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Psychological stress and the immune system: a meta-analytic study of 30 years of inquiry

Psychological Stress and the Human Immune System: A Meta-Analytic
Study of 30 Years of Inquiry
Suzanne C. Segerstrom
University of Kentucky
Gregory E. Miller
University of British Columbia
The present report meta-analyzes more than 300 empirical articles describing a relationship between
psychological stress and parameters of the immune system in human participants. Acute stressors (lasting
minutes) were associated with potentially adaptive upregulation of some parameters of natural immunity
and downregulation of some functions of specific immunity. Brief naturalistic stressors (such as exams)
tended to suppress cellular immunity while preserving humoral immunity. Chronic stressors were
associated with suppression of both cellular and humoral measures. Effects of event sequences varied
according to the kind of event (trauma vs. loss). Subjective reports of stress generally did not associate
with immune change. In some cases, physical vulnerability as a function of age or disease also increased
vulnerability to immune change during stressors.
Since the dawn of time, organisms have been subject to evolu-
tionary pressure from the environment. The ability to respond to
environmental threats or stressors such as predation or natural
disaster enhanced survival and therefore reproductive capacity,
and physiological responses that supported such responses could
be selected for. In mammals, these responses include changes that
increase the delivery of oxygen and glucose to the heart and the
large skeletal muscles. The result is physiological support for
adaptive behaviors such as “fight or flight.” Immune responses to
stressful situations may be part of these adaptive responses be-
cause, in addition to the risk inherent in the situation (e.g., a
predator), fighting and fleeing carries the risk of injury and sub-
sequent entry of infectious agents into the bloodstream or skin.
Any wound in the skin is likely to contain pathogens that could
multiply and cause infection (Williams & Leaper, 1998). Stress-
induced changes in the immune system that could accelerate
wound repair and help prevent infections from taking hold would
therefore be adaptive and selected along with other physiological
changes that increased evolutionary fitness.
Modern humans rarely encounter many of the stimuli that com-
monly evoked fight-or-flight responses for their ancestors, such as
predation or inclement weather without protection. However, hu-
man physiological response continues to reflect the demands of
earlier environments. Threats that do not require a physical re-
sponse (e.g., academic exams) may therefore have physical con-
sequences, including changes in the immune system. Indeed, over
the past 30 years, more than 300 studies have been done on stress
and immunity in humans, and together they have shown that
psychological challenges are capable of modifying various fea-
tures of the immune response. In this article we attempt to con-
solidate empirical knowledge about psychological stress and the
human immune system through meta-analysis. Both the construct
of stress and the human immune system are complex, and both
could consume book-length reviews. Our review, therefore, fo-
cuses on those aspects that are most often represented in the stress
and immunity literature and therefore directly relevant to the
Conceptualizing Stress
Despite nearly a century of research on various aspects of stress,
investigators still find it difficult to achieve consensus on a satisfac-
tory definition of this concept. Most of the studies contributing to this
review simply define stress as circumstances that most people
would find stressful, that is, stressors. We adopted Elliot and
Eisdorfer’s (1982) taxonomy to characterize these stressors. This
taxonomy has the advantage of distinguishing among stressors on
two important dimensions: duration and course (e.g., discrete vs.
continuous). The taxonomy includes five categories of stressors.
Acute time-limited stressors involve laboratory challenges such as
public speaking or mental arithmetic. Brief naturalistic stressors,
such as academic examinations, involve a person confronting a
real-life short-term challenge. In stressful event sequences, a focal
event, such as the loss of a spouse or a major natural disaster, gives
rise to a series of related challenges. Although affected individuals
usually do not know exactly when these challenges will subside,
they have a clear sense that at some point in the future they will.
Chronic stressors, unlike the other demands we have described,
usually pervade a person’s life, forcing him or her to restructure his
or her identity or social roles. Another feature of chronic stressors
Preparation of this work was supported by American Heart Association
Grant 0160367Z, the National Alliance for Research on Schizophrenia and
Depression, National Institute of Mental Health Grant 61531, and Michael
Smith Foundation for Health Research Grant CI-SCH-58. We thank Edith
Chen for her helpful comments on an earlier version of the article and
Jennifer Snedeker for assistance with manuscript preparation.
Correspondence concerning this article should be addressed to Suzanne
C. Segerstrom, Department of Psychology, University of Kentucky, 115
Kastle Hall, Lexington, KY 40506-0044, or Gregory E. Miller, Department
of Psychology, University of British Columbia, 2136 West Mall, Vancou-
ver, British Columbia V6T IZ4, Canada. E-mail: or
Psychological Bulletin Copyright 2004 by the American Psychological Association
2004, Vol. 130, No. 4, 601–630 0033-2909/04/$12.00 DOI: 10.1037/0033-2909.130.4.601
is their stabilitythe person either does not know whether or when
the challenge will end or can be certain that it will never end.
Examples of chronic stressors include suffering a traumatic injury
that leads to physical disability, providing care for a spouse with
severe dementia, or being a refugee forced out of ones native
country by war. Distant stressors are traumatic experiences that
occurred in the distant past yet have the potential to continue
modifying immune system function because of their long-lasting
cognitive and emotional sequelae (Baum, Cohen, & Hall, 1993).
Examples of distant stressors include having been sexually as-
saulted as a child, having witnessed the death of a fellow soldier
during combat, and having been a prisoner of war.
In addition to the presence of difficult circumstances, investi-
gators also use life-event interviews and life-event checklists to
capture the total number of different stressors encountered over a
specified time frame. Depending on the instrument, the focus of
these assessments can be either major life events (e.g., getting
divorced, going bankrupt) or minor daily hassles (e.g., getting a
speeding ticket, having to clean up a mess in the house). With the
more sophisticated instruments, judges then code stressor severity
according to how the average person in similar biographical cir-
cumstances would respond (e.g., S. Cohen et al., 1998; Evans et
al., 1995).
A smaller number of studies enrolled large populations of adults
who were not experiencing any specific difficulty and examined
whether their immune responses varied according to their reports
of perceived stress, intrusive thoughts, or both. Other studies have
examined stressed populations, in which a larger range of subjec-
tive responses may be detected. This work grows out of the view
that peoples biological responses to stressful circumstances are
heavily dependent on their appraisals of the situation and cognitive
and emotional responses to it (Baum et al., 1993; Frankenhauser,
1975; Tomaka, Blascovich, Kibler, & Ernst, 1997).
Overview of the Immune System
As many behavioral scientists are unfamiliar with the details of
the immune system, we provide a brief overview. For a more
complete treatment, the reader is directed to the sources for the
information presented here (Benjamini, Coico, & Sunshine, 2000;
Janeway & Travers, 1997; Rabin, 1999). Critical characteristics of
various immune components and assays are also listed in Table 1.
Components of the Immune System
There are several useful ways of dividing elements of the
immune response. For the purposes of understanding the relation-
ship of psychosocial stressors to the immune system, it is useful to
distinguish between natural and specific immunity. Natural immu-
nity is an immune response that is characteristic not only of
mammals but also lower order organisms such as sponges. Cells
involved in natural immunity do not provide defense against any
particular pathogen; rather, they are all-purpose cells that can
attack a number of different pathogens
and do so in a relatively
short time frame (minutes to hours) when challenged. The largest
group of cells involved in natural immunity is the granulocytes.
These cells include the neutrophil and the macrophage, phagocytic
cells that, as their name implies, eat their targets. The generalized
response mounted by these cells is inflammation, in which neu-
trophils and macrophages congregate at the site of injury or infec-
tion, release toxic substances such as oxygen radicals that damage
invaders, and phagocytose both invaders and damaged tissue.
Macrophages in particular also release communication molecules,
or cytokines, that have broad effects on the organism, including
fever and inflammation, and also promote wound healing. These
proinflammatory cytokines include interleukin(IL)-1, IL-6, and
tumor necrosis factor alpha (TNF
). Other granulocytes include
the mast cell and the eosinophil, which are involved in parasitic
defense and allergy.
Another cell involved in natural immunity is the natural killer
cell. Natural killer cells recognize the lack of a self-tissue molecule
on the surface of cells (characteristic of many kinds of virally
infected and some cancerous cells) and lyse those cells by releas-
ing toxic substances on them. Natural killer cells are thought to be
important in limiting the early phases of viral infections, before
specific immunity becomes effective, and in attacking self-cells
that have become malignant.
Finally, complement is a family of proteins involved in natural
immunity. Complement protein bound to microorganisms can up-
regulate phagocytosis and inflammation. Complement can also aid
in antibody-mediated immunity (discussed below as part of the
specific immune response).
Specific immunity is characterized by greater specificity and
less speed than the natural immune response. Lymphocytes have
receptor sites on their cell surfaces. The receptor on each cell fits
with one and only one small molecular shape, or antigen, on a
given invader and therefore responds to one and only one kind of
invader. When activated, these antigen-specific cells divide to
create a population of cells with the same antigen specificity in a
process called clonal proliferation, or the proliferative response.
Although this process is efficient in terms of the number of cells
that have to be supported on a day-to-day basis, it creates a delay
of up to several days before a full defense is mounted, and the body
must rely on natural immunity to contain the infection during this
There are three types of lymphocytes that mediate specific
immunity: T-helper cells, T-cytotoxic cells, and B cells. The main
function of T-helper cells is to produce cytokines that direct and
amplify the rest of the immune response. T-cytotoxic cells recog-
nize antigen expressed by cells that are infected with viruses or
otherwise compromised (e.g., cancer cells) and lyse those cells. B
cells produce soluble proteins called antibody that can perform a
number of functions, including neutralizing bacterial toxins, bind-
ing to free virus to prevent its entry into cells, and opsonization, in
which a coating of antibody increases the effectiveness of natural
immunity. There are five kinds of antibody: Immunoglobulin (Ig)
A is found in secretions, IgE binds to mast cells and is involved in
allergy, IgM is a large molecule that clears antigen from the
The term pathogen is used here to refer to microorganisms that can
cause disease. This term is most appropriate in the evolutionary context we
proposed in the articles introduction because it focuses on susceptibility to
infection. However, the reader should be aware that pathogens are only a
subset of antigens, that is, all substances that evoke an immune response.
Other antigenic substances include, for example, transformed self-cells
(i.e., cancer cells), transplanted tissue, and allergens (i.e., antigens that
evoke an allergic response).
bloodstream, IgG is a smaller antibody that diffuses into tissue and
crosses the placenta, and IgD is of unknown significance but may
be produced by immature B cells.
An important immunological development is the recognition
that specific immunity in humans is composed of cellular and
humoral responses. Cellular immune responses are mounted
against intracellular pathogens like viruses and are coordinated by
a subset of T-helper lymphocytes called Th1 cells. In the Th1
response, the T-helper cell produces cytokines, including IL-2 and
interferon gamma (IFN
). These cytokines selectively activate
T-cytotoxic cells as well as natural killer cells. Humoral immune
responses are mounted against extracellular pathogens such as
parasites and bacteria; they are coordinated by a subset of T-helper
lymphocytes called Th2 cells. In the Th2 response, the T-helper
cell produces different cytokines, including IL-4 and IL-10, which
selectively activate B cells and mast cells to combat extracellular
Immune Assays
Immune assays can quantify cells, proteins, or functions. The
most basic parameter is a simple count of the number of cells of
different subtypes (e.g., neutrophils, macrophages), typically from
peripheral blood. It is important to have an adequate number of
different types of immune cells in the correct proportions. How-
ever, the normal range for these enumerative parameters is quite
large, so that correct numbers and proportions can cover a wide
range, and small changes are unlikely to have any clinical signif-
icance in healthy humans.
Protein productioneither of antibody or cytokinescan be
measured in vitro by stimulating cells and measuring protein in the
supernatant or in vivo by measuring protein in peripheral blood.
