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7
The Loyal Opposition: A Commentary on “Opportunities and
Challenges in the Study of Biosocial Dynamics in Healthy Aging”
Maxine Weinstein, Dana A. Glei, and Noreen Goldman
Exceptions are interesting: They make us ask “why.” Why is this case different? From a
scientific perspective—at least in principle— we start with a theory or empirical generalization,
and seeks to reject it; too often, however, researchers seek evidence to confirm our hypotheses.
It’s only when we find enough exceptions that we feel compelled to reject the rule or
significantly amend it. Kahneman (2011, p. 81), as always, makes the point regarding “a
deliberate search for confirming evidence, known as positive test strategy,” with elegance and
parsimony: “Contrary to the rules of philosophers of science, who advise testing hypotheses by
trying to disprove them, people (and scientists, quite often) seek data that are likely to be
compatible with the beliefs they currently hold.”
Here we sketch out a case for reconsidering the theoretical motivation for much of the
recent biosocial survey efforts. We concentrate on just a few points. First, we present evidence
that suggests that health disparities by socioeconomic status (SES) are not without exceptions.
Second, we argue that we have only weak evidence showing that biomarkers—at least the ones
that are most commonly collected in population-level biosocial studies—mediate the relationship
between social status and health. Finally, we focus on the allostatic load paradigm. Allostatic
load has been an important guiding framework for much of the biosocial research efforts;
correspondingly, it has been widely critiqued. Because the criticisms are well rehearsed—
although often ignored in practice—we only briefly discuss some of the vulnerabilities with its
application “on the ground.”
These considerations lead us to argue that it is time for our biodemographic
investigations to incorporate and test the kinds of theoretical scaffolding that sociology and
evolutionary biology can provide. We suggest a few directions where we believe such theoretical
articulation might be productive, but especially in light of our own deficiencies in those areas,
we encourage the biodemographic community to be more active in reaching out to the students
of those disciplines. Gruenewald’s chapter provides a well-organized overview of much of the
field, highlighting some of the exciting potential for future work. Our excitement regarding that
potential is perhaps more tempered by some of our experience.
THE SES–HEALTH GRADIENT DOES NOT APPEAR TO BE UNIVERSAL
As noted by Gruenewald, much of the literature documenting links between SES and
health (or mortality) suggests that the lower the position in a social hierarchy—typically
measured as education, income, or occupational status—the higher the risk of poorer health or
earlier death. Much of the evidence for this generalization comes from higher income countries
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—and even among higher income countries there are inconsistent results—but an increasing
body of evidence from middle-income or more recently developed countries appears to show less
consistent or weaker patterns (Goldman et al., 2011). Some of these “exceptions” have been
documented only recently (Rosero-Bixby and Dow, 2009; Smith and Goldman, 2007), but
others, including higher income countries, have been noted for some time. Sweden is such a
case. Vagero and Landberg (1989, cited in Wilkinson, 1996, Figure 5.8), for example, compared
age-standardized death rates across social classes in Sweden with England and Wales and Leon
et al. (1992; cited in P. Wilkinson, 1996) did a similar analysis of infant mortality. Both pictures
show the expected gradient in England and Wales; the picture in Sweden, however, shows little
evidence of an SES gradient. Japanese data for the 1990s (Hirokawa, Tsutusmi, and Kayaba,
2006) show no effect of education (assessed as age at completion of education) or employment
status on all-cause mortality for persons age 60 and older, and no effect of employment status on
all-cause mortality of persons age 59 and younger. Age at completion of education had a
discernible effect on mortality for women below age 59 who finished school before age 15.
