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
    - Be clear, practical, and informative

    Write a short paragraph for the “Policy Implications” section of a policy brief.

    Scientific Research Paper:
    https://doi.org/10.1177/00221465211067209
Journal of Health and Social Behavior
2022, Vol. 63(3) 337–356
© American Sociological Association 2022
DOI: 10.1177/00221465211067209
jhsb.sagepub.com
Original Article
Background
For more than a century, governments have designed
policies intended to address drug use, dependence,
and mortality (Musto 1999). Through what Foucault
(1991) termed governmentality, policies may induce
institutions to foster individual-level behavioral
choices that cohere with the aims of the government.
Throughout the twentieth century, governmental pol-
icy regarding drug supply increasingly focused on the
criminal justice system (MacCoun and Reuter 2001).
By relying on policies that define certain drugs as
illicit, there is a natural reliance by the state on the
criminal justice system to curb supply. According to
Foucault (1982), however, use of policing to influence
individual-level behavior is inefficient and often
counterproductive. Surveillance systems that induce
self-governing behavior rather than direct coercion
are more efficient because they reduce the resources
required to align behavior with governmental aims
(Foucault 1995). Policies may employ influence on
behavior across a continuum from the tacit forms of
behavioral persuasion akin to Foucault’s panopticon
1067209 HSBXXX10.1177/00221465211067209Journal of Health and Social BehaviorVuolo et al.
research-article2022
1The Ohio State University, Columbus, OH, USA
2Purdue University, West Lafayette, IN, USA
Corresponding Author:
Brian C. Kelly, Department of Sociology, Purdue
University, 700 W State St, West Lafayette, IN 47907,
USA.
Email: bckelly@purdue.edu
Surveillance, Self-Governance,
and Mortality: The Impact of
Prescription Drug Monitoring
Programs on U.S. Overdose
Mortality, 2000–2016
Mike Vuolo1
, Laura C. Frizzell1, and Brian C. Kelly2
Abstract
Policy mechanisms shaping population health take numerous forms, from behavioral prohibitions to
mandates for action to surveillance. Rising drug overdoses undermined the state’s ability to promote
population-level health. Using the case of prescription drug monitoring programs (PDMPs), we contend
that PDMP implementation highlights state biopower operating via mechanisms of surveillance, whereby
prescribers, pharmacists, and patients perceive agency despite choices being constrained. We consider
whether such surveillance mechanisms are sufficient or if prescriber/dispenser access or requirements
for use are necessary for population health impact. We test whether PDMPs reduced overdose mortality
while considering that surveillance may require time to reach effectiveness. PDMPs reduced opioid
overdose mortality 2 years postimplementation and sustained effects, with similar effects for prescription
opioids, benzodiazepines, and psychostimulants. Access or mandates for action do not reduce mortality
beyond surveillance. Overall, PDMP effects on overdose mortality are likely due to self-regulation under
surveillance rather than mandated action.
Keywords
drugs, mortality, overdose, policy, surveillance

338
Journal of Health and Social Behavior 63(3)
through direct mandates for action. In this regard,
coercion may occur not only through explicit man-
dates but also in implicit forms that give the appear-
ance of choice as well. Surveillance systems within
the scope of the health care system may appear, at face
value, to be less coercive.
Because the health care system does not monitor
illicit drug supply and largely is reactive to patterns of
substance use, these inefficiencies were further
brought to light by the overdose crisis. By fundamen-
tally altering the source of psychoactive drugs, the
rise of prescription drug misuse in the twenty-first
century disrupted the state’s control of drug use,
dependence, and mortality through criminalization
such that “law enforcement is unlikely to reach PPD
[psychoactive prescription drugs] diversion effec-
tively” (Fischer, Bibby, and Bouchard 2010:2062). In
terms of supply for nonmedical use, most sources are
legally written prescriptions for the users themselves
or for friends or family members as opposed to acqui-
sition from drug dealers (Hulme, Bright, and Nielsen
2018). Furthermore, surges in overdoses during the
twenty-first century outpaced prior drug trends and
undermined the state’s ability to control population-
level health via what Foucault (1990) referred to as
biopower—a “technology of power” for disciplining
and controlling populations. With the health care sys-
tem now a key source of drug supply, combined with
a lapse in biopower, other policies were needed to
address the state’s interests. Questions remain about
how policies may best achieve these ends, particu-
larly whether state surveillance of medical processes
alone may reduce adverse outcomes or mandates for
action are necessary.
Within this article, we examine the role of pol-
icy implementation within U.S. states, specifically
prescription drug monitoring programs (PDMPs),
on overdose mortality. As defined by the Centers
for Disease Control and Prevention (CDC; 2017),
“A prescription drug monitoring program (PDMP)
is an electronic database that tracks controlled sub-
stance prescriptions in a state.” Drugs are deemed
controlled substances by federal government sched-
uling, typically defined as psychoactive drugs with
abuse potential, and include both legally prescribed
medications and drugs with complete prohibitions.
As described in the following, independent state-
level systems create considerable variability in cer-
tain details, such as who can access PDMPs and
who is required to check the system. Typically,
however, PDMPs not only track all prescribed con-
trolled substances but also produce reports at both
the patient and population level to identify overpre-
scribing (Perrone and Nelson 2012).
We consider the role of PDMPs as a means to
reduce overdose mortality by disrupting the circula-
tion of prescription drugs available for consump-
tion by the population. We argue that the
implementation of PDMPs is an example of state
biopower operating via a mechanism of surveil-
lance (Foucault 1990, 1995). This approach allows
multiple actors—physicians, pharmacists, and
patients—to perceive a level of action and agency
despite their choices being constrained (Bird and
Rieker 2008; Foucault 1982) and thus should be
effective regardless of whether providers have
access to or are required to check these systems.
Such surveillance facilitates self-monitoring under
conditions whereby individuals may be checked
and scrutinized, regardless of whether this actually
occurs. That is, unverifiable surveillance is a more
efficient form of biopower than direct behavioral
coercion. Ultimately, a use of biopower in the form
of PDMPs constrains the choices of multiple actors
and in doing so impedes the circulation of con-
trolled substance medications, which in the aggre-
gate impacts overdose mortality. In the analyses
that follow, we test this mechanism by first analyz-
ing the efficacy of PDMPs’ general system of
surveillance prior to analyses of policies that spe-
cifically grant access to PDMP databases to provid-
ers/dispensers or require providers/dispensers to
check this system prior to writing or filling a pre-
scription for a controlled substance. Thus, we con-
sider that unverifiable surveillance of institutional
practices and patient behavior orchestrated by pub-
lic policy may shape patterns of drug-related mor-
tality. We also consider that the effect of such
systems of surveillance may have an institutional
lag given that self-monitoring under conditions of
surveillance may take time to scale up.
The Case of Overdose in the Twenty-
First Century and Policy Responses
Increases in drug-related morbidity, mortality, and
dependence over the past two decades affected U.S.
population health in adverse ways. The rise in pre-
scription drug misuse since the 1990s was consider-
able (Atluri, Sudarshan, and Manchikanti 2014;
Birnbaum et  al. 2011; Dart et  al. 2015; Maxwell
2011), followed by more recent increases in heroin
use and problems related to synthetic opioids such
as fentanyl (Carlson et al. 2016). In this manner, the
crisis has experienced multiple waves and continues
to expand because drugs such as cocaine and meth-
amphetamine are increasingly adulterated with fen-
tanyl (Ciccarone 2021). These multiple waves have