For both antibody and cytokine, higher protein production may
represent a more robust immune response that can confer protec-
tion against disease. Two exceptions are levels of proinflammatory
cytokines (IL-1, IL-6, and TNF
) and antibody against latent
virus. Proinflammatory cytokines are increased with systemic in-
flammation, a risk factor for poorer health resulting from cardiac
disease, diabetes mellitus, or osteoporosis (Ershler & Keller, 2000;
Luster, 1998; Papanicoloaou, Wilder, Manolagas, & Chrousos,
1998). Antibody production against latent virus occurs when viral
replication triggers the immune system to produce antibodies in an
effort to contain the infection. Most people become infected with
Table 1
Immune Parameters Reported and Critical Characteristics
Parameter Arm of immune system Function Cell surface marker
Leukocytes Natural All white cells
Granulocytes Natural Inflammation
Neutrophils Natural Inflammation, phagocytosis
Eosinophils Natural Inflammation
Monocytes/macrophages Natural Inflammation, phagocytosis
Lymphocytes Specific All lymphocytes CD2
T lymphocytes Specific Cellular immunity CD3, CD45RA (naive)
T-helper lymphocytes Specific Cellular (Th1) or humoral (Th2) immunity CD4
T-cytotoxic lymphocytes Specific Cellular (Th1) immunity CD8
B lymphocytes Specific Humoral (Th2) immunity CD19, CD20
Activated B lymphocytes Specific Humoral (Th2) immunity CD23, CD30
Natural killer cells Natural Cellular (Th1) immunity CD16, CD56, CD57
IgA, IgG, IgM Specific Humoral (Th2) immunity
Anti-EBV IgG Specific Index of EBV replication/activation
Anti-HSV IgG Specific Index of HSV replication/activation
Anti-influenza IgG postimmunization Specific Humoral (Th2) immunity
Natural Inflammation, T cell activation
Interleukin-2 Specific T cell activation (Th1)
Interleukin-4 Specific B cell activation, antibody production (Th2)
Interleukin-6 Natural Inflammation
Interleukin-10 Specific Inhibits T cell activation (Th2)
Natural and specific Macrophage, natural killer cell, and T cell
activation (Th1)
Tumor necrosis factor-
Natural Inflammation
Complement Natural Increases effectiveness of natural immunity C3
Functional assay
Neutrophil superoxide release Natural Inflammation
Natural killer cell cytotoxicity Natural Cellular (Th1) immunity
Proliferation to ConA Specific Cellular (Th1) immunity (T cell proliferation)
Proliferation to PHA Specific Cellular (Th1) immunity (T cell proliferation)
Proliferation to PWM Specific Cellular (Th1) and humoral (Th2) immunity
(T and B cell proliferation)
Note. Th1 cells that direct a response to intracellular pathogens; Th2 cells that direct a response to extracellular pathogens; IgA immunoglobulin
A; IgG immunoglobulin G; IgM immunoglobulin M; EBV Epstein-Barr virus; HSV herpes simplex virus; ConA concanavalin A; PHA
phytohemagglutinin; PWM pokeweed mitogen.
latent viruses such as Epstein-Barr virus during adolescence and
remain asymptomatically infected for the rest of their lives. Var-
ious processes can activate these latent viruses, however, so that
they begin actively replicating. These processes may include a
breakdown in cellular immune response (Jenkins & Baum, 1995).
Higher antibody against latent viruses, therefore, may indicate
poorer immune control over the virus.
Functional assays, which are performed in vitro, measure the
ability of cells to perform specific activities. In each case, higher
values may represent more effective immune function. Neutro-
phils function can be quantified by their ability to migrate in a
laboratory assay and their ability to release oxygen radicals. The
natural killer cytotoxicity assay measures the ability of natural
killer cells to lyse a sensitive target cell line. Lymphocyte prolif-
eration can be stimulated with mitogens that bypass antigen spec-
ificity to activate cells or by stimulating the T cell receptor.
Pathways Between Stress and the Immune System
How could stress get inside the body to affect the immune
response? First, sympathetic fibers descend from the brain into
both primary (bone marrow and thymus) and secondary (spleen
and lymph nodes) lymphoid tissues (Felten & Felten, 1994).These
fibers can release a wide variety of substances that influence
immune responses by binding to receptors on white blood cells
(Ader, Cohen, & Felten, 1995; Felten & Felten, 1994; Kemeny,
Solomon, Morley, & Herbert, 1992; Rabin, 1999). Though all
lymphocytes have adrenergic receptors, differential density and
sensitivity of adrenergic receptors on lymphocytes may affect
responsiveness to stress among cell subsets. For example, natural
killer cells have both high-density and high-affinity
receptors, B cells have high density but lower affinity, and T cells
have the lowest density (Anstead, Hunt, Carlson, & Burki, 1998;
Landmann, 1992; Maisel, Fowler, Rearden, Motulsky, & Michel,
1989). Second, the hypothalamicpituitaryadrenal axis, the
sympatheticadrenalmedullary axis, and the hypothalamic
pituitaryovarian axis secrete the adrenal hormones epinephrine,
norepinephrine, and cortisol; the pituitary hormones prolactin and
growth hormone; and the brain peptides melatonin,
and enkephalin. These substances bind to specific receptors on
white blood cells and have diverse regulatory effects on their
distribution and function (Ader, Felten, & Cohen, 2001). Third,
peoples efforts to manage the demands of stressful experience
sometimes lead them to engage in behaviorssuch as alcohol use
or changes in sleeping patternsthat also could modify immune
system processes (Kiecolt-Glaser & Glaser, 1988). Thus, behavior
represents a potentially important pathway linking stress with the
immune system.
Maier and Watkins (1998) proposed an even closer relationship
between stress and immune function: that the immunological
changes associated with stress were adapted from the immunolog-
ical changes in response to infection. Immunological activation in
mammals results in a syndrome called sickness behavior, which
consists of behavioral changes such as reduction in activity, social
interaction, and sexual activity, as well as increased responsive-
ness to pain, anorexia, and depressed mood. This syndrome is
probably adaptive in that it results in energy conservation at a time
when such energy is best directed toward fighting infection. Maier
and Watkins drew parallels between the behavioral, neuroendo-
crine, and thermoregulatory responses to sickness and stress. The
common thread between the two is the energy mobilization and
redirection that is necessary to fight attackers both within and
Models of Stress, the Immune System, and Health
Conceptualizations of the nature of the relationship between
stress and the immune system have changed over time. Selyes
(1975) finding of thymic involution led to an initial model in
which stress is broadly immunosuppressive. Early human studies
supported this model, reporting that chronic forms of stress were
accompanied by reduced natural killer cell cytotoxicity, sup-
pressed lymphocyte proliferative responses, and blunted humoral
responses to immunization (see S. Cohen, Miller, & Rabin, 2001;
Herbert & Cohen, 1993; Kiecolt-Glaser, Glaser, Gravenstein, Ma-
larkey, & Sheridan, 1996, for reviews). Diminished immune re-
sponses of this nature were assumed to be responsible for the
heightened incidence of infectious and neoplastic diseases found
among chronically stressed individuals (Andersen, Kiecolt-Glaser,
& Glaser, 1994; S. Cohen & Williamson, 1991).
Although the global immunosuppression model enjoyed long
popularity and continues to be influential, the broad decreases in
immune function it predicts would not have been evolutionarily
adaptive in life-threatening circumstances. Dhabhar and McEwen
(1997, 2001) proposed that acute fight-or-flight stressors should
instead cause redistribution of immune cells into the compartments
in which they can act the most quickly and efficiently against
invaders. In a series of experiments with mice, they found that
during acute stress, T cells selectively redistributed into the skin,
where they contributed to enhancement of the immune response. In
contrast, during chronic stress, T cells were shunted away from the
skin, and the immune response to skin test challenge was dimin-
ished (Dhabhar & McEwen, 1997). On the basis of these findings
they proposed a biphasic model in which acute stress enhances,
and chronic stress suppresses, the immune response.
A modification of this model posits that short-term changes in
all components of the immune system (natural and specific) are
unlikely to occur because they would expend too much energy to
be adaptive in life-threatening circumstances. Instead, stress
should shift the balance of the immune response toward activating
natural processes and diminishing specific processes. The premise
underlying this model is that natural immune responses are better
suited to managing the potential complications of life-threatening
situations than specific immune responses because they can unfold
much more rapidly, are subject to fewer inhibitory constraints, and
require less energy to be diverted from other bodily systems that
support the fight-or-flight response (Dopp, Miller, Myers, & Fa-
hey, 2000; Sapolsky, 1998).
Even with this modification of the biphasic model, neither it nor
the global immunosuppression model sufficiently explains find-
ings that link chronic stress with both disease outcomes associated
with inadequate immunity (infectious and neoplastic disease) and
disease outcomes associated with excessive immune activity (al-
lergic and autoimmune disease). To resolve this paradox, some
researchers have chosen to focus on how chronic stress might shift
the balance of the immune response. The most well-known of
these models hypothesizes that chronic stress elicits simultaneous
enhancement and suppression of the immune response by altering
patterns of cytokine secretion (Marshall et al., 1998). Th1 cyto-
kines, which activate cellular immunity to provide defense against
many kinds of infection and some kinds of neoplastic disease, are
suppressed. This suppression has permissive effects on production
of Th2 cytokines, which activate humoral immunity and exacer-
bate allergy and many kinds of autoimmune disease. This shift can
occur via the effects of stress hormones such as cortisol (Chiap-
pelli, Manfrini, Franceschi, Cossarizza, & Black, 1994). Th1-to-
Th2 shift changes the balance of the immune response without
necessarily changing the overall level of activation or function
within the system. Because a diminished Th1-mediated cellular
immune response could increase vulnerability to infectious and
neoplastic disease, and an enhanced Th-2 mediated humoral im-
mune response could increase vulnerability to autoimmune and
allergic diseases, this cytokine shift model also is able to reconcile
patterns of stress-related immune change with patterns of stress-
related disease outcomes (Marshall et al., 1998).
Who Is Vulnerable to Stress-Induced Immune Changes?
If the stress response in the immune system evolved, a healthy
organism should not be adversely affected by activation of this
response because such an effect would likely have been selected
against. Although there is direct evidence that stress-related im-
munosuppression can increase vulnerability to disease in animals
(e.g., Ben Eliyahu, Shakhar, Page, Stefanski, & Shakhar, 2000;
Quan et al., 2001; Shavit et al., 1985; Sheridan et al., 1998), there
is little or no evidence linking stress-related immune change in
healthy humans to disease vulnerability. Even large stress-induced
immune changes can have small clinical consequences because of
the redundancy of the immune systems components or because
they do not persist for a sufficient duration to enhance disease
susceptibility. In short, the immune system is remarkably flexible
and capable of substantial change without compromising an oth-
erwise healthy host.
However, the flexibility of the immune system can be compro-
mised by age and disease. As humans age, the immune system
becomes senescent (Boucher et al., 1998; Wikby, Johansson, Fer-
guson, & Olsson, 1994). As a consequence, older adults are less
able to respond to vaccines and mount cellular immune responses,
which in turn may contribute to early mortality (Ferguson, Wikby,
Maxson, Olsson, & Johansson, 1995; Wayne, Rhyne, Garry, &
Goodwin, 1990). The decreased ability of the immune system to
respond to stimulation is one indicator of its loss of flexibility.
Loss of self-regulation is also characteristic of disease states. In
autoimmune disease, for example, the immune system treats self-
tissue as an invader, attacking it and causing pathology such as
multiple sclerosis, rheumatoid arthritis, Crohns disease, and lu-
pus. Immune reactions can also be exaggerated and pathological,
as in asthma, and suggest loss of self-regulation. Finally, infection
with HIV progressively incapacitates T-helper cells, leading to loss
of the regulation usually provided by these cells. Although each of
these diseases has distinct clinical consequences, the change in the
immune system from flexible and balanced to inflexible and un-
balanced suggests increased vulnerability to stress-related immune
dysregulation; furthermore, dysregulation in the presence of dis-
ease may have clinical consequences (e.g., Bower, Kemeny, Tay-
lor, & Fahey, 1998).
The Present Analysis
We performed a meta-analysis of published results linking stress
and the immune system. We feel that this area is in particular need
of a quantitative review because of the methodological nature of
most studies in this area. For practical and economic reasons,
many psychoneuroimmunology studies have a relatively small
sample size, creating the possibility of Type II error. Furthermore,
many studies examine a broad range of immunological parameters,
creating the possibility of Type I error. A quantitative review, of
which meta-analysis is the best example, can better distinguish
reliable effects from those arising from both Type I and Type II
error than can a qualitative review.