Age may be one important factor. There is ongoing debate regarding whether the effects
of SES on health are likely to increase with age—as the advantages of higher status may
cumulate throughout the life cycle—or whether they are expected to decline—as biological
frailty dominates social influences and medical care becomes more widely accessible, for
example, through Medicare for persons 65 and older in the United States (Dannefer, 2003; House
et al., 1994). Most empirical work supports the second argument: SES disparities in health
generally decline from middle to older ages (Smith and Goldman, 2007; Zajacova, Goldman, and
Rodriguez, 2009). Another age-related effect may also be operating: generally, we would expect
the least healthy to die first so one might expect less variability in health as age increases. Either
way, surveys restricted to older adults are less likely than those based on a broader and younger
age range to identify statistically significant (or meaningful) social disparities in health
outcomes.
Another—albeit hotly contested—explanation for these apparent anomalies may be
related to the extent of social inequality. In the aggregate, some work has shown that income
inequality appears to be (inversely) associated with life expectancy in wealthy countries (de
Vogli et al., 2004) and, in the United States, directly related to mortality rates in metropolitan
areas (Ash and Robinson, 2009; Lynch et al., 1998). Other analyses, in some instances by the
same authors, suggest that the apparent inverse relationship disappears when additional
underlying factors are taken into account (Deaton and Lubotsky, 2009; Deaton and Paxson,
2004; Lynch et al., 2000, 2004a,b).
All of this is not to say that SES-associated gradients do not exist. Clearly they do, in
some places, at some times, perhaps even in most places—at least in modern times. Any
generalization is almost certain to have exceptions, and a very extensive literature would
essentially guarantee that we could find at least some support for any claim or counterclaim.
Should we focus simply on survival or some measure(s) of health? How—and when—should we
measure the outcomes? SES may have a very different relationship with survival (or health) at
younger ages from the relationships it has at older ages; some dimensions of health may be more
highly associated than others. “When” may also matter not just in terms of age, but in terms of
historical time or situation in the epidemiological transition. For example, historical data for the
United States reveal few social class differences in child mortality in the late 19th century (e.g.,
the children of physicians had death rates close to the national average), but, as health beliefs
changed and knowledge of hygienic practices spread during subsequent decades, socioeconomic
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gradients in mortality widened (Preston and Haines, 1991). And of course, we could always find
differences among studies in the ways SES is realized or how health is measured.
Still, we would hope that a “good” generalization would be robust. Len Syme says it well
(personal communication, 2012):
We are rightly concerned about defining and measuring variables precisely and perfectly
but, to me, the most important variables withstand our imprecision and vagueness. For
example, if we used different definitions and different methods in different populations
and still always find the same results, I think we have a really important variable. Social
class provides a good example of this. We don't really know what social class is and we
measure it in many different ways. But that doesn't seem to matter; Social class turns out
to be a very important variable because it is always related to the outcomes we study.
Social support provides another example. These variables are so important that they
withstand our clumsy attempts at definition and measurement.
We almost agree: Our point is that the exceptions to the “always” are interesting because
they suggest that we may be missing something should motivate a search for richer more
nuanced explanations of those findings. The exceptions remind us that we shouldn’t expect to
see the same relationships everywhere.
WEAK EVIDENCE THAT BIOMARKERS MEDIATE THE SES–HEALTH LINK
Here we raise two concerns. First, it is not entirely clear whether an unambiguous
relationship between social conditions—broadly construed as position in social hierarchies,
social relationships, and networks—and biomarkers has been documented. And second, the
extent to which currently obtainable biomarkers mediate the relationship between social
conditions and health appears to be an open question. We are not suggesting some magical
connection. At some level, we are biological reductionists: We accept that the association must
be mediated through physiological pathways, and we caution only that a convincing case has not
yet been made. Gruenewald points to the desire to understand how social conditions “get under
the skin” as a motivation for the addition of biomarker collection to epidemiological and social
and demographic studies. Although she identifies some of the apparent vulnerabilities of these
efforts, our overall impression from her commentary is that she is quite optimistic. For a variety
of reasons we have a more guarded view of the landscape. We have concerns about choice of
biomarkers, dealing with complex interactions between genetic endowment and environment, the
large numbers of pathways for which we would want biomarkers, and finally, we suspect that the
physiological influences linking SES to health are comprised of huge numbers of potentially
interactive effects, most of which are not observable or measurable. Other measurement issues
range from determining how, when—or how often—to measure biomarkers to how to measure
environmental influences, especially past environmental influences.
