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

    Summarize the study’s **policy implications**—how the research findings can inform policy or decision-making. Your response should:
    - Interpret what the results suggest for governments, regulators, or institutions
    - Highlight potential actions, strategies, or decisions informed by the study
    - Use policy-appropriate language without speculation beyond the study’s conclusions
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    Write a short paragraph for the “Policy Implications” section of a policy brief.

    Scientific Research Paper:
    https://doi.org/10.1177/0022146520939514
Journal of Health and Social Behavior
2020, Vol. 61(3) 275–289
© American Sociological Association 2020
DOI: 10.1177/0022146520939514
jhsb.sagepub.com
Original Article
An abundance of evidence indicates that the drug
overdose epidemic in the United States is a
national public health emergency (Gomes et  al.
2018; Hedegaard, Miniño, and Warner 2018;
Kariisa et al. 2019). In 2017, 70,237 drug overdose
deaths occurred in the United States, with opioids
involved in 67.8% of these fatal poisonings (Scholl
et  al. 2019). The U.S. drug-related mortality rate
(age-adjusted) increased from 6.1 per 100,000 peo-
ple in 1999 to 21.7 in 2017 (Hedegaard et al. 2018).
From 1999 to 2006, the average annual increase in
the drug-related mortality rate was 10%, and that
rate has risen over time. From 2006 to 2014, the
average increase was 3%, which subsequently
jumped to 16% from 2014 to 2017 (Hedegaard et al.
2018).
Although opioids are often recognized as the
major contributor to drug-related mortality, it is
important to note that in 2017, cocaine and other
psychostimulants were involved in one-third of the
drug overdose deaths in the United States (Kariisa
et al. 2019). Three-fourths of the cocaine-involved
deaths and one-half of the psychostimulant-
involved deaths also included an opioid. Since
2013, drug overdoses involving cocaine and psy-
chostimulants have increased across all demo-
graphic groups and U.S. census regions (Kariisa
et al. 2019). Thus, the current drug overdose epi-
demic in the United States appears to be an evolv-
ing one that is increasingly characterized by
939514 HSBXXX10.1177/0022146520939514Journal of Health and Social BehaviorThombs et al.
research-article2020
1Boston College, Chestnut Hill, MA, USA
2University of North Texas, Fort Worth, TX, USA
3Northeastern University, Boston, MA, USA
Corresponding Author:
Ryan P. Thombs, Department of Sociology, Boston
College, 426 McGuinn Hall, 140 Commonwealth
Avenue, Chestnut Hill, MA 02467, USA.
Email: thombs@bc.edu
What Is Driving the Drug
Overdose Epidemic in the
United States?
Ryan P. Thombs1, Dennis L. Thombs2, Andrew K. Jorgenson1,
and Taylor Harris Braswell3
Abstract
The demand-side perspective argues that the drug overdose epidemic is a consequence of changes in the
economy that leave behind working-class people who lack a college education. In contrast, the supply-side
perspective maintains that the epidemic is primarily due to changes in the licit and illicit drug environment,
whereas a third, distinct perspective argues that income inequality is likely a key driver of the epidemic. To
evaluate these competing perspectives, we use a two-level random intercept model and U.S. state-level
data from 2006 to 2017. Contrary to the demand-side approach, we find that educational attainment is
not associated with drug-related mortality. In support of the supply-side approach, we provide evidence
indicating that opioid prescription rates are positively associated with drug-related mortality. We also
find that income inequality is a key driver of the epidemic, particularly the lack of resources going to the
bottom 20% of earners. We conclude by arguing that considerations of income inequality are an important
way to link the arguments made by the demand-side and the supply-side perspectives.
Keywords
deaths of despair, drug overdose epidemic, income inequality, mortality, population health