We combined studies in such a way as to test the models of
stress and immune change reviewed above. First, we examined
each stressor type separately, yielding separate effects for stressors
of different duration and trajectory. Second, we examined both
healthy and medical populations, allowing comparison of the ef-
fects of stress on resilient and vulnerable populations; along the
same lines, we also examined the effects of age. Finally, we
examined all immune parameters separately so that patterns of
response (e.g., global immunosuppression vs. cytokine shift)
would be clearer.
Article Identification
Articles for the meta-analysis were identified through computerized
literature searches and searches of reference lists. MEDLINE and
PsycINFO were searched for the years 19602001. Following the example
of Herbert and Cohen (1993), we used the terms stress, hassles, and life
events in combination with the term immune to search both databases. The
reference lists of 11 review articles on stress and the immune system
(Benschop, Geenen, et al., 1998; Biondi, 2001; Cacioppo, 1994; S. Cohen
& Herbert, 1996; S. Cohen et al., 2001; Herbert & Cohen, 1993; Kiecolt-
Glaser, Cacioppo, Malarkey, & Glaser, 1992; Kiecolt-Glaser, McGuire,
Robles, & Glaser, 2002; Maier, Watkins, & Fleshner, 1994; OLeary,
1990; Zorrilla et al., 2001) were then searched to identify additional
We selected only articles that met a number of inclusion criteria. The
first criterion was that the work had to include a measure of stress. This
criterion could be met if a sample experiencing a stressor was compared
with an unstressed control group, if a sample experiencing a stressor was
compared with itself at a baseline that could reasonably be considered low
stress, or if differing degrees of stress in a sample were assessed with an
explicit measure of stress. This criterion was not met if, for example,
anxietyan affective statewas used as a proxy for stress, or it seemed
likely that a baseline assessment occurred during periods of significant
stress. The second criterion was that the stressor had to be psychosocial.
Stressors that included a significant physical element such as pain, cold, or
physical exhaustion were eliminated (e.g., Antarctic isolation, space flight,
military training). The third criterion was that the work had to include a
measure of the immune system. This criterion was met by any enumerative
or functional in vitro or in vivo immune assay. However, clinical disease
outcomes such as HIV progression or rhinovirus infection did not meet this
criterion. Finally, we eliminated articles from which a meaningful effect
size could not be abstracted. For example, when between- and within-
subjects observations were treated as independent, the reported effect was
likely to be inflated. In a few cases, effects of stress and clinical status were
confoundedthat is, a stressed clinical group was compared with an
unstressed healthy groupand hence these studies were excluded from the
Stressor Classification
We coded stressors in the articles into five classes: acute time-limited,
brief naturalistic, event sequence, chronic, and distant. The most difficult
distinctions among event sequence, chronic, and distant stressors were
based on temporal and qualitative considerations. Event sequences in-
cluded discrete stressors occurring 1 year or less before immune assess-
ment and could be of any severity. These were most often normative
stressors such as bereavement. Chronic stressors were ongoing stressors
such as caregiving and disability. Distant stressors were severe, traumatic
events that could meet the stressor criterion for posttraumatic stress disor-
der (American Psychiatric Association, 1994), such as combat exposure or
abuse, and had happened more than 1 year before immune assessment.
Most stressors in this category occurred 5 to 10 years before immune
assessment. Disagreements in stressor classification were resolved by
consensus. Subgroups for moderator analyses were similarly decided.
The Meta-Analysis
Overview of procedures. Meta-analysis is a tool for synthesizing re-
search findings. It proceeds in two phases. In the first, effect sizes are
computed for each study. An effect size represents the magnitude of the
relationship between two variables, independent of sample size. In this
context it can be viewed as a measure of how much two groups, one
experiencing a stressor and the other not, differ on a specific immune
outcome. In the second phase, effect sizes from individual studies are
combined to arrive at an aggregate effect size for each immune outcome of
We used Pearsons r as the effect size metric in this meta-analysis. Effect
sizes for individual studies were computed using descriptive statistics
presented in the original published reports. When these statistics were not
available, we requested them from authors. This strategy was successful in
most circumstances. To compute Pearsons r from descriptive statistics in
between-subjects designs, we subtracted the control group mean from the
stressed group mean and divided this value by the pooled sample standard
deviation. The value that emerged from this computation, known as Co-
hens d, was then converted into a Pearsons r by taking the square root of
the quantity d
4). (See Rosenthal, 1994.) To compute Pearsons r
from descriptive statistics in within-subjects designs, we subtracted the
group mean at baseline from the group mean during stress and divided this
quantity by the sample standard deviation at baseline. This d value was
converted into a Pearsons r by taking the square root of the quantity
4). In cases in which descriptive statistics were not available,
Pearsons r was computed from inferential statistics using standard formu-
lae (Rosenthal, 1994). These formulae had to be modified slightly for
studies that used within-subjects designs because effect sizes are system-
atically overestimated when they are calculated from repeated measures
test statistics (Dunlap, Cortina, Vaslow, & Burke, 1996). In these situations
we derived effect size estimates using the formula d t
[2 (1 r)]
where t
corresponds to the value of the t statistic for correlated measures,
and r corresponds to the value of the correlation between outcome mea-
sures at pretest and posttest (Dunlap et al., 1996). Because very few studies
reported the value of r, we used a value of .60 to compute effect sizes in
this meta-analysis. This represents the average correlation between pre-
stress and poststress measures of immune function in a series of studies
performed in our laboratories. To ensure that the meta-analytic findings
were robust to variations in r, we conducted follow-up analyses using r
values ranging from .45 to .75. Very similar findings emerged from these
analyses, suggesting that the values we present below are reliable estimates
of effect size. If anything, they are probably conservative estimates, be-
cause the prepost correlation between immune measures often is substan-
tially lower than .60.
The effect size estimates from individual studies were subsequently
aggregated using random-effects models with the software program Com-
prehensive Meta-Analysis (Borenstein & Rothstein, 1999). The random-
effects model views each study in a meta-analysis as a random observation
drawn from a universe of potential investigations. As such, it assumes that
the magnitude of the relationship between stress and the immune system
differs across studies as a result of random variance associated with
sampling error and differences across individuals in the processes of
interest. Because of these assumptions, random-effects models not only
permit one to draw inferences about studies that have been done but also
to generalize to studies that might be done in the future (Raudenbush, 1994;
Shadish & Haddock, 1994). It also bears noting that in the population of
studies on stress and immunity there is likely to be a fair amount of
nonrandom variance, as researchers who examine ostensibly similar phe-
nomena may still differ in terms of the samples they recruit, the operational
definition of stress they use, and the laboratory methods they utilize to
assess a specific immune process.
Separate random-effects models were computed for each immune out-
come included in the meta-analysis. Prior to computing the random-effects
model, r values derived from each study were z-transformed by the
software program, as recommended by Shadish and Haddock (1994), to
stabilize variance. The z values were later back-transformed into r values
to facilitate interpretation of the meta-analytic findings. In the end, each
random-effects model yielded an aggregate weighted effect size r, which
can be interpreted the same way as a correlation coefficient, ranging in
value from 1.00 to 1.00. Each r statistic was weighted before aggregation
by multiplying its value by the inverse of its variance; this procedure
enabled larger studies to contribute to effect size estimates to a greater
extent than smaller ones. Weighting effect sizes is important because larger
studies provide more accurate estimates of true population parameters
(Shadish & Haddock, 1994). After each aggregate effect size had been
derived, we computed 95% confidence intervals around it, assessed
whether it was statistically significant, and computed a heterogeneity
coefficient to determine whether the studies contributing to it had yielded
consistent findings. Following convention, aggregate effect sizes were
considered statistically different from zero when (a) their corresponding z
value was greater than 1.96 and (b) the 95% confidence intervals around
them did not include the value zero (Rosenthal, 1991; Shadish & Haddock,
To determine whether the studies contributing to each aggregate effect
size shared a common population value, we computed the heterogeneity
statistic Q (Shadish & Haddock, 1994). This statistic is chi-square distrib-
uted with k 1 degrees of freedom, where k represents the number of
independent effect sizes included. When a statistically significant hetero-
geneity test emerged, we searched for moderators (characteristics of the
participants, stressful experience, or measurement strategy) that could
explain the variability across studies. The first step in this process involved
estimating correlations between participant characteristics (e.g., mean age,
percentage female) and immune effects to examine whether the strength of
effects varied according to demographics. When it was possible to do so,
we then stratified the studies according to characteristics of the stressful
experience (e.g., duration, quality) or the measurement strategy (e.g.,
interview, checklist), and computed separate random-effects analyses for
each subgroup.
Handling missing data. Occasionally authors of studies failed to report
the descriptive or inferential statistics needed to compute an effect size. In
some of these cases, the authors noted that there was a significant differ-
ence between a stressed and control group. When this occurred, we
computed effect sizes assuming that p values were equivalent to .05. This
represents a conservative approach because the actual p values were
probably smaller. In other cases, the authors noted that a stressed and
control group did not differ with respect to an immune outcome, but failed
to provide any further statistical information. When this occurred, we
computed effect sizes assuming that there was no difference at all between
the groups (r .00). Because there is seldom no difference at all between
two groups, this also represents a conservative strategy. Imputation was
used in less than 7% of cases.
Handling dependent data. The validity of a meta-analysis rests on the
assumption that each value contributing an aggregate effect size is statis-
tically independent of the others (Rosenthal, 1991). We devised a number
of strategies to avoid violating this independence assumption. First, in
studies that assessed stimulated-lymphocyte proliferation at multiple mi-
togen dosages, we computed the average effect size across mitogen dos-
ages, and we used this value to derive aggregate indices. We used an
analogous strategy for studies that assessed natural killer cell cytotoxicity
at multiple effector:target cell ratios. Second, in studies that utilized de-
signs in which multiple laboratory stressors were compared with a control
condition, the average effect size across stressor conditions was computed
and later used to derive aggregate indices. Because this averaging proce-
dure in most cases yielded an effect size that was smaller than that of the
most potent stressor, we also computed meta-analyses using the larger of
the effect sizes from each study rather than the average. Doing so did not
alter any of the substantive findings we report. Third, in studies in which
immune outcomes were assessed on multiple occasions during a stressful
experience, the average effect size across occasions was used to derive
aggregate indices. Note that we did not conduct meta-analyses of recovery
effects, that is, immune values after a stressor had ended. Although such an
analysis would answer interesting questions about the stress-recovery
process, there were not enough studies that included similar immune
outcomes assessed at similar time points after stress to permit a complete
analysis. Fourth, because some data were published in more than one
outlet, we contacted authors of multiple publications to determine sample
independence or dependence.
Preliminary Findings
The meta-analysis is based on effect sizes derived from 293
independent studies. These studies were reported in 319 separate
articles in peer-reviewed scientific journals (see Table 2). A total
of 18,941 individuals participated in these studies. Their mean age
was 34.8 years (SD 15.9). Although the studies collectively
included a broad range of age groups (range 578 years), most
focused heavily on younger adults. More than half of the studies
(51.3%) had a mean age under 30.0 years, and more than four
fifths (84.8%) had a mean age under 55.0 years. Slightly more than
two thirds of the studies (68.5%) included women; in the average
study almost half (42.8%) of the participants were female. The vast
majority of studies (84.8%) focused on medically healthy adults.
Of those that included medical populations, most focused on
HIV/AIDS (k 18; 38.3%), arthritis (k 6; 12.8%), cancer (k
5; 10.6%), or asthma (k 4; 8.5%).
With respect to the kinds of stressors examined by studies in the
meta-analysis, the most commonly utilized models were acute
laboratory challenges (k 85; 29.0%) and brief naturalistic stres-
sors (k 63; 21.5%). Stressful event sequences (k 30; 10.2%),
chronic stressors (k 23; 7.8%), and distant traumatic experiences
(k 9; 3.1%) were explored less frequently. More than a quarter
of the studies in the meta-analysis modeled the stress process by
administering nonspecific life-event checklists (k 53; 18.1%)
and/or global perceived stress measures (k 21; 7.1%) to partic-
ipants. A small minority of studies examined whether reports of
perceived stress or intrusive memories were associated with the
extent of immune dysregulation within populations who had suf-
fered a specific traumatic experience (k 9; 3.1%).
