Shift-&-Persist and discrimination predicting depression across the life course: An accelerated longitudinal design using MIDUSI-III

Abstract

Life course theorists posit that sensitive periods exist during life span development where risk and protective factors may be particularly predictive of psychological outcomes relative to other periods in life. While there have been between-cohort studies trying to examine differences in discrimination and depressive symptoms, these studies have not been designed to identify these sensitive periods, which are best modeled by examining intra-individual change across time. To identify sensitive periods where discrimination and shift-&-persist (S&P) – a coping strategy that may protect against the negative impact of discrimination – are most strongly predictive of depressive symptoms, we employed latent growth curve modeling using an accelerated longitudinal design to track intra-individual change in depressive symptoms from ages 20–69. Participants were 3,685 adults measured at three time points ~10 years apart from the Midlife in the United States study ( M age = 37.93, SD = 6.948 at Wave I). Results identified two sensitive periods in development where high levels of S&P interacted with discrimination to protect against depressive symptoms; during the 30s and a lagged effect where 40's S&P protected against depressive symptoms when participants were in their 50s. Implications for the life course study of discrimination, coping, and depression are discussed.

Full text

Regular Article
Shift-&-Persist and discrimination predicting depression across the
life course: An accelerated longitudinal design using MIDUSI-III
N. Keita Christophe and Gabriela L. Stein
Department of Psychology, The University of North Carolina at Greensboro, Greensboro, NC, USA
Abstract
Life course theorists posit that sensitive periods exist during life span development where risk and protective factors may be particularly
predictive of psychological outcomes relative to other periods in life. While there have been between-cohort studies trying to examine
differences in discrimination and depressive symptoms, these studies have not been designed to identify these sensitive periods, which
are best modeled by examining intra-individual change across time. To identify sensitive periods where discrimination and shift-&-persist
(S&P) –a coping strategy that may protect against the negative impact of discrimination –are most strongly predictive of depressive symp-
toms, we employed latent growth curve modeling using an accelerated longitudinal design to track intra-individual change in depressive
symptoms from ages 20–69. Participants were 3,685 adults measured at three time points ∼10 years apart from the Midlife in the
United States study (M
age
= 37.93, SD = 6.948 at Wave I). Results identified two sensitive periods in development where high levels of
S&P interacted with discrimination to protect against depressive symptoms; during the 30s and a lagged effect where 40’s S&P protected
against depressive symptoms when participants were in their 50s. Implications for the life course study of discrimination, coping, and
depression are discussed.
Keywords: depression, discrimination, life course, shift-&-persist, trajectories
Discrimination, or the behaviors and unfair treatment reflecting
negative attitudes and judgments about individuals in a group
(Pascoe & Richman, 2009), has been consistently found through
meta-analysis to be associated with poor mental health across
the life span (Schmitt, Branscombe, Postmes, & Garcia, 2014;
Yip, Wang, Mootoo, & Mirpuri, 2019). While the greatest amount
of attention has been paid to ethnic–racial discrimination (Yip
et al., 2019), where individuals are discriminated against as a func-
tion of their racial or ethnic group membership, discrimination
has negative implications for psychological well-being regardless
of attribution and across racial/ethnic groups, including White
populations (Kessler, Mickelson, & Williams, 1999; Schmitt
et al., 2014). Indeed, the inherently negative and uncontrollable
nature of discrimination may make it particularly harmful to
health and well-being (Pascoe & Richman, 2009; Williams &
Mohammed, 2009). Shift-&-persist (S&P) is a coping strategy
that has been shown to lead to positive health outcomes for
youth (Chen & Miller, 2012) and adults (Chen, Miller,
Lachman, Gruenewald, & Seeman, 2012) who face uncontrollable
stress. More recent work has also found S&P to be protective in
the context of general discrimination (Lam et al., 2018) and eth-
nic–racial discrimination (Christophe et al., 2019). To better
understand how S&P may protect against negative mental health
in the face of discrimination, we utilize the Midlife in the United
States (MIDUS) dataset, and adopt a life course approach to
examine how these factors impact the trajectory of depressive
symptoms from age 20 to age 69.
Discrimination and Depression from a Life Course
Perspective
Discrimination is a chronic and pernicious stressor that has been
associated with greater concurrent and prospective depressive
symptomatology (Luo, Xu, Granberg, & Wentworth, 2012;
Schmitt et al., 2014; Williams, Neighbors, & Jackson, 2003).
Despite scholarly work outlining discrimination’s ability to con-
currently and prospectively predict depressive symptoms, there
is much less known from a life course perspective about whether
there are particular periods in life course development where
discrimination may operate as a particularly harmful stressor in
predicting depressive symptoms and depression.
Life course theorists have proposed that the timing of exposure
to discrimination may be particularly important in understanding
its impact across the life span (Gee, Hing, Mohammed, Tabor, &
Williams, 2019). To date, however, only a small number of studies
are available that allude to possible changes in discrimination at
different stages throughout the life span, possibly due to the scar-
city of such wide-ranging data. For instance, using Wave I of
MIDUS, Kessler and colleagues (1999) observed that, while
overall reports of daily discrimination are lower in older cohorts
compared to younger cohorts, discrimination had an equal and
negative impact on mental health across age cohorts. While this
Author for Correspondence: N. Keita Christophe, Department of Psychology,
University of North Carolina at Greensboro, 296 Eberhart Building, PO Box 26170,
Greensboro, NC, 27402-6170; E-mail: nnchrist@uncg.edu
© The Author(s), 2021. Published by Cambridge University Press
Cite this article: Christophe NK, Stein GL (2021). Shift-&-Persist and discrimination
predicting depression across the life course: An accelerated longitudinal design using
MIDUSI-III. Development and Psychopathology 1–16. https://doi.org/10.1017/
S0954579421000146
Development and Psychopathology (2021), 1–16
doi:10.1017/S0954579421000146
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may, initially, imply is that the impact of discrimination on men-
tal health is equivalent across age, this claim cannot be made, as
Kessler and colleagues’study does not measure the intra-
individual changes in associations between discrimination and
depression across time. This study, and all studies focused solely
on inter-individual change, cannot examine sensitive periods in
individuals’lives where risks such as discrimination pose a partic-
ularly strong risk for their negative mental health. While not
examining links to psychological functioning, Gee and colleagues’
(2007) study of age discrimination among working women fol-
lowed for 17 years provides a window into potential intra-
individual change in exposure to discrimination. Specifically,
the authors observed nonlinear trajectories of discrimination fre-
quency, where discrimination starts high in the 20s, drops in the
30s, then increases and peaks when women were in their 50s
(Gee, Pavalko, & Long, 2007). These meaningful changes in expo-
sure to discrimination across decades may impact individuals’
concurrent and future endorsement of depressive symptoms.
In other words, as exposure to discrimination changes across
the life course, its impact on concurrent and later psychological
functioning may be dependent on the specific developmental
stage in the life course.
The life stress literature provides a particularly useful perspec-
tive when thinking of sensitive periods where discrimination is
more strongly associated with the occurrence or recurrence of
depressive symptoms. In an attempt to highlight how stress expo-
sure impacts the likelihood of depression as a function of age,
Mazure and Maciejewski (2003) created a model, derived from
data, illustrating that risk for depression due to life stressors
increases in adulthood, peaks between ages 40 and 50, before
declining to low levels of risk in the 60s and beyond. This
would imply that discrimination may pose a particular risk for
depression in midlife relative to early adulthood or old age.
While the overall trajectory of depression risk due to life stressors
does not fully map onto the trajectory of discrimination exposure
outlined by Gee and colleagues (2007), it is important to note that
frequency of exposure to discrimination may not be a driver of
depressive symptoms as much as the impact of discrimination
during different decades/developmental stages of life. For
instance, the stress sensitization model of depression (see
Monroe & Harkness, 2005 for review) asserts that, while initial
onset of depression is more likely to be triggered by major life
stressors, which typically first occur earlier in development, we
become progressively more sensitized to stress and more chronic
daily stressors –including discrimination –become more capable
in inciting recurrences of depressive symptomatology than they
did earlier in life. The authors contend that chronic daily life
stressors, relative to major life stressors such as job loss and rela-
tionship dissolution, are in fact the modal stressors that precede
increases in depressive symptoms later in adulthood.
