Question:
You are given the full text of a scientific research paper.

    Identify and summarize the **policy problem** that this research addresses. Your response should:
    - Clearly state the societal, environmental, or strategic issue motivating the study
    - Be written in accessible, policy-brief style sentences
    - Focus on the core challenge the research aims to solve
    - Avoid excessive detail or technical jargon unless necessary for clarity

    Write a short paragraph suitable for inclusion under the “Policy Problem” section of a policy brief.

    Scientific Research Paper:
    https://doi.org/10.1177/0022146519899115
Journal of Health and Social Behavior
2020, Vol. 61(1) 4–23
© American Sociological Association 2020
DOI: 10.1177/0022146519899115
jhsb.sagepub.com
Original Article
The mental health of racial-ethnic minorities some-
times presents a paradox. Despite expectations to
the contrary, minorities report mental health that is
either somewhat better or no worse than that of the
non-Hispanic white population (Williams and Earl
2007). The 12-month prevalence of most psychiat-
ric disorders tends to be lower among minorities
(Breslau et al. 2005; Miranda et al. 2008; Vilsaint
et al. 2019), including for depression (Hasin et al.
2018). Other patterns are consistent with this
observation. Death rates from suicide among non-
Hispanic whites are two to three times higher than
among blacks, Asians, and Latinos (Miranda et al.
2008). Once adjustments for socioeconomic status
are included, African Americans almost always
report better mental health on dimensional measures
of depression (Barnes and Bates 2017). Hispanics,
too, tend to report a lower prevalence of psychiatric
disorders (Breslau et al. 2005), and among Asian
populations, the prevalence of psychiatric disorders
is especially low (Takeuchi et al. 2007). To be sure,
relative to whites, African Americans sometimes
report worse mental health on cognitive measures of
899115 HSBXXX10.1177/0022146519899115Journal of Health and Social BehaviorSchnittker and Do
research-article2020
1University of Pennsylvania, Philadelphia, PA, USA
Corresponding Author:
Jason Schnittker, Department of Sociology, University
of Pennsylvania, 3718 Locust Walk, Philadelphia, PA
19104-6299, USA.
Email: jschnitt@ssc.upenn.edu
Pharmaceutical Side Effects
and Mental Health Paradoxes
among Racial-Ethnic Minorities
Jason Schnittker1 and Duy Do1
Abstract
Sociologists have long struggled to explain the minority mental health paradox: that racial-ethnic minorities
often report better mental health than non-Hispanic whites despite social environments that seem less
conducive to well-being. Using data from the 2008–2013 Medical Expenditure Panel Survey (MEPS), this
study provides a partial explanation for the paradox rooted in a very different disparity. Evidence from
MEPS indicates that non-Hispanic whites consume more pharmaceuticals than racial-ethnic minorities for
a wide variety of medical conditions. Moreover, non-Hispanic whites consume more pharmaceuticals that
although effective in treating their focal indication, include depression or suicide as a side effect. In models
that adjust for the use of such medications, the minority advantage in significant distress is reduced, in
some instances to statistical nonsignificance. Although a significant black and Hispanic advantage in a
continuous measure of distress remains, the magnitude of the difference is reduced considerably. The
relationship between the use of medications with suicide as a side effect and significant distress is especially
large, exceeding, for instance, the relationship between poverty and significant distress. For some minority
groups, the less frequent use of such medications is driven by better health (as in the case of Asians),
whereas for others, it reflects a treatment disparity (as in the case of blacks), although the consequences
for the mental health paradox are the same. The implications of the results are discussed, especially with
respect to the neglect of psychological side effects in the treatment of physical disease as well as the
problem of multiple morbidities.
Keywords
comorbidity, disparities, pharmaceuticals, psychological distress, race-ethnicity

Schnittker and Do
5
well-being, such as life satisfaction (Hughes and
Thomas 1998), though they usually have a lower
risk of clinically significant distress (Breslau et al.
2005; Riolo et al. 2005).
Differences of this sort confound easy explana-
tions. For one, the apparent racial-ethnic advantage
in mental health does not align with racial-ethnic
disparities in physical health. On most indicators,
African Americans report worse physical health
than non-Hispanic whites (Williams 2005). The
Hispanic population between the ages of 15 and 34
reports worse health, though overall the health of
Hispanics is somewhat better than whites (Williams
2005). Even when racial-ethnic minorities maintain
a clear relative advantage in health, as with the
Asian population (Acciai, Noah, and Firebaugh
2015), racial-ethnic minorities are often exposed to
more stress, especially when considering race-spe-
cific stressors (Williams 2018). Black and Hispanic
populations, for instance, report higher levels of
discrimination along with lower socioeconomic sta-
tus (Kessler, Mickelson, and Williams 1999; Pérez,
Fortuna, and Alegría 2008). Asian populations, too,
report high levels of discrimination, and some
specific Asian populations (e.g., Hmong and
Burmese) also report lower socioeconomic status
than whites (Yip, Gee, and Takeuchi 2008).
In light of these facts, sociologists interested in
explaining the racial-ethnic minority advantage
have focused less on stress exposure and more on
protective factors. These efforts, however, have not
been entirely successful. Research has uncovered
some important protective factors, to be sure, but
these factors are not always relevant or suitable to
explaining the minority paradox. They either apply
only to coping with certain forms of stress, have
highly complex and contingent associations with
mental health, or are not uniformly more common
among racial-ethnic minorities (Ellison, Musick,
and Henderson 2008; Jackson, Knight, and Rafferty
2010; Odom, Garrett-Peters, and Vernon-Feagans
2014; Phinney and Haas 2003). As an alternative
explanation for the paradox, some scholars have
turned to measurement biases in common measures
of mental health (Sue et  al. 2012). Yet here, too,
such explanations are limited, at least for purposes
of explaining the paradox. Measurement differ-
ences between groups are generally not large or
consistent enough to explain the paradox altogether
(Alegría and McGuire 2003). Mixed findings of
this sort have prompted some to argue that the liter-
ature on minority mental health, no matter how well
developed, still has more questions than answers
(Williams and Earl 2007).
This study seeks to push the literature in a dif-
ferent direction and provides a different answer for
the paradox. In particular, this study explores
whether the paradox is a function of the side effects
of medications frequently prescribed for common
physical conditions. This study presents evidence
that much of the paradox in mental health can be
explained by the more frequent use of medications
that have either depression or suicide as a side
effect among non-Hispanic whites. In this way, this
study argues that the resolution of one paradox
might rest on another: The elusive protective factor
apparent among racial-ethnic minorities might be,
in part, in their relatively low levels of medical
intervention.
Background
Patterns in racial-ethnic minority mental health are
confounding in large part because most sociological
theory points to the many ways that minorities are
disadvantaged (Williams 2018). For good reason,
most explanations for minority mental health begin
with socioeconomic status (Williams et al. 1997). At
least in the context of physical health, socioeco-
nomic status explains some though not all of the
black disadvantage, including with respect to life
expectancy (Geruso 2012; Hayward et  al. 2000).
Socioeconomic status also affects mental health,
largely through chronic stress (Thoits 2010), but
given the relatively low socioeconomic status of
blacks, socioeconomic status only suppresses the
black–white difference in depression. For some
dimensional measures of depression specifically,
African Americans report less depression even
before adjustments for socioeconomic status,
though such adjustments further inflate the advan-
tage (Barnes and Bates 2017). A similar pattern is
found for other groups and is sometimes even more
pronounced. The Mexican immigrant disadvantage
in socioeconomic status, for instance, is quite large
even as the 12-month prevalence of most psychiat-
ric disorders is very low (Vega et al. 1998; Vilsaint
et al. 2019).
As research on minority mental health has
evolved, it has increasingly focused on uncovering
other sources of stress. This focus has done much to
forefront previously neglected race-based stressors
(Williams 2018). Perhaps the most prominent of
such stressors is discrimination, which is both com-
mon among racial-ethnic minorities and conse-
quential for mental health. Blacks, Hispanics, and
Asians all report more discrimination than the white
population, including major lifetime experiences