The studies in the meta-analysis examined 292 distinct immune
system outcomes. A minority of these outcomes were assessed in
three or more studies (k 87; 30.0%), and as such, they are the
focus of the meta-analyses we present in the rest of this article (see
Table 1). The most commonly assessed enumerative outcomes
were counts of T-helper lymphocytes (k 90; 30.7%), T-cytotoxic
lymphocytes (k 81; 27.6%), natural killer cells (k 67; 22.9%),
and total lymphocytes (k 52; 17.7%). The most commonly
assessed functional outcomes were natural killer cell cytotoxicity
(k 94; 32.1%) and lymphocyte proliferation stimulated by the
mitogens phytohemagglutinin (PHA; k 65; 22.2%), concanava-
lin A (ConA; k 39; 13.3%), and pokeweed mitogen (PWM; k
26; 8.9%).
Interpreting the Meta-Analytic Findings
Table 1 lists the immune parameters analyzed with the arm of
the immune system to which they belong (natural or specific) and,
briefly, their function. Where relevant, cell surface markers used to
identify classes of immunocytes in flow cytometry are given. For
example, the cell surface marker CD19 is used to identify B
lymphocytes. Recall that different models of stress and the im-
mune system posit differential effects of stress on subsets of the
immune systemfor example, natural versus specific immunity or
cellular (Th1) versus humoral (Th2) immunity. Table 1 acts as a
guide for interpreting the pattern of results in light of these models.
In the following sections we describe the meta-analytic results
for each stressor category. A useful rule of thumb for judging
effect sizes is to consider values of .10, .30, and .50 as correspond-
ing to small, medium, and large effects, respectively (J. Cohen &
Cohen, 1983); more generally, the aggregate effect size r can be
interpreted in the same fashion as a correlation, with values rang-
ing from 1.00 to 1.00. Positive values indicate that the presence
of a stressor increases a particular immune parameter relative to
some baseline (or control) condition. We should caution the reader
that in some analyses, our statistics are derived from as few as
three independent studies. Although meta-analyses of small num-
bers of studies do not pose any major statistical problems, it is
important to remember that they have limited power to detect
statistically significant effect sizes. What a meta-analysis can
accurately provide in these instances, however, is an estimate of
how much and what direction a given stressors presence influ-
ences a specific immune outcome (i.e., an effect size estimate).
Meta-Analytic Results for the Effects of Stressors
Acute time-limited stressors. Acute time-limited stressors in-
cluded primarily experimental manipulations of stressful experi-
ences, such as public speaking and mental arithmetic, that lasted
between 5 and 100 min. Reliable effects on the immune system
included increases in immune parameters, especially natural im-
The proportion of student samples varied across stressor categories.
Nearly all of the studies of brief naturalistic stressors used student samples
(k 60; 95.2%) because these stressors were predominantly examinations.
Student samples were also used in a large minority of acute time-limited
stressor studies (k 31; 40.5%) but constituted a small minority of
samples used in studies of life-event checklists (k 8; 14.0%) and studies
of event sequences (k 2; 6.6%), and student samples were not used in
studies of chronic stressors or stress appraisals and intrusions. These are
rough estimates, as some studies did not specify whether young adult
samples were drawn from a student population.
Table 2
Studies Used in the Meta-Analysis by Type of Stressor
Acute time-limited Brief naturalistic Event sequence Chronic Distant Life event Stress appraisal
Ackerman et al., 1996, 1998 Baker et al., 1984, 1985 Antoni et al., 1990 Bauer et al., 2000 Boscarino &
Chang, 1999
Abdeljaber et al., 1994 Andersen et al., 1998
Aloe et al., 1994 Bisselli et al., 1993 Aragona et al., 1996 Dekaris et al., 1993 Inoue-Sakurai et
al., 2000
Benschop, Jabaaij, et
al., 1998
de Gucht et al., 1999
Arber et al., 1992 Borella et al., 1999 Arnetz et al., 1991 Dimsdale et al., 1994 Laudenslager et
al., 1998
Biondo et al., 1994 Halim et al., 2000
Bachen et al., 1992, 1995 Bosch et al., 1996 Bartrop et al., 1977 Drummond & Hewson-
Bower 1997
Mosnaim et al.,
Birmaher et al., 1994 Hall et al., 1998
Barger et al., 2000 Boyce et al., 1993,
Beem et al., 1999 Esterling et al., 1994,
Spivak et al., 1997 Byrnes et al., 1998 Ironson et al., 1997
Beck et al., 2000 Davidson et al., 1999 Cruess et al., 2000 Gennaro, Fehder,
Cnaan, et al., 1997
Watson et al.,
F. Cohen et al., 1999 Kawakami et al.,
Benschop, Brosschot, et al., 1994 Deinzer & Schu¨ller,
Delahanty et al., 1997 Gennaro, Fehder,
Nuamah, et al., 1997
Wilson et al., 1999 Evans et al., 1995 Kawamura et al.,
Benschop et al., 1995 Deinzer et al., 2000 Dworsky et al., 1989 Glaser & Kiecolt-
Glaser, 1997
Gomez et al., 1994 Kusaka et al., 1992
Benschop, Jacobs, et al., 1996 Dobbin et al., 1991 Goodkin et al., 1996 Glaser et al., 1998,
2000, 2001
Gonza´lez-Quijano et
al., 1998
Lerman et al., 1999
Benschop, Nieuwenhuis, et al., 1994 Fittschen et al., 1990 Ironson et al., 1990,
Irwin et al., 1991, 1997 Goodkin, Blaney, et
al., 1992
Maes et al., 1999
Bongartz et al., 1987 Gilbert et al., 1996 Irwin et al., 1986, 1988 Kiecolt-Glaser et al.,
1991, 1995, 1996
Goodkin, Fuchs, et al.,
Marsland et al., 2001
Bosch et al., 2001 Glaser, Kiecolt-Glaser,
Speicher, & Holliday,
Irwin, Daniels, Smith,
et al., 1987
Kiecolt-Glaser, Glaser,
et al., 1987
Graham et al., 1988 McClelland et al.,
Breznitz et al., 1998 Glaser, Kiecolt-Glaser,
Stout, et al., 1985
Irwin, Daniels, &
Weiner, 1987
Lauc et al., 1998 Howland et al., 2000 McDade, 2001
Bristow et al., 1997 Glaser et al., 1986,
1987, 1990, 1991,
1993, 1994, 1996,
Kiecolt-Glaser, Fisher,
et al., 1987
Lutgendorf et al., 1999 Irwin, Daniels, Bloom,
et al., 1987
Nakamura et al.,
Brosschot et al., 1991, 1992, 1994 Gruzelier et al., 2001 Kiecolt-Glaser et al.,
McKinnon et al., 1989 Irwin et al., 1990 Nakata et al., 2000
Burleson et al., 1998 Guidi et al., 1999 Lane et al., 1983 Mills et al., 1997, 1999 Jabaaij et al., 1993,
Scanlan et al., 1998
Cacioppo et al., 1995, 1998 Halvorsen & Vassend,
Lutgendorf et al., 1997,
Nakano et al., 1998 Kemeny et al., 1989 Schaubroeck et al.,
Caggiula et al., 1995 Jemmott & Magloire,
McClelland et al., 1991 Pariante et al., 1997 Kessler et al., 1991 So¨derfeldt et al.,
Caudell & Gallucci, 1995 Jemmott et al., 1983 Nagabhushan et al.,
Sabioncello et al., 2000 Kubitz et al., 1986 Song et al., 1999
Chi et al., 1993 Kamei et al., 1997,
Pettingale et al., 1994 Scanlan et al., 1998 Leserman et al., 1997 Theorell et al., 1990
S. Cohen et al., 2000 Kang et al., 1996, 1997,
Solomon et al., 1997 Schlesinger & Yodfat,
Levy et al., 1989 Tjemsland et al.,
Cruse et al., 1993 Kiecolt-Glaser et al.,
1986, 1993, 1994,
1997, 2001
Spratt & Denney, 1991 Stowell et al., 2001 Liang et al., 1997 Værnes et al., 1991
Delahanty et al., 1996, 1998, 2000 Kugler et al., 1996 Udelman, 1982 Vedhara et al., 1999 B. S. Linn et al., 1988 Vitaliano et al., 1998
Dopp et al., 2000 Lacey et al., 2000 Weiss et al., 1996 Vitaliano et al., 1998 M. W. Linn et al.,
1983, 1984
Wilcox et al., 2000
Table 2 (continued)
Acute time-limited Brief naturalistic Event sequence Chronic Distant Life event Stress appraisal
Dugue´ et al., 1993 Lowe et al., 2000 Zisook et al., 1994 Martin & Dobbin 1988
Endresen et al., 1991 Maes et al., 1997, 1998,
McClelland et al.,
Geenen et al., 1998 Marchesi et al., 1989 McDade et al., 2000
Gerits & DeBrabander, 1999 Marshall et al., 1998 McIntosh et al., 1993
Gerritsen et al., 1996 Marucha et al., 1998 McNaughton et al.,
Goebel & Mills, 2000 McClelland et al., 1985 Miletic et al., 1996
Goebel et al., 2000 Ockenfels et al., 1994 H. Moss et al., 1998
Herbert et al., 1994 Paik et al., 2000 R. B. Moss et al.,
Jacobs et al., 2001 Segerstrom, 2001 Mulder et al., 1995
Jern et al., 1989 Segerstrom et al., 1998 Patterson et al., 1995
Johnson et al., 1996 Song et al., 1999 Perry et al., 1992
Kamei et al., 1998 Uchakin et al., 2001 Petrey et al., 1991
Kang & Fox, 2000 Van Rood et al., 1995 Rabkin et al., 1991
Landmann et al., 1984 Vassend & Halvorsen,
Ravindran et al., 1996
Larson et al., 2001 Vedhara & Nott, 1996 Schlesinger & Yodfat,
Manuck et al., 1991 Wadee et al., 2001 Shea et al., 1991
Marsland et al., 1995, 1997, 2001 Whitehouse et al., 1996 Thomason et al., 1996
Matthews et al., 1995 Wolf et al., 1994 Thornton et al., 2000
McDonald & Yagi, 1960 Workman & La Via,
Vialettes et al., 1989
Miller, Dopp, et al., 1999 Zautra et al., 1989
Mills & Dimsdale, 1996
Mills, Berry, et al., 1995
Mills et al., 1996, 1998
Mills, Haeri, & Dimsdale, 1995
Mills, Ziegler, et al., 1995
Moyna et al., 1999
Naliboff et al., 1991
Naliboff, Solomon, Gilmore,
Benton, et al., 1995
Naliboff, Solomon, Gilmore, Fahey,
et al., 1995
Neumann & Chi, 1999
Neumann et al., 1998, 2000
Ohira et al., 1999
Olff et al., 1995
Pawlak et al., 1999, 2000
Pehlivanog˘lu et al., 2001
Peters et al., 1999
Pike et al., 1997
Redwine et al., 2001
Ring et al., 2000
Rohleder et al., 2001
Sauer et al., 1995
(table continues)
munity. The most robust effect of this kind of experience was a
marked increase in the number of natural killer cells (r .43) and
large granular lymphocytes (r .53) in peripheral blood (see
Table 3). This effect is consistent with the view that acute stressors
cause immune cells to redistribute into the compartments in which
they will be most effective (Dhabhar & McEwen, 1997). However,
other types of lymphocytes did not show robust redistribution
effects: B cells and T-helper cells showed very little change (rs
.07 and .01, respectively), and this change was not statistically
significant across studies. T-cytotoxic lymphocytes did tend to
increase reliably in peripheral blood, though to a lesser degree than
their natural immunity counterparts (r .20); this increase drove
a reliable decline in the T-helper:T-cytotoxic ratio (r ⫽⫺.23).