Vuolo et al.
339
contributed to growth in drug consumption, drug
dependence, and overdose mortality over time. In
addition to opioids, consumption and adverse out-
comes from other prescribed controlled substances
also escalated (Bachhuber et al. 2016; Scholl et al.
2019). Increasing dependence on opioids and other
drugs led to growth in the unmet treatment burden
(McCabe, Cranford, and West 2008). Beyond drug
dependence, the United States experienced massive
growth in overdoses over the past 20 years, which
contributed to lower life expectancies (Case and
Deaton 2015; Dowell et  al. 2017; Imtiaz, Probst,
and Rehm 2018). Surging overdose mortality con-
tributed greatly to years of life lost across socioeco-
nomic groups (Ho 2017). Between 1999 and 2019,
nearly 850,000 people died in the United States
from a drug overdose, and early evidence indicates
further escalation of overdose deaths during 2020
(CDC 2020). Considering the adverse effects on
population health, the drug overdose crisis poten-
tially undermined state biopower.
In response to problems emerging from these
patterns of substance use, state-level governments
implemented various policy measures to combat
escalating trends in abuse, dependence, and over-
dose. Within the United States, drug policies operate
in a multilevel manner. Whereas federal-level poli-
cies intervene on the international drug trade,
including heroin and illicitly manufactured fentanyl,
PDMPs represent actions taken at the U.S. state
level to curb pharmaceutical drugs acquired within
the context of the medical system. Although modest
within the larger umbrella of U.S. drug control,
PDMPs nonetheless directly target medicalized sub-
stances recognized as fueling the overdose crisis.
Evaluation of such policies and the distinctions in
how they are implemented remains imperative to
identify efficacious solutions to reduce overdose
mortality and other substance abuse problems. We
focus on the implementation of PDMPs—given
their role in surveillance, long history, wide cover-
age, and application to several classes of substances
(Gugelmann and Perrone 2011)—in shifting over-
dose mortality while accounting for the broader pol-
icy context by considering the passage of other
relevant policies over the period of observation.
On a basic level, all PDMPs create a database of
prescriptions for controlled substances that allow for
surveillance over potential exploitations by either
providers or patients, although these systems of sur-
veillance primarily affect the institutional duties of
prescribers/dispensers. Although PDMPs have
drawn attention during the overdose crisis, some
PDMPs preceded the rise in overdose mortality
during the twenty-first century; the earliest were
implemented in New York approximately a century
ago, and even electronic systems emerged approxi-
mately 30 years ago. Thus, many currently practic-
ing physicians and pharmacists have been familiar
with these systems in principle even if not directly
subject to their regulation until more recently. With
respect to recent implementation, prescribers and
pharmacists often received information about these
systems from state medical and pharmacy boards
and through continuing education. Information on
how prescribers/dispensers can access the state
PDMP is typically maintained online via state web-
sites. Use of PDMPs can be variable, especially
given that practitioners indicate it can be time con-
suming; nonetheless, as of 2014, 87% of physicians
in states with a PDMP indicated they had the capac-
ity to access PDMP information at the site of their
clinical practice and had used a PDMP to check
patient prescriptions (Rutkow et al. 2015).
As of 2000, PDMPs were active in only 12 U.S.
states. In other U.S. states, essentially no surveil-
lance system existed to assist in identifying poten-
tial misuse. As the crisis became more visible,
PDMP implementation was rapid, with PDMPs in
22 U.S. states by 2005, 39 by 2010, and 49 by 2015,
with Missouri the lone holdout; St. Louis County
established a PDMP during 2017 in response to the
state-level absence. Prior to that, all PDMP policies
were passed at the state level (and Washington,
D.C.) rather than locally. Although PDMPs have a
lengthy history, there is variability across and
within U.S. states over time (Pauly et  al. 2018),
summarized in Figure 1. For example, although
prescribers/dispensers have not always had access
to PDMP records, all active PDMPs now grant such
access. Furthermore, as of 2012, none required
physicians/dispensers to check the PDMP database
when filling a prescription. By 2016, 16 U.S states
had passed such laws for prescribers, and 5 states
had passed such a law for dispensers. Given this
small proportion and timeframe within which
requirements to check the PDMP registry were
active, we argue and empirically demonstrate that
any effect of PDMPs observed over the last two
decades is due to the invisible system of surveil-
lance they create rather than newer, explicit require-
ments for professionals to check them.
Policy and Surveillance as State
Biopower
Policy interventions on behavior can take a range of
forms, from the provision of information for

