American Journal of Epidemiology
© The Author 2013. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of
Public Health. All rights reserved. For permissions, please e-mail: [email protected].
Vol. 178, No. 6
DOI: 10.1093/aje/kwt143
Advance Access publication:
September 5, 2013
Invited Commentary
Invited Commentary: On the Future of Social Epidemiology—A Case for Scientific
Realism
Carles Muntaner*
* Correspondence to Dr. Carles Muntaner, 155 College Street, Health Sciences Building 386, Toronto, ON M5T 1P8, Canada
(e-mail: [email protected]).
Initially submitted February 12, 2013; accepted for publication April 26, 2013.
In their article in this issue of the Journal (Am J Epidemiol. 2013;178(6):843–849), Galea and Link identify important heuristics for our discipline. In this commentary, I build upon their ideas by arguing that 1) social epidemiology
has become an Asian, European, Latin American, and African rather than just North American endeavor, 2) realism
is better suited to social epidemiology than positivism, 3) more work on social mechanisms (social class relations,
racial discrimination) is needed to increase the explanatory power of social epidemiology, 4) increased attention on
(social) causal models will generate more innovative social interventions, and 5) social interventions should be
conducted in full partnerships with affected populations.
causality; epistemology; global health; history of epidemiology; philosophy of science; race; social class; social
epidemiology
In their article “Six Paths for the Future of Social Epidemiology” (1), Professors Galea and Link provide a timely look
at the major challenges facing social epidemiology and discuss 6 important areas on which to focus in the future. In this
commentary, I build upon their ideas, giving alternative interpretations of these challenges and what might be desirable for
the future of social epidemiology.
Milton Terris, the celebrated Brazilian and American epidemiologist, believed that epidemiology was a biosocial science
because the process of studying the causes and patterns of population health was inherently a social one. To say he had a point
would be a gross understatement. Consider this example:
Deaths due to hemorrhaging are biologic facts. However,
deaths caused by firearms are social facts that also implicate
economic (i.e., the production and commerce of firearms),
political (i.e., the laws that regulate fire arm access), and cultural (i.e., the social acceptability of problem solving via violence) relations (2). According to Galea and Link (1), social
epidemiology has evolved into a valuable and established
subdiscipline within epidemiology, starting in the mid-1990s
around the time of President Clinton’s first administration.
With an increase in new sources of funding and publication
forums, social epidemiologists have identified a plethora of new
problems (e.g., neighborhood effects over the life course),
developed novel hypotheses (e.g., impact of income inequality
within and between nations), advanced methods (e.g., multilevel designs), and reached provocative findings (e.g., nongradients in health using social class concepts). Indeed, the impact
of social epidemiology has reached and influenced new areas
in health scholarship, including global health (3). Despite this
progress, Galea and Link express concern that social epidemiology is at risk of losing its hard-won status because other
subdisciplines within epidemiology are also studying social
factors. In the present article, I contend that if social epidemiology is losing its identity and traction, it is not because
other specialties have become more aware or interested in the
social aspects of population health. On the contrary, if social
epidemiology has become stagnant, it is because the field tends
to ask conventional questions such as, “What social risk factors predict health outcomes?” rather than posing questions
that interrogate, “Why do health inequities occur in the first
place?” or “How are health inequities generated and reproduced
over time?” That is, social epidemiology finds itself caught
within an empiricist and pragmatist epistemology that favors
descriptive summaries and observational accounts over social
explanations and causal mechanisms. This state of affairs limits
the depth of its inquiries, results in a dearth of causal thinking,
and generates little in the way of interventions.
852
Am J Epidemiol. 2013;178(6):852–857
Social Epidemiology and Scientific Realism 853
SOCIAL EPIDEMIOLOGY IS GLOBAL
My first reaction to the article by Galea and Link was to note
the degree to which it is centered on social epidemiology in
the United States. There is little doubt that the enhanced status
(including name recognition) of the specialty has been achieved
mainly within the United States. New research departments
(e.g., Leo Syme’s department at the University of California,
Berkeley and his students at Harvard and the University of
Michigan), theories (4), methods (5–7), and findings (8, 9) testify to the emergence of social epidemiology within the United
States, with global repercussions. Social epidemiologists at the
Society for Epidemiologic Research, the American Public Health
Association, or the International Epidemiological Association can
present their work under that label instead of the philosophically unsatisfying “Mental and Psychosocial Epidemiology.”