However, natural killer cells as well as T-cytotoxic cells can
express CD8, the marker most often used to define the latter
population. Because some studies did not use the T cell receptor
(CD3) to differentiate between CD3CD8 natural killer cells
and CD3CD8 T-cytotoxic cells, it is possible that the effect for
T-cytotoxic cells is actually being driven by natural killer cells
(Benschop, Rodriguez-Feuerhahn, & Schedlowski, 1996).
The results for cell percentages roughly parallel those for num-
ber. However, the percentage data are harder to interpret because
any given parameter is linearly dependent on the other parameters:
For example, the enumerative data suggest that the decrease in
percentage T-helper cells (r ⫽⫺.24) is probably an artifact of the
increases in percentage natural killer cells (r .24) and percentage
T-cytotoxic cells (r .09).
Another effect that may be considered a redistribution effect is
the significant increase in secretory IgA in saliva (r .22). The
time frame of these acute stressors is too short for the synthesis of
a significant amount of new antibody; therefore, this increase is
probably due to release of already-synthesized antibody from
plasma cells and increased translocation of antibody across the
epithelium and into saliva (Bosch, Ring, de Geus, Veerman, &
Amerongen, 2002). This effect therefore represents relocation,
albeit of an immune protein rather than an immune cell.
There were also a number of functional effects. First, natural
killer cell cytotoxicity significantly increased with acute stressors
(r .30), but only when the concomitant increase in proportion of
natural killer cells in the effector mix was not removed statisti-
cally. When examined on a per-cell basis, cytotoxicity did not
significantly increase (r .12). One could, therefore, consider the
increase in cytotoxicity a methodological artifact of the definition
of effector in effector:target ratios. However, to the degree that one
is interested in the general cytotoxic potential of the contents of
peripheral blood rather than that of a specific natural killer cell, the
uncorrected value is more illustrative. Second, mitogen-stimulated
proliferative responses decreased significantly. Again, this could
be a methodological artifact of the mix of cells in the assay.
However, the proportion of total T and B cells, which are respon-
sible for the proliferative response to PWM and ConA, did not
decrease as reliably or as much as did the proliferative response
(rs ⫽⫺.05 to .11 vs. .10 to .17), suggesting that acute
stressors do decrease this function of specific immunity. Finally,
the production of two cytokines, IL-6 and IFN
, was increased
significantly following acute stress (rs .28 and .21,
The data for acute stressors, therefore, support an upregulation
of natural immunity, as reflected by increased number of natural
Table 2 (continued)
Acute time-limited Brief naturalistic Event sequence Chronic Distant Life event Stress appraisal
Schedlowski, Jacobs, Alker, et al.,
Schedlowski, Jacobs, & Stratmann,
et al., 1993
Schmid-Ott et al., 1998, 2001
Sgoutas-Emch et al., 1994
Sieber et al., 1992
Spangler, 1997
Stone et al., 1993
Tsopanakis & Tsopanakis, 1998
Uchino et al., 1995
Van der Pompe et al., 1997, 1998
Van der Voort et al., 2000
Wang et al., 1998
Weisse et al., 1990
Willemsen et al., 1998
Winzer et al., 1999
Zakowski, 1995
Zakowski et al., 1992, 1994
Zeier et al., 1996
killer cells in peripheral blood, and potential downregulation of
specific immunity, as reflected by decreased proliferative re-
sponses. Other indicators of upregulated natural immunity include
increased neutrophil numbers in peripheral blood (r .30), in-
creased production of a proinflammatory cytokine (IL-6), and
increased production of a cytokine that potently stimulates mac-
rophages and natural killer cells as well as T cells (IFN
). The only
exception to this pattern was the increased secretion of IgA anti-
body, which is a product of the specific immune response. An
interesting question for future research is whether this effect is part
of a larger nonspecific protein release in the oral cavity in response
to acute stress (cf. Bosch et al., 2002).
It bears noting that a number of the findings presented in Table
3 are accompanied by significant heterogeneity statistics. To iden-
tify moderating variables that might explain some of this hetero-
geneity, we examined whether effect sizes varied according to
demographic characteristics of the sample (mean age and percent-
age female) or features of the acute challenge (its duration and
nature). Neither of the demographic characteristics showed a con-
sistent relationship with immune outcomes. Although these find-
ings suggest that acute time-limited stressors elicit a similar pattern
of immune response for men and women across the life span, this
conclusion needs to be viewed somewhat cautiously given the
narrow range of ages found in these studies. We also did not find
Table 3
Meta-Analysis of Immune Responses to Acute Time-Limited Stress in Healthy Participants
Immune marker kN rSE
95% CI pQ
Leukocyte subset count
Leukocytes 25 1,129 .17 .04 .10, .25 .001 34.61
Granulocytes 12 397 .08 .06 .04, .19 .18 31.77
Neutrophils 3 86 .30 .12 .08, .50 .009 2.13
Eosinophils 3 81 .10 .16 .39, .21 .53 2.99
Monocytes 15 590 .04 .05 .05, .13 .43 15.43
Lymphocytes 24 828 .18 .05 .09, .26 .001 31.77
T lymphocytes 33 1,452 .07 .03 .01, .12 .01 25.48
T-helper lymphocytes 42 1,678 .01 .03 .05, .05 .86 23.72
T-cytotoxic lymphocytes 42 1,678 .20 .03 .15, .25 .001 34.05
T-helper:T-cytotoxic ratio 19 920 .23 .10 .40, .04 .02 17.98
Naive T lymphocytes 3 241 .09 .11 .29, .12 .41 2.46
B lymphocytes 18 739 .07 .04 .14, .01 .08 16.23
Activated B lymphocytes 4 60 .15 .14 .40, .14 .31 0.48
Natural killer cells 41 1,635 .43 .06 .33, .51 .001 172.75***
Large granular lymphocytes 8 362 .53 .30 .00, .83 .05 165.64***
Leukocyte subset percentage
Granulocytes 5 295 .13 .10 .31, .07 .20 7.24
Neutrophils 5 217 .04 .07 .10, .18 .56 3.75
Monocytes 7 277 .06 .09 .12, .23 .55 10.82
Lymphocytes 7 350 .06 .06 .05, .16 .30 1.34
T lymphocytes 10 497 .05 .09 .22, .13 .62 28.05***
T-helper lymphocytes 14 642 .24 .04 .31, .16 .001 13.61
T-cytotoxic lymphocytes 15 692 .09 .04 .01, .16 .03 9.28
B lymphocytes 5 248 .11 .07 .24, .02 .09 1.46
Natural killer cells 15 693 .24 .11 .03, .42 .02 90.19***
Total immunoglobulins
Serum IgA 4 91 .12 .11 .10, .33 .30 0.95
Serum IgM 3 67 .14 .13 .12, .37 .30 0.61
Secretory IgA secretion rate 6 293 .22 .08 .06, .37 .008 6.92
Secretory IgA concentration 8 337 .22 .09 .05, .38 .01 13.05
Basal cytokine levels
489.01 .11 .23, .21 .91 0.25
Natural killer cell function
Natural killer cell cytotoxicity 37 1,398 .30 .05 .20, .39 .001 108.85***
Per-cell cytotoxicity 8 287 .12 .11 .09, .32 .26 18.12*
Lymphocyte proliferation
Proliferation to ConA 17 706 .17 .04 .24, .09 .001 14.12
Proliferation to PHA 26 1,120 .17 .04 .23, .10 .001 35.36
Proliferation to PWM 10 480 .10 .05 .19, .01 .03 5.84
Cytokine production
3 78 .01 .12 .23, .23 .98 5.78
Interleukin-4 3 136 .19 .11 .39, .03 .08 2.38
Interleukin-6 3 143 .28 .09 .13, .44 .001 12.84**
3 96 .21 .11 .01, .40 .05 0.24
Note. CI confidence interval; IgA immunoglobulin A; IgM immunoglobulin M; ConA concanavalin
A; PHA phytohemagglutinin; PWM pokeweed mitogen.
* p .05. ** p .01. *** p .001.
a consistent pattern of relationships between features of the acute
challenge and immune outcomes. Acute stressors elicited similar
patterns of immune change across a wide spectrum of durations
ranging from 5 though 100 min and irrespective of whether they
involved social (e.g., public speaking), cognitive (e.g., mental
arithmetic), or experiential (e.g., parachute jumping) forms of
stressful experience.
Brief naturalistic stressors. Table 4 presents the meta-analysis
of brief naturalistic stressors for medically healthy adults. The vast
majority of these stressors (k 60; 95.2%) involved students
facing academic examinations. In contrast to the acute time-limited
stressors, examination stress did not markedly affect the number or
percentage of cells in peripheral blood. Instead, the largest effects
were on functional parameters, particularly changes in cytokine
production that indicate a shift away from cellular immunity (Th1)
and toward humoral immunity (Th2). Brief stressors reliably
changed the profile of cytokine production via a decrease in a
Th1-type cytokine, IFN
(r ⫽⫺.30), which stimulates natural and
cellular immune functions, and increases in the Th2-type cytokines
IL-6 (r .26), which stimulates natural and humoral immune
functions, and IL-10 (r .41), which inhibits Th1 cytokine pro-
duction. Note that IFN
and IL-6 share the property of stimulating
natural immunity but differentially stimulate cytotoxic versus in-
flammatory effector mechanisms. Their dissociation after brief
Table 4
Meta-Analysis of Immune Responses to Brief Naturalistic Stress in Healthy Participants
Immune marker kN rSE
95% CI pQ
Leukocyte subset count
Leukocytes 9 249 .20 .07 .07, .32 .002 12.95
Granulocytes 3 56 .01 .15 .27, .29 .93 0.01
Neutrophils 5 103 .11 .11 .07, .34 .18 2.33
Monocytes 6 120 .06 .10 .13, .25 .52 3.90
Lymphocytes 9 236 .06 .08 .10, .23 .46 10.46
T lymphocytes 5 110 .03 .10 .18, .22 .81 0.05
T-helper lymphocytes 7 197 .06 .08 .09, .21 .43 1.08
T-cytotoxic lymphocytes 6 185 .05 .08 .10, .20 .50 1.74
T-helper:T-cytotoxic ratio 12 351 .01 .07 .11, .14 .84 13.68
B lymphocytes 5 126 .48 .56 .51, .92 .35 99.48***
Natural killer cells 5 103 .15 .11 .35, .06 .16 2.06
Leukocyte subset percentage
Monocytes 4 98 .11 .11 .10, .32 .30 2.33
Lymphocytes 3 97 .13 .11 .33, .08 .23 2.05
T lymphocytes 5 160 .16 .18 .47, .19 .36 13.67**
T-helper lymphocytes 11 350 .11 .10 .29, .09 .28 26.56**
T-cytotoxic lymphocytes 12 362 .03 .06 .14, .08 .60 8.84
B lymphocytes 3 121 .07 .53 .74, .80 .89 42.48***
Natural killer cells 5 163 .02 .19 .38, .35 .93 18.20**
Total immunoglobulins
Serum IgA 6 243 .11 .07 .02, .24 .10 1.28
Serum IgG 7 290 .06 .06 .06, .17 .37 2.54
Serum IgM 7 290 .02 .10 .17, .21 .83 13.41*
Secretory IgA rate 4 139 .09 .33 .50, .63 .78 31.31***
Secretory IgA concentration 9 350 .19 .18 .20, .46 .40 66.97***
Specific immunoglobulin
Epstein-Barr virus 7 359 .20 .04 .10, .30 .001 6.56
Herpes simplex virus 4 225 .18 .08 .02, .34 .08 4.97
Complement molecule
C3 3 116 .16 .10 .34, .03 .09 1.77
Natural killer cell function
Natural killer cell cytotoxicity 14 468 .11 .05 .21, .01 .04 14.55
Lymphocyte proliferation
Proliferation to ConA 9 220 .32 .15 .56, .03 .03 27.08***
Proliferation to PHA 14 443 .19 .09 .35, .02 .03 33.38***
Proliferation to PWM 3 106 .17 .15 .43, .12 .24 4.75
Cytokine production
6 149 .11 .08 .05, .27 .17 15.07***
Interleukin-2 4 107 .17 .36 .71, .49 .63 27.34***
Interleukin-4 3 81 .10 .12 .32, .13 .39 0.69
Interleukin-6 3 100 .26 .11 .06, .44 .01 0.79
Interleukin-10 3 95 .41 .11 .21, .57 .001 1.65
8 314 .30 .13 .51, .05 .02 28.76***
Tumor necrosis factor-
3 100 .18 .19 .19, .51 .34 5.10
Note. CI confidence interval; IgA immunoglobulin A; IgG immunoglobulin G; IgM immunoglob-
ulin M; ConA concanavalin A; PHA phytohemagglutinin; PWM pokeweed mitogen.