340
Journal of Health and Social Behavior 63(3)
decision-making to the elimination of choice through
prohibition, and these imply varying impacts on per-
sonal autonomy (Griffiths and West 2015). A key
component of the implementation of PDMPs is the
creation of a condition of surveillance. The aware-
ness that one may be monitored can create a self-
governing system of social control even when it is
unverifiable that one is actively being monitored
(Foucault 1995). When prescribers and pharmacists
become immersed in a system of state-level over-
sight on the prescription and distribution of con-
trolled substances, they may subtly self-check and
alter their behavior. This self-check on institution-
ally based practices can take multiple forms. For
example, when aware of PDMP surveillance, pre-
scribers may consciously or subconsciously ques-
tion whether to write a prescription, resulting in
fewer prescriptions overall. Similarly, they may pre-
scribe fewer doses per prescription, particularly for
new patient cases, resulting in fewer pills in circula-
tion overall. In rarer instances, PDMPs may
facilitate reporting behaviors by which medical pro-
fessionals under conditions of unverifiable surveil-
lance alert authorities to suspected misuses by
patients or even other prescribers/dispensers. In a
PDMP system where prescribers/dispensers do not
have direct access to the database, this surveillance
process would be the only possibility, such that any
effect would be strictly through a mechanism of
self-regulation under surveillance. In other words,
behavior can change even in the absence of inten-
tional efforts to access the system. In a PDMP sys-
tem where prescribers/dispensers have database
access, they can also self-govern by actively check-
ing the system either as habit or if something seems
amiss with a patient or request, with use of PDMPs
common in U.S. states providing access (Rutkow
et al. 2015). Importantly, these mechanisms may be
in effect even if providers are not required to explic-
itly check a monitoring system when writing or fill-
ing a prescription. As such, a system of surveillance
looming over prescribing, dispensing, and care-
seeking behaviors may instill self-governance even
if individuals are not mandated to act under these
policies.
For Foucault (1995), this internalized self-
governance is the perfect exercise of state power
because control is exercised over the greatest
number of individuals while reducing costs to the
system from third-party oversight, a form of power
best exemplified by the panopticon. Along with
institutions such as schools and criminal justice,
health was one of the key examples within which
Foucault (1995) described panopticon-like power.
Foucault conceptualized biopower in referring to
the power states possess to influence population-
level health, life, death, reproduction, and longev-
ity. In this manner, biopower is a technology of
power for disciplining and controlling populations
through techniques such as surveillance, which
stimulate the population to govern its behavior in
ways that conform to governmental aims (Foucault
1990). Although Foucault was critical of power in
many instances, he also recognized the potential for
biopower to be a positive mechanism contributing
to population-level well-being (Foucault 1990).
Infectious disease surveillance remains a key exam-
ple of the opportunity to leverage surveillance in
the interest of population health (Fairchild 2003). In
the case of overdose mortality, states have capital-
ized on biopower to implement PDMPs that
constrain the autonomy of health professionals at
the individual level for an overall population-level
benefit via increased surveillance.
State power, however, is not simply disciplining
individuals into conformity through coercion.
Summarizing Foucault (1982), Garland (1997:175)
states that governmental power “constructs individ-
uals who are capable of choice and action, shapes
them as active subjects, and seeks to align their
choices with the objectives of governing authori-
ties.” Within medical sociology, constrained
choices theory contends directly with this dynamic
(Bird and Rieker 2008). Although originally used to
explain gender differences in health, it provides a
conceptual framework for multilevel processes that
lead to differential shaping of health behaviors
(Vuolo, Kadowaki, and Kelly 2016). That is, indi-
viduals are agentic actors in terms of their
Figure 1.  Prescription Drug Monitoring
Programs (PDMPs) across State and Year.
Source: Data from Prescription Drug Abuse Policy
System.