Taken together, these studies provide evidence that midlife
may be a particularly sensitive period where discrimination
may, more strongly than is typical across the life course, predict
greater levels of depression. Those in midlife are at a crossroads
where their life stressors, both major and chronic, intersect lead-
ing to greater symptoms (Mazure & Maciejewski, 2003), and at
the same time, they may also be more sensitized to stressors
and more likely to become depressed in response to chronic,
daily stressors, such as discrimination (Monroe & Harkness,
2005). In the current study, we attempt to test the claim that
there is a sensitive period during midlife where discrimination
is a particularly strong predictor of depression by employing an
accelerated longitudinal design (Bollen & Curran, 2005) to assess
intra-individual change across an almost 50-year time period
using Waves I–III of the MIDUS study.
Shift-&-Persist
In addition to considering timing effects of discrimination, a life
course perspective also posits that psychological resources may be
differentially protective at specific sensitive periods in develop-
ment (Gee et al., 2019). S&P coping may be such a resource
that can mitigate the longitudinal negative impact of discrimina-
tion on depressive symptoms at specific points in development.
Outlined by Chen and Miller (2012), S&P is a class of coping
strategies that involve a combination of accepting and reapprais-
ing uncontrollable stressors (shift) coupled with maintaining opti-
mism, purpose in life, and an orientation towards the future in
spite of this uncontrollable stress exposure (persist). This coping
strategy is theorized to be particularly well suited for uncontrolla-
ble stressors relative to direct attempts to eliminate or stop uncon-
trollable stressors (e.g., poverty, discrimination, etc.) and the
combination of shifting and persisting in the context of uncon-
trollable stress that has been asserted (Chen, 2012) and empiri-
cally demonstrated (Chen et al., 2012) to be more effective than
either strategy in isolation. In this sense, S&P coping may be con-
ceptualized as what Lazarus and Folkman (1984) would refer to as
a secondary-control coping strategy, or a strategy where one
changes themselves to better fit the demands of the stressful envi-
ronment. This is contrasted to primary-control coping strategies
(Lazarus & Folkman, 1984), where individuals cope by actively
altering the conditions of their environment. Attempts to actively
and immediately “alter”or changes one’s impoverished or dis-
criminatory environment are likely to be unsuccessful, and
these unsuccessful attempts to cope with uncontrollable stress
may, themselves, elicit subsequent and exaggerated maladaptive
stress responses (Chen, 2012).
When individuals come from disadvantaged backgrounds,
S&P, has been associated with a number of positive physiological
health outcomes. For instance, when low-socioeconomic status
(SES) individuals utilize high levels of S&P coping, they have
been shown to be protected against exhibiting greater body
mass indexes (BMIs) (Kallem et al., 2013), experiencing greater
asthma-related impairment (Chen et al., 2011), and demonstrat-
ing greater inflammation (Chen, McLean, & Miller, 2015).
Particularly relevant to the current study, which draws from
Waves I through III of the MIDUS study, Chen and colleagues
(2012) conducted a study using a subset of 1,255 MIDUS II par-
ticipants who provided biological samples to examine how S&P
impacted allostatic load (i.e., physiological “wear and tear”) for
those from disadvantaged backgrounds. The authors observed
that S&P protected against greater allostatic load for those from
low-SES backgrounds. Adults who grew up in low-SES conditions
but used high levels of S&P coping displayed the lowest levels of
allostatic load (Chen et al., 2012). In addition, among youth who
faced high levels of general discrimination, another form of
uncontrollable stress, individuals who utilized S&P coping in
the face of discrimination reported better asthma control and
asthma quality of life compared to those who less frequently uti-
lized S&P coping (Lam et al., 2018). Similar to what has been
found with economic stress, the protective effects of S&P coping
were not observed among those who faced little general
discrimination.
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Recent scholarship has attempted to extend the work on S&P
to examine whether S&P coping may be similarly protective
depressive symptoms, an important mental health outcome that
is, itself, influenced in part by inflammatory processes (Slavich
& Irwin, 2014). While originally conceptualized as being protec-
tive for physiological health (Chen & Miller, 2012), secondary-
control coping strategies, which encompass strategies such as
S&P, have been meta-analytically associated with fewer internaliz-
ing symptoms (Compas et al., 2017). Meaning and purpose in life,
key components of persisting, have also been directly associated
with fewer depressive symptoms (Sumner, Burrow, & Hill,
2018). Successful S&P coping with uncontrollable stressors may
not only lead to adaptive downregulations in the stress response
system (Chen, 2012), but may also reduce the impact that these
stressors have on mental functioning. This conception of S&P
as potentially adaptive for physiological and mental well-being
stands, at face value, in contrast to recent work on “skin-deep
resilience”(see Brody, Yu, Chen, & Miller, 2020 for brief sum-
mary of this work), which suggests that upwardly mobile individ-
uals, especially those from minority groups, may demonstrate
good mental health and poor physical health. However, the fac-
tors suggested to lead to a skin-deep resilience pattern, such as
self-control (Brody et al., 2020; Miller, Yu, Chen, & Brody,
2015) and striving, which includes components such as high edu-
cational aspirations, an intense commitment to hard work, and a
“single-minded”focus on success (Brody, Yu, Miller, & Chen,
2016), are not the same as factors characteristic of S&P coping.
Stated differently, while striving and very high levels of self-
control have been associated with positive mental health but
poor physiological health, it remains to be seen whether S&P cop-
ing in the context of uncontrollable stress is or is not protective of
mental health in the same way that it has been shown to be pro-
tective of poor physical health.
Initial work examining the impact of S&P in the context of
depressive symptoms has shown that S&P is protective against
greater depressive symptoms depending on the nature of the
uncontrollable stressor. For instance, in a sample of Latinx
youth, Christophe et al. (2019) found that S&P coping was protec-
tive against the harmful effects of economic hardship on youth
depressive symptoms. Further, when discrimination was exam-
ined as an uncontrollable stressor, discrimination was not associ-
ated with depressive symptoms when youth with relatively weak
ethnic racial identities (i.e., meaning and importance of race/eth-
nicity; Umaña-Taylor et al., 2014) used high levels of S&P coping
(Christophe et al., 2019). This protective effect of S&P against
greater depressive symptoms has been found in an independent
sample of 674 Mexican-origin youth (Stein et al., under review).
Specifically, low ethnic-racial identity youth who faced high levels
of discrimination endorsed relatively lower levels depressive
symptoms in 9th grade and had marginally more steeply declining
trajectories of symptoms throughout the high school years (Stein
et al., under review). These two studies, thus, provide initial evi-
dence that S&P coping may, contrary to striving (Brody et al.,
2020), be protective against poor mental (Christophe et al.,
2019) and physical health (Chen et al., 2015; Lam et al., 2018).
While these studies provide the basis for the notion that S&P
might protect against discrimination, and specifically protect
against depressive symptoms due to discrimination, this concept
has not yet been tested in an adult sample, nor has it been tested
across a large swath of the life span. Furthermore, while sensitive
periods are most commonly explored for risk factors, Gee and col-
leagues (2019) assert that sensitive periods may also exist for
protective factors. Specifically, there may be times throughout
development where protective factors such as S&P display partic-
ularly potent effects. Theoretically, these periods may overlap with
those for stressors such as discrimination and also occur during
midlife. If individuals are particularly sensitized to the effects of
daily stressors in midlife (Monroe & Harkness, 2005), and most
likely to experience depression at that time period due to those
stressors (Mazure & Maciejewski, 2003), coping methods used
during those times may be particularly efficacious. Therefore, in
the current study we examine whether main effects of S&P coping
and interactions between S&P and discrimination predict trajecto-
ries of depression across an almost 50-year window, as well as
examining whether sensitive periods exist where S&P coping
interacts with discrimination in predicting less concurrent and
future depressive symptoms.
Across many of these studies, and in line with Chen and
Miller’s(2012) original theoretical assertion, S&P coping is
often not directly associated with positive physiological outcomes
as a main effect (Chen, Lee, Cavey, & Ho, 2013; Chen et al., 2015;
Kallem et al., 2013; Lam et al., 2018), but instead moderates the
effect of the uncontrollable stressor such that those who face
greater levels of uncontrollable stress –be it economic stress or
discrimination –are the ones who benefit from S&P coping.
Individuals who do not face pervasive uncontrollable stressors
likely do not benefit from employing coping strategies such as
S&P coping that are especially suited to help people cope with
uncontrollable stress (Chen, 2012; Chen & Miller, 2012); the
effectiveness of S&P, thus, may be observable only for those facing
frequent uncontrollable stress (i.e., a Stressor×S&P interaction).