276
Journal of Health and Social Behavior 61(3)
polysubstance use (Jones, Einstein, and Compton
2018; McCall Jones, Baldwin, and Compton 2017).
Although drug-related mortality has been
increasing for nearly 20 years, there is no consensus
about its causes (Case and Deaton 2020; Monnat
2018, 2019; Ruhm 2019). The debate emerges into
two broad schools of thought—the demand side
versus the supply side. The demand-side perspec-
tive, most recently framed as the “deaths of despair”
narrative, argues that the current drug epidemic is a
product of macroeconomic changes over the past
half century that leave behind vulnerable members
of the working class who lack a college education
(Case and Deaton 2020). In contrast, the supply-
side perspective argues that the increase in drug-
related mortality is a product of drugs, particularly
opioids, becoming more readily available and
affordable through both licit and illicit means (Lin,
Liu, and Ruhm 2020; Ruhm 2019; Singhal, Tien,
and Hsia 2016). Although these two perspectives
are often positioned as competing paradigms, a
third approach—the income inequality perspective—
provides a way to incorporate insights from both
approaches because it explains how macrolevel
changes in the political economy directly affect
individuals and the ways by which they relate to
one another. The role of income inequality in the
current drug overdose epidemic is given negligible
discussion in both the demand-side and the supply-
side approaches. However, we argue that drug-
related mortality is likely a product of both financial
opportunities cultivated by wealthy and powerful
elites as well as the vulnerabilities present in the
working class, particularly nonmobile and dis-
placed workers.
In the current study, we evaluated these three
approaches to understanding the drug overdose epi-
demic. To do so, we used a panel data set of the 50
U.S. states and the District of Columbia covering
the 2006 to 2017 period. We analyzed these data
using a two-level random intercept model. In con-
trast to fixed-effects models that estimate within
effects, multilevel models allow for the simultane-
ous estimation of within and between effects, which
permitted us to evaluate how the drivers of the epi-
demic are associated with drug-related mortality
through time and across states.
Before reporting the results of the analysis, we
discuss the key arguments made by the demand-
side and the supply-side approaches concerning the
current drug overdose epidemic and how insights
from the income inequality and health literature can
potentially serve as a link between the two. Our
findings support the arguments made by the
supply-side perspective and the inequality–health
literature more so than the demand-side approach.
However, the effect of income inequality on drug-
related mortality is complex. Our findings indicate
that the share of income going to the top 5%, the top
20%, and the Gini coefficient are not associated
with drug-related mortality but that the share of
income of the bottom 20% is associated with drug-
related mortality. In other words, and in the context
of income inequality, the lack of resources going to
the earners at the bottom of the income distribution
is driving the drug overdose epidemic. We conclude
that a broader discussion of inequality provides a
way to integrate the demand-side and the supply-
side approaches to enhance our understanding of
drug-related mortality.
Background
Divergent Perspectives on the Drivers of
the Drug Overdose Epidemic
The demand-side perspective.  The demand-side per-
spective argues that the current drug overdose epi-
demic is a product of macroeconomic changes in the
United States beginning in the 1970s (Case and
Deaton 2015, 2017, 2020; Dasgupta, Beletsky, and
Ciccarone 2017; Monnat 2018). Case and Deaton
(2015, 2017, 2020) showed that in some segments
of the U.S. population, particularly among middle-
aged white men with a high school education or
less, there is an association between diminished
economic opportunity and increased overall mortal-
ity. The phenomenon, recently described as deaths
of despair, includes deaths related to drugs, suicide,
and alcohol. They argue that this pattern of mortal-
ity is a consequence of structural changes in the U.S.
economy that have greatly reduced the number of
well-paying jobs (particularly in the manufacturing
sector), which many once had access to regardless
of their level of education (Case and Deaton 2017;
Charles, Hurst, and Schwartz 2018).
Scholars working within this framework argue
that a variety of processes are behind these jobs
disappearing—production moving overseas, the
weakening of labor unions, and computer and
robotic technology replacing skilled workers
(Desilver 2017; Pierce and Schott 2020). These
economic changes led to the rise of a postindustrial
economy, causing elevated hopelessness and
despair (Case and Deaton 2017, 2020). According
to Case and Deaton (2020), deaths of despair are
primarily restricted to those without a college edu-
cation regardless of race or gender. Although it is

Thombs et al.
277
true that deaths of despair are highest among white
men without a college degree, deaths of despair
have also increased for women and African
Americans without a college education (Case and
Deaton 2020). Thus, the demand-side approach
places significant emphasis on differences in educa-
tional attainment to explain the drug overdose
epidemic.1
The supply-side perspective.  The second para-
digm used to explain the drug overdose epidemic is
the supply-side perspective, which argues that drugs,
particularly opioids, have become more readily
available since the 1990s (Paulozzi, Mack, and
Hockenberry 2014; Pezalla et al. 2017; Ruhm 2019).
This perspective argues that although there may be a
link between macroeconomic conditions and drug-
related mortality, the association disappears once
supply-side characteristics are accounted for (Ruhm
2019). Moreover, this approach suggests that the epi-
demic is not due to changes in the macroeconomic
environment for two reasons. First, states with rela-
tively strong economies (e.g., Massachusetts) have
experienced high rates of drug-related mortality, and
second, whites have a higher drug-related mortality
rate than minority groups who have long lived in
greater economic precarity (Ruhm 2019).
Moreover, scholars working within this approach
point to the increase in drug-related mortality
occurring at the same time that opioids started to
become more readily prescribed by physicians
(Ruhm 2019). The supply-side paradigm highlights
the role that pharmaceutical companies play in
increasing opioid availability by encouraging phy-
sicians to prescribe opioid analgesics (Hadland
et  al. 2018; Makary, Overton, and Wang 2017).
Although the number of prescriptions have declined
since 2010, the supply of illicit fentanyl and other
opioids, such as heroin, have markedly increased in
response (Ruhm 2019). For example, illicit fen-
tanyl, produced in China, has dramatically
increased over the past decade (Drug Enforcement
Agency 2016; Suzuki and El-Haddad 2017). Thus,
the supply-side perspective is primarily focused on
the role of specific sectors in the economy—the
health care/pharmaceutical industry and the illicit
drug industry—rather than changes in the economy
as a whole.
The income inequality–health relationship: a frame-
work to integrate the demand-side and supply-side per-
spectives.  Both the demand-side and supply-side
perspectives downplay the role of income inequality
in the drug overdose epidemic. Case and Deaton
(2020) suggested that the rise in inequality is a prod-
uct of the aforementioned macroeconomic changes,
but they did not believe it influences drug-related
mortality to any discernable degree. Relying on cor-
relations, they showed that highly unequal states,
like California and New York, have relatively low
rates of drug-related mortality, whereas more equal
states, like New Hampshire, have high mortality
rates (Case and Deaton 2020). Similarly, from the
supply-side perspective, Ruhm (2019) argued that
although demand-side conditions and inequality
may explain some portion of the drug epidemic, the
overall expanded supply and availability of drugs
provides a much stronger explanation for the over-
dose epidemic.
A large body of research in the social sciences
suggests that inequality is a key driver of a range of
health-related outcomes (see Pickett and Wilkinson
2015; Wilkinson and Pickett 2010, 2019). Recent
studies that used relatively more sophisticated sta-
tistical modeling techniques have provided empiri-
cal evidence of the detrimental health impacts of
macro levels of income inequality, especially
reductions in country-level and U.S. state-level
average life expectancy (e.g., Curran and Mahutga
2018; Hill and Jorgenson 2018; Jorgenson et  al.
2020). A number of studies have also observed
inequality to be associated with adult and infant
mortality, obesity, HIV infections, mental illness,
and homicides (Buot et  al. 2014; Daly 2016;
Ribeiro et  al. 2017; Torre and Myrskylä 2014;
Wilkinson and Pickett 2010, 2019). Overall, the
majority of these studies suggest that inequality is
harmful to human health, well-being, economies,
and social cohesion.
Although the demand-side and the supply-side
approaches underplay the role of inequality in the
drug overdose epidemic, multiple theoretical per-
spectives that can be applied to drug use help
explain why income inequality is related to various
population health outcomes (Hill, Jorgenson et al.
2019; Hill and Jorgenson 2018; Jorgenson et  al.
2020). The psychosocial and social capital perspec-
tives take a micro point of view to the inequality–
health relationship, whereas neomaterialism takes a
macro perspective. The psychosocial perspective
suggests that the stress of relative deprivation, from
the unequal distribution of income, contributes to
low self-esteem, emotional distress, and risky cop-
ing behaviors, such as drug use (Wilkinson and
Pickett 2010).
A similar framework—the social capital
perspective—argues that income inequality facili-
tates widespread status competition, which tends to