Vuolo et al.
341
health-related decision-making but constrained
within the policy, community, and family environ-
ments (among others) within which they are situ-
ated. A Foucauldian interpretation adds that the
state has a vested interest in guiding these con-
straints. Furthermore, most work has focused on
constraints for individual health decisions rather
than also incorporating how constraints affect pro-
viders and institutions.
Notably, surveillance through PDMPs constrains
the decision-making of multiple actors (Bird and
Rieker 2008)—patients, physicians, and pharmacists—
and in doing so provides the state with opportuni-
ties to guide the constraints of multiple types of
individuals central to the circulation of prescribed
controlled substances and ultimately, overdose
mortality. With choices effectively constrained but
with enough perceived agency, surveillance affects
the decision-making processes that shape the diver-
sion of prescription drugs and drug dependence that
facilitates illicit drug use. For health professionals,
the suspension of professional licensing by the state
creates a potential sanction should surveillance
detect irregularities on the part of these prescribers
and pharmacists. The PDMP system may also con-
strain and reduce the practice of “doctor shopping”
by patients. This constraint may occur after patients
become aware that the state may be monitoring
their receipt of prescriptions and that this catalog of
prescriptions filled by multiple doctors may be used
as evidence against them, particularly those who
may face distribution charges. The opportunity to
intervene on multiple actors through self-regulation
under surveillance enhances policy impact, which
indicates the full scope of biopower.
Within the context of constrained choices, the
surveillance effects of PDMPs apply across popu-
lations when implemented in a U.S. state. In this
manner, the comprehensive nature of surveillance
grounded within PDMPs allows their effects to be
noticeable precisely because they create a system of
surveillance that applies universally across the pop-
ulation; this universality is a key characteristic of
biopower. Although even in a strong system of sur-
veillance, some individuals find ways to evade
detection (Goffman 2009). Thus, a small pool of
unscrupulous prescribers/dispensers could still seek
ways to divert prescriptions, whereas users may
determine ways to work within the current system
of surveillance. Yet the overall system of surveil-
lance creates constraints for a large proportion of
prescribers, pharmacists, and patients that inhibit
the circulation and potential diversion of medica-
tions. The one-on-one doctor–patient behavior,
although surely a component, does not alone indi-
cate how changes in aggregate have an effect. Most
individuals who misuse prescription medications
obtain these drugs from friends and family, with
primarily heavy users turning to the black market or
doctor shopping (Hulme et al. 2018). As such, the
problem of mortality extends beyond any single
individual momentary constrained behavior for
how a provider writes a prescription; the aggregate
effect of individual constrained choices on local
availability matters for misuse and mortality.
Of policies that have been passed, PDMPs most
directly address the state’s interest in controlling
drug supply because they create a system of surveil-
lance advantageous to the state in its universal
application across all controlled substance prescrip-
tions. Other policies enacted, such as enhanced nal-
oxone access and Good Samaritan laws, require
individual-level action, which may depend on vary-
ing individual and community resources, and are
not subject to the same level of governmental over-
sight in their use. In some ways, these policies
release a certain level of governmental control. By
absolving users of responsibility should they call
state actors such as first responders, Good
Samaritan laws prevent scrutiny by the criminal
justice system (Haegerich et  al. 2014)—albeit in
ways that may yield better mortality outcomes. By
reducing the need for hospital admission after use
of an overdose reversal drug, naloxone access laws
(Davis and Carr 2017) curtail surveillance by the
health system. Notably, these policies create a
mechanism whereby the state can intervene only on
adverse outcomes rather than patterns of drug con-
sumption more generally.
Importantly, surveillance-driven constraints
may require time to shift self-governance in a man-
ner that produces measurable results. Clinical
interactions have cultural components rather than
simply being crude measurements of physiology
(Dubbin, Chang, and Shim 2013; Light and Levine
1988). As such, there may be a lag period as cul-
ture within institutions changes. Cultural shifts in
cognition often occur in a tacit and incremental
manner rooted in practice rather than a paradig-
matic shift (Luhrmann 1991). Within the context of
the institution of medicine, shifts requiring behav-
ioral change in clinical interactions via self-moni-
toring may require time to scale up. In this regard,
there is a lag between the recognition of surveil-
lance and the routinization of self-governance in
clinical practice. As awareness of surveillance and
resulting self-consciousness gradually become
ingrained in clinical practice, institutional and