6
Journal of Health and Social Behavior 61(1)
with unfair treatment, such as losing a job, as
well as everyday experiences of discrimination
and disrespect, such as inferior service (American
Psychological Association 2016). Discrimination
has, in turn, been related to subsequent psycho
logical distress and an increased risk of major
psychiatric disorders (Brown et  al. 2000; Lewis,
Cogburn, and Williams 2015; Williams 2018).
Although this research is revealing, it only deep-
ens the paradox of minority mental health. In effect,
it shows that once stress is measured more compre-
hensively, racial-ethnic minorities report even more
stress than the white population. In this light, some
scholars have shifted their focus from documenting
risks to uncovering protective factors among
minorities. A variety of factors have been put for-
ward. Much of this work has focused on social sup-
port and religious involvement, and both are
demonstrably important for coping with stress
(Brody et al. 2006; Ellison et al. 2008). In a similar
vein, though positing something more novel, Keyes
(2009) pointed to the possibility that blacks are bet-
ter able to create positive meaning from negative
events. A somewhat smaller literature has taken a
different tact and attempted to identify risk factors
that are more common among whites. For instance,
some studies point to inflexible coping styles
among non-Hispanic whites, including a belief in
meritocracy, which can lead to more self-blame in
situations of low socioeconomic status (Kwate and
Meyer 2010). Other research has explored the ways
in which whiteness can sometimes diminish health
(Malat, Mayorga-Gallo, and Williams 2018). Yet
even as research identifies these racial-ethnic dif-
ferences, it generally does not address the mental
health paradox directly. Moreover, research rarely
finds coping resources that are uniformly more
common among racial-ethnic minorities or signifi-
cant risk factors that are plainly more frequent
among whites. Although racial-ethnic minorities
experience more discrimination, for instance, they
are not necessarily better able to cope with that dis-
crimination than non-Hispanic whites (Phinney and
Haas 2003). Even among populations that expect
discrimination, discrimination remains detrimental
to their mental health (Kessler et al. 1999). Neither
family relationships nor relationships of choice can
explain the paradox, though both are important for
well-being (Mouzon 2013, 2014). Although blacks
report considerably more religious involvement,
religion, too, fails to explain the paradox (Mouzon
2017). In this light, recent research has framed its
findings in a different and perhaps narrower fash-
ion, arguing only for a set of offsetting mechanisms,
as when studies find evidence for higher self-
esteem among blacks relative to whites but greater
exposure to trauma (Louie and Wheaton 2019).
Explaining the minority mental health paradox
requires something more: It requires thinking about
risk factors that both are more common in the most
advantaged population and have less contingent
relationships with mental health. Very little research
has focused on precisely this set of empirical issues,
though a different and emerging literature could
provide clues (Qato, Ozenberger, and Olfson 2018).
Treatment Disparities in Health Care
A very well-developed literature has focused on dis-
parities in the receipt and quality of health care
(Institute of Medicine 2003). African Americans
tend to receive less medical care than whites, and
when they do receive care, it tends to be of lower
quality. Some of this difference is driven by access
to care, including the availability of health insur-
ance (Sohn 2017). Yet even when adjusting for
health insurance, African Americans still report
lower quality care. Consistent with this, some evi-
dence links treatment disparities to physicians’
implicit preference for white patients over patients
of color (Sabin et al. 2009; van Ryn et al. 2011). The
same pattern is found in prescription medications,
the most common form of medical intervention.
African Americans receive fewer prescription medi-
cations, and even among insured patients with the
same condition, black and Hispanic patients use sig-
nificantly fewer medications that white patients
(Briesacher, Limcangco, and Gaskin 2003). The
interpretation of this difference is not always consis-
tent. In some instances, the difference reflects over-
treatment on the part of non-Hispanic white patients,
as in the case of the overuse of antibiotics (Goyal
et al. 2017). Much of the evidence, though, points to
significant
undertreatment
among
minorities,
including with respect to the use of new essential
drugs (Wang et al. 2006) and the use of analgesics in
the treatment of pain (Meghani, Byun, and Gallagher
2012). Pharmaceutical innovations, like medical
innovations more generally, are usually given to the
most advantaged patients first, producing disparities
even as the medical armamentarium improves.
Treatment disparities of this sort have a number
of consequences, including higher mortality among
African American patients who are not adequately
treated (Morris et al. 2010). Yet some treatment dis-
parities might have the paradoxical effect of reduc-
ing the burden of depression among minority
populations, who either take fewer medications

Schnittker and Do
7
because they are prescribed fewer medications at a
given level of illness (i.e., blacks and Hispanics) or
take fewer medications because they have better
health (i.e., Asians). Prescription drug use is, in gen-
eral, very common, representing the average
American’s most routine medical treatment. In 2012,
nearly 60% of adults reported the use of at least one
prescription medication, and 15% reported using five
or more (Kantor et al. 2015). Such medications are
often highly effective in treating the condition they
were designed to treat or in reducing a known risk
factor. More directly relevant to mental health,
though, evidence points to a significant increase in
the use of medications with depression or suicide as
a significant side effect (Qato et al. 2018). Indeed, at
present, about 37% of U.S. adults take at least one
such medication, and 15% take three or more (Qato
et  al. 2018). The same data show a significant
increase in the risk of major depression associated
with using such medications, especially when taking
more than one (Qato et al. 2018).
To date, no study has attempted to link such
medications to racial-ethnic differences in depres-
sion. It remains unclear whether side effects of this
sort are relevant to understanding racial-ethnic
differences in depression, both because racial-
ethnic differences in the use of such medications
are not well understood and because few studies
have explored the implications of such medications
for population mental health. The present study
addresses these issues by using the Medical
Expenditure Panel Survey (MEPS), a high-quality
nationally representative survey useful for assess-
ing medication use.
Data and Methods
Data came from the 2008–2013 MEPS (see Cohen,
Cohen, and Banthin 2009), including the file made
available at the Integrated Public Use Microsample
repository at the University of Minnesota (Blewett
et al. 2018). The MEPS is a nationally representa-
tive probability survey of the civilian noninstitu-
tionalized U.S. population, drawn from a subsample
of households that participated in the prior year’s
National Health Interview Survey (NHIS). Both
MEPS and NHIS include oversamples: Black,
Hispanic, and low-income respondents are overs-
ampled to improve the precision of estimates for
these subgroups. The overall MEPS response rate
(after taking into account the nonresponse rate from
the NHIS) ranges from 46% to 71%. All the analyses
presented here employed survey weights to account
for oversampling and nonresponse.
The data are organized as annual files, though
data collection occurred through a series of overlap-
ping household panels. MEPS respondents enter the
survey annually as members of a panel. A new panel
is selected each year, and data are collected over five
rounds in two calendar years. The data are then orga-
nized into annual files, each of which will include
multiple panels. For each calendar year, data from
interview rounds one, two, and three are included for
individuals in their first of two years, and data from
interview rounds three, four, and five are included
for individuals in their second of two years.
Collectively, respondents from the two overlapping
panels in each year provide nationally representative
estimates for that year. More information is available
at https://meps.ahrq.gov/survey_comp/hc_data_col-
lection.jsp. We began with 2008 because it was the
first year to include comprehensive chronic disease
indicators. Listwise deletion was used to eliminate a
small number of missing cases, less than 3% for the
dependent variable and an additional 1% across the
set of remaining covariates.
Nonspecific Psychological Distress
The MEPS survey includes the Kessler K6, a well-
validated instrument assessing nonspecific psycho-
logical distress (Kessler et al. 2003). The K6 was
not fielded in every round: In each calendar year,
respondents in the first year of their panel were
given the K6 in the second round of interviews, and
respondents in the final year of their panel were
given the K6 in the fourth round. The K6 asks about
the frequency of six symptoms in the past 30 days.
Respondents were asked: “how often did you feel so
sad that nothing could cheer you up; nervous; rest-
less or fidgety; hopeless; that everything was an
effort; and worthless,” with response categories
ranging from none of the time (0) to all of the time
(4). The six items were combined to create a sum-
mary measure with scores ranging from 0 to 24. In
addition to a continuous score, some models
employed a categorical measure of significant dis-
tress, based on a threshold score of 13 or higher, as
used in previous studies (Reeves et al. 2011).
Medication Use
Among the most valuable features of MEPS is its
collection of detailed medication use. MEPS
includes the names of all the drugs taken by the
respondent. Although MEPS collected information
on drugs in every round, we did not include drugs
reported in rounds after respondents reported their