* p .05. ** p .01. *** p .001.
naturalistic stress indicates differential effects between Th1 and
Th2 responses rather than natural and specific responses.
The functional assay data are consistent with this suggestion of
suppression of cellular immunity via decreased Th1 cytokine pro-
duction: The T cell proliferative response significantly decreased
with brief stressors (r ⫽⫺.19 to .32), as did natural killer cell
cytotoxicity (r ⫽⫺.11). Increased antibody production to latent
virus, particularly Epstein-Barr virus (r .20), is also consistent
with suppression of cellular immunity, enhancement of humoral
immunity, or both.
There was also evidence that age contributed to vulnerability to
stress-related immune change during brief naturalistic stressors,
even within a limited range of relatively young ages. When we
examined whether effect sizes varied according to demographic
characteristics of the sample, sex ratio did not show a consistent
pattern of relations with immune processes. However, the mean
age of the sample was strongly related to study effect size. To the
extent that a study enrolled participants of older ages, it was likely
to observe more pronounced decreases in natural killer cell cyto-
toxicity (r ⫽⫺.58, p .04; k 14), T lymphocyte proliferation
to the mitogens PHA (r ⫽⫺.58, p .04; k 13) and ConA (r
.31, p .38; k 9), and production of the cytokine IFN
.63, p .09; k 8) in response to brief naturalistic stress. The
strength of these findings is particularly surprising given the nar-
row range of ages found in studies of brief natural stress; the mean
participant age in this literature ranged from 15.7 to 35.0 years.
We also calculated effect sizes for three studies examining the
effects of examination stress on individuals with asthma (see Table
5). These three studies, all emanating from a team of investigators
at the University of WisconsinMadison, found that stress reli-
ably increased superoxide release (r .20 to .37) and decreased
natural killer cell cytotoxicity (r ⫽⫺.33). Because natural killer
cells are stimulated by Th1 cytokines, this change is consistent
with a Th1-to-Th2 shift. However, stress also reliably increased T
cell proliferation to PHA (r .32), which is not consistent with
such a shift. The generally larger effect sizes are consistent with
the idea that individuals with immunologically mediated disease
are more susceptible to stress-related immune dysregulation, but
the reversed sign for T cell proliferation also indicates that that
pattern of dysregulation may also be more disorganized. That is,
the organized pattern of suppression of Th1 but not Th2 immune
responses in healthy individuals undergoing brief stressors may
reflect regulation in the healthy immune system. In contrast, the
lack of regulation in a diseased immune system may lead to more
chaotic changes during stressors.
Stressful event sequences. The meta-analysis of stressful event
sequences is presented in Table 6. With the exception of signifi-
cant increases in the number of circulating natural killer cells and
the number of antibodies to the latent Epstein-Barr virus, the
findings indicate that stressful event sequences are not associated
with reliable immune changes. For many immune outcomes, how-
ever, significant heterogeneity statistics are evident. Studies of
healthy adults generally fell into two categories that yielded dis-
parate patterns of immune findings. The largest group of studies
focused on the death of a spouse as a stressor and, as such, used
samples consisting primarily of older women. Collectively, these
studies found that losing a spouse was associated with a reliable
decline in natural killer cell cytotoxicity (r ⫽⫺.23, p .01; k
6) but not with alterations in stimulated-lymphocyte proliferation
by the mitogens ConA (r ⫽⫺.04, p .45; k 4), PHA (r
.01, p .93; k 7), or PWM (r ⫽⫺.08, p .76; k 3) or with
changes in the number of T-helper lymphocytes (r .07, p .52;
k 6) or T-cytotoxic lymphocytes (r ⫽⫺.13, p .45; k 5) in
peripheral blood. The next largest group of studies in this area
examined immune responses to disasters, which may have differ-
ent neuroendocrine consequences than loss; whereas loss is gen-
erally associated with increases in cortisol, trauma may be asso-
ciated with decreases in cortisol (Yehuda, 2001; Yehuda,
McFarlane, & Shalev, 1998). Natural disaster samples tended to
focus on middle-aged adults of both sexes who were direct victims
of the disaster, rescue workers at the scene, or personnel at nearby
medical centers. There were medium-size effects suggesting in-
creases in natural killer cell cytotoxicity (r .25, p .53; k 4)
and stimulated-lymphocyte proliferation by the mitogen PHA (r
.26, p .33; k 2), as well as decreases in the number of T-helper
lymphocytes (r ⫽⫺.20, p .43; k 2) and T-cytotoxic lym-
phocytes (r ⫽⫺.23, p .55; k 2) in the circulation. However,
none of them was statistically significant because of the small
number of studies involved, and therefore these effects should be
considered suggestive but not reliable.
An additional group of studies in this area examined immune
responses to a positive initial biopsy for breast cancer in primarily
middle-aged female participants before and after the procedure.
The three studies of this nature did not yield a consistent pattern of
relations with any of the immune outcomes.
In summary, stressful event sequences did not elicit a robust
pattern of immune changes when considered as a whole. When
these sequences are broken down into categories reflecting the
stressors nature, the meta-analysis yields evidence of declines
in natural immune response following the loss of a spouse,
Table 5
Meta-Analysis of Immune Responses to Brief Naturalistic Stress in Participants With Asthma
Immune marker kN r SE
95% CI pQ
Neutrophil function
Superoxide release with FMLP 3 216 .20 .07 .06, .32 .004 0.39
Superoxide release with PHA 3 216 .37 .07 .24, .49 .001 0.68
Natural killer cell function
Natural killer cell cytotoxicity 3 216 .33 .07 .45, .21 .001 0.50
Lymphocyte proliferation
Proliferation to PHA 3 216 .32 .07 .19, .43 .001 0.35
Note. CI confidence interval; FMLP N-formyl-met-leu-phe; PHA phytohemagglutinin.
nonsignificant increases in natural and specific immune re-
sponses following exposure to natural disaster, and no immune
alterations with breast biopsy. Unfortunately, we cannot deter-
mine whether these disparate patterns of immune response are
attributable to features of the stressors, demographic or medical
characteristics of the participants, or some interaction between
these factors.
Chronic stressors. Chronic stressors included dementia
caregiving, living with a handicap, and unemployment. Like
other nonacute stressors, they did not have any systematic
relationship with enumerative measures of the immune system.
They did, however, have negative effects on almost all func-
tional measures of the immune system (see Table 7). Both
natural and specific immunity were negatively affected, as were
Th1 (e.g., T cell proliferative responses) and Th2 (e.g., antibody
to influenza vaccine) parameters. The only nonsignificant
change was for antibody to latent virus; this effect size was
substantial (r .44), but there was also substantial heteroge-
neity. Further analyses showed that demographics did not
moderate this effect: Immune responses to chronic stressors
were equally strong across the age spectrum as well as across
Distant stressors. Distant stressors were traumatic events such
as combat exposure or abuse occurring years prior to immune
assessment. The meta-analytic results for distant stressors appear
in Table 8. The only immune outcome that has been examined
regularly in this literature is natural killer cell cytotoxicity, and it
is not reliably altered in persons who report a distant traumatic
Meta-Analytic Results for the Effects of Checklists and
Nonspecific life events. Most of the studies in this area exam-
ined whether immune responses varied as a function of the number
of life events a person endorsed on a standard checklist, a persons
rating of the impact of those events, or both. As Table 9 illustrates,
this methodology yielded little in the way of significant outcomes
in healthy participants. To determine whether vulnerability to life
events might vary across the life span, we divided studies into two
categories on the basis of a natural break in the age distribution.
These analyses provided evidence that older adults are especially
vulnerable to life-eventinduced immune change. In studies that
used samples of adults who had a mean age above 55, life events
were associated with reliable declines in lymphocyte-proliferative
responses to PHA (r ⫽⫺.40, p .05; k 2) and natural killer cell
cytotoxicity (r ⫽⫺.59, p .001; k 2). These effects were much
weaker in studies with a mean age below 55: Life events were not
associated with proliferative responses to PHA (r ⫽⫺.22, p .24;
k 2), and showed a reliable but modest relationship with natural
killer cell cytotoxicity (r ⫽⫺.10, p .03; k 8). The differences
in effect size between older and younger adults were statistically
significant for natural killer cell cytotoxicity (p .001) but not
PHA-induced proliferation (p .15). None of the other modera-
tors we examinedsex ratio, kind of life event assessed (daily
hassle vs. major event), or the method used to do so (checklist vs.
interview)was related to immune outcomes.
Table 10 presents the relationship between life events and
immune parameters in participants with HIV/AIDS. The presence
Table 6
Meta-Analysis of Immune Responses to Stressful Event Sequences in Healthy Participants
Immune marker kN rSE
95% CI pQ
Leukocyte subset count
Monocytes 3 113 .02 .10 .21, .17 .87 0.39
Lymphocytes 5 223 .05 .07 .09, .18 .49 2.65
T lymphocytes 5 213 .02 .07 .16, .12 .82 0.37
T-helper lymphocytes 9 566 .03 .11 .19, .25 .81 39.29***
T-cytotoxic lymphocytes 8 544 .14 .15 .41, .15 .35 58.22***
T-helper:T-cytotoxic ratio 6 296 .06 .08 .09, .21 .44 7.54
B lymphocytes 5 185 .02 .08 .13, .17 .76 0.35
Natural killer cells 4 370 .17 .09 .00, .34 .05 5.06
Leukocyte subset percentage
T lymphocytes 3 129 .02 .09 .16, .19 .85 0.11
T-helper lymphocytes 5 279 .00 .06 .12, .12 .94 0.00
T-cytotoxic lymphocytes 5 279 .05 .06 .17, .07 .43 3.65
B lymphocytes 3 129 .04 .09 .22, .14 .67 0.57
Specific immunoglobulin
Epstein-Barr virus 3 198 .21 .07 .07, .34 .003 1.18
Natural killer cell function
Natural killer cell cytotoxicity 13 698 .03 .17 .29, .34 .87 164.40***
Lymphocyte proliferation
Proliferation to ConA 6 297 .04 .06 .15, .08 .53 2.53
Proliferation to PHA 11 675 .10 .10 .09, .28 .32 42.25***
Proliferation to PWM 7 284 .12 .16 .19, .40 .45 28.72***
Note. CI confidence interval; ConA concanavalin A; PHA phytohemagglutinin; PWM pokeweed
*** p .001.
of life events was associated with a significant reduction in the
number of natural killer cells and a marginal reduction in the
number of T-cytotoxic lymphocytes. It is unrelated to the number
of T-helper lymphocytes, the percentage of T-cytotoxic lympho-
cytes, and the T-helper:T-cytotoxic ratio, all of which are recog-
nized indicators of disease progression for patients with
We have already proposed that immunological disease dimin-
ishes the resilience and self-regulation of the immune system,
making it more vulnerable to stress-related disruption, and this
may be the case in HIV-infected versus healthy populations. How-
ever, studies of HIV-infected populations also utilized more re-
fined measures of life events (interviews that factor in biographical
context) than did studies of healthy populations (typically, check-
list measures). Unfortunately, we cannot differentiate between
these explanations on the basis of the available data.
Global stress appraisals and intrusive thoughts. The meta-
analysis of stress appraisals and intrusive thoughts is displayed in
Table 11. These studies generally enrolled large populations of
adults who were not experiencing any specific form of stress and
examined whether their immune responses varied according to
stress appraisals and/or intrusive thoughts. This methodology was
unsuccessful at documenting immune changes related to stress.