Evidence linking social conditions and biomarkers is not unambiguous. As Gruenewald
discusses, even a relatively well-measured marker such as blood pressure, with established ties to
disease processes, exhibits markedly different relationships with SES across studies or even
within studies depending on sex or measure of SES (Goldman et al., 2011). Other biomarkers
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raise even more complex issues of assay comparability and, more fundamentally, the processes
that the markers reflect, processes that may differ across time and setting.
Work on the relationship between social factors and markers of immune function or
inflammation serves as a good example of such problems. Recent reviews of the literature by
Uchino (2006) and Kiecolt-Glaser, Gouin, and Hantsoo (2010) provide insights into factors that
make it difficult to generalize about the links. One example is the fact that social interactions
typically entail both positive and negative aspects; another is that commonly measured
biomarkers of inflammation (IL-6, for example, which can have both pro- and anti-inflammatory
influences [Uchino, 2006]) have highly complex mechanisms that could easily be misinterpreted
in observational study designs.
One account, proposed by Hillard Kaplan during the course of the workshop, suggests
that some of the difficulties could lie in the causal pathways that underlie the inflammatory
markers (Kaplan, personal communication, 2011). For example, the history of exposure to
infection might influence inflammation throughout the life course. More generally, Kaplan
suggested that the causal pathways would be likely to differ not only across environmental
conditions, but also with age so that researchers might confound adaptive aspects of aging with
potentially correlated, but not necessarily causal, social exposures. Thus, for instance, higher
levels of inflammation might be an adaptive response to age-related changes rather than a marker
of poor regulation.
Our own recent work in collaboration with Carol Ryff and Yu-Hsuan Lin (Glei et al.,
2012a) using U.S. and Taiwanese data provides little encouragement. We examined the relation
between two components of social relationships—perceived support and social integration—and
six inflammatory markers. Results yielded only weak evidence of a link between the biomarkers
and the social relationships. Along the lines of Kaplan’s suggestion, one might expect that
exposure to infection, especially when today’s older adults were children, would be a more
important promoter of inflammation in Taiwan than in the United States. If so, that could weaken
the potential effect of social relationships in Taiwan. However, even this very plausible
suggestion is not supported by the data in this instance: If anything, the association between
social relationships and inflammation appeared stronger in Taiwan than in the United States. We
recognize that our data cannot support a conclusive test: We do not have direct information on
childhood exposure to infectious disease in Taiwan, although, perhaps, a comparison of
inflammation in cohorts who were born early versus late in the epidemiological transition might
shed some light. We also do not know whether, as discussed earlier, exposure would have pro- or
anti-inflammatory effects in adulthood. To move forward, we need to have hypotheses that direct
our attention to the complex interactions and links among social organization, physical
conditions, macro-level change in these factors, and individual-level response to exposures.
Such hypotheses present heavy—perhaps insupportable—demands on any data collection
initiative and will almost certainly require an approach that articulates data across time and
place. Such worthwhile efforts at integration, as we discuss later, face their own challenges.
We are also collaborating with colleagues using data from the Survey on Stress, Aging
and Health in Russia, a survey of Muscovites aged 55 and older (Glei et al., 2012b; Shkolnikova
et al., 2009). Russia might be the “poster child case” for establishing an association between
social disparities and mortality: On the one hand, the most greatly disadvantaged Russians
(especially men) suffered the greatest declines in life expectancy during the mortality crisis; on
the other, highly educated Russians experienced an increase in life expectancy (Murphy, 2006;
Shkolnikov et al., 2006). If a link between SES and biomarkers corresponding to a link between
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SES and mortality could be documented anywhere, we expected to find strong evidence in
Russia. Indeed, we found substantial educational disparities in physiological dysregulation
based on 20 biomarkers. However, more detailed analysis revealed that the size of the
differentials varied across systems. Both sexes exhibited a large educational disparity in standard
cardiovascular and metabolic factors, but heart rate parameters (based on 24h ECG) and
inflammation showed substantial differences only in men. These results are consistent with the
excess cardiovascular mortality that is a major contributor to high levels of mortality among
Russians, particularly men. Yet, social disparities in neuroendocrine dysregulation were
negligible in both sexes. If social disparities in allostatic load and in health outcomes reflect a
differential burden of stress, it seems surprisingly to find so little social variation in these stress
hormones, although we recognize the many measurement issues surrounding the collection of
these markers.