8
Journal of Health and Social Behavior 61(1)
mental health to ensure proper time ordering. Drug
information was then linked to information on
whether the specific drug taken by the respondent
involved suicide or depression as a side effect (in
separate categories). This linkage was based on pro-
cedures described in detail in Qato et al. (2018) and,
in particular, on lists provided in an online appendix
to that article. In that study, Qato and colleagues
(2018) linked information on medications, as
reported in the National Health and Nutritional
Examination Study (NHANES), to information on
adverse effects provided in Micromedex, a proprie-
tary database created by Truven Health Analytics,
which, in turn, is based on information provided by
the U.S. Food and Drug Administration (Barrons
2004; Cheng et al. 2010). After using the informa-
tion provided in Qato et al. (2018) we created two
variables, the first corresponding to the number of
drugs the respondent was taking with depression as
a side effect and the second corresponding to the
number of drugs the respondent was taking with sui-
cide as a side effect. In each case, we divided the
sum into four categories, consistent with the Qato
et  al. (2018) analysis of the NHANES: no drugs
with such a side effect (the reference category), one
drug, two drugs, or three or more drugs. In some of
the descriptive information presented in our study,
we refer to actual counts rather than categories. In
addition, in some analyses, we removed antidepres-
sants from the suicide side-effect count (a sensitiv-
ity test discussed in more detail shortly). The list of
drugs included both prescription and over-the-
counter medications, though most were prescrip-
tion. The Qato et al. (2018) analysis was based on
reports of prescription medications in NHANES,
though some prescription medications have over-
the-counter formulas.
Sociodemographic Variables
Race-ethnicity was divided into four groups: non-
Hispanic white, non-Hispanic black, Hispanic, and
Asian. Less than 2% of the sample consisted of an
“other” race-ethnicity group, and because of their
small numbers, they were dropped from the analy-
sis. The models also included controls for the sex of
the respondent, marital status (married; widowed,
divorced, or separated; never married), age (18–to
39, 40–64, 65–79, and 80 and over), education (less
than high school, high school or equivalent, college
or higher), insurance status, and poverty (below
100% of the federal poverty line for household
income). All the models also controlled for the year
of the survey using a series of dummy variables.
Chronic Conditions
Many of the medications with either depression or
suicide as a side effect are used in the treatment of
chronic conditions (as opposed to prevention). To
control for physical health comorbidities, which
themselves could play a powerful role in depres-
sion, the models included controls for whether the
respondent was ever told they had each of 11 condi-
tions: angina pectoris, arthritis, asthma, coronary
heart disease, diabetes, high cholesterol, emphy-
sema, a heart attack, heart disease or other heart
condition, hypertension, or a stroke. These condi-
tions were entered into the regression model as
separate covariates, and in some specifications,
interactions among all pair-wise conditions were
explored to assess the influence of comorbidity.
Results
The results begin by describing basic differences in
the use of medications with depression and suicide
as side effects, as presented in Tables 1, 2, and 3.
Table 1 presents basic demographic information,
Table 2 presents drug use information, and Table 3
presents means for the distress measures arrayed
over race-ethnicity and drug use. Table 1 shows,
among other things, few significant differences in
distress on average. There is no black-white or
Hispanic-white paradox, though in basic multivari-
ate models (see Table 3), a significant difference
emerges. Table 2 shows large racial-ethnic differ-
ences in the use of medications with side effects.
About 60% of non-Hispanic whites do not use med-
ications with depression as a side effect, but this
percentage is much higher among minorities, espe-
cially Asians and Hispanics. About 5% of whites
use three or more such medications, relative to
slightly over 4% of blacks, 1% of Asians, and 2% of
Hispanics. The same pattern is found for drugs
involving suicide as a side effect, though the racial-
ethnic differences are, if anything, larger. About
72% of whites use no such medications, relative to
85% of blacks, 91% of Asians, and 87% of
Hispanics. The white population uses three or more
such medications at levels that are at least two-to-
one relative to racial-ethnic minorities. These differ-
ences are not driven by any particular class of
medication. For most specific subclasses, the same
pattern is evident, with the notable exception of
analgesics with depression as a side effect, where
blacks are slightly more likely to take such medica-
tions than non-Hispanic whites. The single largest
contributor to the overall count is antidepressants
with suicide as a side effect. These medications are

Schnittker and Do
9
taken by nearly 16% of non-Hispanic whites but
fewer than 7% of racial-ethnic minorities.
Table 2 also presents the prevalence of specific
chronic conditions, once again arrayed over race-
ethnicity. In this case, the racial-ethnic differences
are somewhat different. In general, many chronic
conditions are more common among non-Hispanic
whites than minorities, though there are notable
exceptions. Blacks report a considerably higher
prevalence of diabetes, hypertension, stroke, and
asthma. For many chronic conditions, Asians and
Hispanics report a considerably lower prevalence
than non-Hispanic whites.
Table 3 presents means for the distress variables
arrayed by race-ethnicity and drug use. It also
shows two patterns in particular that set the stage
for the multivariate models. In general, the use of
medications with side effects increases the preva-
lence of significant distress and the K6 score.
Furthermore, racial-ethnic differences in distress
are different within the rows denoting the use of no
drugs with side effects. Within this group, the
prevalence of significant distress is generally higher
among blacks than among whites (the difference is
statistically significant for three of four compari-
sons). Similarly, the prevalence of distress is higher
among Hispanics (for three of four comparisons).
The regression models presented in Table 4
explore the consequences of such medications for
the minority advantage in mental health. Pairs of
models are presented for each model specification,
the first using linear regression for the K6 summary
score and the second using logit regression for the
categorical coding of the K6. The use of control
variables is critical both for understanding the mag-
nitude of the paradox and for contextualizing the
influence of pharmaceutical use. The baseline
model controls for socioeconomic status (as well as
other variables) and shows a minority advantage,
both with respect to a dimensional measure of dis-
tress and a categorical one. The magnitude of the
difference is large, with minorities showing at least
a 20% reduction in the odds of reporting significant
distress. The second pair of models inserts controls
Table 1.  Descriptive Statistics for Socioeconomic and Demographic Variables.
Race and Ethnicity

Non-Hispanic
White
Non-Hispanic
Black
Non-Hispanic
Asian
Hispanic
Significant distress (K6 ≥ 13) (%)
4.95
5.36
3.09***
5.35
K6 score (continuous, minimum = 0,
maximum = 24)
3.38
3.29
2.87***
3.34
Female (%)
51.60
54.94***
53.16*
49.30***
Age (%)
  18–44
34.13
43.71***
45.09***
53.68***
  45–64
45.13
43.67*
41.90**
37.48***
  65–79
15.18
9.82***
10.14***
6.93***
  80+
5.56
2.80***
2.87***
1.90***
Marital status (%)
  Married
57.96
34.19***
62.02**
48.82***
  Widowed, divorced, separated
20.27
23.44***
11.54***
16.45***
  Never married
21.78
42.37***
26.44***
34.73***
Educational attainment (%)
  Less than high school
9.90
18.38***
13.03***
38.30***
  High school graduate
29.79
34.02***
16.36***
27.44***
  College or above
60.31
47.60***
70.61***
34.26***
Less than 100% of poverty guideline (%)
8.16
20.70***
9.73*
19.38***
Has any type of insurance (%)
89.28
81.50***
85.30***
63.81***
Number of observations
58,508
24,899
9,796
34,055
Note: All estimates are weighted using survey weights. Data from Medical Expenditure Panel Survey 2008–2013.
*p < .05, **p < .01, ***p < .001 (for test of significant differences between non-Hispanic whites and other racial/ethnic
groups).