342
Journal of Health and Social Behavior 63(3)
personal behavioral shifts emerge that lead to
observable effects on the circulating pool of con-
trolled medication and its subsequent misuse.
These forms of self-governance may also initially
occur unevenly, depending on familiarity and
experience
with
patients
(Chiarello
2015).
Specifically, the surveillance effect of PDMPs may
become more readily situated within clinical
encounters with new patients, whereas such regu-
lation within the context of long-term patient rela-
tionships may require more time to build as
consciousness of surveillance more fully takes
hold.
Although Foucault’s arguments about the power
inherent in surveillance is persuasive, we must also
consider the possibility that surveillance and self-
governance alone may not be sufficient to affect
overdose mortality. As noted previously, U.S. states
more recently implemented policies with specific
requirements for action on the part of prescribers/
dispensers (Pauly et al. 2018). The requirement that
individuals engage in specific types of action can be
a key policy lever for shifting health behaviors by
creating a system of constraints that influences
decision-making within a broader context (Bird and
Rieker 2008). Additionally, direct mandates for pre-
scriber/dispenser action may create a more immedi-
ate effect by eliminating any lag in behavioral
scale-up. As such, we also assess whether require-
ments for action drive any effect on overdose mor-
tality, which would shift evidence away from the
surveillance and self-governance effect described
previously. However, we note that for Foucault
(1982), such requirements that apply power through
behavioral coercion are unnecessary, less efficient,
and even counterproductive. He stressed that power
is exercised over free subjects and that governing is
cultivated through the ability of individuals to make
choices for themselves while being steered toward
choices aligned with governmental aims.
The Empirical Case for Studying PDMPs
In addition to considering an important theoretical
mechanism of surveillance on a critical population
health outcome, there is an empirical case to be
made for our approach. While U.S. states increas-
ingly enacted policies in response to growing drug
dependence and overdose problems, research
toward comprehensively examining the influence of
PDMPs on trends in fatal overdose over a period of
rapid policy implementation continues to build.
Recent reviews have categorized these results as
“mixed” (Finley et al. 2017), “insufficient” (Fink
et  al. 2018), “limited” (Rhodes et  al. 2019), and
“conflicting” (Moride et al. 2019). Limitations of
existing studies on these policies include: (a) exami-
nations of policies in single locations, (b) consider-
ations of single policies without attention to the
wider policy context, (c) truncated periods of obser-
vation, and (d) inability to account for unobserved
heterogeneity. Most studies also do not account for
tremendous heterogeneity within states.
Utilizing sociological theories and integrating
multiple data sets, we build on this prior research to
move beyond each of these limitations through the
inclusion of U.S. counties across the nation, con-
trolling for multiple policies to account for broader
policy contexts, an extended time period of policy
implementation, and use of lead and lag structures
within fixed effects models of drug-related mortal-
ity. We examine overdose rates at the county level
using restricted access mortality data. Although
overdose-related policies are enacted at the state
level, analysis of mortality at the county level per-
mits a more nuanced assessment given considerable
within-state heterogeneity, an important distinction
given noted differences between rural and urban
areas in terms of prescription drug misuse and mor-
tality (Keyes et al. 2014; Peters et al. 2020). For
instance, rural counties in central Pennsylvania
present a very different context than Philadelphia
County despite being within the same state (Monnat
2018). We first examine the possible impact of
PDMPs, net of other policies, on overall opioid
overdose mortality in the United States. Importantly,
we incorporate the time since policy passage
because we anticipate that the effects of surveil-
lance may not be immediate. We follow by looking
at these effects on overdose mortality specific to
prescription opioids, prescription benzodiazepines,
and stimulants to show the potential effects of
PDMPs beyond the general opioid overdose crisis.
Additionally, we consider whether these effects
hold for subsequent PDMP policies that grant pre-
scriber/dispenser access or require prescribers/dis-
pensers to check the system before writing or filling
a prescription. In this manner, our analyses permit
an assessment that contrasts the effects of surveil-
lance-stimulated self-governance and mandated
action. Furthermore, most existing studies are
descriptive and lack a theoretical foundation, creat-
ing an urgent need for theoretically driven studies
of prescription drug misuse (Watkins 2016). By
incorporating sociological theories, we are better
able to highlight the mechanisms by which PDMPs
might mitigate mortality and extend considerations
of policy mechanisms to other health domains.

Vuolo et al.
343
Data and Methods
Overdose Mortality Data
We utilized CDC restricted access mortality files
from 2000 to 2016. The database contains every
death in the United States in each year. Cause of
death codes allowed identification of drug over-
doses, which we then aggregated to the county
level. We calculated age-adjusted overdose rates per
100,000 for each county. Unlike public use data,
restricted data contain no suppressed information,
which permits more precise analysis of counties
with fewer than 10 deaths per year.
Our first outcome is age-adjusted death rate due
to any opioid overdose within counties each year.
We utilized the CDC’s operational definition for
opioid overdose (CDC 2014). Such deaths have
ICD-10 underlying cause codes of “drug poison-
ing,” X40-X44, X60-X64, X85, and Y10-Y14,
together with ICD-10 multiple causes codes
T40.0-T40.4 and T40.6. The latter multiple cause
codes specify opioids as a cause of death, which
include prescription opioids, such as hydrocodone
(Vicodin) and oxycodone (OxyContin), and mor-
phine, heroin, and synthetic opioids, such as fen-
tanyl. In addition to total opioid overdoses, we
examined overdoses from prescription opioids
(T40.2) separately as an outcome and benzodiaze-
pines (T42.4) and psychostimulants with abuse poten-
tial (T43.6) in separate analyses. Benzodiazepines,
also known as tranquilizers, are prescribed for condi-
tions such as anxiety and insomnia; well-known
examples include alprazolam (Xanax), clonazepam
(Klonopin), and diazepam (Valium). Psychostimulants
include prescriptions, such as methylphenidate (Ritalin)
and amphetamines (Adderall), and methamphet-
amine but not cocaine, which is classified sepa-
rately (T40.5). We also note that deaths attributed to
overdose can include multiple coded substances.
Because polydrug use heightens overdose risk
beyond that of a single substance, examining the
role of PDMPs on reductions in deaths involving
prescription drugs in conjunction with another sub-
stance remains important. We considered polydrug
use by additionally examining each of the separate
classes in combination with an opioid.
Policy Data
We utilized the Prescription Drug Abuse Policy
System for a comprehensive listing of policy pas-
sage in each U.S. state between 2000 and 2016. We
then created a state-year data set of the presence of
policies across the observation period. Our central
variable of interest is presence of a state-level
PDMP (coding described in the following). We also
considered alternative specifications via variables
for whether prescribers/dispensers have access to
the PDMP and whether prescribers/dispensers are
required to check the PDMP when writing/filling a
prescription. To account for other policies that may
affect overdose rates, we included covariates for
naloxone access expanded beyond medical settings,
Good Samaritan laws absolving criminal/civil lia-
bility in reporting an overdose, pain clinic restric-
tions, and medical marijuana laws (not passed
because of the overdose crisis but an important pol-
icy covariate given their pertinence to managing
pain).
County- and State-Level Covariates
From the U.S. Census Bureau’s American Community
Survey (ACS) and decennial censuses, we included
county-level time-varying covariates, specifically
population density, median household income, and
percentages foreign-born, female-headed house-
holds, non-Hispanic Black, Hispanic, and over 25
with a bachelor’s degree. We included an interaction
between percentage foreign-born and Hispanic
given their high correlation and a significant inter-
action effect in several model specifications. To
include all counties, we required 5-year ACS esti-
mates because only larger counties are available
over shorter estimates. Because these begin in 2009,
we linearly interpolated years between the 2000
census and 2009 (with the exception of population
density because estimates are available annually).
We also included county-level unemployment rate,
available annually from the Bureau of Labor Statistics.
From the Annual Survey of State Government
Finances, we included state-level per capita spending
on education, public welfare, hospitals, and health.
Analysis
We used fixed effects models to determine the effect
of PDMPs on overdose mortality (Allison 2009;
Halaby 2004; Winship and Morgan 1999). The
method’s strength is the elimination of unobserved
heterogeneity by differencing all predictors and the
outcome from its county-specific average. Fixed
effects estimators are robust to observed or unob-
served time-invariant variables, which removes any
constant county-level effects. To account for the
well-known increase in overdose rates and provide
estimates of the effect of PDMPs independent of
that increase, all models included fixed effects for