Trajectories of Depressive Symptoms
While there exist a multitude of studies examining trajectories of
depression from adolescence into emerging adulthood, far fewer
studies have examined trajectories of depressive symptoms across
and through the midlife period. Regardless of the population or
trajectory modeling techniques employed, these studies point to
meaningful, intra-individual change in depressive symptoms
across time. For instance, employing multigroup growth curve
modeling using the National Longitudinal Survey of Youth,
Walsemann and colleagues (2009) found decreasing linear trajec-
tories of depression from ages 27 to 43 among White, Black, and
Latinx participants. By contrast, in a study utilizing Waves I–Vof
the National Longitudinal Study of Adolescent to Adult Health,
Chen and Harris (2019) observed nonlinear trajectories of depres-
sion where unconditional depression scores, stratified by sex,
tended to decrease throughout the 20s, and increase throughout
the 30s until the end of the study window at age 40. In a study
of 8,801 women studied across a 20-year period, Gillis et al.
(2019) observed four different trajectories of depressive symptoms
from ages 60 to 80 using latent growth curve mixture modeling: a
small persistently high depression group (5%), and increasing
depression group (14%), a decreasing trajectory group (19%),
and a low and stable group that also appeared to be slightly
decreasing over time (62%). It is important to note that 81% of
this sample across the decreasing and low stable trajectories
displayed at least some decline in symptomatology over time.
In a study examining trajectories of negative affect in a large
(N= 1992 at Wave I, 85% White), multigenerational sample of
individuals, Charles, Reynolds, and Gatz (2001) observed a linear
decrease in negative affect from ages 15 to 55 that then changed
trajectory and began to decrease linearly at a slower rate from
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age 60 through age 85. Finally, and most relevant to our current
study that also uses the MIDUS sample, Blöchl and Nestler (2019)
used second-order latent growth curve modeling on Waves
I through III of the MIDUS data (see Isiordia & Ferrer, 2018
for explanation of this model) to examine how vascular risk
factors (e.g., hypertension, diabetes, and smoking) impacted the
linear trajectory of depression across the three study waves.
Largely similar to past studies (Charles et al., 2001; Gillis et al.,
2019; Walsemann, Gee, & Geronimus, 2009) the authors also
found evidence for a slightly decreasing unconditional linear
slope of depressive symptoms.
1
To summarize across these findings from varying samples and
using varying modeling techniques, these studies point towards
meaningful differences in the intra-individual trajectory of
depression during and beyond the midlife period as a function
of age. Furthermore, several of these studies (with Chen &
Harris, 2019 as a notable exception) broadly point to an
on-average decrease in depressive symptoms across the life course.
Because intra-individual change across development has been
observed for depressive symptoms (Charles et al., 2001; Chen &
Harris, 2019; Gillis et al., 2019; Kessler, Foster, Webster, &
House, 1992; Walsemann et al., 2009), it is crucial to employ
modeling designs that account for developmental stage when
examining both predictors of depression across the life course
and sensitive periods during development when factors such as
discrimination and S&P coping are particularly predictive of
symptomatology relative to other developmental stages. This
study builds upon these previous examinations of depressive tra-
jectories by transforming the MIDUS study into an accelerated
longitudinal design to both examine trajectory of depression
across the life course as well as how exposure to discrimination
and use of S&P coping influences depressive symptomatology.
Current Study
Based on life course theory, the current study attempted to con-
duct a developmentally informed examination of the trajectory
of depression from ages 20 to 69 and examine how discrimina-
tion, S&P, and interactions between the two may concurrently
and prospectively predict depressive symptoms during sensitive
periods in development –specifically decades –controlling for
participants’expected trajectory of depressive symptoms across
the life course. While it is, theoretically, possible to examine the
effect of discrimination and S&P on depressive symptoms from
year to year, we assert that, when attempting to identify specific
sensitive periods during development across the life course,
using a decade-based approach to approximate each period in
development is more appropriate. Given past studies finding dif-
ferences in initial levels and the slope of depression from young
adulthood through midlife (Walsemann et al., 2009), we also
examine how initial levels of depression and the trajectory of
depression may vary based on race and relevant covariates.
Specifically, this study uses multigroup latent growth curve mod-
eling with data from Waves I through III of the MIDUS study to
estimate the trajectory of depression across each decade of this
nearly 50-year timespan. We hypothesized that (a) depressive
symptoms will show a linear decline throughout the life course
and (b) controlling for covariates and the downward trajectory
of depressive symptoms, discrimination will concurrently predict
greater than expected levels of depression during each decade of
life. We also predicted, based on the work of Mazure and
Maciejewski (2003) that (c) a sensitive period will exist during
midlife (40s/50s) where discrimination will be an especially strong
and predictor of concurrent and later depressive symptoms.
Finally, we hypothesized that (d) while S&P will not be associated
with depression as a main effect, it will interact with discrimina-
tion during midlife such that discrimination will not be associated
with depressive symptoms during midlife for those using high lev-
els of S&P coping.
Method
Participants & procedure
Participants were 3,685 adults (M
age
= 37.93, SD = 6.948 range =
20–49 at Wave I) from the MIDUS dataset, a national study of
health and well-being conducted by the MacArthur Foundation
Research Network on Successful Midlife Development where par-
ticipants were first selected through random-digit phone dialing
(see Radler, 2014 for brief description). The participants in this
study represent a subsample of MIDUS participants from
Project 1, the original project launched in 1995 assessing psycho-
social and health-related outcomes, who were not missing on
important demographic information such as race and gender.
Participants included those from the national probability sample
(N= 1716), the sibling sample (N= 456), the twin sample (N=
1143), and the city oversample (N= 370). Our sample was
89.2% monoracial White, with 5.7% of participants identifying
as Black/African American and 2.2% identifying as Hispanic/
Latina(o). Given the skewed race/ethnicity distribution, sampling
weights were used to increase the influence of responses from
racially minoritized participants, thus improving generalizability
and partially correcting for the increased attrition observed
among racially minoritized participants (see “Sample weights
for race”section for greater detail). The sample was fairly evenly
split with regards to biological sex (51.6% female) (only binary sex
collected in study). A small proportion of our sample (5.3%) was
born outside of the United States. At Wave 1, 25.6% of the sample
had obtained a high school diploma, an additional 22.2% had
obtained at least a bachelor’s degree, and an additional 10.7%
had obtained a masters of other graduate degree. (e.g., MD, JD,
PhD, etc.).
Wave I was collected during 1995 and 1996, while Wave II was
collected about 10 years later in 2004 and 2005. Wave III was
collected during 2013 and 2014. Missingness was 25.3% at
Wave II of data collection (N= 2751 present), and 46.4% at
Wave III (N= 1974 present). To facilitate our comparison of tra-
jectories of depression across development, participants were sep-
arated into three separate birth cohorts based on age at Wave I of
data collection. Participants from Cohort 1 were born between
1965 and 1974 and were thus between the ages of 20 and 29 at
Wave I in 1995. Participants in Cohort 2 were between 30 and
39 (birth years 1955–1966) at Wave I and Cohort 3 was between
40 and 49 (birth years 1945–1956). This study focused on under-
standing depression risk and resilience during sensitive develop-
mental periods in adulthood, versus the impact of
discrimination and S&P coping on depression in advanced ages
(70–90+ years old), where significant physical declines, cognitive
declines, and declines of social connectedness may disrupt one’s
previously observed trajectory of depressive symptoms. Given
1
This study solely examines change over time, not change over time based on an indi-
vidual’s age as do the previously cited studies. Further explanation of this is provided in
the Discussion section.
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our focus on the former question, individuals above age 49 at
Wave I were excluded from our sample, meaning that the most
elderly participants in our sample were between ages 60 and 69
at Wave III of data collection. Additional demographic informa-
tion for the whole sample and each cohort may be seen in Table 1.
Measures
Depressive symptoms
Depressive symptoms were measured at each time point using the
five items from the Kessler-6 (Kessler et al., 2002). Participants
were asked how often they felt, for example, “hopeless”,“worth-
less”, and “that everything was an effort”in the last 30 days for
1(all of the time)to5(none of the time). One item asking partic-
ipants how often they felt “nervous”was dropped based on a con-
firmatory factor analysis with the full MIDUS I –MIDUS III data
that indicated that adequate fit of a unidimensional model for
depressive symptoms were obtained only when this item was
dropped (Blöchl & Nestler, 2019). The remaining five items
were reverse-coded such that greater scores indicated more
depressive symptoms. The full Kessler-6 has displayed reliability
of .89 in the nationally representative telephone-based pilot sur-
vey for MIDUS (Kessler et al., 2002). At Waves I, II, and III of
our sample, reliability was .857, .853, and .849, respectively,
for the five-item measure. Test–retest reliability between
Waves 1 and 2 was .479, and test–retest reliability between
Waves 2 and 3 was .567.