Whether the current battery of biomarkers actually mediates the relation between social
conditions and health is an even more vexed question. Unlike several other studies (see below),
the Russia data do show that the biomarkers—cumulated across systems—explain a substantial
proportion (albeit only about one-third at best) of the variation in health across SES groups.
Other studies that have examined whether biomarkers mediate social disparities in self-assessed
health status or physical functioning show that the biomarkers explain only a small proportion of
the socioeconomic differentials in Taiwan (Dowd, 2006; Goldman et al., 2011; Hu et al., 2007),
relatively little of the variation in the US (Goldman et al., 2011; Koster, 2005), and none of the
SES gap in Costa Rica (Goldman et al., 2011). As Gruenewald notes in her chapter, “… evidence
demonstrating that social disparities in biomarkers underlie social disparities in actual health
outcomes” is needed. To date, such evidence is sparse at best, and must be stacked up against a
growing body of null or inconsistent findings—at least from population-based studies. The
likelihood that null findings are underrepresented because of publication bias simply serves to
underscore this point. Overall then, we have not done well at explaining the physiological
pathways linking SES to health.
ALLOSTATIC LOAD – A FEW CONCERNS
Never underestimate the power of a narrative (Kahneman, 2011, p. 81)– and the story
behind allostatic load is compelling. A recent review by Juster, McEwen, and Lupien, (2010, p.
3) provides a simple summary of the plot: “Allostatic load (AL) represents the ‘wear and tear’
the body experiences when repeated allostatic responses are activated during stressful situations
(McEwen and Steller, 1993).” In turn, allostatic response (Juster, McEwen, and Lupien, 2010, p.
2) is the “process whereby an organism maintains physiological stability by changing parameters
of its internal milieu by matching them appropriately to environmental demands (Sterling and
Eyer, 1988).” The Juster et al. review summarizes some 58 studies of allostatic load; we estimate
that—perhaps—15 of them include measures of stressors or perceived stress. Five of those were
based on the Taiwan SEBAS data and yielded only modest support for links between AL and
stress (Gersten, 2008; Glei et al., 2007; Goldman et al. 2005; Seeman et al., 2004; Weinstein et
al., 2003). Studies in the United States have also yielded some evidence of a modest association
(Roepke et al., 2011; von Känel et al., 2003) but others have found mixed results (Gallo et al.,
2011; Mair, Cutchin, and Kirsten Peek, 2011) or no association (Seeman et al., 2002). Still other
research found a weak relationship in Australia (Clark, Bond, and Hecker, 2007) and Germany
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(Schnorpfeil et al., 2003), and a modest association in China (Sun et al., 2007) and Sweden
(Gustafsson et al., 2011). In short, while there is substantial evidence that multisystem
dysregulation—to use that term rather than AL, which implies a link to stressful experience—is
related to many health outcomes, its links to stressful experience are not well established.
Concerns with allostatic load are nothing new. The NIA Exploratory Workshop on
Allostatic Load, held under the auspices of the Behavioral and Social Research Program,
National Institute on Aging, was convened November 29-30, 2007, in part to shed some light on
the issues. Background materials from the workshop provide a laundry list of such concerns
(Nielson, Seeman, and Hahn, 2007). A full discussion of the concerns is not what we would want
to accomplish here, but we note that the participants raised issues with (among others): how
“stress” is defined (Cacioppo, Crimmins, Epel, Goldman); the choice of biomarkers that capture
dynamics or reflect cumulative dysregulation (Cohen, Coles, Epel, Goldman); and understanding
the role of the timing of exposure (Maestripieri)—a question also raised by Gruenewald. One
might add assay comparability across time and place, the need for a developmental approach that
incorporates exposure and health across the life course, and the various logistical and financial
hurdles involved in incorporating well-designed biomarker collection in population surveys.