344
Journal of Health and Social Behavior 63(3)
year. Finally, we included a standard error cluster
correction to account for dependencies between
counties within states. There are three additional
assumptions of fixed effects models: exogenous
treatment selection, the parallel trends assumption,
and causal direction (Vaisey and Miles 2017).
Appropriate tests demonstrate no evidence of
endogenous selection (Morgan and Winship 2007;
Vaisey and Miles 2017). The coding of our indepen-
dent variable addressed the latter two assumptions.
Because policies may take time to reach effec-
tiveness for outcomes related to overdose (Bao
et al. 2016), we took advantage of the fact that all
U.S. states but one (Missouri) implemented a
PDMP prior to 2016 to recode the measure of the
policy as centered around the year of implementa-
tion. That is, the point of passage was coded as 0;
years prior were coded −1, −2, and so on; and years
after were coded 1, 2, and so on. This approach
incorporated lead and lag effects directly into the
model without sacrificing data. As the number of
U.S. states at the extremes becomes small and thus
the estimates become less precise, we capped the
number of years in either direction at ±5.1 In mod-
els that consider PDMP prescriber/dispenser access
and requirements, we used traditional leads and
lags because the lack of consistent implementation
of these forms of PDMPs prohibit the prior
approach, and as noted previously, requirements for
action may yield more immediate effects. The tests
for leads for both approaches allowed us to test for
reverse causality and parallel trends. For the for-
mer, significant leads would indicate that an
increase or decrease in overdoses preceded policy
implementation and that the relationship actually is
temporally reversed.2 For the latter, plotting the
leads allows determination of whether the period
prior to adoption was different across counties prior
to the point of adoption. All other policies were
coded as their status on the first of any given year.
We lagged all other covariates by 1 year to ensure
appropriate temporal order.
The United States contains 3,142 counties,
amounting to 53,414 possible county-year observa-
tions from 2000 to 2016. However, 13 counties
restructured boundaries such that we started those
series at later years when their boundaries cohered
with the present because prior years were not com-
parable within the CDC data. In our main PDMP
models (not including Missouri), there were 3,025
counties across 17 years, which (accounting for
restructuring) included 48,239 observations. In our
models for PDMP access and restrictions, the use of
traditional leads and lags eliminated data on both
sides of the time series (2 years at the beginning for
lags and 1 year at the end for leads). In these mod-
els, there were 3,140 counties and 46,939
observations.
Results
Descriptive Statistics
Table 1 provides descriptive statistics, pooled over
counties and years. Although our models use differ-
ent subsets of data, we show the descriptive statis-
tics from 2000 to 2016 (accounting for restructuring)
because this covers the years used in our main mod-
eling approach (but includes Missouri). On average
across the observation period, the county-level age-
adjusted overdose rate for all opioids was 5.54 per
100,000. Overdoses involving prescription opioids
(2.79), benzodiazepines (1.58), and stimulants (.66)
were notable. Figure 2 shows trends in these out-
comes over time averaged at the county level, con-
firming well-known increases in all opioid
overdoses and flattening of those due specifically to
prescription opioids.
Benzodiazepines stabilized about the same time
as prescription opioids, whereas psychostimulants
have seen increases in recent years. Given more
recent implementation in most U.S. states,
expanded naloxone laws (13.6%), Good Samaritan
laws (9.0%), and pain clinic restriction laws
(10.5%) have lower coverage than PDMPs across
the entire period. County-years with medical mari-
juana laws comprise 15.0% over the period. Finally,
Table 1 displays averages for demographic and
expenditure covariates.
Although Figure 1 showed the growth in PDMPs
across U.S. states, we also note the total percentage
of county-years because this is our level of analysis
for each PDMP variable. With relatively wide cover-
age, 66.1% of county-years had a PDMP during the
period of observation. Prescribers/dispensers were
granted access in 58.5% and 56.0% of county-years,
respectively. As a recent change, only 3.9% and
1.8% of county-years have PDMP checking require-
ments for prescribers and dispensers, respectively.
Presence of a PDMP
Next, we move to the results of fixed effects models
for the presence of any PDMP system, which
accounts for the creation of a system of surveillance.
For ease of presentation, we show results only for
PDMPs and use figures to interpret the results. The
full models, inclusive of all controls and fixed effects

