The Opioid Crisis and Educational Performance

AuthorRajeev Darolia,Sam Owens,John Tyler
Published date01 September 2022
Date01 September 2022
188 ANNALS, AAPSS, 703, September 2022
DOI: 10.1177/00027162231151523
The Opioid
Crisis and
1151523ANN The Annals of the American AcademyThe Opioid Crisis and Educational Performance
We propose a simple model of how opioids in a com-
munity can impact the educational outcomes of chil-
dren based on both the extent of exposure to opioids in
the community and on the child’s vulnerability to any
given level of exposure. We then use national data to
document where and how the opioid crisis has inter-
sected with students’ performance on standardized test
scores in the U.S., focusing particularly on rural com-
munities. Finally, we estimate the extent to which vari-
ation in one measure of the opioid crisis, drug-related
mortality, is related to variation in test scores. We find
strong relationships between the two, as well as evi-
dence that the relationship is particularly salient for
third-grade students in rural communities.
Keywords: opioids; rural schools; at-risk students;
children’s vulnerability; educational per-
The opioid crisis is now widely recognized as
one of the most important public health
emergencies of our time. Opioid overdoses led
to over forty thousand deaths in 2016, more
than fivefold the levels from the late 1990s
(U.S. Department of Health and Human
Services 2019). The issue is particularly acute
for rural communities, where residents may
face higher opioid prescription and drug-
related mortality (DRM) rates, and which may
Rajeev Darolia ( is the Wendall
H. Ford Professor of Public Policy in the Martin School
of Public Policy and Administration at the University
of Kentucky.
Sam Owens ( is a PhD candi-
date in the Martin School of Public Policy and
Administration at the University of Kentucky.
John Tyler ( is a professor of edu-
cation, economics, and public policy at Brown University.
Correction (April 2023): Article updated online to
correct the citation Drescher, 2023 in the text and
face relatively high barriers to effective policy responses that rely on the ade-
quacy of infrastructure like transportation and healthcare (García et al. 2019;
Hancock et al. 2017).
Recent high-profile litigation and settlements among states and local govern-
ments with drug companies have highlighted some of the costs of the opioid
epidemic. The dollar amounts discussed in some of these cases have been huge;
for example, Purdue Pharma and Mallinckrodt agreed to national settlements of
about $10 billion and $1.6 billion, respectively, and a judge in Oklahoma recently
awarded a settlement of $465 million in a suit brought against Johnson and
Johnson (although this award was later overturned). The settlements in these
cases brought by various state attorneys general are based on estimated addi-
tional costs to state and local governments generated by the opioid crisis such as
public healthcare, treatment facilities, law enforcement, criminal justice, and jail
expenses. While these figures are notable, the total societal costs of the opioid
epidemic are likely much higher when the less direct harm that is visited on com-
munities by the crisis is factored into the equation. In this study, we examine one
such source of indirect cost: the extent to which exposure to the opioid crisis may
be negatively affecting the education outcomes of children.
Drawing from literature on the effects of childhood exposure to environmental
stressors and violence, we propose a simple model of how opioids in a community
can impact the education outcomes of young children. The model suggests that
children’s education outcomes will be influenced by the level or intensity of the
crisis in a community, the extent of a child’s exposure to the community-wide crisis
level, and the child’s vulnerability given their level of exposure.
Following a discussion of the model relating the opioid crisis to education out-
comes, we use national data to document where and how the opioid crisis has inter-
sected with students’ performance on standardized test scores in the U.S., focusing
particularly on rural communities. County-level DRM rates are our primary meas-
ure of the intensity of the opioid crisis in a given county and year, and third-grade
and eighth-grade test scores from the years 2009 to 2014 represent the education
outcomes of interest. Mapping the intersection of DRM and test scores reveals
remarkable spatial differences, with notable “hot spots” in the Appalachian Belt and
the industrial Midwest. It also shows areas of concern in the Southwest and West,
suggesting a more acute need for concern in some areas of the country than in oth-
ers. We then go on to estimate the extent to which variation in one measure of the
opioid crisis—average lifetime DRM rate—is related to variation in test scores,
finding strong relationships between the two, as well as evidence that the relation-
ship is particularly salient for third-grade students in rural communities.
Education can be a pathway to economic and social mobility, especially for
children from disadvantaged backgrounds; and when this pathway is imperiled, it
is the most vulnerable children who have the most to lose. The fact that some of
the areas hardest hit by the opioid crisis—the Appalachian belt, the industrial
Midwest, impoverished rural communities across the nation—are also areas asso-
ciated with markers of childhood disadvantage, such as high levels of poverty and
parental unemployment, lends urgency to the opioid crisis–education question. In
other words, if the opioid crisis has negative spillover effects on the educational
achievement of children, it is likely that the opioid crisis will exacerbate already
existing gaps in the structure of economic opportunity for the next generation.
The Phases and Geography of the Opioid Crisis
The opioid crisis is generally divided into three waves, with each wave character-
ized by the category of opiate—first natural and semisynthetic substances, then
heroin, and finally synthetically derived substances—that is the primary driver of
overdose rates at the time.1 The first wave began in the 1990s with a steady rise
in overdose deaths from prescription natural and semisynthetic opioids, as well
as prescribed methadone. In 2010, the second phase began with a dramatic rise
in heroin overdose deaths, tripling between 2010 and 2015. An even steeper
increase in overdose deaths from synthetic opioids brought the third and current
phase, which began around 2013 (Dasgupta, Beletsky, and Ciccarone 2018). The
timeframe for our analysis coincides with the end of the first wave and beginning
of the third wave, capturing the changing face of the crisis as legislative actions
on prescription drugs lowered the supply of semisynthetic drugs, setting the
stage for increased heroin and synthetic opioid usage across the nation.
Our primary measure for the intensity of opioid use in our analysis is county-
level DRM rates from the Institute for Health Metrics and Evaluation (IHME; Centers for Disease Control and Prevention (CDC)
DRM data are limited due to issues including the suppression of counties with
low DRM numbers and the potential underreporting of overdose deaths. IHME
DRM data are imputed using CDC DRM data, Census population counts, and
small area Bayesian estimation models. It is important to note that while these
data are not exact counts of overdose deaths by county, they are the best approxi-
mation and allow comparison across counties and years. See Appendix A for a
more detailed description of the data used in this article. While IHME DRM
rates are based on all drug-related deaths which overestimates mortality rates
due strictly to opioid-related deaths, the latter are estimated to account for about
70 percent of DRM in recent years and opioid-related deaths are the primary
driver of the growth in drug-related deaths over the past 20 years (CDC 2018a).
Moreover, mortality related to all types of drugs, not just opioids, would be
expected to affect students according to our conceptual model, presented below.
Of course, in addition to fatalities, there are other potential negative effects of
opioid use, including nonfatal overdose emergencies that lead to hospitalization
and ongoing addiction with all the associated negative societal spillovers.
Moreover, opioid abuse can co-occur with other substance use disorders, depres-
sion, and other physical and psychological ailments. Thus, our measure repre-
sents a relatively extreme consequence of opioid use.
We display the trend in average county-level DRM rates from 1980 to 2014 in
Figure 1 (see Monnat Forthcoming for more trends in more recent years). DRM
rates have been increasing since the early 1980s, with an uptick in mortality rates
in the 1990s and a steeper increase starting around the year 2000. As displayed
in panel A, from 2000 to 2014, average DRM in counties rose from 3.7 to 10.0

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