The definition and measurement of “stress” is a particularly thorny problem (Cohen,
Kessler, and Underwood, 1997; Monroe, 2008). A related issue pertains to how “stress” fits into
the allostatic load framework. One viewpoint suggests that allostatic load provides a measure of
physiological stress. This perspective—which tautologically links stress to allostatic load—fails
to provide us with testable hypotheses regarding the impact of life challenges or other
environmental factors on physiological dysregulation. An alternative framework, which
underlies much of the research described in this paper, posits that dysregulation is a result of
prolonged or repeated exposure to life stressors. In this case, there is a testable relationship, but
one that has not been studied systematically and, to date, has yielded only weak evidence of
causal linkages.
WHERE CAN WE GO FROM HERE?
We are not yet ready to deny more generally the utility of documenting physiological parameters
of a population, but we would argue that future forays into biosocial survey data collection need
to be grounded in well-formulated theory. No one is advocating throwing the wheat away with
the chaff, and we recognize that it may be too early to decide what to keep and what to toss. We
see several areas for development. Gruenewald talks about geographic variation and we agree
that it is a potentially fruitful area for investigation. “Geographic” encompasses a multitude of
possible explanatory factors including variation in environmental conditions, gender roles and
relations, epidemiologic history, social structures and institutions, culture, developmental
histories, and genetic endowment. We have now amassed an impressive array of biosocial
studies across a wide range of geographies, and now the question is how can we best exploit
these data.
If we want to move beyond purely descriptive “comparisons” to understand the deeper,
possibly causal explanations of variation—and it does seem like a worthy goal – we will need
better theory to inform our investigations. It is here that we see a large potential contribution
from sociology, from both cultural and biological anthropology, from psychology, and from
evolutionary biology. As a field, we are spending too much time talking among ourselves. This
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commentary has noted or implied a few areas from which to begin: we need help understanding,
for example, how social stratification varies across geographies, how social institutions and
structures mutually contribute to, and reinforce each other’s formation and perpetuation (Sewell,
1992), and the role of priming in relative deprivation (Kahneman, 2011). We have data that will
allow us to perform similar analyses in the United States, Taiwan, Costa Rica, and Russia, but
how do we explain differences when we find? As noted by Goldman and her colleagues (2011,
p. 313):
Despite a justified appeal for international comparisons of social gradients in health that
integrate biological mechanisms, such undertakings are generally unable to establish
whether divergent findings reflect true variability in the physiological pathways linking
SES to health across countries, regions, and time periods; differences across data sets in
measurement error or definitions of biomarkers, SES and health outcomes; differences in
analytic strategies; or differences in sample size.
These questions are not only limited to different geographies, but also apply to group
differences more generally. How can we explain different relationships among variables when
we find them between, for example, men and women? Underlying physiological differences by
sex may be only one factor. As Gruenewald says, social factors interact with biology in complex
ways: those differences between men and women are almost certain to also have a basis in the
social interpretation and expectations for each sex. Similarly, we would look for deeper
explanations of black/white differentials.
We have also mentioned the need for both epidemiologic history and evolutionary
biology in our discussion of inflammation, but one could easily imagine that those questions
factor in to just about any physiological–social link that we would want to examine. Overall, we
would advocate for comparative studies that bring together diverse explanations for the links
between physiology and social conditions. Are there ways to test (i.e., reject) the theories? If—
as seems likely—additional data collection initiatives continue to be funded, we would advocate
for carefully targeted, theoretically driven studies.
So, is the glass half empty or is it half full? As always, the answer is “both.” We remain
both skeptical and cautiously optimistic.
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