Because of the small number of studies in this category, moderator
analyses could not be performed.
The meta-analysis results shown in Table 12 address a similar
question with regard to persons who are in the midst of a specific
event sequence or a chronic stressor. To the extent that they
appraise their lives as stressful or report the occurrence of intrusive
thoughts, these individuals exhibit a significant reduction in natu-
ral killer cell cytotoxicity. Although this effect does not extend to
the number of T-helper and T-cytotoxic lymphocytes in the circu-
lation, it suggests that a persons subjective representation of a
stressor may be a determinant of its impact on the immune
Evidence Regarding Type I Error and Publication Bias
The large number of effect sizes generated by the meta-analysis
raises the possibility of Type I error. One strategy for evaluating
this concern involves dividing the number of significant findings
in a meta-analysis by the total number of analyses conducted.
When we performed this calculation, a value of 25.6% emerged,
Table 7
Meta-Analysis of Immune Responses to Chronic Stress in Healthy Participants
Immune marker kN rSE
95% CI pQ
Leukocyte subset count
Leukocytes 4 240 .07 .07 .06, .19 .32 2.12
Neutrophils 3 124 .36 .36 .33, .79 .31 20.45***
Eosinophils 3 124 .07 .22 .47, .35 .75 8.07*
Monocytes 4 240 .04 .17 .36, .29 .83 14.33**
Lymphocytes 4 240 .06 .10 .25, .13 .54 5.24
T lymphocytes 5 470 .03 .05 .12, .06 .55 2.75
T-helper lymphocytes 10 786 .05 .04 .12, .03 .22 8.54
T-cytotoxic lymphocytes 10 786 .08 .08 .23, .08 .34 33.44***
T-helper:T-cytotoxic ratio 6 528 .11 .08 .29, .08 .26 17.47**
Activated B lymphocytes 3 138 .02 .09 .19, .15 .82 0.03
Natural killer cells 4 158 .14 .32 .65, .45 .65 33.61***
Leukocyte subset percentage
Monocytes 3 224 .08 .10 .11, .26 .42 3.18
T lymphocytes 5 522 .03 .05 .13, .07 .59 4.93
T-helper lymphocytes 10 860 .07 .06 .18, .03 .19 19.45*
T-cytotoxic lymphocytes 10 860 .02 .05 .08, .11 .75 13.72*
Natural killer cells 6 246 .04 .09 .13, .21 .64 7.85
Specific immunoglobulin
Antibody to herpes simplex virus 1 3 185 .44 .34 .19, .81 .17 20.78***
Antibody to influenza after vaccination 3 304 .22 .05 .33, .11 .001 0.38
Natural killer cell function
Natural killer cell cytotoxicity 8 563 .12 .05 .20, .01 .04 11.58
Lymphocyte proliferation
Proliferation to ConA 4 486 .13 .06 .24, .02 .02 4.06
Proliferation to PHA 6 636 .16 .06 .27, .05 .004 8.75
Cytokine production
Interleukin-2 3 355 .21 .05 .31, .11 .001 1.50
Note. CI confidence interval; ConA concanavalin A; PHA phytohemagglutinin.
* p .05. ** p .01. *** p .001.
Table 8
Meta-Analysis of Immune Responses to Distant Stressors and
Posttraumatic Stress Disorder in Healthy Participants
Immune marker kN r SE
95% CI pQ
Natural killer cell cytotoxicity 3 94 .05 .25 .49, .41 .84 7.67*
Note. CI confidence interval.
* p .05.
suggesting that more than one fourth of the analyses yielded
reliable findings. This exceeds the 5% value at which investigators
typically become concerned about Type I error rates and gives us
confidence that the meta-analytic findings presented here are
A second concern arises from the publication bias toward pos-
itive findings, which could skew meta-analytic results toward
larger effect sizes. Fortunately, recent advances in meta-analysis
enable one to evaluate the extent of this publication bias by using
graphical techniques. A funnel plot can be drawn in which effect
sizes are plotted against sample sizes for any group of studies.
Because most studies in any given area have small sample sizes
and therefore tend to yield more variable findings, the plot should
end up looking like a funnel, with a narrow top and a wide bottom.
If there is a bias against negative findings in an area, the plot is
shifted toward positive values or a chunk of it will be missing
We drew funnel plots for all of the immune outcomes in the
meta-analysis for which there were a sufficient number of obser-
vations. Although not all of them yielded perfect funnels, there
was no systematic evidence of publication bias. Space limitations
prevent us from including all plots; however, Figure 1 displays
three plots that are prototypical of those we drew. As is evident
from the data in the figure, psychoneuroimmunology researchers
Table 10
Meta-Analysis of Immune Responses to Major and Minor Life Events of Unknown Duration in
Participants With HIV/AIDS
Immune marker kN rSE
95% CI pQ
Leukocyte subset count
T-helper lymphocytes 11 998 .01 .03 .08, .05 .70 7.70
T-cytotoxic lymphocytes 6 669 .14 .08 .29, .01 .08 17.92**
T-helper:T-cytotoxic ratio 3 356 .02 .05 .13, .09 .70 0.09
Natural killer cells 3 261 .27 .06 .38, .15 .001 0.30
Leukocyte subset percentage
T-helper lymphocytes 4 1,026 .02 .06 .15, .10 .73 7.58
T-cytotoxic lymphocytes 3 223 .00 .07 .13, .13 .99 0.00
Note. CI confidence interval.
** p .01.
Table 9
Meta-Analysis of Immune Responses to Major and Minor Life Events of Unknown Duration in
Healthy Participants
Immune marker kN rSE
95% CI pQ
Leukocyte subset count
Lymphocytes 5 537 .18 .17 .47, .14 .27 20.28***
T lymphocytes 4 237 .00 .07 .13, .13 .99 0.00
T-helper lymphocytes 5 227 .00 .07 .13, .13 .99 0.00
T-cytotoxic lymphocytes 5 227 .05 .07 .09, .18 .48 3.02
T-helper:T-cytotoxic ratio 3 70 .14 .38 .54, .71 .71 12.11**
Natural killer cells 4 194 .08 .07 .22, .07 .28 2.72
Leukocyte subset percentage
T lymphocytes 3 151 .20 .21 .21, .55 .34 7.61*
T-helper lymphocytes 7 285 .01 .06 .11, .13 .83 0.54
T-cytotoxic lymphocytes 6 205 .01 .07 .15, .14 .92 0.07
Natural killer cells 5 261 .00 .06 .12, .12 .99 0.00
Total immunoglobulins
Serum IgA 3 124 .07 .10 .26, .14 .52 2.19
Serum IgG 3 124 .06 .10 .24, .13 .54 2.06
Serum IgM 3 124 .03 .09 .15, .21 .72 0.72
Secretory IgA rate 3 276 .08 .10 .26, .11 .43 3.97
Secretory IgA concentration 4 101 .16 .14 .42, .12 .25 4.34
Specific immunoglobulin
Epstein-Barr virus 3 317 .02 .11 .23, .19 .86 5.65
Natural killer cell function
Natural killer cell cytotoxicity 12 672 .07 .07 .20, .07 .35 29.39***
Lymphocyte proliferation
Proliferation to ConA 3 72 .13 .15 .35, .16 .38 2.49
Proliferation to PHA 4 131 .26 .15 .50, .03 .08 6.11
Note. CI confidence interval; IgA immunoglobulin A; IgG immunoglobulin G; IgM immunoglob-
ulin M; ConA concanavalin A; PHA phytohemagglutinin.
* p .05. ** p .01. *** p .001.
seem to be reporting positive and negative findingsand not
hiding unfavorable outcomes when they do emerge. Thus, we do
not have any major concerns about publication bias leading this
meta-analysis to dramatically overestimate effect sizes.
The immune system, once thought to be autonomous, is now
known to respond to signals from many other systems in the body,
particularly the nervous system and the endocrine system. As a
consequence, environmental events to which the nervous system
and endocrine system respond can also elicit responses from the
immune system. The results of meta-analysis of the hundreds of
research reports generated by this hypothesis indicate that stressful
events reliably associate with changes in the immune system and
that characteristics of those events are important in determining the
kind of change that occurs.
Models of Stress and the Immune System
Selyes (1975) seminal findings suggested that stress globally
suppressed the immune system and provided the first model for
how stress and immunity are related. This model has recently been
challenged by views that relations between stress and the immune
system should be adaptive, at least within the context of fight-or-
flight stressors, and an even newer focus on the balance between
cellular and humoral immunity. The present meta-analytic results
support three of these models. Depending on the time frame,
stressors triggered adaptive upregulation of natural immunity and
suppression of specific immunity (acute time-limited), cytokine
shift (brief naturalistic), or global immunosuppression (chronic).
When stressors were acute and time-limitedthat is, they gen-
erally followed the temporal parameters of fight-or-flight stres-
sorsthere was evidence for adaptive redistribution of cells and
preparation of the natural immune system for possible infection,
injury, or both. In evolution, stressor-related changes in the im-
mune system that prepared the organisms for infections resulting
from bites, puncture wounds, scrapes, or other challenges to the
integrity of the skin and blood could be selected for. This process
would be most adaptive when it was also efficient and did not
divert excess energy from fight-or-flight behavior. Indeed, changes
in the immune system following acute stress conformed to this
pattern of efficiency and energy conservation. Acute stress upregu-
lated parameters of natural immunity, the branch of the immune
system in which most changes occurred, which requires only
minimal time and energy investment to act against invaders and is
also subject to the fewest inhibitory constraints on acting quickly
(Dopp et al., 2000; Sapolsky, 1998). In contrast, energy may
actually be directed away from the specific immune response, as
indexed by the decrease in the proliferative response. The specific
immune response in general and proliferation in particular demand
time and energy; therefore, this decrease might indicate a redirec-
tion away from this function. Similar redirection occurs during
Table 11
Meta-Analysis of Immune Responses to Global Stress Appraisals in Healthy Participants
Immune marker kN r SE
95% CI pQ
Leukocyte subset count
T lymphocytes 3 241 .15 .09 .31, .03 .10 3.15
T-helper lymphocytes 3 241 .14 .10 .32, .06 .18 3.80
T-cytotoxic lymphocytes 4 279 .02 .09 .19, .15 .80 5.09
Naive T lymphocytes 3 241 .09 .11 .29, .12 .41 4.29
Natural killer cells 3 205 .20 .13 .42, .04 .10 4.28
Leukocyte subset percentage
T-helper lymphocytes 3 143 .02 .09 .19, .15 .79 0.08
T-cytotoxic lymphocytes 3 143 .03 .09 .23, .11 .48 0.60
Total immunoglobulin
Serum IgG 4 332 .02 .10 .18, .20 .87 7.51
Natural killer cell function
Natural killer cell cytotoxicity 4 151 .11 .09 .27, .06 .21 1.85
Note. CI confidence interval; IgG immunoglobulin G.
Table 12
Meta-Analysis of Immune Responses to Stress Appraisals and Intrusive Thoughts Within Healthy
Stressed Populations
Immune marker kN r SE
95% CI pQ
Leukocyte subset count
T-helper lymphocytes 3 462 .10 .11 .31, .11 .35 7.52*
T-cytotoxic lymphocytes 3 462 .26 .32 .71, .34 .40 57.99***
Natural killer cell function
Natural killer cell cytotoxicity 3 566 .15 .06 .27, .02 .02 7.97
Note. CI confidence interval.
* p .05. *** p .001.
fight-or-flight stressors with regard to other nonessential, future-
oriented processes such as digestion and reproduction. As stressors
became more chronic, the potential adaptiveness of the immune
changes decreased. The effect of brief stressors such as examina-
tions was to change the potency of different arms of specific
immunityspecifically, to switch away from cellular (Th1) im-
munity and toward humoral (Th2) immunity.
The stressful event sequences tended to fall into two substantive
groups: bereavement and trauma. Bereavement was associated
with decreased natural killer cell cytotoxicity. Trauma was asso-
ciated with nonsignificantly increased cytotoxicity and increased
proliferation but decreased numbers of T cells in peripheral blood.