10
Journal of Health and Social Behavior 61(1)
Table 2.  Descriptive Statistics for Medications with Depression or Suicide as a Potential Side Effect.
Race and Ethnicity

Non-Hispanic
White
Non-Hispanic
Black
Non-Hispanic
Asian
Hispanic
Taking any drugs with depression side effects (%)
  None
60.14
66.85***
79.44***
75.52***
  1 drug
25.20
20.80***
15.43***
16.77***
  2 drugs
9.79
8.10***
3.84***
5.48***
  3+ drugs
4.88
4.25**
1.29***
2.23***
Taking any drugs with depression side effects by subclass (%)
  Analgesics
14.62
15.52*
5.82***
11.05***
  Antihypertensives
11.53
9.79***
6.45***
5.46***
  Corticosteroids
5.50
3.63***
2.03***
2.48***
  Gastrointestinal agents
9.20
6.98***
4.13***
5.56***
  Hormones/hormone modifiers
9.05
4.89***
4.47***
4.09***
  Respiratory agents
.54
.65
.24***
.48
  Others
5.18
4.63*
1.94***
2.65***
Taking drugs with suicide side effects (%)
  None
72.45
84.98***
91.05***
86.82***
  1 drug
16.38
9.57***
6.42***
8.49***
  2 drugs
6.51
3.36***
1.50***
2.65***
  3+ drugs
4.66
2.09***
1.02***
2.04***
Taking any drugs with suicide side effects by subclass (%)
  Analgesics
2.67
2.51
.72***
1.83***
  Anticonvulsants
7.96
5.00***
2.61***
3.86***
  Anxiolytics, hypnotics, sedatives
7.28
2.90***
1.87***
3.27***
  Antidepressants
15.85
6.17***
3.34***
5.98***
  Gastrointestinal agents
.38
.43
.22*
.31
  Hormones/hormone modifiers
1.04
.47***
.55***
.38***
  Respiratory agents
1.74
1.18***
.92***
.88***
  Others
5.65
3.61***
2.19***
3.21***
Chronic conditions (%)
  Angina pectoris
3.04
2.14***
1.43***
1.58***
  Arthritis/gout/lupus/fibromyalgia
28.94
24.27***
12.04***
13.88***
  Asthma
9.66
11.07***
5.42***
6.76***
  Coronary heart disease
6.52
4.35***
2.84***
3.62***
  High cholesterol
33.90
26.25***
27.35***
23.80***
  Diabetes
8.53
12.13***
8.88
9.32*
  Emphysema
2.82
1.30***
.49***
.55***
  Heart attack
4.30
3.14***
1.56***
2.01***
  Heart disease (exclude coronary heart
disease, angina, heart attack)
13.06
8.62***
4.27***
4.53***
  Hypertension
34.02
40.67***
24.17***
23.59***
  Stroke
3.85
4.53**
1.57***
1.95***
Number of observations
58,508
24,899
9,796
34,055
Note: All estimates are weighted using survey weights. Data from Medical Expenditure Panel Survey 2008–2013.
*p < .05, **p < .01, ***p < .001 (for test of significant differences between non-Hispanic whites and other racial-ethnic
groups).

Schnittker and Do
11
for each of the chronic health conditions. As
expected, this adjustment alters the magnitude of
the racial-ethnic differences, though in ways spe-
cific to each racial-ethnic group. The black-white
difference is suppressed somewhat by physical
health, such that the black advantage would be even
larger—about 7% larger for the K6 score—were it
not for a black disadvantage in physical health. For
both Hispanics and Asians, however, the inclusion
of controls for chronic conditions explains some of
the apparent mental health advantage. The Asian
advantage for the K6 is reduced by about 67%,
whereas the Hispanic advantage is reduced by 46%.
The difference between Hispanics and whites in
significant distress, meanwhile, is reduced to statis-
tical nonsignificance.
The next two models introduce controls for the
use of pharmaceuticals involving depression and
suicide as side-effects. In the first pair of models
(Model 3), the count of medicines involving suicide
includes antidepressants, but in the second pair
(Model 4), the count excludes them. The inclusion
of the side-effect variables explains what remains
of much of the racial-ethnic advantage. Although
the black-white difference in the K6 summary
remains significant and shows a black advantage, it
is reduced a great deal, by 60%, relative to Model 1.
The difference in significant distress, meanwhile, is
reduced to statistical nonsignificance. A similar pat-
tern is found for the Asian-white difference, and in
fact, the Asian advantage in the K6 turns into a sig-
nificant Asian disadvantage. Similarly, the Hispanic
advantage with respect to significant distress in
Model 1 is reversed in Model 4, turning into a sig-
nificant Hispanic disadvantage. Formal tests of
mediation using structural equations and the contin-
ued K6 outcome provide further evidence for medi-
ation. In Model 3, the indirect effect of black was
–.359 (p < .001), and the direct effect was –.214
(p < .001). The parallel effects for Asians and
Hispanics were, respectively, –.375 (p < .001) and
.235 (p < .001) and –.222 (p < .001) and –.031
(p = .583). In Model 4, similar patterns of signifi-
cance among direct and indirect effects were found
Table 3.  Descriptive Statistics for Nonspecific Psychological Distress by Race-Ethnicity and the Use of
Medications with Depression and Suicide Side Effects.
Race and Ethnicity

Non-Hispanic
White
Non-Hispanic
Black
Non-Hispanic
Asian
Hispanic
Significant distress (K6 ≥ 13), % (SE)
  No drugs with depression side effects
3.26 (.13)
3.96 (.20)**
2.29 (.23)**
3.80 (.17)**
  1 drug with depression side effects
5.46 (.23)
6.05 (.33)
4.49 (.66)
7.77 (.59)***
  2 drugs with depression side effects
9.33 (.45)
10.03 (.73)
10.06 (1.86)
12.17 (1.19)*
  3 drugs with depression side effects
14.35 (.78)
15.16 (1.30)
15.00 (3.75)
22.93 (2.60)***
  No drugs with suicide side effects
2.46 (.11)
3.55 (.17)***
2.20 (.22)
3.21 (.15)***
  1 drug with suicide side effects
6.52 (.27)
10.21 (.78)***
9.05 (1.34)*
12.39 (.90)***
  2 drugs with suicide side effects
12.39 (.69)
19.45 (1.62)*** 14.79 (3.75)
25.41 (1.80)***
  3 drugs with suicide side effects
27.67 (1.16)
34.04 (2.27)**
28.15 (6.27)
41.10 (2.60)***
K6 score, mean (SE)
  No drugs with depression side effects
2.86 (.03)
2.81 (.05)
2.62 (.07)**
2.87 (.05)
  1 drug with depression side effects
3.62 (.05)
3.73 (.09)
3.40 (.13)
4.27 (.10)***
  2 drugs with depression side effects
4.62 (.09)
4.57 (.14)
5.02 (.37)
5.36 (.21)**
  3 drugs with depression side effects
5.99 (.14)
6.30 (.22)
5.68 (.65)
7.16 (.39)**
  No drugs with suicide side effects
2.63 (.03)
2.79 (.04)**
2.61 (.06)
2.80 (.05)**
  1 drug with suicide side effects
4.16 (.06)
4.89 (.13)***
4.74 (.28)*
5.52 (.16)***
  2 drugs with suicide side effects
5.90 (.11)
7.12 (.29)***
6.37 (.56)
8.14 (.30)***
  3 drugs with suicide side effects
8.74 (.17)
9.97 (.32)**
9.06 (1.00)
10.91 (.39)***
Number of observations
58,508
24,899
9,796
34,055
Note: All statistics are weighted using survey weights. Data from Medical Expenditure Panel Survey 2008–2013.
*p < .05, **p < .01, ***p < .001 denote whether the estimates are significantly different between non-Hispanic whites
and other racial-ethnic groups.