However, there are clear instances in which US social epidemiology seems to have held general social epidemiology back.
This includes social epidemiologists’ reluctance to understand
and measure race as a social relation rather than as the property of an organism (10) or persistence in using ranks of economic resources rather than conceptualizing and measuring
the social relations that generate them (11). Failing to account
for social relations has resulted in large amounts of data without much explanatory power and an overall lack of support for
policy interventions that reduce social inequities in health.
Whereas US epidemiology has played it relatively safe in
its social epidemiologic approaches, other research programs
around the world have been more willing to advance the discipline in critical ways. For example, Europe has been a constant source of new findings in social epidemiology, at least
since the Whitehall Studies (if not earlier), on the enduring
effects on health of (social) class stratification (12), the population health approach (13), income inequality (14, 15), precarious employment (16), the great recession (17), and politics
and welfare-state regimes on health (18). Latin America has
also contributed to the development of social epidemiology, in
particular with respect to theory and philosophy (19, 20).
Beyond Australia and New Zealand, language barriers may
prevent us from appreciating the recent contributions from
Asian social epidemiologists, notably from Korea (21) but also
from Japan, Taiwan, China, India, Thailand, Malaysia, Indonesia, and Sri Lanka, among other countries. Other regions
also seem to be catching up and are contributing to the field
(e.g., Arab nations in the Middle East) (22).
WHY EPIDEMIOLOGY IS (A BIT) SOCIAL BUT SOCIAL
EPIDEMIOLOGY IS NOT (FULLY SO)
Galea and Link reviewed the recent history of social epidemiology, with special attention paid to the United States.
With regard to the timing of disciplinary evolution, I would
situate the emergence of multilevel analyses to test the effects
of social processes on health before 2000. A multilevel ontology was provided by Krieger’s theoretical papers and models
(23, 24), and in a matter of a few years, innovative work using
multilevel analyses to examine social mechanisms and associations appeared in the literature (5–7). The evolution of the
discipline since the early 1990s has been at best incremental,
with negligible advances in theory or methods. Only Nancy
Am J Epidemiol. 2013;178(6):852–857
Krieger’s pioneering work (25) between the mid-1980s and
mid-1990s seemed ahead of its time, with an integration of
themes that would define social epidemiology in the years to
come (i.e., racism, sexism, social class, multilevel ontology,
social causation, contextual data, and historical analyses).
Galea and Link claim that social epidemiology is at risk
of losing its hard-won identity because of its ubiquitous status within epidemiology. Despite insights gleaned from studies
on smoking and obesity that point to social mechanisms, what
I think is the major weakness of the discipline is precisely what
they present as one of its major strengths, namely “epidemiologists have been at the leading edge of demonstrating all these
observations” (emphasis mine) (1, p. 844). Thus, social epidemiology favors an empiricist framework that yields thousands
of studies, with ever larger samples and longer follow up periods yielding only associations with few causal explanations
(e.g., 26). It is the avoidance of mechanisms and explanation
(e.g., scientific realism; 10, 27) in favor of associations (empiricism, pragmatism) that leads to a disciplinary cul-de-sac. Most
studies use socioeconomic status indicators such as educational level, occupational class, or even income without any
serious consideration of the potential social mechanisms that
link exposure and outcome. For example, a recent major
global health study showed that educational level is inversely
related to several health indicators in several countries (3).
This implies that health disparities can be reduced or eliminated if population levels of education increase (e.g., everyone
earned graduate degrees). Yet, this assumption is simply unrealistic: The proportion of highly educated persons occupying
coveted jobs in market economies (i.e., capitalist) represents a
diminutive share of the overall working population (28). Multilevel analyses provide another example. Its development and use
was meant to explicitly test for social mechanisms (e.g., how
Catholic schools influence student performance; 29) rather
than to search for independent associations between exposures
and outcomes like with traditional risk-factor regression models. Thus, there is a danger that under the banner of complex
methods innovation, one may simply perpetuate the associationist status quo.