The different results for loss and trauma mirror neuroendocrine
effects of these two types of adverse events. Lossmaternal
separation in nonhuman animals and bereavement in humansis
commonly associated with increased cortisol production (Irwin,
Daniels, Risch, Bloom, & Weiner, 1988; Laudenslager, 1988;
McCleery, Bhagwagar, Smith, Goodwin, & Cowen, 2000). In
contrast, trauma and posttraumatic stress disorder are commonly
associated with decreased cortisol production (see Yehuda, 2001;
Yehuda et al., 1998, for reviews). To the degree that cortisol
suppresses immune function such as natural killer cell cytotoxicity,
these results have the potential to explain the different effects of
loss and trauma event sequences.
The most chronic stressors were associated with the most global
immunosuppression, as they were associated with reliable de-
creases in almost all functional immune measures examined. In-
creasing stressor duration, therefore, resulted in a shift from po-
tentially adaptive changes to potentially detrimental changes,
initially in cellular immunity and then in immune function more
broadly. It is important to recognize that although the effects of
chronic stressors may be due to their duration, the most chronic
stressors were associated with changes in identity or social roles
(e.g., acquiring the role of caregiver or refugee or losing the role
of employee). These chronic stressors may also be more persistent,
that is, constantly rather than intermittently present. Finally,
chronic stressors may be less controllable and afford less hope for
control in the future. These qualities could contribute to the sever-
ity of the stressor in terms of both its psychological and physio-
logical impact.
Increasing stressor chronicity also impacted the type of param-
eter in which changes were seen. Compared with the natural
immune system, the specific immune system is time and energy
intensive and as such is expected to be invoked only when cir-
cumstances (either a stressor or an infection; cf. Maier & Watkins,
1998) persist for a longer period of time. Affected immune do-
mainsnatural versus specificwere consistent with the duration
of the stressorsacute versus chronic. Furthermore, changing
immune responses via redistribution of cells can happen much
faster than changes via the function of cells. The time frames of the
stressor and the immune domain were also consistent; acute stress
affected primarily enumerative measures, whereas stressors of
longer duration affected primarily functional measures.
The results of these analyses suggest that the dichotomization of
the immune system into natural and specific categories and, within
specific immunity, into cellular and humoral measures, is a useful
starting point with regard to understanding the effects of stressors.
Categorizing an immune response is a difficult process, as each
immune response is highly redundant and includes natural, spe-
cific, cellular, and humoral immune responses acting together.
Given this redundancy, the differential results within these theo-
retical divisions were remarkably, albeit not totally, consistent. As
further immunological research defines these divisions more sub-
tly, the results with regard to stressors may become even clearer.
However, the present results suggest that the categories used here
are meaningful.
The results of this meta-analysis reflect the theoretical and
empirical progress of this literature over the past 4 decades. In-
creased differentiation in the quality of stressors and the immuno-
logical parameters investigated have allowed complex models to
be tested. In contrast, previous meta-analyses were bound by a
Figure 1. Funnel plots depicting relationship between effect size and
sample size. PHA phytohemagglutinin.
small number of more homogenous studies. Herbert and Cohen
(1993) reported on 36 studies published between 1977 and 1991,
finding broadly immunosuppressive effects of stress. Zorrilla et al.
(2001) reported on 82 studies published between 1980 and 1996,
finding potentially adaptive effects of acute stressors in addition to
evidence for immunosuppression with longer stressors. It is im-
portant to note that meta-analytic findings are bound by the models
tested in the literature. As more complex models are tested, more
complex relationships emerge in meta-analysis. We next consider
some such areas of complexity that should be considered in future
psychoneuroimmunology research.
Individual Differences and Immune Change Under Stress
The meta-analytic results indicate that organismic variables
such as age and disease status moderate vulnerability to stress-
related decreases in functional immune measures. Both aging and
HIV are associated with immune senescence and loss of respon-
siveness (Effros et al., 1994; Effros & Pawelec, 1997), and both
are also associated with disruption of neuroendocrine inputs to the
immune system (Kumar et al., 2002; Madden, Thyagarajan, &
Felten, 1998). The loss of self-regulation in disease and aging
likely makes affected people more susceptible to negative immu-
nological effects of stress. Finally, the meta-analysis did not reveal
effects of sex on immune responses to stressors. However, these
comparisons simply correlated the sex ratio of the studies with
effect sizes. Grouping data by sex would afford a more powerful
comparison, but few studies organized their data that way. Gender
may moderate the effects of stress on immunity by virtue of the
effects of sex hormones on immunity; generally, men are consid-
ered to be more biologically vulnerable (Maes, 1999), and they
may be more psychosocially vulnerable (e.g., Scanlan, Vitaliano,
Ochs, Savage, & Borson, 1998).
It seems likely to us that individual differences in subjective
experience also make a substantive contribution to explaining this
phenomenon. Studies have convincingly demonstrated that peo-
ples cardiovascular and neuroendocrine responses to stressful
experience are dependent on their appraisals of the situation and
the presence of intrusive thoughts about it (Baum et al., 1993;
Frankenhauser, 1975; Tomaka et al., 1997). Although the same
logic should apply to peoples immune responses to stressful
experience, few of the studies in this area have included measures
of subjective experience, and those reports were limited by meth-
odological issues such as aggregation across heterogeneous stres-
sors. As a consequence, measures of subjective experience were
not significantly associated with immune parameters in healthy
research participants, with the exception of a modest (r ⫽⫺.10)
relationship between intrusive thoughts and natural killer cell
cytotoxicity. Psychological variables such as personality and emo-
tion can give rise to individual differences in psychological and
concomitant immunological responses to stress. Optimism and
coping, for example, moderated immunological responses to stres-
sors in several studies (e.g., Barger et al., 2000; Bosch et al., 2001;
Cruess et al., 2000; Segerstrom, 2001; Stowell, Kiecolt-Glaser, &
Glaser, 2001).
Mechanisms of Stress Effects on the Immune System
Virtually nothing is known about the psychological pathways
linking stressors with the immune system. Many theorists have
argued that affect is a final common pathway for stressors (e.g., S.
Cohen, Kessler, & Underwood, 1995; Miller & Cohen, 2001), yet
studies have enjoyed limited success in attempting to explain
peoples immune responses to life experiences on the basis of their
emotional states alone (Bower et al., 1998; Cole, Kemeny, Taylor,
Visscher, & Fahey, 1996; Miller, Dopp, Myers, Stevens, & Fahey,
1999; Segerstrom, Taylor, Kemeny, & Fahey, 1998). Furthermore,
many studies have focused on the immune effects of emotional
valence (e.g., unhappy vs. happy; Futterman, Kemeny, Shapiro, &
Fahey, 1994), but the immune system may be even more closely
linked to emotional arousal (e.g., stimulated vs. still), especially
during acute stressors (S. Cohen et al., 2000). Finally, it is possible
that emotion will prove to be relatively unimportant and that other
mental processes such as motivational states or cognitive apprais-
als will prove to be the critical psychological mechanisms linking
stress and the immune system (cf. Maier, Waldstein, & Synowski,
In terms of biological mechanisms, the field is further along, but
much remains to be learned. A series of studies in the mid-1990s
was able to show via beta-adrenergic blockade that activation of
the sympathetic nervous system was responsible for the immune
system effects of acute stressors (Bachen et al., 1995; Benschop,
Nieuwenhuis, et al., 1994). Apart from these findings, however,
little is known about biological mechanisms, especially with re-
gard to more enduring stressors that occur in the real world.
Studies that have attempted to identify hormonal pathways linking
stressors and the immune system have enjoyed limited success,
perhaps because they have utilized snapshot assessments of hor-
mones circulating in blood. Future studies can maximize their
chances of identifying relevant mediators by utilizing more inte-
grated measures of hormonal output, such as 24-hr urine collec-
tions or diurnal profiles generated through saliva collections
spaced throughout the day (Baum & Grunberg, 1995; Stone et al.,
Future studies could also benefit from a greater emphasis on
behavior as a potential mechanism. This strategy has proven useful
in studies of clinically depressed patients, in which decreased
physical activity and psychomotor retardation (Cover & Irwin,
1994; Miller, Cohen, & Herbert, 1999), increased body mass
(Miller, Stetler, Carney, Freedland, & Banks, 2002), disturbed
sleep (Cover & Irwin, 1994; Irwin, Smith, & Gillin, 1992), and
cigarette smoking (Jung & Irwin, 1999) have been shown to
explain some of the immune dysregulation evident in this popu-
lation. There is already preliminary evidence, for instance, that
sleep loss might be responsible for some of the immune system
changes that accompany stressors (Hall et al., 1998; Ironson et al.,
Stress, the Immune System, and Disease
The most pressing question that future research needs to address
is the extent to which stressor-induced changes in the immune
system have meaningful implications for disease susceptibility in
otherwise healthy humans. In the 30 years since work in the field
of psychoneuroimmunology began, studies have convincingly es-
tablished that stressful experiences alter features of the immune
response as well as confer vulnerability to adverse medical out-
comes that are either mediated by or resisted by the immune
system. However, with the exception of recent work on upper
respiratory infection (S. Cohen, Doyle, & Skoner, 1999), studies
have not yet tied these disparate strands of work together nor
determined whether immune system changes are the mechanism
through which stressors increase susceptibility to disease onset. In
contrast, studies of vulnerable populations such as people with
HIV have shown changes in immunity to predict disease progres-
sion (Bower et al., 1998).
To test an effect of this nature, researchers need to build clinical
outcome assessments into study designs where appropriate. For
example, chronic stressors reliably diminish the immune systems
capacity to produce antibodies following routine influenza vacci-
nations (see Table 7). Yet as far as we are aware, none of these
studies has tracked illness to explore whether stress-related dis-
parities in vaccine response might be sufficient to heighten sus-
ceptibility to clinical infection with influenza. Cytokine expression
represents a relatively new and promising example of an avenue
for research linking stress, immune change, and disease. For ex-
ample, chronic stress may elicit prolonged secretion of cortisol, to
which white blood cells mount a counterregulatory response by
downregulating their cortisol receptors. This downregulation, in
turn, reduces the cells capacity to respond to anti-inflammatory
signals and allows cytokine-mediated inflammatory processes to
flourish (Miller, Cohen, & Ritchey, 2002). Stress therefore might
contribute to the course of diseases involving excessive nonspe-
cific inflammation (e.g., multiple sclerosis, rheumatoid arthritis,
coronary heart disease) and thereby increase risk for excess mor-
bidity and mortality (Ershler & Keller, 2000; Papanicoloaou et al.,
1998; Rozanski, Blumenthal, & Kaplan, 1999). Another example
of the importance of cytokines to clinical pathology is in asthma
and allergy, in which emerging evidence implicates excess Th2
cytokine secretion in the exacerbation of these diseases (Busse &
Lemanske, 2001; Luster, 1998).
Sapolsky (1998) wrote,
Stress-related disease emerges, predominantly, out of the fact that we
so often activate a physiological system that has evolved for respond-
ing to acute physical emergencies, but we turn it on for months on
end, worrying about mortgages, relationships, and promotions. (p. 7)
The results of this meta-analysis support this assertion in one
sense: Stressors with the temporal parameters of the fight-or-flight
situations faced by humans evolutionary ancestors elicited poten-
tially beneficial changes in the immune system. The more a stres-
sor deviated from those parameters by becoming more chronic,
however, the more components of the immune system were af-
fected in a potentially detrimental way.
Further research is needed to support two other ideas elicited by
this quote: the idea that subjective experience such as worry is
more likely to result in stress-related immune change than objec-
tive experience and the idea that stress-related immune change
results in stress-related disease. Though the results of the meta-
analysis were not encouraging on the first point, many of these
studies suffered from methodological limitations. We hope that
these results will inform investigations that go beyond the rela-
tionship between a stressful event and an immune parameter to
investigate the psychological phenomena that mediate that rela-
tionship. Finally, these results can also inform investigations into
stress, immunity, and disease process. Whether the disease is
characterized by natural or specific immunity, its cytokine profile,
and its regulation by anti-inflammatory agents such as cortisol,
may determine the disparate effects of different kinds of stressors.
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