12
Journal of Health and Social Behavior 61(1)
Table 4.  Linear Regression and Logistic Regression Analysis of K6 Score and Significant Distress (K6 ≥ 13).
Model 1
Model 2
Model 3
Model 4

(Basic Controls)
(Model 1 + Chronic
Conditions)
(Model 2 + Drugs
with Depression/
Suicide Side Effects)
(Model 2 + Drugs with
Depression/Suicide
Side Effects, Excluding
Antidepressants)

K6 Score
(K6 ≥
13)
K6 Score
(K6 ≥
13)
K6 Score
(K6 ≥ 13)
K6 Score
(K6 ≥ 13)

Coeff. (SE)
OR (SE)
Coeff. (SE)
OR (SE)
Coeff. (SE)
OR (SE)
Coeff. (SE)
OR (SE)
Race and ethnicity (non-Hispanic white is reference)
  Non-Hispanic
black
–.534***
(.057)
.779***
(.039)
–.573***
(.057)
.754***
(.040)
–.214***
(.052)
1.019
(.059)
–.362***
(.053)
.900
(.050)
  Non-Hispanic
Asian
–.424***
(.068)
.654***
(.056)
–.140*
(.066)
.822*
(.069)
.235***
(.065)
1.158
(.099)
.103
(.065)
1.020
(.087)
  Hispanic
–.468***
(.066)
.795***
(.043)
–.254***
(.061)
.940
(.050)
–.031
(.057)
1.139*
(.062)
–.122*
(.058)
1.051
(.056)
Whether taking drugs with depression side effects (none is reference)
  1 drug
.266***
(.033)
1.166***
(.050)
.348***
(.034)
1.254***
(.053)
  2 drugs
.543***
(.061)
1.358***
(.079)
.725***
(.063)
1.535***
(.090)
  3+ drugs
.860***
(.103)
1.363***
(.100)
1.156***
(.110)
1.590***
(.123)
Whether taking drugs with suicide side effects (none is reference)
  1 drug
1.381***
(.048)
2.600***
(.119)

  2 drugs
2.840***
(.093)
4.502***
(.280)

  3+ drugs
5.258***
(.141)
10.295***
(.735)

Whether taking drugs with suicide side effects (exclude antidepressant drugs; none is reference)
  1 drug
1.447***
(.055)
2.386***
(.105)
  2 drugs
2.625***
(.114)
3.832***
(.267)
  3+ drugs
5.081***
(.186)
7.916***
(.640)
Female (0/1)
.473***
(.032)
1.237***
(.044)
.451***
(.032)
1.200***
(.046)
.160***
(.028)
.974
(.037)
.258***
(.029)
1.051
(.040)
Age
  18–39 (reference)
  40–64
.345***
(.045)
1.582***
(.081)
–.309***
(.043)
.932
(.055)
–.365***
(.039)
.873*
(.050)
–.351***
(.040)
.884*
(.051)
  64–79
–.272***
(.062)
.968
(.075)
–1.825***
(.069)
.346***
(.032)
–1.679***
(.066)
.380***
(.035)
–1.762***
(.068)
.363***
(.034)
  80+
.015
(.101)
.974
(.089)
–1.754***
(.102)
.334***
(.032)
–1.435***
(.098)
.423***
(.042)
–1.653***
(.102)
.371***
(.036)
Marital status (married is reference)
  Widowed,
divorced,
separated
1.035***
(.056)
1.944***
(.099)
.812***
(.055)
1.692***
(.088)
.657***
(.050)
1.552***
(.081)
.725***
(.053)
1.621***
(.085)
  Never married
.438***
(.050)
1.371***
(.077)
.499***
(.048)
1.445***
(.077)
.424***
(.044)
1.377***
(.073)
.439***
(.046)
1.393***
(.075)
(continued)

Schnittker and Do
13
Model 1
Model 2
Model 3
Model 4

(Basic Controls)
(Model 1 + Chronic
Conditions)
(Model 2 + Drugs
with Depression/
Suicide Side Effects)
(Model 2 + Drugs with
Depression/Suicide
Side Effects, Excluding
Antidepressants)

K6 Score
(K6 ≥
13)
K6 Score
(K6 ≥
13)
K6 Score
(K6 ≥ 13)
K6 Score
(K6 ≥ 13)

Coeff. (SE)
OR (SE)
Coeff. (SE)
OR (SE)
Coeff. (SE)
OR (SE)
Coeff. (SE)
OR (SE)
Educational attainment (less than high school is reference)
  High school
graduate
–.435***
(.064)
.776***
(.036)
–.341***
(.061)
.826***
(.039)
–.359***
(.055)
.802***
(.038)
–.347***
(.056)
.816***
(.039)
  College or
above
–.943***
(.064)
.475***
(.027)
–.707***
(.060)
.557***
(.031)
–.726***
(.055)
.531***
(.029)
–.709***
(.058)
.546***
(.031)
Under federal
poverty
guideline
1.600***
(.068)
2.522***
(.107)
1.379***
(.063)
2.188***
(.095)
1.189***
(.058)
1.979***
(.085)
1.228***
(.059)
2.007***
(.088)
Has any insurance
–.005
(.061)
1.022
(.053)
–.191**
(.058)
.870*
(.047)
–.415***
(.056)
.687***
(.037)
–.370***
(.055)
.728***
(.039)
Chronic
conditionsa
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Year fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Number of
observations
127,258
127,258
127,258
127,258
127,258
127,258
127,258
127,258
Note: All analyses are weighted using survey weights. Data from Medical Expenditure Panel Survey 2008–2013. OR =
odds ratio; coeff. = coefficient.
aChronic conditions include a set of binary variables that indicate whether a person has ever been told by a doctor
or health professional that he or she has the following conditions: angina pectoris, arthritis, asthma, coronary heart
disease, high cholesterol, diabetes, emphysema, heart attack, heart condition/disease (excluding coronary heart
disease, angina, and heart attack), hypertension, and stroke.
*p < .05, **p < .01, ***p < .001.
Table 4.  (continued)
with the exception of the direct effect of Asian was
no longer significant (b = .103, p = .115) and the
direct effect of Hispanic emerged as borderline sig-
nificant (b = –.122, p = .037).
These patterns are due in part to the particularly
powerful relationship between drugs with suicide
as a side effect and the risk of significant distress,
especially when using three or more such medica-
tions. Taking three or more medications with sui-
cide as a side effect results in an increase in the
odds of significant distress by a factor of more than
10. Even one such medication more than doubles
the odds of significant distress. Drugs with depres-
sion as a side effect are consequential, too, though
the relationship is considerably weaker than it is for
suicide. To be sure, some of the relationship
between drugs with suicide as a side effect and sig-
nificant distress could be driven by the inclusion of
antidepressants in the count. Such drugs involve
suicide as a side effect and therefore are relevant to
considering nonspecific psychological distress, but
such drugs obviously do not involve depression as a
side effect. The next pair of models eliminates anti-
depressants from the count. This reduces the rela-
tionship between drugs with suicide as a side effect
and the risk of significant distress, though it does
not reduce the relationship by much. For instance,
in the K6 models, the coefficient for three or more
such drugs is reduced by only 3% between Models
3 and 4. Moreover, pharmaceutical side effects con-
tinue to explain the racial-ethnic minority advan-
tage with respect to significant distress. The
mediating effect for the black-white difference in
the K6 score is weaker than for Model 3, though
side effects still explain 32% of the difference.
Table 5 employs several sensitivity tests. The
primary threat to the models presented in Table 4 is
that the relationship between the use of pharma
ceuticals with side effects and the K6 reflects
unobserved heterogeneity rather than side effects