The absence of causal explanations has consequences not
only for basic social epidemiology but for applied social epidemiology and public health, its associated social technology.
An excess of descriptive studies and a lack of causal mechanisms leads to a very poor understanding about which interventions actually improve population health or reduce health
inequities (30). Large gaps exist when it comes to knowledge
of what kinds of potential interventions matter (e.g., What are
the social policies that have the greatest impact on reducing
health inequalities? What types of labor markets and what
combination of labor market flexibility and social protection
promotes worker health (31)? When do intersectoral policies
and health in all policies reduce health inequalities) (32)?
IS MACRO HARDER THAN MICRO?
Galea and Link claim that the study of macrosocial determinants of health remains disproportionately small because
of the intrinsic difficulty of the subject matter. There is a contemporary emergent literature on political traditions, macro
policies, and welfare state policies that provides some theoretical
854 Muntaner
guidance and methodological heuristics (33–35). Economic
macro-analyses are even more common, stemming from a tradition of research on the effects of economic downturns and
recessions (36), which continues to date (37, 38). What has
become arguably the most popular topic in social epidemiology involves a macroeconomic indicator, namely income
inequality (14, 39, 40). There are also new theoretic and empiric
developments in neighborhood research (41) and a new generation of housing studies that do not dwell on the personal
characteristics of the homeless (42). Yet, without articulating
the main questions of macrosocial factors in social epidemiology (e.g., whether liberal democracies better than authoritarian regimes for population health) (43), it is difficult to
know where the challenges may reside. Other social disciplines, such as economics (44), sociology (45), or political economy (46), routinely study macrosocial factors. It seems that
despite its limitations, macrosocial epidemiology has started
down a promising path.
Regarding methods, the trend is to rely increasingly on new
and larger datasets that involve a global sample of countries.
Large multilevel country datasets that contain individual-level
data are powerful tools to investigate hypotheses about macrosocial determinants (e.g., whether egalitarian welfare regimes
associated with better health outcomes; 47). Although these
datasets are increasingly available (48–50), they do not allow
researchers to understand how social mechanisms at the country level affect individual health outcomes (e.g., how specific
policies are generated, implemented, and evaluated). Social
sciences have developed a series of comparative historical
methods that complement crossnational surveys (51) among
other tools for the analysis of macro-level determinants (realist
reviews, hypothesis testing case studies; 30). There is no reason why social epidemiology should not open its doors to use
them when appropriate.
DO WE NEED EVER BIGGER INDIVIDUAL LEVEL
DATASETS WITH LONGER FOLLOW UPS?
Galea and Link seem to assume that we will be using large
(and overpowered) quantitative, individual-level longitudinal
data as the sine qua non in social epidemiology. This assumption is consistent with a tradition of the parent discipline, empiricist epistemology, which prefers observations over theoretic
constructs, mechanisms, and explanations, at least when it comes
to social variables (e.g., socioeconomic status, race). However,
(social) science needs explanations and mechanisms (scientific realism) to reveal what our senses cannot detect. (This is
also why we use surveys, microscopes, and telescopes.) When
individual-level data do not provide insights into the workings of society, we need to find other methods that allow us to
uncover these social mechanisms. Thus, the counterfactual
method is not the problem underlying the causal inferences
on race in social epidemiology (10, 52, 53). The problem is
that race is conceptualized as an observable property
of an organism (skin color and other visible phenotypic features) rather than as a social relation that generates economic, political, and cultural inequalities as revealed with a
realist epistemology (10). Similarly, the reciprocal relationships between social capital and violence can be tested using
nonrecursive structural equation models (54). The problem
with social capital is not one of methods but its vague theoretical construct (55) that leads to identification problems
(56). What appears as a lack of methodological innovation is
often a lack of good theory.