278
Journal of Health and Social Behavior 61(3)
undermine social cohesion and interpersonal trust
and as a consequence, reduces collective political
efforts to support vulnerable populations (Kawachi
et  al. 1997; Truesdale and Jencks 2016). A third
approach, the neomaterialist perspective, suggests
that income inequality concentrates wealth and
power among elites and weakens broader commit-
ments to the general interests of society. These con-
ditions create political pressure to cut taxes,
deregulate industry, and limit investments in public
resources and social services that promote public
health, all of which disproportionately impact those
in lower income groups (Neumayer and Plümper
2015).
Although the inequality–health link is a well-
established body of research, few empirical investi-
gations have studied the relationship between
income inequality and the drug epidemic, and those
that have often did so indirectly. Three important
studies that either directly or tangentially study the
relationship are Monnat (2018, 2019) and Peters
et  al. (2019). Monnat (2018) found that income
inequality (operationalized as the Gini coefficient) is
associated with higher rates of drug-related mortal-
ity. Monnat (2019), although not explicitly investi-
gating inequality, found that more economically
distressed counties have higher drug-related mortal-
ity rates. Peters et  al. (2019), whom also did not
investigate inequality explicitly, found that places
hit hardest by the prescription-opioid epidemic are
those that have been economically left behind.
These studies illustrate that economic distress plays
a significant role in the drug epidemic and that
supply-side factors also matter, but they only tan-
gentially linked their findings to the unequal distri-
bution of resources and power in society.
The income inequality–health literature and the
aforementioned studies by Monnat (2018, 2019)
and Peters et al. (2019) also highlight that different
parts of the income distribution may affect drug-
related mortality more so than others. The inequality–
health literature has generally focused on inequality
measures that quantify the concentration of income at
the top of the distribution or the Gini coefficient that
takes into account the entire distribution while giving
less attention to the bottom of the income distribution
(Hill and Jorgenson 2018; Pickett and Wilkinson
2015; Wilkinson and Pickett 2010). However, the
studies by Monnat (2018, 2019) and Peters et  al.
(2019) suggest that economic distress, or the lack of
resources going to earners at the bottom of the income
distribution, is driving the epidemic. Thus, whether
inequality is associated with drug-related mortality
may depend on how inequality is measured.
Even though it has been given limited attention
in the drug epidemic literature, we argue that the
income inequality–health paradigm can serve as a
bridge between the demand-side and supply-side
perspectives for four reasons. First, the resources in
a society have to be distributed in some way. When
they are concentrated at the top of the income distri-
bution, the rich not only have more resources, but
also they have the power to influence political deci-
sions (Cole 2018; Saez and Zucman 2019). As the
neomaterialist perspective argues, the elite try to
actively preserve that power by undermining social
welfare programs and legislation that benefit the
working class and poor (Hill and Jorgenson 2018;
Neumayer and Plümper 2015).
Second, because inequality is a measure of the
distribution of social power, higher inequality also
means the elite have more economic power relative
to everyone else. When the elite have more power,
working-class people have less, which is associated
with a weakening of labor unions and stagnant
wages (Piketty 2014; Saez and Zucman 2019;
Stiglitz 2012). The economic distress and alienation
that this creates is a key aspect of the demand-side
paradigm (Case and Deaton 2020).
Third, from the supply side, higher inequality is
directly tied to the rise and reproduction of monop-
olistic and oligopolistic sectors (Piketty 2014; Saez
and Zucman 2019). In the United States, a prime
example is the private health care/pharmaceutical
industry, which the supply-side approach criticizes
for their role in the drug overdose epidemic.
However, it should be noted that this industry also
produces instability and hardship for people through
the very existence of private health care insurance
(Saez and Zucman 2019). If we consider health care
insurance to be a tax on labor (because it is essen-
tially mandatory), it increases the effective labor
tax rate in the United States from 29% to 37%,
which disproportionately burdens the poor and
working class (Saez and Zucman 2019). In contrast
to a tax levied by the government, this money goes
primarily to industry executives, which further
reproduces inequality.
Fourth, Case and Deaton (2020) suggested that
in contrast to what inequality scholars argue, people
only compare themselves to those in their surround-
ing community—not to the elite. However, a long
line of research suggests that people do compare
themselves to the elite and that they do it more so in
highly unequal societies (e.g., the United States),
which can lead to poor health outcomes (Bourdieu
1984; Schor 1993; Veblen 1994; Wilkinson and
Pickett 2010, 2019).