14
Table 5.  Sensitivity Analysis of K6 Score and Significant Distress (K6 ≥ 13) Regression Models.
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6

All Adults 18+
All Adults 18+
All Adults 18+
All Adults 18+
Young Adults 18–30
Young Adults 18–30

K6 Score (K6 ≥ 13) K6 Score (K6 ≥ 13) K6 Score (K6 ≥ 13) K6 Score (K6 ≥ 13) K6 Score (K6 ≥ 13) K6 Score
(K6 ≥ 13)

Coeff. (SE) OR (SE) Coeff. (SE) OR (SE) Coeff. (SE) OR (SE) Coeff. (SE) OR (SE) Coeff. (SE) OR (SE) Coeff. (SE) OR (SE)
Race and ethnicity (non-Hispanic white is reference)
  Non-Hispanic black
−.364***
(.053)
.904
(.050)
−.350***
(.053)
.903
(.050)
−.707***
(.094)
.767*
(.083)
−.463***
(.087)
.977
(.106)
−.500***
(.104)
.811
(.093)
−.276**
(.104)
.979
(.115)
  Non-Hispanic Asian
.115
(.065)
1.042
(.088)
.128
(.065)
1.033
(.088)
−.245
(.129)
.552*
(.130)
.018
(.129)
.696
(.163)
−.395**
(.131)
.589*
(.121)
−.165
(.133)
.699
(.149)
  Hispanic
−.123*
(.058)
1.061
(.057)
−.103
(.058)
1.061
(.057)
−.447***
(.097)
.684***
(.069)
−.260**
(.097)
.818
(.084)
−.514***
(.087)
.779*
(.077)
−.315***
(.086)
.912
(.096)
Whether taking drugs with depression side effects (none is reference)
  1 drug
.339***
(.034)
1.223***
(.053)
.284***
(.034)
1.204***
(.052)
.413***
(.077)
1.317**
(.111)
.262**
(.089)
1.058
(.100)
  2 drugs
.729***
(.064)
1.507***
(.090)
.656***
(.065)
1.468***
(.090)
.665***
(.135)
1.702***
(.198)
.433**
(.166)
1.513*
(.264)
  3+ drugs
1.172***
(.110)
1.585***
(.121)
1.094***
(.111)
1.527***
(.120)
1.089***
(.287)
1.768**
(.375)
1.373***
(.387)
1.948*
(.503)
Whether taking drugs with suicide side effects (exclude antidepressant drugs; none is reference)
  1 drug
1.440***
(.055)
2.358***
(.104)
1.406***
(.055)
2.320***
(.099)
1.466***
(.123)
2.624***
(.248)
1.236***
(.166)
2.006***
(.269)
  2 drugs
2.609***
(.114)
3.777***
(.265)
2.584***
(.114)
3.722***
(.255)
2.600***
(.247)
3.949***
(.567)
2.580***
(.365)
3.501***
(.813)
  3+ drugs
5.071***
(.183)
7.796***
(.621)
5.043***
(.185)
7.684***
(.612)
5.959***
(.456)
13.792***
(2.298)
5.367***
(.624)
8.440***
(2.158)
(continued)

15
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6

All Adults 18+
All Adults 18+
All Adults 18+
All Adults 18+
Young Adults 18–30
Young Adults 18–30

K6 Score (K6 ≥ 13) K6 Score (K6 ≥ 13) K6 Score (K6 ≥ 13) K6 Score (K6 ≥ 13) K6 Score (K6 ≥ 13) K6 Score
(K6 ≥ 13)

Coeff. (SE) OR (SE) Coeff. (SE) OR (SE) Coeff. (SE) OR (SE) Coeff. (SE) OR (SE) Coeff. (SE) OR (SE) Coeff. (SE) OR (SE)
Whether taking drugs without depression/suicide side effects (none is reference)
  1 drug
.344***
(.041)
1.180**
(.067)
.507***
(.073)
1.419**
(.153)
  2 drugs
.322***
(.049)
1.266***
(.086)
.600***
(.112)
1.593***
(.218)
  3+ drugs
.330***
(.056)
1.243**
(.084)
.690***
(.147)
1.336*
(.195)
Chronic conditionsa
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Comorbidityb
Yes
Yes
No
No
No
No
No
No
No
No
No
No
Year fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Number of observations
127,258
127,258
127,258
127,258
27,494
27,494
27,494
27,494
30,841
30,841
30,841
30,841
Note: All analyses are weighted using survey weights. All analyses control for gender, age, marital status, education, poverty status, and insurance status as in Table 3. Data from
Medical Expenditure Panel Survey 2008–2013. OR = odds ratio; coeff. = coefficient.
aChronic conditions include a set of binary variables that indicate whether a person has ever been told by a doctor or health professional that he or she has the following conditions:
angina pectoris, arthritis, asthma, coronary heart disease, high cholesterol, diabetes, emphysema, heart attack, heart condition/disease (excluding coronary heart disease, angina, and
heart attack), hypertension, and stroke.
bComorbidity includes all pairwise interactions between chronic conditions.
*p < .05, **p < .01, ***p < .001.
Table 5.  (continued)

16
Journal of Health and Social Behavior 61(1)
specifically, most likely with respect to poor physi-
cal health. The models presented in Table 4 already
control for a broad set of chronic conditions,
though there are other possibilities that can be
addressed empirically. The first model of Table 5
presents a regression model identical to Model 3 of
Table 4 but includes all possible two-way interac-
tions between the chronic conditions dummy vari-
ables (coefficients not shown). This model, in
effect, tests whether patterns in comorbidity are
important in a nonadditive fashion. The inclusion
of these interactions does little to change the
parameters for race-ethnicity or the parameters for
pharmaceutical side effects.
There are other ways to explore unobserved het-
erogeneity with respect to health. It is possible that
drugs involving depression or suicide as a side
effect have no more of a relationship with the K6
than drugs that do not have depression or suicide as
a side effect—that the entire relationship between
pharmaceutical use and the K6 is driven by under-
lying treated illness. Other studies have explored
this possibility and found that drugs without depres-
sion or suicide as a side effect are statistically unre-
lated to depression (Qato et al. 2018), though it is
important to see if the same holds true in the present
study, which uses both a different data set and a dif-
ferent outcome. Model 2 suggests other drugs have
a significant relationship with the K6, too, though
the magnitude of the relationship is much weaker
than it is for drugs involving suicide as a side effect.
For each pair-wise coefficient comparison (i.e., one
medication with suicide as a side effect is equal to
one drug with neither depression nor suicide as a
side effect), the difference is statistically significant
at p < .001.
A narrower sensitivity test is to estimate the
same models among those with only one chronic
condition, thereby eliminating respondents with
complex comorbidities altogether. Model 3 pres-
ents a baseline model, including all the controls
presented in Model 1 of Table 4 but limiting the
sample to those with a single condition. Model 4
then introduces controls for medications with
depression and suicide as side effects. The same
pattern is evident for this subsample as it is for the
full sample. All the coefficients indicating a racial-
ethnic minority advantage are eliminated once con-
trols for medications with depression or suicide as a
side effect are introduced.
The final two models provide an even more
stringent test. Pharmaceuticals are taken for many
reasons, including the treatment of disease but also
its prevention and the treatment of risk rather than
disease itself. Younger people are, on average,
healthier than older people and so consume phar-
maceuticals under different conditions. Models 5
and 6 limit the sample to those between the ages of
18 and 30. Although the magnitude of the racial-
ethnic difference in distress itself differs in this sub-
sample, the influence of pharmaceuticals with side
effects is very similar to that estimated in the full
sample, greatly reducing or eliminating the minor-
ity advantage.
Discussion
This study used nationally representative data to
explore the consequences of pharmaceutical side
effects for racial-ethnic differences in distress. The
results indicate that medications play a significant
role in shaping the apparent mental health advan-
tage among racial-ethnic minorities, though there is
some variation between groups and between the
continuous and categorical codings of the K6. The
black-white difference in significant distress is
reduced to statistical nonsignificance in a model
controlling for side effects. The black-white differ-
ence in the continuous distress score remains sig-
nificant, though the difference, as estimated with
basic controls, is reduced by 32% in a model that
excludes antidepressants (Model 4) and by 60% in a
model that includes them (Model 3). The advantage
in significant distress for Asians is reduced to statis-
tical nonsignificance, and at least in Model 3, the
advantage for significant distress turns into a sig-
nificant disadvantage among Hispanics. In the case
of Asians, the advantage for continuous distress is
reduced to nonsignificance in Model 4. Although
differences between whites and both Hispanics and
blacks in continuous distress remain in Model 4,
pharmaceutical side effects play an important role in
explaining the minority mental health paradox.
These patterns are determined by two things.
First, racial-ethnic differences in the use of phar-
maceuticals are large. For virtually every class of
medication, non-Hispanic whites consume more
medications with side effects than do racial-ethnic
minorities. The Asian-white difference and Hispanic-
white difference is larger than the black-white differ-
ence, though all three differences are statistically
significant. The fact that medications with side
effects cut across disease categories suggests that the
role of medications is not limited to domains where
treatment disparities are well established or where
medications are especially consequential for health,
including, for instance, the more aggressive treat-
ment of cardiovascular disease among non-Hispanic