Daily discrimination
Daily discrimination was assessed at each time point using the
nine-item daily scale (Williams, Yan, Jackson, & Anderson,
1997). Participants were asked to respond to items such as how
often “people ask as if you are dishonest”and “you are treated
with less respect than other people”on a 1 (often)to4(never)
scale. Items were reverse-coded and summed together such that
higher scores indicated more frequent daily discrimination. This
measure has displayed a reliability of .88 in a diverse sample of
1,139 participants from the Detroit Area Study (Williams et al.,
1997). Reliability in our sample at Wave 1, 2, and 3 was .855,
.918, and .916, respectively. Test–retest reliability between
Waves I and II was .52, and test–retest reliability between
Waves II and III was .569.
Participants who indicated that they have faced some form of
daily discrimination were then asked to select the main reason
they were subjected to discrimination. Response options available
across all three waves of data were: age, gender, race, ethnicity/
nationality, religion, height/weight, physical appearance, physical
disability. Participants were allowed to select multiple main rea-
sons for facing daily discrimination. Asking about the prevalence
of discrimination before asking about the attribution has been
theorized to help reduce underestimation of discrimination –a
phenomenon that can occur when victims struggle to clearly
identify the reason for which they were discriminated against
(Williams et al., 2003).
Shift-&-Persist
Shift was measured at each of the three time points using the four-
item positive reappraisal subscale of the Primary and Secondary
Control Questionnaire (Wrosch, Heckhausen, & Lachman,
2000). Participants responded to sample items such as “I can
find something positive, even in the worst situations”and
“when I am faced with a bad situation, it helps to find a different
way of looking at things”on a 4-point scale from 1 (A lot)to4
(Not at all). Items were reverse-coded and averaged such that
greater values indicated greater shifting, or positive reappraisal.
Reliability in our sample at Wave I, II, and III was .796, .801,
and .788, respectively. Test–retest reliability between Waves I
and II was .562, and test–retest reliability between Waves II and
III was .646.
Persist was measured at each of the three time points using the
four-item “live for today”subscale of the “Planning and Making
Sense of the Past Questionnaire”(Prenda & Lachman, 2001).
Participants responded to sample items such as “I have too
many things to think about today to think about tomorrow”
and “I live one day at a time”on a 4-point scale from 1 (a lot)
to 4 (not at all). Items were reverse-coded and averaged, where
greater scores indicated a higher propensity to persist through
life. Reliability in our sample at Wave I, II, and III was .641,
.637, and .609, respectively. Test–retest reliability between
Waves I and II was .522, and test–retest reliability between
Waves II and III was .571.
Consistent with past work with the S&P construct (Chen et al.,
2015; Kallem et al., 2013), and the theoretical assertion that S&P
exerts a joint influence greater than the sum of its component
parts (Chen, 2012; Lam et al., 2018), we computed a single
“S&P”score by creating separate mean scores for shift and for
persist, standardizing these mean scores, then averaging these
standardized scores together. Higher scores on this composite
variable indicate greater use of S&P coping.
2
Test–retest reliability
between Waves I and II was .530, and test–retest reliability
between Waves IIand IIIwas .619.
Covariates
Covariates in our study include biological sex, nativity status, race,
education as a measure of SES (higher values = greater education/
SES), and neuroticism. Sample was also added through three
dummy-coded variables for sibling sample, twin sample, and
city oversample, with the main probability sample as the reference
group. Due to low Ns for non-White participants, race was
dichotomized such that those who identify as monoracial white
were coded “0”, while those who are multiracial or of other racial
minority groups were coded as “1”. Similar to our measures of
discrimination and S&P, SES was entered as a time varying covar-
iate, as education/SES may change across time. Neuroticism,
which has been proposed as a factor that may influence relations
between stressors such as discrimination, and psychological
adjustment (Stokes, 2019), was measured at all three time points
using a four-item neuroticism subscale generated from previous
“Big-Five”inventories for the MIDUS study (Keyes, Shmotkin,
& Ryff, 2002; Rossi, 2001). On a 1 (a lot)to4(not at all) scale,
participants rated how well the words, moody, worrying, nervous,
and calm described them. The moody, worrying, and nervous
items were reverse coded before creating a mean score, where
higher numbers indicate greater trait neuroticism. Reliability
was .747 in our sample. Finally, dummy-coded variables indicat-
ing the sample a participant was drawn from (i.e., city oversample,
2
This measure of S&P differs slightly from that used by Chen et al.’s(2012) study
examining S&P during Wave II of MIDUS. To assess shift, Chen et al. (2012) use the pos-
itive reappraisals subscale and the emotional reactivity subscale from the
Multidimensional Personality Questionnaire (Patrick, Curtin, & Tellegen, 2002).
Because the emotional reactivity subscale is not available at all three waves of data, we,
unlike Chen et al., do not use it as part of our measure of shift. For persist, we use the
same measure as Chen et al. (2012), the live for today subscale (Prenda & Lachman,
2001).
Development and Psychopathology 5
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sibling sample, or twin sample) were entered as covariates, with
the national probability sample serving as the reference group.
Sample weights for race
Because racial minority groups were underrepresented in our
sample relative to the population, post-stratification sampling
weights were used adjusting the weight of responses by race.
Using the same convention as is done with the full MIDUS sam-
ple, weights for our subsample were created by comparing the
percentages of White, Black, and “Other”(i.e., non-Black minor-
ity and multiracial participants) of our sample against those from
the 2013 Current Population survey from the US Census Bureau.
Dividing the demographic percentages in the Current Population
Survey by the percentages in our sample produced weights of .90
for Whites, 2.76 for Blacks, and 2.13 for “Others”. Using sampling
weights partially corrects for the underrepresentation of minority
groups being in our MIDUS I sample and partially corrects for
the observed differential attrition by race in MIDUS II and III.
Results
Descriptive statistics
Before running our multigroup latent growth curve model, we cal-
culated descriptive statistics for the whole sample and each sample
separately (see Table 2). To summarize, depressive symptoms
were positively associated with discrimination at each time
point, and discrimination was also positively associated with
S&P across time points. Neuroticism was also moderately associ-
ated with depressive symptoms at every time point (rs≥.328),
whereas minority status and immigrant status were only weakly
associated with depressive symptoms (rs .037−.136). Across
cohorts and waves of data, gender was the most commonly
Table 1. Sample demographics for final analytical sample
Variable Total sample (N= 3685) Cohort 1 (N= 538) Cohort 2 (N= 1518) Cohort 3 (N= 1629)
Age at Wave I (SD) 37.93 (6.948) 26.98 (1.476) 34.72 (2.806) 44.55 (2.849)
Birth years 1945–1974 1965–1974 1955–1966 1945–1956
Biological sex
Male 48.4% 47.2% 47.5% 49.5%
Female 51.6% 52.8% 52.5% 50.5%
Race
White 89.2% 84.0% 88.1% 91.9%
Black/African American 5.7% 7.4% 6.1% 4.7%
Native American/Aleutian Islander/ Eskimo .7% .9% .6% .6%
Asian/ Pacific Islander 1.2% 2.6% 1.6% .4%
Multiracial .7% 1.3% .5% .6%
Other 2.6% 3.3% 3.0% 1.7%
Latinx origin*
Yes 2.2% 2.1% 2.7% 2.0%
No 72.2% 66.4% 70.6% 75.6%
Missing 25.6% 31.6% 26.8% 22.4%
Born in United States
Yes 94.5% 93.7% 93.9% 95.5%
No 5.3% 5.9% 5.9% 4.5%
Missing .2% .4% .3% .1%
Parental Education
Below high school 15.1% 6.7% 12.3% 18.5%
Above high school 80.1% 89.0% 82.4% 75.0%
Missing 5.6% 4.3% 5.3% 6.4%
Sample
Main 46.6% (1716) 52.4% (282) 45.7% (694) 45.4% (740)
Sibling 12.4% (456) 6.9% (37) 11.7% (178) 14.8% (241)
Twin 31.0% (1143) 27.7% (149) 32.1% (487) 31.1% (507)
City oversample 10.0% (370) 13.0% (70) 10.5% (159) 8.7% (141)
Note. Questions asking about Latinx/Hispanic origin were first asked at Wave II.