Thombs et al.
279
Overall, the arguments made by the demand-
side and supply-side approaches suggest that
income inequality is central to the underlying argu-
ments of each perspective even though it is given
limited recognition by both. We suggest that both
approaches are concerned with how the unequal
distribution of resources and power drives drug-
related mortality and as such should not be consid-
ered antithetical. Thus, we believe a focus on
income inequality is key to integrating the demand-
side and the supply-side approaches.
Given the discussion of the three approaches to
the drug overdose epidemic—the demand-side per-
spective, the supply-side perspective, and the
income inequality–health perspective—we test the
following hypotheses:
The Demand-Side Hypothesis: Educational
attainment is negatively associated with
drug-related mortality.
The Supply-Side Hypothesis: The opioid pre-
scription rate is positively associated with
drug-related mortality.
The Income–Inequality Hypothesis: The income
share of the top 5%, top 20%, and the Gini
coefficient are positively associated with
drug-related mortality, and the income share
of the bottom 20% is negatively associated
with drug-related mortality.
Data and Methods
Sample
We analyzed state-level annual observations for the
temporal period 2006 to 2017 for the 50 U.S. states
and the District of Columbia. The time period of this
study corresponds to the first year in which the opi-
oid prescription data were made available by the
Centers for Disease Control (CDC 2019) and the
last year of available mortality data.
Model Estimation Technique: Two-Level
Random Intercept Model
We used a two-level random intercept model to test
our hypotheses. Our model nested annual state-level
observations within states. In total, 611 observations
(Level 1) were nested within the 50 states and the
District of Columbia (Level 2) from 2006 to 2017.
The two-level random intercept model, also known
as the within-between random-effects model
(REWB) or the hybrid model,2 allowed us to model
the within and between effects for each driver of
drug-related mortality. The model is written as
follows:
y
x
x
x
u
e
ij
ij
j
j
j
ij
=
+
−
+
+
+
−
−
β
β
β
0
1
2
(
)
.
b1 represents the within effects, which are estimated
by group mean centering the variables, and b2 is the
group mean, which represents the between effects. uj
is the Level 2 error term, and eij is the Level 1 error
term.3 The primary advantage of this model is that it
allows the researcher to obtain both the within and
between effects simultaneously. Doing so is not pos-
sible in the standard fixed-effects model, which relies
on within variance only, or the standard random-effects
model, which uses a weighted average of within and
between variance (Bell, Fairbrother, and Jones 2019).
In the context of this study, it allowed us to test
whether an increase in a driver within a state had the
same effect as cross-sectional differences (the average
level of the driver) between states.
Before group mean centering the variables, we
grand mean centered all of the independent vari-
ables to provide a meaningful interpretation of the
intercepts. The dependent variable and indepen-
dent variables were converted to natural loga-
rithms, making them equivalent to elasticity
models. We logged the variables to (1) correct for
skewness and (2) because we posited that the rela-
tionship between drug-related mortality and its
determinants are multiplicative in nature; that is,
the determinants are not independent of one
another but rather, act proportionally. To correct
for autocorrelation, each model was estimated with
an exponential covariance structure, which models
an autoregressive process (Rabe-Hesketh and
Skrondal 2012). We also estimated robust standard
errors to correct for heteroskedasticity.
Drug-Related Mortality Rate
per 100,000 People
Our dependent variable was the annual drug-related
mortality rate per 100,000 people by state. We
obtained these data from CDC WONDER’s (CDC
2018) multiple cause of death database. As defined
by the CDC, drug-related deaths are those that are
unintentional (ICD-10 codes X40-X44), by suicide
(ICD-10 codes X60-X64), by homicide (ICD-10
code X85), undetermined (ICD-10 codes Y10-
Y14), and all other drug-induced causes (deaths not