Vuolo et al.
345
for county and year, can be found in Appendix A.
Figure 3 shows the relationship between the timing of
PDMP adoption and the overall opioid overdose rate.
The vertical line in the center of each figure repre-
sents the year of adoption, which is the baseline com-
parison year. Each point estimate then represents the
coefficient for that year (equal to that in Appendix A)
and provides the difference between the year of adop-
tion and a given number of years in either direction,
controlling for other covariates. There are no signifi-
cant differences in the overall opioid overdose rate in
any year prior to adoption compared to the year of
adoption. Statistically, this finding means there are no
“lead” effects, which would indicate that change pre-
ceded policy implementation. Such significant leads
could imply state-level policy was passed in response
to overdose rates in a county and that the relationship
is temporally reversed. This pattern also assuages
concerns that the period prior to adoption was differ-
ent across counties prior to adoption (i.e., the parallel
trends assumption). The years after adoption then
provide differences occurring due to the presence of a
Table 1.  Descriptive Statistics (Pooled over Counties and Year 2000–2016, N = 53,277).
Average (SD)/%
County-level age-adjusted overdose rates

  Overall
5.54 (14.82)
  Prescription opioid
2.79 (13.11)
  Prescription opioid with another opioid
.63 (11.87)
  Benzodiazepines
1.58 (4.93)
  Benzodiazepines with an opioid
1.28 (4.31)
  Psychostimulants
.66 (2.68)
  Psychostimulants with an opioid
.23 (1.36)
  Heroin
.71 (2.57)
State-level policies

  PDMP system
66.09%
  PDMP with prescriber access
58.51%
  PDMP with dispenser access
56.04%
  PDMP prescriber requirement
3.89%
  PDMP dispenser requirement
1.80%
  Good Samaritan law
9.00%
  Naloxone access law
13.55%
  Medical marijuana
14.98%
  Pain management clinic law
10.50%
State-level per capita expenditures ($)

  Education
1.73 (.43)
  Public welfare
1.28 (.44)
  Hospitals
.18 (.13)
  Health
.16 (.08)
County-level demographics

  Unemployment rate
6.32 (2.74)
  Population density (per mi2)
213.21 (1235.20)
  Median household income (thousands)
42.13 (11.63)
  % living in poverty
11.43 (5.66)
  % with bachelor’s degree
13.38 (6.58)
  % foreign-born
4.12 (5.35)
  % female-headed households
6.38 (2.45)
  % Black
9.26 (14.35)
  % Hispanic
7.90 (12.92)
Note: Data from Centers for Disease Control and Prevention, Prescription Drug Abuse Policy System, American
Community Survey, and decennial censuses. PDMP = prescription drug monitoring program.

346
Journal of Health and Social Behavior 63(3)
PDMP. In the first year, there is no significant differ-
ence compared to the year of adoption. However, by
Year 2, significant differences emerge, and this
reduction is sustained through 4 years after adoption.
Specifically, county-level overdoses per 100,000 are
predicted to be .88, 1.17, and 1.59 lower in years 2, 3,
and 4, respectively, from adoption.
We would presume that PDMPs will be more
directly effective for prescription opioids than illicit
opioids, although we recognize there is potential for
a cascading effect as opioid-dependent individuals
transition to heroin use. Thus, in Figure 4, we dis-
play two versions of the prescription opioid over-
dose rate. On the left, we show the model for all
prescription opioid overdoses. Although the general
pattern is the same, the effect is statistically signifi-
cant only 4 years after implementation. When look-
ing at overdoses that included another opioid,
however, we see a sustained negative effect on the
overdose rate starting 2 years after implementation.
Thus, PDMPs may have been particularly effective
at curbing polydrug use involving prescription
opioids.
Given that PDMPs have a general effect on pre-
scribed controlled substances, there is no reason to
confine the analysis strictly to opioids. In Figure 5,
we provide evidence that PDMPs are also effective
at lowering the overdose mortality rate for benzodi-
azepines and psychostimulants. Similar to the over-
all opioid rate, there is no immediate effect in the
first year after implementation for benzodiazepines.
However, the rate is significantly lower in Years 2,
3, and 4 by .51, .82, and .87, respectively. For stim-
ulants, the results are significant in Year 1 (–.26)
and sustained through Year 4 (–.45). Although not
shown, if we limit our analysis to benzodiazepine
or stimulant overdoses including an opioid, the pat-
tern is virtually identical, again indicating a benefit
for polydrug-related mortality rates.
For fixed effects models, a common validity
assessment to a significant finding is to consider an
outcome where there should be no effect. As a
robustness check, we ran our models for cocaine,
which has no prescription analogue outside a hospi-
tal setting and should not be affected by PDMPs,
and indeed found no significant effects of PDMPs
on cocaine overdose mortality (results not shown).
PDMPs with Prescriber/Dispenser
Access or Requirements to Check
To clarify whether surveillance is the mechanism
by which PDMPs operate, we also isolate PDMP
systems that either provide access to prescribers/
dispensers or mandate prescribers/dispensers to
check the system. Unlike our prior model specifica-
tion that centered PDMP on year of implementa-
tion, we include traditional lags and leads. Although
this results in data loss on both sides of the time
series, this approach is necessary because it is not
possible to center on year of implementation
because these forms of PDMPs are more recent and
Figure 3.  PDMP Effect on County-Level Opioid
Overdose Rate Using Fixed Effects Regression.
Note: Data from Centers for Disease Control and
Prevention, Prescription Drug Abuse Policy System,
American Community Survey (ACS), and decennial
censuses. Model includes all policy and contextual
controls described in text and year and county
fixed effects. Differences from the horizontal 0 line
represent effect sizes, with confidence intervals that
do not overlap indicating statistical significance at p <
.05. See Appendix A for table of full model. PDMP =
prescription drug monitoring program; OD = overdose;
diff. = difference.
Figure 2.  Average County-Level Overdose
Rates over Time.
Note: Data from Centers from Disease Control and
Prevention.