The call for complexity is well taken. Yet, advancing epidemiologic methods as a distinct branch of statistics (with no
social epidemiologic theory content) is a risky proposition
(57). The common practice of imposing a mathematical (e.g.,
additive) model without considering theory-informed social
mechanisms may lead to unrealistic conclusions about the
relation between social factors and health. A simple 2 × 2
table to express the theoretical ideas of social epidemiology
may be preferable than the “. . . ad hoc models suggested by
complex statistical techniques” (55, p. 239). Large individual-level datasets will not tell us why the murder rate in
Chicago is dramatically higher than that in the United Kingdom
(58). That would require data on arms production, distribution, laws regulating their purchase, racial segregation, and
social cohesion, among other processes, and few if any of
these variables are accessible from individual-level surveys. A
realist epistemologist is open to a variety of methods (e.g.,
mixed methods) if they help uncover the social mechanisms
that influence health outcomes (26). We need to augment
social epidemiology by complementing it with other methods
if we want to succeed in the 6 areas of social epidemiology
outlined by Galea and Link.
SHOULD SOCIAL EPIDEMIOLOGY DEVOTE ITSELF
MOSTLY TO THE STUDY OF BIOLOGIC MECHANISMS?
Social epidemiology being a biosocial science has led to
repeated calls for uncovering how society determines proximal
biologic mechanisms that lead to ill health (2). Some interesting findings have been produced in recent years (59), yet what
sets social epidemiology apart is its unique attempt to uncover
social mechanisms (60), whereas other specialties that also
include social variables in their studies (cardiovascular,
cancer, psychiatric) are in a better position to deal with the biological end of the spectrum. Moreover, health, as defined
by the World Health Organization, is not just about biology.
The field would veer towards biologic reductionism if studies
attempted to link, say, income support policies to specific biologic processes. This is the same way in which it is reductionist
to link social policies to a single disease, as is the norm in epidemiology. When we look for the association of poverty with
the risk of major depression disorder, the nondiseased group
is likely to include patients with substance abuse, anxiety, disorders, cardiovascular disorder, diabetes, and many other disorders affected by poverty. Estimates of attributable risk due to
poverty on population health (important for applied social epidemiology) will be affected because of misclassification of
the outcome. We need better models to appropriately capture
the total impact of social exposures and not simply rely on the
single-outcome approach (61, 62).
At the same time, some of the traditional problems of social
epidemiology could be laid to rest, for example, the “selection/
causation” issue, with its social Darwinist (i.e., biologic Spenglerism) connotations that naturalize inequities (63). That is,
if poverty is a risk factor for mental illness among youths,
society is deemed responsible and assistance is seen as
Am J Epidemiol. 2013;178(6):852–857
Social Epidemiology and Scientific Realism 855
legitimate. On the other hand, if mental disorders are a risk
factor for poverty, the process is deemed as selection and can
be attributed to genetic predispositions (the individual); therefore, society is not held responsible. Yet, there is no moral or
public health justification for why patients with schizophrenia should be allowed to be poor (also an instance of social
causation) if poverty follows the onset of the disorder rather
than precedes it (64). Rather, the efforts of social epidemiologists may be better used to figure out the set of social protection policies (housing, active labor markets, health services)
that may prevent poverty among these patients. Yet, underlying the selection/causation issue, we find conflicting views
about political philosophy (i.e., individual versus collective
responsibility) (65) and even scientific metaphysics (66) that
are unlikely to be overcome in the near future.
SOCIAL INTERVENTIONS WITH POPULATIONS RATHER
THAN ON POPULATIONS
Galea and Link acknowledge that experiments are often
inadequate for social interventions. This strict approach to
what constitutes evidence is compatible with scientific realism
that, contrary to the dominant empiricist view in epidemiology (67), views causality as an ontologic, rather than epistemologic, category.
Another issue regarding social interventions is that social
epidemiology may not realize its full potential to reduce social
inequalities in health alone (29). Rather, understanding how to
change the social production of health requires a multidisciplinary or transdisciplinary approach. Galea and Link assume
that we should seek the solutions to social inequalities alone
and remain silent on the need for such partnerships. Instead of
competing with allied or disciplines, social epidemiologists
would benefit by embracing and partnering with similarly
minded fields to yield a fuller, more complete understanding of
which social changes might bring about the greatest good and
how such changes should come about. The existing literature
offers few answers to these pressing questions, but partnerships with academics in other disciplines, community members
in deprived locales, and decision makers in multiple levels of
government can increase the relevance of the evidence generated by social epidemiologists (68, 69). More than other subfields, social epidemiology is uniquely placed to benefit from
partnerships to help generate new questions and to ensure findings are used to inform population health interventions. Partnerships especially might be formed with persons implementing
social policies and programs to help determine the impact of
social change on health, especially given that most social programs do not measure health as an outcome (70).