280
Journal of Health and Social Behavior 61(3)
categorized in any of the aforementioned ICD-10
codes).
Key Independent Variables: Educational
Attainment, Opioid Prescription Rates,
and Income Inequality
Our three main variables of interest were educational
attainment (the percentage of the state population
with a bachelor’s degree), the opioid prescription
rate (per 100 people), and four different measures of
income inequality: the income share of the (1) top
5%, (2) the top 20%, (3) the bottom 20%, and (4) the
Gini coefficient (range = 0–1). Educational attain-
ment (used to test the demand-side hypothesis) and
the inequality measures (used to test the income
inequality hypothesis) were obtained from the
American Community Survey (ACS) 1-year esti-
mates (U.S. Census Bureau 2018a), and the opioid
prescription rates (used to test the supply-side
hypothesis) were garnered from the CDC (2019).
Each measure of income inequality provided a
sufficiently different approach to understanding
how inequality is associated with drug-related mor-
tality. The share of income going to the top 5%, top
20%, and bottom 20% are measures of concentra-
tion toward the tail ends of the income distribution,
whereas the Gini coefficient takes into account the
entire income distribution but does not indicate spe-
cifically where the inequality lies within the distri-
bution (Burns, Tomita, and Lund 2017; Hill and
Jorgenson 2018; Jorgenson, Schor, and Huang
2017). Therefore, the Gini coefficient provided a
general measure of how unequal a distribution is,
whereas income shares provided more specific
measures of how resources are concentrated at spe-
cific locations along the income distribution. The
income inequality measures were estimated from
income data that were calculated as the sum of
wages net of all other forms of income, such as
government assistance, interest, and dividends
(U.S. Census Bureau 2018b).
Additional Covariates
Following the social determinants of health litera-
ture (e.g., Monnat 2018; Solar and Irwin 2010), we
included additional covariates that controlled for the
structure of each state’s economy and potential
regional differences that could be driving the epi-
demic. Our models included two trend terms, one
that was centered on the year 2006 and a second one
that was the quadratic trend term, which we deemed
appropriate based on the Bayesian information
criterion (BIC) statistic. In addition, we included the
annual state-level median household income in
2017 inflation-adjusted dollars and the percentage
of the labor force in manufacturing, which con-
trolled for the affluence and structure of the econ-
omy for each state, respectively. To control for
regional differences, we included a set of indicator
variables denoting census region (1 = Northeast,
reference group; 2 = Midwest; 3 = South; 4 = West).
The median household income data and the percent-
age of the labor force in manufacturing were gath-
ered from the ACS 1-year estimates (U.S. Census
Bureau 2018a). The census regions followed the
U.S. Census Bureau’s (2015) categorization. We
report sensitivity analyses with additional covari-
ates in the Sensitivity Analysis section following the
results.
Results
Descriptive Statistics: Where Is the
Variance?
The univariate, nonlogged descriptive statistics for
the dependent and independent variables are
reported in Table 1. The table includes the mean and
the overall, within, and between standard deviation
(SD) for each variable. For all of the variables, the
variance is greater between states rather than within
them over time. Particularly notable is the mean of
the drug-related mortality rate, which is 15.98
deaths per 100,000 people. The mortality rate varies
substantially within states (within SD = 4.80) and
between states (between SD = 4.98). Figure 1 illus-
trates the average drug-related mortality rate from
2006 to 2017, and Figure 2 shows the change in the
mortality rate over the same period.
We also found that much of the variance for our
three main independent variables of interest (the
percentage of the population with a bachelor’s
degree, the opioid prescription rate, and income
inequality) is between states rather than within
them. The mean percentage of people with a bache-
lor’s degree is 28.84 (between SD = 5.86%; within
SD = 1.63%), and the mean opioid prescription rate
is 79.41 opioids per 100 people (between SD =
21.88; within SD = 8.14). Regarding income
inequality, the mean share of income going to the
top 5% is 21.50% (between SD = 1.37%; within
SD = .68%), and the mean Gini coefficient is .46
(between SD = .02; within SD = .01). The average
share of income going to the top 20% is 49.37%
(between SD = 1.86%; within SD = .70%), whereas
the mean share of income going to the bottom 20%
is 3.46% (between SD = .41%; within SD = .15%).

Thombs et al.
281
Random Intercept Model Results
We first estimated a model that included all of the
within and between effects for each independent
variable (not reported here) and used Wald tests
(Table 2) to determine whether the between and
within effects for the continuous variables are statis-
tically different from one another. Of the five con-
tinuous independent variables included in the
model, only the opioid prescription rate’s within and
between effects are statistically different at the .05
Table 1.  Descriptive Statistics: CDC Wonder and American Community Survey 1-Year Estimates,
2006 to 2017.
Measurement
Mean (SD)
Between SD
Within SD
n
Drug-related
mortality
Per 100,000
people
15.98
(6.89)
4.98
4.80
612
% Bachelor’s degree
%
28.84
(6.03)
5.86
1.63
612
Opioid prescription
rate
Per 100 people
79.41
(23.16)
21.88
8.14
612
Top 5%
%
21.50
(1.52)
1.37
.68
612
Top 20%
%
49.37
(1.97)
1.86
.70
612
Bottom 20%
%
3.46
(.43)
.41
.15
612
Gini
Index
.46
(.02)
.02
.01
612
Median household
income
2017 inflation-
adjusted $
59,386.58
(11,563.06)
9,705.46
6,419.06
612
Manufacturing
% of labor force
10.22
(3.98)
3.99
.57
611
Figure 1.  Average Drug-Related Mortality Rate, 2006 to 2017.