6 N.K. Christophe and G.L. Stein
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Table 2. Means and correlations among key study variables
Variable Cohort 1 Cohort 2 Cohort 3 Full Sample (1) (2) (3) (4) (5) (6) (7) (8) (9)
(1) Dep 1 1.616
(.675)
1.526
(.640)
1.511
(.645)
1.534
(.649)
1
(2) Dep 2 1.612
(.766)
1.538
(.677)
1.502
(.609)
1.531
(.660)
.456 1
(3) Dep 3 1.561
(.701)
1.459
(.610)
1.428
(.619)
1.455
(.627)
.397 .599 1
(4) Disc 1 14.981
(5.634)
14.442
(5.543)
13.657
(5.268)
14.192
(5.465)
.200 .197 .184 1
(5) Disc 2 14.383
(5.397)
13.946
(5.001)
13.493
(4.977)
13.787
(5.051)
.199 .315 .287 .606 1
(6) Disc 3 13.765
(5.266)
13.420
(5.085)
12.695
(4.579)
13.096
(4.878)
.153 .219 .275 .590 .641 1
(7) S&P 1 −.012
(.731)
.003
(.702)
.066
(.725)
.028
(.718)
−.072 .022 .041 .066 .077 .107 1
(8) S&P 2 .022
(.646)
.009
(.717)
.052
(.727)
.031
(.713)
.021 .050 .022 .092 .131 .107 .545 1
(9) S&P 3 .037
(.694)
−.031
(.704)
.038
(.705)
.011
(.704)
.039 .037 .019 .081 .080 .116 .510 .628 1
Note. Correlation are for the whole sample. Correlations significant at p< .05 are in bold. Dep = depressive symptoms. Disc = discrimination. S&P = shift-&-persist. The number after variable name corresponds to the Wave of data.
Development and Psychopathology 7
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cited reason for discrimination (36.2%), followed by race (20.4%)
and height/weight (19.6%). Discrimination based on physical dis-
ability (2.8%) was the most uncommon perceived reason for
discrimination.
Data analytic plan
After running descriptives on observed study variables, inferential
data analysis advanced in four steps. In the first step we estimated
a multigroup latent intercept model and compared its fit relative
to an unconditional linear growth model, where depressive symp-
toms were entered as indicators of the latent intercept and latent
linear slope factors. Time was rescaled such that the intercept for
the latent curve model corresponded to participants’20s (ages
20–29). Scaling of the slope factor for each cohort therefore cor-
responded to the deviation in decades from participants’20s (see
Table 2 for visual representation). This allows the loadings for the
slope factor to be identical at overlapping ages and for us to thus
model growth in depressive symptoms from age 20 through age
69 (Mehta & West, 2000). Second, we tested whether homosce-
dasticity, or constraining the residual variances in our depressive
symptom factors across time to equality, fit as well as a latent
intercept and slope model where residual variances were freely
estimated. This allowed us to test whether the impact of depres-
sive symptoms on the intercept and slope varied across time.
In Step 3, we added time-invariant covariates (TICs), namely
race, biological sex, immigrant status, sample, neuroticism, and
parental education predicting the intercept and linear growth fac-
tors of depressive symptoms. We also added our main variables of
interest, daily discrimination and S&P, as well as their product
term, to the model as time-varying covariates (TVCs). In this
model, which included both TICs and TVCs, TVCs predicted
depressive symptoms at each time point controlling for the impact
of the latent intercept and slope factor, while TICs predicted the
growth factor of depressive symptoms over and above the effects
of the TVCs (Bollen & Curran, 2005). We tested for contempora-
neous and lagged effects of our TVCs (e.g., Wave I discrimination
predicting Wave IIdepression) given theoretical work proposing
that sensitive periods may exist throughout development where
exposure to discrimination at that time is particularly harmful
for later health and well-being (Gee et al., 2019). These sensitive
periods are also theorized to exist for protective factors. Finally, in
Step 4, we compare this conditional model to a model where the
covariance between the latent intercept and slope is constrained to
equality across our three cohorts of participants. This tests
whether initial depression in the 20s is equally related to the
slope of depression across all three cohorts. Throughout these
steps, model fit was determined based on a combination of indi-
ces, where a nonsignificant chi-square, a comparative fit index
(CFI) ≥.95, an root mean square error of approximation
(RMSEA) ≤.05, and standardized root mean square residual
(SRMR) ≤.08 indicate “good”fit (Hu & Bentler, 1999). When
appropriate, competing models are compared using the Satorra–
Bentler scaled chi-square likelihood ratio test (Satorra, 2000).
Model parameters were not interpreted in depth until we had
decided upon our well-fitting, final model. Because we used sam-
pling weights, model parameters were estimated using a maxi-
mum likelihood estimator that was robust to nonnormality
(MLR). Because of our accelerated longitudinal design, some
data were missing by design
3
and, thus, intentionally not handled
using missing data procedures. Missing data that were not missing
by design were estimated using full information maximum
likelihood (FIML). All analyses were conducted using Mplus
Version 8.4.
Competing latent curve models
We first fit an intercept-only model using summary scores for
depression as indicators of the latent intercept (Model 1). This
model provided an adequate fit to the data but did not fit better
than a subsequent unconditional model (see Table 3 for model fit
statistics from competing models) including a random intercept
and slope factor (Scaled ΔX
2
(1) = 31.007, p< .0001) where resid-
ual variances for depression were freely estimated (i.e., heterosce-
dastic). This unconditional heteroscedastic model (Model 2)
provided a good fit to the data (X
2
(11) = 17.359, p= .0077,
RMSEA = .022, CFI = .975, SRMR = .045). There was a significant
intercept (M= 1.575, p< .001) and negative slope (M= -.026,
p= .001), indicating that depressive symptoms across the sample
is expected to go down by .026 points per ∼10-year period
from ages 20 to 69. The intercept was not associated with the
slope for any cohort, meaning that initial levels of depressive
symptoms during the 20s was not associated with the downward
trajectory of depressive symptoms across time. In this uncondi-
tional model, there was significant individual variability around
the intercept of depressive symptoms (.200, p= .007) but not
the slope (.021, p= .118). The unconditional heteroscedastic
model was then tested against a homoscedastic model (Model
3), where residual variances for the depression factors were con-
strained to equality. The more parsimonious homoscedastic
model provided a significantly worse fit to the data (Scaled ΔX
2
(8) = 134.782, p< .0001). Therefore, a heteroscedastic structure
was retained and used in subsequent model evaluation.
Next, we added TICs (biological sex, immigrant status, paren-
tal education, neuroticism, and race) and TVCs (discrimination,
S&P, and their interactions at each time point) to our model
(Model 4). Contemporaneous and lagged effects (e.g., discrimina-
tion in 20s predicting depressive symptoms in one decade later)
were assessed for our TVCs. Again, at points where our three
cohorts are assessed at overlapping ages (see Supplementary
Table 1), parameters were constrained to equality to facilitate
the study of quasi-intra-individual change through our accelerated
longitudinal design. Sex, immigrant status, and our dummy-
coded MIDUS sample variables (i.e., sibling, twin, or city over-
sample –national probability sample is reference group) were
not associated with the slope and intercept of depressive symp-
toms and were thus dropped for greater model parsimony. This
trimmed model provided a good fit to the data (X
2
(121) =
185.896, p= .0001, RMSEA = .021, CFI = .958, SRMR = .031).
Given nonsignificant covariances between the intercept and
slope between each of the three cohorts, we tested whether a
more parsimonious model where these covariances were set to
equality would fit the data equally well. This model (Model 5)
provided an adequate fit to the data (X
2
(123) = 214.059, p<
.0001, RMSEA = .025, CFI = .940, SRMR = .035), but fit the data
significantly worse than the Model 4, where these covariances
were freely estimated by cohort (Scaled ΔX
2
(2) = 19.020, p<
.0001). Given the good model fit and good fit relative to an
3
By creating cohorts to facilitate our accelerated longitudinal design, we have intro-
duced missingness by design. For example, Cohort 2, who were between ages 30 and
39 during Wave I and between 50 and 59 at Wave III, do not have data during the 20s
or 60s by design (see Supplemental Table 1 for visual depiction for which decades cohorts
are missing data by design).
8 N.K. Christophe and G.L. Stein
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alternative model, we used Model 4, our conditional model with
TICs and TVCs where intercept slope covariance is freely esti-
mated across cohort, as our final analytical model.
Interpreting final analytical model
In our final conditional analytical model (see Table 4), there con-
tinued to be a significant intercept (intercept = 1.578, p< .001),
and a significant downward linear slope (slope = −.026, p<.001)
of depressive symptoms. Again, the intercept is interpreted for
all cohorts as the level of depression when participants were in
their 20s. There was significant residual variation in the value
of depressive symptoms in the 20s (residual variance = .107,
p= .030) and the slope of depression across time (residual vari-
ance = .023, p= .018) that was not accounted for by our covariates.