WILL WE EVER REDUCE SOCIAL INEQUALITIES IN
HEALTH?
Still, Galea and Link consider health inequalities a core
problem for social epidemiology. Given this, the authors
express “hope” and “luck” in seeing health inequalities reduced
and anticipate that such a value-laden commitment will meet
some opposition from epidemiologists (i.e., scientists). I see
no reason for such caution, given the high stakes and consequences of health inequalities. Applied sciences such as social
epidemiology are meant to produce knowledge that can be
Am J Epidemiol. 2013;178(6):852–857
used for social change (e.g., reducing racial or class inequalities in health). Studying social inequalities in health has implicit values because inequalities are considered avoidable and
unfair (71). Values are not only implicit (we study cancer
because this disease has a negative value, causes immense
human suffering, and we want to eliminate it); values are also
explicit when social interventions and evaluations are conducted (i.e., in applied social epidemiology and its associated public health technology). This is analogous to a
surgeon operating on a patient, performing this duty for the
sake of procedural science, and pretending to have no vested
interest in whether the patient lives or dies. Such a value-free
approach to health is not only unrealistic in our social profession but also harmful to the production of meaningful
and actionable science.
The ethos of value-free science that originated from Max
Werber’s ideas is not followed by basic scientists (whether
made explicit or not, scientists have value systems and are
heavily invested in their research outcomes) (72) and is inadequate for applied scientists because the goal of their research
is to act upon the world. This involves making a decision on
what is the proper action, which relies partially on one’s
values. The unfortunate effects of this ethos among social
epidemiologists might be self-censoring, ignoring mechanisms, and discounting intervention research (i.e., their duty
as applied scientists) for fear of appearing partial. Inevitably,
this leads to more studies finding associations with race
but no social mechanisms of racism or racial segregation
(10, 73). Moreover, the supposedly value-free decision to
circumvent possible mechanisms that link power or politics
and health is anything but neutral. The decision to avoid
social mechanisms such as racism or social class implies
that nothing will be done to address those inequalities. Partiality is not incompatible with objectivity. For example,
nothing is holding back applied social epidemiologists from
on one hand designing and evaluating a housing intervention for the homeless and mentally ill while on the other
hand believing in the immorality of allowing the homeless to
remain on the streets (68). Applied scientific methods have a
built-in corrective mechanism (74) that gives them a unique
advantage over other forms of knowledge production—failed
interventions will either be improved upon by others or will
not be replicated at all.
A CONFIDENT DISCIPLINE IN AN UNCERTAIN
FUTURE
Disciplinary developments are influenced by external social
forces out of their control (e.g., funding, politics) (75). The
opposition to safe injection sites in spite of the evidence of
its public health benefits is a case in point (76). As long as
social epidemiology continues to identify new problems and
produce interesting findings, the discipline will undoubtedly
thrive. Prudently, Galea and Link embrace a future in which
social epidemiologists borrow insightful theories from other
disciplines to augment existing frameworks. Yet, this is already
happening (29, 30, 77).
Time will tell whether the social in social epidemiology will
blossom, whether positivism will make room for (scientific)
realism, whether the discipline will close upon itself (and risking
856 Muntaner
collapse), or whether it will open up further to multidisciplinary and transdisciplinary developments. In any event, the areas
identified by Galea and Link will be crucial to distinguish ourselves from the parent field.
ACKNOWLEDGMENTS
Author affiliation: Bloomberg Faculty of Nursing, Dalla
Lana School of Public Health, and Department of Psychiatry, University of Toronto and Center for Research in Inner
City Health, St Michael’s Hospital, Toronto, Ontario, Canada
(Carles Muntaner).
The research leading to these results forms part of the
SOPHIE project (Evaluating the Impact of Structural Policies on Health Inequalities and Their Social Determinants
and Fostering Change), which has received funding from the
European Community’s Seventh Framework Program (FP7/
2007–2013) under grant agreement no. 278173.
I thank Edwin Ng from the Dalla Lana School of Public
Health for his comments on earlier drafts.
Conflict of interest: none declared.
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