282
Journal of Health and Social Behavior 61(3)
level. As such, we included the within and between
effects for the opioid prescription rate in the models
and report the random effects (because they are
more efficient than the within effects) for the other
variables.
Table 3 reports the results of the two-level ran-
dom intercept models for drug-related mortality by
income inequality measure, and Figure 3 visually
presents the point estimates and confidence inter-
vals for each key variable (the percentage of people
with a bachelor’s degree, the opioid prescription
rate, and each inequality measure). All of the two-
level intercepts are statistically significant, indicat-
ing that the two-level model fits the data well and is
superior to the linear model. The percentage of
people with a bachelor’s degree is not statistically
significant at the .05 level in any of the models,
which does not support the demand-side hypothe-
sis. The opioid prescription rate’s within effects are
not statistically significant in any model, but all of
the between effects are, which supports the supply-
side hypothesis. In other words, states with a higher
rate of opioid prescriptions, on average, have a
higher drug-related mortality rate.
The results for the income inequality measures
indicate that inequality has a complex association
with drug-related mortality. In contrast to what
the income inequality hypothesis expects, the
share of income of the top 5%, the top 20%, and
Gini coefficient are not associated with drug-
related mortality, but the share of income of the
bottom 20% is negatively associated with
Figure 2.  Percentage Change in the Drug-Related Mortality Rate from 2006 to 2017.
Table 2.  Wald Tests of the within and between Effects: CDC WONDER and American Community
Survey 1-Year Estimates, 2006 to 2017.
(1)
Top 5%
(2)
Top 20%
(3)
Bottom 20%
(4)
Gini
% Bachelor’s degree
.19
.03
.05
.02
Opioid prescription rate
22.15*
22.43*
22.66*
22.53*
Income inequality
1.65
2.51
3.24
2.45
Median household income
.50
1.21
1.70
1.30
Manufacturing
.21
.16
.00
.12
Note: The test statistics are χ2 values.
*p < .05. H0: The coefficients are equivalent.

Thombs et al.
283
drug-related mortality. Moreover, the values of
the BIC statistic for each model indicate that the
bottom 20% model best fits the data. Based on
Raftery’s (1995) grades of evidence,4 there is
“strong” evidence that the bottom 20% model is
better than the top 5% model and “positive” evi-
dence that it is better than the top 20% model and
the Gini model. Overall, these results indicate that
the lack of resources going to the bottom 20% of
earners best explains the income inequality–
drug-related mortality relationship—rather than
the concentration of resources at the top of the
income distribution.
Sensitivity Analysis
As a sensitivity analysis, we estimated models with
two additional covariates that may be associated
with the drug epidemic (Ariizumi and Schirle 2012;
Case and Deaton 2015; Ruhm 2005): (1) the per-
centage of the population that is white and (2) a
dichotomous variable corresponding to the years of
Table 3.  Two-Level Random Intercept Model for the Regression of the Drug-Related Mortality Rate,
2006 to 2017 (N = 611): CDC WONDER and American Community Survey 1-Year Estimates.
(1)
(2)
(3)
(4)

Top 5%
Top 20%
Bottom 20%
Gini
% Bachelor’s degree
.49
(.26)
.49
(.26)
.50
(.26)
.49
(.26)
Opioid prescription rate,
within
−.05
(.16)
−.05
(.16)
−.04
(.16)
−.05
(.16)
Opioid prescription rate,
between
1.15*
(.24)
1.17*
(.24)
1.23*
(.24)
1.18*
(.24)
Income inequality
.18
(.22)
.89
(.63)
−.48*
(.23)
.83
(.53)
Median household income
.11
(.07)
.11
(.07)
.12
(.07)
.12
(.07)
Manufacturing
−.27*
(.11)
−.27*
(.11)
−.25*
(.10)
−.27*
(.11)
Midwest
−.32*
(.12)
−.30*
(.12)
−.28*
(.11)
−.30*
(.11)
South
−.33*
(.08)
−.35*
(.08)
−.36*
(.08)
−.35*
(.08)
West
−.25*
(.10)
−.23*
(.10)
−.20*
(.09)
−.22*
(.10)
Time
−.02
(.02)
−.02
(.02)
−.03
(.02)
−.03
(.02)
Time2
.01*
(.00)
.01*
(.00)
.01*
(.00)
.01*
(.00)
Constant
3.04*
(.70)
3.05*
(.69)
3.02*
(.70)
3.04*
(.69)
Random components
  State-level intercept
.04*
(.01)
.04*
(.01)
.04*
(.01)
.04*
(.01)
  Year intercept
.05*
(.01)
.05*
(.01)
.05*
(.01)
.05*
(.01)
  Rho
.76*
(.06)
.76*
(.06)
.76*
(.06)
.76*
(.06)
  BIC
−450.18
−452.51
−457.66
−453.18
Note: Robust standard errors are in parentheses. The models are estimated with an exponential covariance structure
to correct for autocorrelation. Census region reference group = Northeast. BIC = Bayesian information criterion.
*p < .05.