Schnittker and Do
17
whites (e.g., Davis et al. 2007). For this reason, the
role of pharmaceutical side effects in the minority
mental health advantage is likely to be preserved
even if some especially prominent treatment dis-
parities are eliminated. Second, the pattern is
shaped by the large impact of these pharmaceutical
on psychological distress, a pattern found in other
data sets with different measures of depression
and, of course, a pattern anticipated based on the
observation of significant side effects in drug trials
and postmarketing surveillance (Qato et al. 2018).
In our study, drugs with suicide as a side effect are
especially consequential. A single such medication
more than doubles the odds of significant distress.
Relative to other factors implicated in minority
mental health, this relationship is large. For
instance, it is as large as the relationship between
poverty and significant distress and much larger
when it involves three or more such medications.
Furthermore, the percentage of the population
exposed to this risk is not small. About 28% of
non-Hispanic whites take at least one such medica-
tion, though no more than 15% of racial-ethnic
minorities do. To be sure, the reason minority
patients consume fewer such pharmaceuticals dif-
fers between racial-ethnic groups. Among African
Americans, the lower consumption of such phar-
maceuticals likely reflects a treatment disparity,
whereas among Asians, it likely reflects better
health. For this reason, controls for comorbid med-
ical conditions play a different role between these
groups, suppressing the difference for the former
and reducing it for the latter. Furthermore, the
mediating effect of drugs with side effects is
smaller for the black-white difference than for
other race-ethnic differences. Nonetheless, the fact
that the non-Hispanic white population consumes
more medications with significant side effects
plays an important role in undermining their men-
tal health relative to other racial-ethnic groups.
More research is needed on the social process
that produces between-group differences in the use
of pharmaceuticals. In this regard, there are at least
two distinct topics to address: first, the process that
leads to a race-ethnic difference in prescription
drug use overall and second, the process that leads
to the frequent use of medications with depression
or suicide as a side effect. The relatively low levels
of pharmaceutical use on the part of African
Americans and Hispanics likely reflects a combina-
tion of patient and provider influences. On the one
hand, racial-ethnic minorities might be less
demanding with respect to requesting prescriptions.
Some evidence indicates that black and Hispanic
patients express greater concern than white patients
regarding the quality-of-life effects of pharmaceuti-
cals, including concerns about side effects (Adams
et al. 2018; Huang et al. 2009). It is unclear where
this concern comes from, though it need not reflect
a misapprehension of the risks and benefits of phar-
maceuticals. Indeed, recent evidence indicates that
non-Hispanic white populations may be, if any-
thing, too credulous of pharmaceuticals, rarely dis-
cussing alternative forms of treatment (Adams et al.
2018) or assuming that the use of a pharmaceutical
is the privilege and right of a properly treated
“pharmaceutical person” (Ballantyne et al. 2018).
In addition, evidence indicates that the relationship
between physical health and mental health is gener-
ally stronger for whites, perhaps increasing the
demand for treatment irrespective of the taste for
pharmaceuticals (Assari and Lankarani 2016). On
the other hand, though, there is evidence that differ-
ences in prescription drug use emerge further
upstream and involve the behavior of physicians.
Physicians may hold beliefs that lead to significant
undertreatment among racial-ethnic minorities,
including the belief that African Americans tolerate
pain better than non-Hispanic whites and therefore
require fewer analgesics (Hoffman et al. 2016) or
that white patients are more “medically coopera-
tive” and therefore are more likely to follow
through with treatment recommendations (Oliver
et  al. 2014). Understanding the white advantage
with respect to pharmaceuticals requires under-
standing the reasons they are prescribed medica-
tions at higher rates as well as the reasons they
might persist in taking such medications even when
those medications present significant side effects.
It is also important to understand whether and
how physicians consider psychological side effects
when treating physical disease. One possibility is
that physicians largely ignore the risk of depres-
sion as a side effect, either because the medications
involved have proven value in treating illnesses of
putatively greater significance or because physi-
cians infer that a patient’s depression is caused by
disease. Many drugs with depression or suicide as
a side effect are effective in treating the conditions
they were designed to treat, to the point of being
essential. For instance, some common antihyper-
tensives involve depression as a side effect, though
they also reduce blood pressure quickly and reduce
the risk of cardiac events (Beers and Passman
1990). When presented with a significant risk fac-
tor like hypertension, a physician might ignore
potential psychological side effects in favor of
effectively treating the risk at hand. At the same

