [20]
and in an assessment of evidence for a causal association between service in the Gulf War
and Illness in U.S. Veterans [21]. Criteria-based methods provide only general guidelines
for assessing the causality of associations rather than a strict checklist for identifying a
causal relationship [22, 23].
The model of sufficient component causes proposed by Rothman [1] more than
twenty years ago is still widely used in epidemiology as a framework for teaching and
understanding multicausality. A sufficient component cause is made up of a number of
components, no one of which is sufficient for the disease or adverse health condition on
its own [1, 23]. However, a sufficient cause exists when all the components are present.
Diseases and adverse health conditions can be caused by more than one causal
mechanism and each causal mechanism involves the combined action of several
component causes. For example, post traumatic stress disorder (PTSD) may be the result
of interpersonal violence, sexual trauma, wartime traumatic experiences, automobile
accidents, or other traumatic experiences and a variety of factors may interact to
determine the severity and persistence of PTSD [24, 25]. This may include the
cumulative number and salience of traumatic experiences, biological and constitutional