284
Journal of Health and Social Behavior 61(3)
the Great Recession (1 = the years 2008 and 2009;
0 = otherwise). Measures of the percentage of the
population that is white were obtained from the
ACS 1-year estimates (U.S. Census Bureau 2018a),
and the recessionary years were coded according to
the National Bureau of Economic Research (2010).5
The results are reported in Appendix A available in
the online version of the article.
The inclusion of the additional variables makes
the bottom 20% slope coefficient only marginally
statistically significant (p = .06). However, neither
the percentage of the population that is white nor
the Great Recession dichotomous variable are sta-
tistically significant in the models. Additionally, the
model’s BIC values are significantly larger than
the ones reported in the aforementioned results.
The difference between the BIC value for the bot-
tom 20% model in the main results compared to the
sensitivity analysis is 11.19, indicating “very
strong” evidence that the main results fit the data
better than the sensitivity analysis. Overall, these
findings suggest that the inclusion of the additional
variables is unwarranted and that their addition
leads to inflated standard errors.
At a reviewer’s request, we also interacted the
Great Recession variable with our main variables of
interest—the percentage of people with a bachelor’s
degree, the opioid prescription rate (between effect),
and each inequality measure. The results are reported
in the Appendix B available in the online version of
the article. The interaction is not statistically signifi-
cant for the percentage of people with a bachelor’s
degree or the opioid prescription rate, but it is for all
of the inequality measures except for the share of the
top 5%.6 The results suggest that none of the
inequality measures had an effect on drug-related
mortality during the Great Recession but did so dur-
ing nonrecessionary years. The slope coefficient of
the top 20% during the Great Recession was –.28
(p = .77), compared to its nonrecession slope coeffi-
cient of 1.13 (p = .04). The slope coefficient of the
bottom 20% during the Great Recession was –.08
(p = .74), compared to its nonrecession slope coeffi-
cient of –.58 (p = .02), and the Gini’s Great
Recession slope coefficient was –.10 (p = .75), com-
pared to its nonrecession slope coefficient of 1.06
(p = .46). These findings align with prior research
showing that some population health characteristics
Figure 3.  Key Results by Income Inequality Model.
Note: The circle represents the point estimate, and the black bars correspond to the 95% confidence interval.

Thombs et al.
285
improve during recessionary periods (Ariizumi and
Schirle 2012; Ruhm 2000, 2005). Regardless, the
primary results reported in this study appear to be
more robust compared to alternative specifications
and both opioid prescription rates and the income
share of the bottom 20% of earners are drivers of the
drug overdose epidemic.
Discussion
The results of this study have important implica-
tions for understanding the drivers of the U.S. drug
overdose epidemic and the policies needed to com-
bat it. Regarding the drivers of drug-related mortal-
ity, the findings support the arguments made by the
supply-side perspective more so than the demand-
side perspective. As we discussed, the supply-side
perspective argues that changes in the drug environ-
ment are driving the epidemic. For the licit drug
market, prior research points to the exploitive prac-
tices of pharmaceutical companies that made opi-
oids more available to the general public (Ruhm
2019). Even though the prescribing rates of licit opi-
oids have declined during the past several years, the
illicit drug market has picked up where it left off by
making fentanyl and heroin increasingly cheap and
widely available. Undoubtedly, the increase in
cheap opioids (and other drugs) plays a significant
role in the epidemic.
However, the arguments made by the supply-
side approach have limited explanatory power
when it comes to the underlying mechanism of why
the epidemic emerged. For example, the supply-
side argument does not provide an explanation for
what is driving people to use drugs, and it does not
offer a compelling reason for why the epidemic is
disproportionately affecting white (and increas-
ingly black) working-class people without a college
degree (Case and Deaton 2020). Although the
demand side’s emphasis on educational attainment
is not supported in this study, we contend that
income inequality can act as a link between the
arguments made by the demand-side and the sup-
ply-side approaches. On one hand, increasing
inequality both produces and is reproduced by the
health care and pharmaceutical industries at the
center of the epidemic. Americans are reporting
higher levels of pain than ever before, but the
reported increase appears to be largely from social
and economic distress rather than from physical ail-
ments (Case and Deaton 2020). Beginning in the
1990s, the pharmaceutical industry quickly “phar-
maceuticalized” this phenomenon by treating it as a
medical issue to be addressed by pharmaceuticals
and drugs rather than by policy interventions or
social change (Abraham 2010; Bell and Figert
2012). On the other hand, income inequality is also
an important demand-side factor because it concen-
trates resources and power among a small number
of elites, which increases alienation and under-
mines the well-being of lower income groups
(Neumayer and Plümper 2015; Wilkinson and
Pickett 2019). As we show, the best inequality pre-
dictor of drug-related mortality is the lack of
resources going to the poorest 20% of earners rela-
tive to everyone else.
Building on this discussion, the results indicate
that health policy should take on a wider set of mea-
sures to combat the drug overdose epidemic. The
actions taken by federal and state governments in
the United States have primarily focused on the
practices of pharmaceutical companies and pre-
scribing physicians (Gross and Gordon 2019).
Although we find that addressing the supply side of
the epidemic is necessary—it is an inadequate pre-
vention response by itself (also see Monnat 2019;
Peters et al. 2019). Policymakers must also address
structural factors like economic inequality, which
will require implementing policies that redistribute
income and resources. Although wealth is not
explicitly discussed in this article, implementing a
wealth tax may be an even more effective strategy
because the wealthiest often structure their assets so
they have relatively low levels of taxable income
(Saez and Zucman 2019). Furthermore, eliminating
private insurance and moving to a single-payer sys-
tem could potentially not only check the power of
pharmaceutical companies and limit the harmful
prescribing practices of physicians but also serve as
a large pay increase for employees. Today, an
employee’s health care costs an average of $13,000
a year, and a single-payer system funded through
progressive taxation would shift the burden of pay-
ing for health care from workers to the rich (Saez
and Zucman 2019).
Although the findings for this study are rela-
tively robust, they should be interpreted with two
limitations in mind. First, due to the availability of
opioid prescription rates, our analysis dates back
to 2006. However, as Case and Deaton (2015)
showed, drug-related mortality has been on the rise
since the beginning of the twenty-first century. This
longer-term trend is not captured in the present
study. Second, there may also be important spatial
differences within states, such as at the county or
city levels, that are not captured in this state-level
study. Future research should evaluate whether this
is the case.