Overall, the time-invariant and time-varying predictors in the
model explained a significant amount of variance in the intercept
(R
2
s = .447−.633, ps < .001) but not in the linear slope (R
2
’s = .079
−.136, ps = .062−.186). The intercept of depression was not asso-
ciated with the slope for any of the three cohorts. Greater neurot-
icism predicted a higher latent intercept of depression in the 20s
(B= .446, p< .001) and a more steeply decreasing trajectory of
depressive symptoms across the life course (B=−.060, p= .001),
again controlling for our TVCs. Moving to our TVCs, whose lev-
els and effects may vary by decade of life, we first examined the
effects of SES (measured by education level) on depressive symp-
toms. During each decade of life, greater SES was associated with
fewer same-decade depressive symptoms. Controlling for the neg-
ative slope of depression and all TICs, there were no contempora-
neous nor lagged effects of S&P on depressive symptoms during
any decade of life. During all decades of life, discrimination was
concurrently associated with greater depressive symptoms
controlling for the TICs and the negative linear slope of depres-
sive symptoms across time (see Figure 1 for visual representation).
There was one significant lagged effect where discrimination
experienced in participants’40s led to greater depression 10
years later when participants were in their 50s (B= .010, p= .019),
implying a sensitive period where discrimination during the 40s
was particularly harmful in predicting later depression. This
lagged effect occurred over and above the significant contempora-
neous effect of discrimination in the 50s.
Examining interactions between S&P and discrimination,
when participants were in their 30s, S&P and discrimination sig-
nificantly interacted with each other and predicted less concurrent
depression than would be expected given the negative trajectory of
depression (B=−.011, p= .015). Specifically, at mean levels of
S&P, discrimination predicted greater concurrent depression
when participants were in their 30s (B= .019, p< .001); this effect
was intensified for participants who endorsed low S&P coping in
their 30s (B= .027, p< .001), but was attenuated for those who
were high in S&P coping at this time (B= .011, p= .018). This
means that, when participants were in their 30s, high levels of
S&P coping partially buffered against the concurrent association
between discrimination and depressive symptoms (see Figure 2).
In addition, we found evidence of a lagged interactive effect
where the interaction between S&P and discrimination during
the 40s was predictive of depression during the 50s than what
would be expected given the negative slope of depression
(B=−.012, p= .022). Probing this interaction (see Figure 3)
revealed that, while discrimination when participants were in
their 40s predicted greater depression 10 years later if participants
endorsed low (B= .019, p< .001) and mean (B= .010, p= .019)
S&P coping at that earlier time, this association did not hold
for those who had displayed high S&P coping while in their 40s
Table 3. Model fit statistics from principled evaluation of competing curve of factors models
Model Χ
2
(df) pRMSEA CFI TLI SRMR
MLR
scaling
factor
Satorra–
Bentler
Scaled Χ
2
(df) pDecision
Unconditional models
(1) Intercept Only 28.306
(12)
.0050 .033 .936 .952 .072 2.9159 –––
(2) Unconditional
heteroscedastic
17.359
(11)
.0977 .022 .975 .980 .045 2.2510 31.007
(1)
<.0001 Reject
model 1
Retain
model 2
(3) Unconditional
homoscedastic
46.364
(19)
.0004 .034 .893 .949 .088 3.1041 134.782
(8)
<.0001 Reject
model 3
Retain
model 2
Conditional models
*(4) Conditional model
with TICs & TVCs
185.896
(121)
.0001 .021 .958 .953 .031 1.3901 –––
(5) Conditional model
with TICs & TVCs -
intercept & slope
covariance set to
equality
214.059
(123)
<.0001 .025 .940 .935 .035 1.4034 19.020
(2)
<.0001 Reject
model 5
Retain
model 4
Note. * = final analytical model. Maximum likelihood robust (MLR) scaling factor is needed in the calculation of the Satorra-Bentler Scaled X
2
difference test (Satorra, 2000). Depressive
symptoms are the dependent variable for all models. Unconditional models do not contain any covariates, while conditional models include the following covariates: socioeconomic status
(SES) (time-varying), neuroticism (time-invariant), and race (time-invariant).
CFI = comparative fit index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual; TLI = Tucker-Lewis Index.
Development and Psychopathology 9
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Table 4. Standardized parameter estimates for final analytical model (model 4)
Path Cohort 1 (N = 538) pCohort 2 (N = 1518) pCohort 3 (N = 1629) p
Intercept 1.578 <.001 1.578 <.001 1.578 <.001
Slope −.026 <.001 −.026 <.001 −.026 <.001
R
2
Intercept .469 <.001 .447 <.001 .633 <.001
R
2
Slope .079 .186 .070 .180 .136 .062
Intercept ↔Slope .007 .792 −.012 .542 −.033 .120
Effects of time-invariant covariates
Neuroticism →Intercept .446 (.030) <.001 .446 (.030) <.001 .446 (.030) <.001
Neuroticism →Slope −.060 (.018) .001 −.060 (.018) .001 −.060 (.018) .001
Race (1 = non-White) →Intercept .073 (.054) .174 .073 (.054) .174 .073 (.054) .174
Race →Slope −.039 (.039) .317 −.039 (.039) .317 −.039 (.039) .317
Concurrent effects by age
SES
20’s SES −.031 (.013) .019 ––– –
30’s SES −.022 (.007) .001 −.022 (.007) .001 ––
40’s SES −.011 (.005) .042 −.011 (.005) .042 −.011 (.005) .042
50’s SES ––−.019 (.006) .001 −.019 (.006) .001
60’s SES ––––−.035 (.009) <.001
S&P
20’s Depression −.102 (.061) .094 ––––
30’s Depression −.029 (.022) .200 −.029 (.022) .200 ––
40’s Depression −.010 (.019) .603 −.010 (.019) .603 −.010 (.019) .603
50’s Depression ––−.013 (.025) .592 −.013 (.025) .592
60’s Depression ––––−.032 (.038) .399
Discrimination
20’s Depression .016 (.007) .023 ––––
30’s Depression .019 (.004) <.001 .019 (.004) <.001 ––
40’s Depression .021 (.003) <.001 .021 (.003) <.001 .021 (.003) <.001
50’s Depression ––.020 (.004) <.001 .020 (.004) <.001
60’s Depression ––––.020 (.010) .044
S&P×Discrimination interaction
20’s Depression −.031 (.016) .055 ––––
30’s Depression −.011 (.005) .015 −.011 (.005) .015 ––
40’s Depression .001 (.004) .886 .001 (.004) .886 .001 (.004) .886
50’s Depression ––.002 (.006) .756 .002 (.006) .756
60’s Depression ––––.004 (.009) .649
10-year lagged effects
S&P
20’s S&P →30’s Depression .152 (.105) .149 ––––
30’s S&P →40’s Depression .014 (.031) .644 .014 (.031) .644 ––
40’s S&P →50’s Depression ––−.003 (.025) .901 −.003 (.025) .901
50’s S&P →60’s Depression ––––−.021 (.039) .581
Discrimination
20’s Discrimination →30’s Depression .009 (.013) .500 ––––
(Continued)
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(B= .001, p= .902). This means that while discrimination was
associated with concurrent greater depression in the 40s regard-
less of S&P, high levels of S&P prevented discrimination from
having an impact on depression 10 years later; this fully protective
effect may only be observed during this specific sensitive period of
development.
Discussion
This study took a life course perspective to examine how expo-
sure to discrimination and S&P coping impacted depressive tra-
jectories from ages 20 to 69. The accelerated longitudinal design
improves our ability both to track the trajectory of depressive
symptoms throughout a large swath of the life span by modeling
intra-individual change over time while also testing whether
sensitive periods in development exist where discrimination,
S&P, and their interactions are especially strong predictors of
concurrent and prospective deviations from individuals
expected trajectory of depressive symptoms. Broadly, this
study aligns with past work demonstrating change across the
life span in the expression of depressive symptoms (Charles
et al., 2001; Gillis et al., 2019;Walsemannetal.,2009)and
affirms that discrimination is a significant predictor of depres-
sive symptoms throughout much of the life span (Schmitt
et al., 2014). Furthermore, our findings expand what is known
regarding the efficacy of S&P coping beyond the realm of phys-
iological health outcomes by contributing to a small but grow-
ing body of literature demonstrating the protective effects of
S&P coping for depressive symptoms. Specifically, our findings
provide additional evidence that S&P coping may protect
Table 4. (Continued.)
Path Cohort 1 (N = 538) pCohort 2 (N = 1518) pCohort 3 (N = 1629) p
30’s Discrimination →40’s Depression .006 (.004) .186 .006 (.004) .186 ––
40’s Discrimination →50’s Depression ––.010 (.004) .019 .010 (.004) .019
50’s Discrimination →60’s Depression ––––.014 (.009) .104
S&P × Discrimination interaction
20’s interaction →30’s Depression −.035 (.019) .063 ––––
30’s Interaction →40’s Depression −.009 (.007) .161 −.009 (.007) .161 ––
40’s Interaction →50’s Depression ––−.012 (.005) .022 −.012 (.005) .022
50’s Interaction →60’s Depression ––––.005 (.011) .617
Note. Significant values are bolded for increased readability. –= estimate is missing by design. At points where the same parameter is assessed across multiple cohorts, that parameter has
been constrained to equality across those cohorts. ↔indicates a correlation. →indicates a regression path.