18
Journal of Health and Social Behavior 61(1)
time, physicians might disregard depression in
those patients who present with other significant
illnesses. Oncologists’ sensitivity to depression in
their patients, for instance, can be as low as 6%
(Newell et  al. 1998). There are several explana-
tions for why physicians overlook depression, but
one is a straightforward causal inference: that phy-
sicians expect depression in cases of severe physi-
cal illness and that any depression will resolve if
and when the illness that occasioned it is treated.
Whatever the explanation, it is almost certainly the
case that physicians either disregard the psycho-
logical side effects of medications or regard them
as less clinically significant.
In general, the results of this study speak to the
problem of multiple morbidity, hinting at some of its
complexities for understanding population health.
The use of multiple pharmaceuticals has increased
over time, in tandem with the increasing intensity of
medical intervention (Aronowitz 2012; Swinglehurst
and Fudge 2017), a pattern some refer to as “pharma-
ceuticalization” in reference to the parallel concept
of medicalization (Abraham 2010). Polypharmacy
itself has been linked to adverse events and nonad-
herence, and there has been some discussion on how
to assist physicians in managing the practice (Grando
et al. 2012). Yet little is known about multiple mor-
bidity per se or the ways in which the treatment of
one disease has consequences for another, in large
part because medical science continues to be orga-
nized around discrete disease categories. It is
unlikely that medical treatment always involves
complementarities between diseases. Some evidence
points to a rise, for instance, in emergency room vis-
its for adverse effects of medical treatment (Bernstein
et al. 2003). But a lack of complementarities is per-
haps especially likely when mental illness has con-
sistently played a secondary role to physical illness.
The scope of the side-effects issue is broad, and
future research should consider the role of pharma-
ceutical side effects in other psychiatric disorders,
including side effects related to anxiety, confusion,
and psychotic syndromes.
As implied in the introduction, the results do not
speak directly to any theory in medical sociology
for understanding the mental health paradox given
how the literature has mostly centered on adum-
brating risks among the disadvantaged, though the
results are at least thematically consistent with cer-
tain facets of the literature. Research on fundamen-
tal causes, for instance, has occasionally uncovered
countervailing mechanisms, providing some degree
of advantage for an otherwise disadvantaged popu-
lation (Lutfey and Freese 2005). Mechanisms of
such sort are not unanticipated when disease is
caused by a complex social process involving a
mix of intentional and unintentional influences.
Subsequent refinements of the fundamental cause
theory have explicitly called for a deeper consider-
ation of such countervailing mechanisms and has
cast such mechanisms either in terms of well-
resourced people pursuing multiple goals, some of
which occasionally cut against health, or in terms of
imperfect knowledge regarding how to pursue bet-
ter health (Phelan, Link, and Tehranifar 2010). Yet
Phelan and colleagues (2010) cautioned scholars to
avoid using the concept of countervailing mecha-
nisms to provide only a post hoc justification for
results that are inconsistent with fundamental cause
theory. They further suggested that some behaviors
can be well accommodated in the theory, as when
the pursuit of status undermines health. The results
of the current study provide a different way for-
ward. They suggested that countervailing mecha-
nisms can appear even in the context of pursuing
better health, in this case, poor mental health
emerging in pursuit of optimal physical health. The
results also suggest that countervailing mechanisms
can be born of something other than imperfect
knowledge or asymmetric goals. In the present
study, the lack of consideration of side effects likely
reflects a structured neglect of certain side effects
on the part of both patients and providers, rather
than simple ignorance or lack of awareness.
More generally, the results speak to the potential
adverse consequences of exceptionally high levels
of confidence in medicine. Americans maintain
enormous confidence in their personal physicians
and in the medical treatment of disease. As medical
authority continues to expand over a wider set of
conditions and the pharmaceutical armamentarium
continues to grow (Clarke et al. 2003), the apparent
benefits of pharmaceuticals might loom so large as
to overwhelm serious consideration of their numer-
ous side effects. To be sure, the neglect of side
effects might be especially prominent with respect
to mental health given that patients and physicians
might be willing to trade some measure of emo-
tional well-being for an improvement in disease,
physical functioning, or longevity. But the tendency
to neglect side effects is more general, and other
side effects are almost certainly overlooked as well.
Adapting theories regarding the minority mental
health advantage only requires a more complete
accounting of risks and resources, arrayed over a
somewhat more complex ledger. It also requires
integrating sociology’s understanding of the causes
of mental health, informed by fundamental cause

Schnittker and Do
19
theory, with its understanding of how patients inter-
face with medicine.
Limitations
This study has several limitations on both the inde-
pendent and dependent variable sides. Although the
medication data provided in MEPS is among the
best among nationally representative surveys for
evaluating health care, there are several reasons to
suspect the effect of medications with depression
and suicide as side effects, as estimated here, is
understated. The analyses are premised on drugs
that formally list depression or suicide as a side
effect, though there are likely other medications that
involve such side effects. It is not uncommon, for
instance, for new side effects to become apparent
only in Phase IV of drug development, during post-
marketing surveillance, when drugs are effectively
evaluated in a naturalistic setting (Suvarna 2010).
At the same time, respondents may neglect to report
all the medications they are taking, especially over-
the-counter medications taken on an occasional
basis. Most medications with significant side effects
are prescription medications, though a number of
over-the-counter medications also involve depres-
sion as a side effect, including proton-pump inhibi-
tors, used to reduce stomach acid production
(Laudisio et al. 2018). It is unlikely, however, that a
perfectly comprehensive assessment of medication
use would substantially change the findings with
respect to race-ethnicity and mental health. Racial-
ethnic differences in the use over-the-counter medi-
cations are similar to those in prescription
medications, suggesting a general reluctance to use
drugs and not a specific reluctance to use what a
doctor prescribes (Fillenbaum et al. 1993).
The dependent variable used in this study is a
well-validated measure of psychological distress,
and empirically, the models produce the pattern
than research has tried to explain. Yet much of the
research has focused on major depression, for
which the K6 cannot provide a direct analogue.
Furthermore, some reviews of the racial-ethnic dif-
ferences in major depression and distress find that
the black-white difference in the K6 is closer to par-
ity than a black advantage (Barnes and Bates 2017).
Furthermore, the impact of pharmaceutical side
effects among blacks differs between the categori-
cal and dimensional coding of the K6. Other depen-
dent variables are likely to yield similar patterns to
those found here, though the magnitude of the
minority advantage as well as the mediating effects
of pharmaceutical side effects are likely to differ.
Although we performed numerous sensitivity
analyses with respect to the relationship between
drug use and depression, there remains the possibil-
ity of unobserved confounding, perhaps premised on
prodromal rather than manifest disease and therefore
based on diseases not reported by the respondent.
The relationship between physical disease and
depression is not limited to the most significant ill-
nesses, the kind that are usually measured in omni-
bus health surveys. The risk of suicide, for instance,
increases among those who have been hospitalized
for any illness (Qin et al. 2013). At least some of the
relationship between the use of pharmaceuticals and
depression might be due to unmeasured morbidity,
especially among those taking multiple medications.
But given the breadth of controls for chronic disease
employed in this study as well as specifications that
specifically focused on comorbidity, it is unclear
what the relevant set of other morbidities might be.
Furthermore, the evidence linking pharmaceuticals
to depression and suicide is strong. A significant
upward bias in the coefficients seems unlikely.
Conclusion
The racial-ethnic minority advantage in mental health
has long puzzled social scientists. Although there is
much to be gained from thinking about protective fac-
tors among minorities, the present study suggests it is
still worth considering risk factors that are more com-
mon among non-Hispanic whites. Such risk factors
are not easy to identify, given social scientists’ interest
in uncovering stressors among racial-ethnic minori-
ties. And this interest is theoretically well founded—
the idea of fundamental causes suggests multiple
overlapping risk factors flowing from one fundamen-
tal cause such that any one risk can be replaced by
another and still preserve the effects of the cause (Link
and Phelan 1995). To explain paradoxes, though, a
different perspective is required. Identifying relevant
risk factors among non-Hispanic whites might require
considering the shadow side of otherwise desirable
therapeutic interventions. And explaining disparities
in one domain of health might require considering dis-
parities in a seemingly very different one.
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Author Biographies
Jason Schnittker is professor of sociology at the
University of Pennsylvania. His research focuses on the
social, cultural, and biological determinants of health, with
a particular focus on mental health.
Duy Do is a graduate student in demography at the
University of Pennsylvania. He is interested in health pol-
icy, health economics, and the social and medical determi-
nants of health.

    Policy Problem:

Answer: U.S. mental-health policy continues to frame racial-ethnic gaps in psychological distress almost exclusively in terms of minority disadvantage, yet recent evidence shows the opposite pattern: non-Hispanic whites report equal or worse distress than Black, Hispanic, and Asian adults largely because whites are far more likely to be taking multiple medications whose documented side-effects include depression and suicidality.1 This “hidden iatrogenic burden” is driven by (a) clinical guidelines and insurance formularies that privilege pharmacological management of chronic conditions while giving little weight to downstream mental-health effects, and (b) race-linked prescribing biases that simultaneously over-medicate insured white patients and under-treat many minorities. As a result, current prescribing practices both erode population mental health and mask the true scale of undertreatment among minorities, while quality-of-care metrics ignore whether drugs that improve blood pressure, pain, or reflux are also generating avoidable depressive morbidity. Without policies that systematically track, disclose, and mitigate the psychological side-effects of widely used medicines—and that align prescribing incentives with holistic health outcomes—efforts to close mental-health disparities will remain misdirected and ineffective.