286
Journal of Health and Social Behavior 61(3)
We conclude by reiterating that drug-related mor-
tality is likely a deleterious “downstream” conse-
quence of changes in the U.S. economy over the past
half-century that have led to increased income
inequality and an exploitive private health care and
pharmaceutical industry. Our emphasis on the role of
income inequality in the current drug overdose epi-
demic is consistent with recent research demonstrat-
ing that growing income inequality is a key
determinant of other health-related outcomes, includ-
ing overall life expectancy, crime, and mental illness
as well as anthropogenic greenhouse gas emissions
that cause climate change and exacerbate air pollu-
tion’s impact on public health (Hill, Jorgenson et al.
2019; Hill and Jorgenson 2018; Jorgenson et al. 2016,
2017, 2020; Knight, Schor, and Jorgenson 2017;
Pickett and Wilkinson 2015). Therefore, addressing
the supply side of the drug overdose epidemic is cer-
tainly warranted, but taking a more structural per-
spective to the epidemic that involves reducing
income inequality would likely not only lead to
reduced drug-related mortality but also have positive
economic and environmental benefits.
Acknowledgments
We would like to thank Amy Burdette, editor of JHSB,
and the three anonymous reviewers for their helpful
comments.
Notes
1.
Case and Deaton’s emphasis on education aligns
with sociological work that explains why educa-
tion is important to health (e.g., Mirowsky and
Ross 2015). In the postindustrial economy, educa-
tional attainment is critical to accessing well-paying
jobs (Case and Deaton 2020). Mirowsky and Ross
(2015:297) also argued that education is important
for individuals to be able to overcome the unhealthy
“default American lifestyle.”
2.
Some researchers refer to it as the hybrid model, but
this term is misleading because it is a random-effects
model—not a combination of the fixed-effects and
random-effects models (Bell and Jones 2015).
3.
Although the within effects are not subject to constant,
time-invariant unobserved heterogeneity bias, the
between effects can be biased if time-invariant (Level
2) variables are omitted from the model. However, the
within effects are still subject to time-variant omitted
variable bias (also see Hill, Davis, et al. 2019).
4.
Raftery’s (1995) grades of evidence provide a way
to compare how well different, nonnested models fit
the data.
5.
Technically, the Great Recession began in December
2007, but we code 2007 as a 0 because it is the last
month of the year.
6.
The percentage of the population that is white is
excluded because the BIC values of the models are
significantly smaller when it is removed from the
analysis, and it is not statistically significant in any
of the analyses.
Supplemental Material
Appendices A and B are available in the online version of
the article.
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Author Biographies
Ryan P. Thombs is a PhD student in the sociology depart-
ment at Boston College. His primary research interests are
in the areas of political economy, environmental sociol-
ogy, the structural determinants of health, and quantitative
methods. His published work appears in such journals as
Sociological Forum, Energy Research & Social Science,
Climatic Change, and Sociology of Development.
Dennis L. Thombs, PhD, FAAHB, is professor and dean
of the School of Public Health at the University of North
Texas Health Science Center in Fort Worth. For the past
30 years, his scholarship has focused on substance abuse
prevention and policy. He has authored more than 100
articles in peer-reviewed national and international jour-
nals and is the coauthor of Introduction to Addictive
Behaviors, published by the Guilford Press, which is now
in its fifth edition. Dr. Thombs’s research has been sup-
ported by the National Institute on Alcohol Abuse and
Alcoholism, the National Institute of Mental Health, and
the U.S. Department of Education.
Andrew K. Jorgenson is professor and chair in the
Department of Sociology at Boston College. He conducts
research in the areas of environmental sociology, global polit-
ical economy, and sustainability science. His published work
appears in such venues as the American Journal of Sociology,
Nature Climate Change, Social Forces, Environmental
Research Letters, Social Problems, and Sociological Theory,
and he is the coauthor of Super Polluters: Tackling the
World’s Largest Sites of Climate-Disrupting Emissions,
forthcoming from Columbia University Press. He is the 2020
recipient of the Fred Buttel Distinguished Contribution
Award from the American Sociological Association’s
Section on Environmental Sociology.
Taylor Harris Braswell is a sociology PhD candidate at
Northeastern University. He works at the intersection of
urban and environment sociology, with a particular focus
on urbanization, energy systems, and spatial data science.
His ongoing dissertation research is about the relationship
between energy governance and urbanization processes in
the United States. He has also been involved in a number
of projects that use spatial methods to examine other
issues related to urban geography, such as gentrification,
vacancy, and urban governance.

    Policy Implications:

Answer: Our findings suggest that an adequate prevention response will require both (1) limiting the availability of opiates and (2) redistributing income, especially to earners at the bottom of the income distribution. Policy makers should strengthen government regulations to restrict the manufacturing, distribution, and prescription of legal opioid medications. However, given the availability and low cost of illicit opioids, addressing structural factors like income inequality will also be key to reducing drug-related demand and use in low-resource communities where stress, despair, and lack of economic opportunity are predominant. Prior research indicates that lower-income people disproportionately make up drug-related overdoses, and as our analysis shows, the lack of resources going to the bottom 20% of earners is a driving force.