Figure 1. Graph of depressive trajectory across the life course and the same-decade
impact of discrimination on depressive symptoms. Note. The symptom trajectory
value should be interpreted as the level of depressive symptoms predicted based
on one’s symptom trajectory across time. The effect of same decade discrimination
value should be interpreted as the predicted level of depressive symptoms based on
one’s exposure to discrimination during that decade of life. The difference between
these values is equal to the significant ( p< .05) beta weight for discrimination. The
gray bar indicates the predicted level of depressive symptoms if participants experi-
enced discrimination and used mean levels of S&P coping.
Figure 2. Simple slopes plot of the Discrimination×Shift-&-Persist (S&P) interaction
predicting same-decade depressive symptoms in the 30s.
Figure 3. Simple slopes plot of the 10-year lagged interaction effect predicting
depressive symptoms in the fifties.
Development and Psychopathology 11
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individuals against depressive symptomatology during certain
sensitive periods in life course development.
We first found evidence for a significant downward trajectory
of depression from ages 20 to 69. This finding builds off of pre-
vious work by Blöchl and Nestler (2019) examining the trajectory
of depression in the MIDUS sample. By transforming the MIDUS
data into an accelerated longitudinal design, we, contrary to
Blöchl and Nestler (2019), were not only able to model trajectories
of depressive symptoms as a function of time (i.e., the 20 years
that pass between Wave I and Wave III), but also as a function
of development (i.e., estimating intra-individual change from
ages 20 to 69). This emphasis on intra-individual change is espe-
cially needed given previously documented within-person
changes in symptomatology across the life course (Charles
et al., 2001; Chen & Harris, 2019; Gillis et al., 2019; Kessler
et al., 1992; Walsemann et al., 2009). Our findings coincide
with much of this previous work and suggest that, on average,
individuals endorse fewer depressive symptoms as they age up
until age 69.
Examining predictors of slope of depression, neuroticism
emerged as a significant predictor. Across the life course, neurot-
icism has been strongly associated with greater internalizing
symptoms (Tackett & Lahey, 2016) and trajectories of negative
affect (Charles et al., 2001). This corresponds with our finding
that neuroticism in the 20s is related to initial depressive symp-
toms in the 20s and the slope of depressive symptoms through
the 60s. Given that 20’s neuroticism predicted a steeper decline
in depressive symptoms over time, our finding may represent a
“regression to the mean”where individuals who are highly neu-
rotic in their 20s endorse fewer depressive symptoms across devel-
opment –effectively placing these individuals back on the modal
downward trajectory of depressive symptoms.
In terms of our time-varying effects, discrimination, largely
consistent with Hypothesis 1, predicted greater concurrent levels
of depression than would be expected based on the downward tra-
jectory of depressive symptoms. The findings that discrimination
predicts greater depressive symptoms at each decade of develop-
ment is unsurprising and aligns with a large body of work (see
Paradies et al., 2015; Pascoe & Richman, 2009; Schmitt et al.,
2014; Williams & Mohammed, 2009 for reviews and meta-
analyses) research attesting to cross sectional positive associations
between discrimination and depression. In addition, we did not
observe any significant main effects of S&P on depressive symp-
toms. While this may cast doubt on the efficacy of S&P as a cop-
ing strategy, these findings also align with previous work finding
interactive, but not main effects of S&P (Chen et al., 2015;
Christophe et al., 2019; Kallem et al., 2013). Indeed, S&P is con-
ceptualized as a set of coping strategies that is particularly effective
in the context of uncontrollable stressors (Chen & Miller, 2012).
In the absence of uncontrollable stressors such as low-SES or dis-
crimination, S&P is not theorized and has not been shown to be
associated either with improved physical (Chen et al., 2012; Lam
et al., 2018) or mental health (Stein et al., under review;
Christophe et al., 2019). “Goodness of fit”is an important factor
to consider when examining coping; other active coping strategies
that are aimed at altering the environment itself may be more
suited to coping with controllable stressors, while changing one-
self to the demands of the environment (e.g., shifting and persist-
ing) may be more adaptive when stressors are uncontrollable
(Lazarus & Folkman, 1984; Stanisławski, 2019). Therefore, theory
would assert that S&P should be principally adaptive in the con-
text of high levels of uncontrollable stress, which is what we found
when examining time-specific interactions between S&P and
discrimination.
In terms of context-specific effects, which imply especially sen-
sitive decades in development where the effects risk and protective
factors may be particularly pronounced, we found evidence for
Hypothesis 2 in the form of two time-specific interactions
between S&P and discrimination. The first was a concurrent inter-
active effect observed during the 30s. Specifically, the positive
relation between discrimination and depressive symptoms in the
30s were weakened at high levels of S&P coping. We also observed
a lagged interactive effect where discrimination when participants
were in their 40s predicted greater depression than would be
expected in the 50s. This lagged effect, however, was attenuated
by S&P coping during the 40s and nonsignificant when S&P
was at high levels. This lends credence to the hypothesis proposed
by Gee and colleagues (2019) that sensitive periods exist both for
risk factors (i.e., discrimination), but also for protective factors
(i.e., S&P).
Relative to other points along the life course, there may be
something unique about facing discrimination and using S&P
coping in the 30s and especially in the 40s where these two factors
interact to predict depressive symptoms a full 10 years later.
Historically, psychologists such G. Stanley Hall conceptualized
midlife as spanning from 25 or 30 to 40 or 45 years of age and
described this as when we are, generally, at the peak of our abil-
ities (1922). While other theorists commonly place “midlife”
slightly later in the life cycle, Lachman and colleagues (2015)
highlight that Jung and Erikson also saw midlife as a critical
time where individuals are “negotiating and regulating growth
and decline”(p. 21). Given that midlife likely constitutes the
intersection of various areas of growth and decline (see
Lachman, Teshale, & Agrigoroaei, 2015 for visual representation),
uncontrollable stressors such as discrimination when individuals
are supposed to be approaching and at this peak of life may be
particularly detrimental to their psychological well-being.
Specifically, midlife may be theorized as a time of relative social
and financial stability (Alwin, McCammon, Mortimer, &
Shanahan, 2003); to the extent that discrimination in different
social and occupational contexts has the ability to disrupt this
life stability, one may expect meaningful concurrent and long-
lasting impacts on well-being and on depressive symptoms.
These life course perspectives explaining the justification for
our finding that sensitive periods for risk and protective factors
occur when entering and in midlife align with those from the
life stress field. For instance, stress sensitization theory would pre-
dict that, during midlife, daily and chronic stressors such as dis-
crimination may more easily trigger bouts of depressive
symptoms than at points earlier in development (Monroe &
Harkness, 2005). Interestingly, the partially protective, buffering
interactive effect of S&P occurs during the 30s a time where
risk for depression due to different life stressors is gradually
increasing (Mazure & Maciejewski, 2003). However, it is only at
the peak of risk for depression due to life stressors, the 40s,
where S&P demonstrated a fully protective effect in minimizing
the typically harmful effect of discrimination on depressive symp-
toms. Our results, therefore, may illustrate the gradual start of this
sensitive period in the 30s that then reaches its zenith during the
40s; S&P coping may be, thus, increasingly important in the 30s
and maximally important and effective in the 40s relative to other
times during adult development.
While theoretical scholarship has posited the existence and
highlighted the potential importance of identifying sensitive
12 N.K. Christophe and G.L. Stein
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periods during adulthood (Gee, Walsemann, & Brondolo, 2012;
Gee et al., 2019), the broader field of psychology has studied sen-
sitive periods almost exclusively in the context of early childhood
development. Indeed, much work has focused on increased capac-
ity for how factors such as early infant–mother separation and
child maltreatment influences epigenetic changes implicated in
the development of mental disorders (Roth & Sweatt, 2011),
while other work has broadly pointed to the benefits of positive
parenting “the earlier the better”to take advantage of sensitive
periods in neurodevelopment occurring during the first months
of life (Zeanah, Gunnar, McCall, Kreppner, & Fox, 2011). Our
findings, in concert with previously cited empirical work propos-
ing sensitive periods in adulthood for discrimination (e.g., Gee
et al., 2012) and life–stress work pointing to variable risk for
depressive episodes at different points in the life course
(Mazure & Maciejewski, 2003), suggest that sensitive periods are
not, however, exclusively found during early development. In
studying sensitive periods during other periods of development,
scholars’conceptions of the potential “window of opportunity”
during which sensitive period may occur may need to be adjusted
based on the developmental stage and phenomenon being stud-
ied. In our study, for instance, we operationalize a sensitive period
for depression during the life course as a 10-year window, which
stands in stark contrast to the month-long to week-long sensitive
periods observed in human and animal models of early develop-
ment (see Knudsen, 2004 for examples). Ultimately, much more
empirical work is needed to uncover and understand the environ-
mental, social, neural, genetic, and epigenetic mechanisms behind
potential sensitive periods in midlife. These nascent efforts may
be strengthened by adapting and synthesizing the schemas, mod-
els, and methods used when studying early childhood sensitive
periods with those employed by those studying life course
development.
Controlling for the effect of discrimination and S&P at each
wave of data, we failed to find significant differences in the inter-
cept and slope of depression between White and non-White par-
ticipants. It is important to note that tests for racial differences in
initial levels of depression and its trajectory were done controlling
for discrimination, a robust predictor of greater depressive symp-
toms among ethnic/racial minorities at different stages of devel-
opment (Benner et al., 2018; Paradies et al., 2015). Therefore,
by controlling for discrimination, there may be no remaining dif-
ference in depressive trajectories between Whites and racially
minoritized individuals, as the construct of race may have no lon-
ger served as a proxy variable for social disadvantage. Indeed,
only 10.8% of our sample were from ethnically/racially minori-
tized groups, and despite our use of sampling weights to partially
correct for this low level of representation, our sample sizes are
too imbalanced to draw definitive conclusions as to the impact
of race on the trajectory of depressive symptoms across the
life course. In addition, given our small percentage of
non-White participants, we made the decision to dichotomize
race and preserve statistical power despite the ready admission
that the lived experiences of various marginalized groups may
differ widely and that this approach may not be sensitive to
important between (i.e., between minoritized groups) and
within-group variability (Hall, Yip, & Zárate, 2016). Future cul-
turally sensitive longitudinal work on discrimination may be
better suited to tease apart potential differences in the course
of depression throughout the life span if they include balanced
samples of Whites and individuals from multiple ethnically/
racially minoritized groups.
Limitations and Conclusions
Despite its contribution, this study has several limitations that set
the stage for future work informed by a life course perspective.
First, while our cohort sequential, or accelerated longitudinal
design, allowed us to examine the trajectory of depression across
a 49-year period, each cohort included individuals as far as 9 years
apart in age. Our inferences are, thus, limited to the level of the
decade, whereas the effects of discrimination and S&P coping
likely also have more short-term effects on depressive trajectories.
While difficult to extend over such a time period, future work
should test for both short-term (e.g., 1-, 3-, or 5-year-) and long-
term (10+-year) effects of these factors on the course of depres-
sion throughout the life span. These types of short-term longitu-
dinal tests of S&P may be particularly useful in the study of child
and adolescent development, where there are already well-
established, albeit shorts sensitive periods that spur rapid develop-
mental change.
Secondly, we chose to measure depressive symptoms and S&P
as TVCs which assumes that these factors themselves do not
change over time in a systematic way. While cognitively based
coping strategies, such as S&P, may come online during adoles-
cence (Compas et al., 2017), we are aware of no scholarly work
that maps changes in the frequency of S&P coping across time.
A limited amount of work has begun to examine how discrimina-
tion unfolds across long periods of time. For instance, using 3,032
participants from Wave I of MIDUS, Kessler and colleagues
(1999) found that younger cohorts (age 25–44) reported greater
everyday discrimination than did those over 65. However, because
these differences are between individuals, inferences for how dis-
crimination unfolds across the life span (i.e., age effects) cannot be
inferred. Furthermore, it is unclear whether those observed differ-
ences in discrimination reflect what Gee et al. (2012) refer to as
“age-patterned responses”, or differences in prevalence due to
stage in life, or period effects, or changes brought on by historical
events and/or social transition. Indeed, participants who were
over the age of 65 during MIDUS I data collection in 1995/
1996 were young adults during the civil rights movement; living
through this time may have both influenced what these individu-
als perceive (and report) as discrimination relative to those youn-
ger individuals in Kessler et al.’s(1999) MIDUS I sample who
were not alive during this period of social change.
By contrast, in a sample of 7,225 working women followed for
15 years, Gee and colleagues (2007) found that age-based discrim-
ination peaked in the 20s and 50s, was low during the 30s, and
declined from the 50s peak around age 55 age discrimination,
which implies that an individual’s exposure to discrimination
changes over as a function of their age. While Gee et al.’s
(2007) study design is particularly useful in its ability to illustrate
within-person change over time, such a design is not well suited
to account for significant legal or social changes during this
15-year time frame (i.e., any period effects) that may have
impacted women’s exposure to workplace discrimination. While
we have no reason to believe specific period effects significantly
impacted our findings related to individual’s trajectory depressive
symptoms over time or age-based changes in the effects of dis-
crimination and S&P on symptomatology, it is impossible to
rule out the potential influence of small and unmeasured influ-
ences due to time and context. To better disaggregate potential
period effects, which may be observed between cohorts, from
time patterned responses, which may only be observed via intra-
individual change over time, scholars should endeavor to examine
Development and Psychopathology 13
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and compare intra-individual changes in factors such as discrim-
ination between samples exposed and not exposed to potentially
meaningful period effects (e.g., pre-post civil rights legislature,
pre-post federal legalization of same-sex marriage, etc.).
Finally, a notable limitation examining the effects of discrim-
ination in the MIDUS sample is the relative racial/ethnic homoge-
neity of the MIDUS sample. Although we utilized sampling
weights to increase the influence of non-White participants in
the estimation of our parameters, the fact remains that White par-
ticipants made up almost 90% of our sample. This sample homo-
geneity limited both the amount of reported racial/ethnic
discrimination we observed across the life course and limited
our power to detect reliable differences in our estimates based
on race. Future work would benefit from examining large, tar-
geted samples of racially/ethnically minoritized individuals to
better and more reliably observe the impact of racial discrimi-
nation on depressive symptoms throughout the life course.
Indeed, if systematic change over time exists in discrimination,
the pattern of change over time may vary based on the type of
discrimination. While used as control variables, Gee and col-
leagues (2007) observed that patterns of racial and gender-
based discrimination did not mirror patterns of age-based dis-
crimination. Future work may, therefore, benefit from examin-
ing multiple types of discrimination concurrently and from
using more restricted samples if the substantive interest is on
understanding specific types of discrimination (e.g., using a
Muslim-American sample to study religious and ethnic–racial
discrimination).
Despite its limitations, the current study adds to our under-
standing of how depressive symptoms change over the life course,
as well as how discrimination and S&P coping impacts depressive
symptomatology both contemporaneously and up to 10 years
later during sensitive periods in adult development. Our findings
demonstrated that, among a representative sample of adults in the
US, depression follows a slightly downward linear trajectory from
ages 20 through 69. Our use of an accelerated longitudinal design
allows us to approximate intra-individual change in depressive
symptoms over time and move beyond population-based epide-
miological studies that compare rates of depression between indi-
viduals at different ages. In addition, our findings add to the
bodies of work that have independently examined the impact of
discrimination at different points along the life course by showing
that discrimination is predictive of greater depressive symptoms
throughout human development over and above the natural neg-
ative trajectory of depression observed with aging. Finally, our
study provides further evidence that, similar to what has been
found with physiological health, S&P may also be protective for
mental health. Chen et al. (2012), using MIDUS Waves I and
II, found that adults who grew up in poverty but displayed high
levels of S&P coping endorsed the lowest levels of physiological
wear and tear, whereas our findings using MIDUS Waves I, II,
and III suggest that S&P buffers against the harmful impact of
discrimination on depressive symptoms in the 30s and is crucial
in the 40s at protecting against the long-term impact of discrim-
ination 10 years later. These findings point to potential sensitive
periods of human development (Gee et al., 2019) where the
impact of S&P may be particularly important in promoting psy-
chological well-being. In conclusion, this study adds to the field
by adopting developmental and life course perspective to the
study of how discrimination and S&P coping impact depressive
symptoms from ages 20 to 69.
Supplementary material. The supplementary material for this article can
be found at https://doi.org/10.1017/S0954579421000146
Financial Support. This work was supported in part by a predoctoral fellow-
ship provided by the National Institute of Child Health and Human
Development (T32-HD07376) through the Carolina Consortium on Human
Development, University of North Carolina at Chapel Hill, to N. Keita
Christophe
Conflicts of Interest. None.
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