Vuolo et al.
347
less common, particularly requirements to check.
We include a 2-year lag because 2 years is where
we start to see effects in the models for any PDMP
system. We include a single lead because any addi-
tional leads would remove data from the most
recent years when PDMP checking requirements
were being implemented.
Table 2 summarizes results for these four PDMP
variables by the same five outcomes previously
described and heroin. Simply put, we find no evi-
dence that overdose mortality was associated with
policies that specifically grant prescriber/dispenser
access to the PDMP system. Although almost all the
lag coefficients are in the hypothesized negative
Figure 4.  PDMP Effect on County-Level Prescription Opioid Overdose Rate Using Fixed Effects
Regression.
Note: Data from Centers for Disease Control and Prevention, Prescription Drug Abuse Policy System, American
Community Survey, and decennial censuses. Models include all policy and contextual controls described in text and
year and county fixed effects. Differences from the horizontal 0 line represent effect sizes, with confidence intervals
that do not overlap indicating statistical significance at p < .05. See Appendix A for table of full models. PDMP =
prescription drug monitoring program; OD = overdose; diff. = difference.
Figure 5.  PDMP Effect on County-Level Benzodiazepine and Stimulant Overdose Rate Using Fixed
Effects Regression.
Note: Data from Centers for Disease Control and Prevention, Prescription Drug Abuse Policy System, American
Community Survey, and decennial censuses. Models include all policy and contextual controls described in text and
year and county fixed effects. Differences from the horizontal 0 line represent effect sizes, with confidence intervals
that do not overlap indicating statistical significance at p < .05. See Appendix A for table of full models. PDMP =
prescription drug monitoring program; OD = overdose; diff. = difference; implem. = implementation.

348
Journal of Health and Social Behavior 63(3)
direction for access, the only evidence of a signifi-
cant reduction is for the effect of prescriber access
on psychostimulants, but only in the first lag, and a
coterminous effect for heroin. Given the significant
lead, there is even some evidence that fatal psycho-
stimulant overdoses were already on the decline
when states implemented dispenser access.
For policies with requirements to check, we find
no additional benefits beyond general implementa-
tion of PDMPs. There is little evidence that requir-
ing prescribers/dispensers to check the PDMP
reduced overdose mortality. We find a significant
lead for all fatal opioid overdoses, implying that
these requirements may have been implemented in
response to high overdose rates. Considering when
these particular policies were passed (Figure 1),
they were implemented as prescription opioid over-
dose mortality was stabilizing and heroin overdoses
were increasing. In fact, in models for heroin over-
doses, we find a significant lead (b = 1.42, p < .01,
for prescribers; b = .89, p < .001, for dispensers).
Thus, as all opioid overdose mortality increased,
U.S. states may have tried to strengthen PDMP
systems via these requirements, but this overdose
rise was due to substances (heroin, fentanyl) less
likely to be affected directly by such changes. We
also find a significant lead for benzodiazepines.
Separating benzodiazepine overdoses that did and
did not include an opioid (not shown), we find only
a significant lead when the overdose included an
opioid such that it was unlikely that overdoses due
solely to benzodiazepines drove this particular policy
adoption.
Discussion
For over a century, the state has demonstrated a
vested interest in controlling drug use, dependence,
and overdose. Until recently, criminal justice has
been the main institution used to curb supply and
wider patterns of use (MacCoun and Reuter 2001).
The overdose crisis and rates of drug dependence
escalating during the twenty-first century funda-
mentally shifted the focal institution for the
Table 2.  Fixed Effects Panel Models of Overdose Rates by PDMP Type.
All Opioid
Overdoses
All Prescription
Opioid
Overdoses
Prescription Opioid
Overdoses with
Another Opioid
All
Benzodiazepines
Overdoses
All
Psychostimulant
Overdoses
All Heroin
Overdoses
Access granted

Prescriber

  Lead 1
–.622
–.592
–.077
–.522
–.113
–.058
  Coterminous
.225
.124
.056
.016
.129
–.112*
  Lag 1
.169
.061
–0.038
–.156
–.204**
–.135
  Lag 2
–.473
–.220
–.122
–.279
–.020
–.099
Dispenser

  Lead 1
–.675
–.602
–.063
–.538
–.160*
–.022
  Coterminous
.277
.117
.005
–.001
.125
–.076
  Lag 1
.004
–.039
–.064
–.139
–.186*
–.167
  Lag 2
–.346
–.191
–.079
–.362
–.029
–.060
Required to check

Prescriber

  Lead 1
2.886**
.912
.177
1.872*
.024
1.416**
  Coterminous
.112
.698
.187
.061
.119
–.066
  Lag 1
.993
.618
.235
1.591**
.241
.387
  Lag 2
2.084
.796
.659
.640
1.091***
.907
Dispenser

  Lead 1
3.310**
2.109
.260
4.138**
.186
.885***
  Coterminous
–.204
.588
.948*
–1.670*
.608
.634
  Lag 1
.884
–.745
–.841
1.386
.130
.778
  Lag 2
1.237
.887
.888
–.086
.664
–.024
Note: Data from Centers for Disease Control and Prevention, Prescription Drug Abuse Policy System, American
Community Survey, and decennial censuses. Models include all controls, county and year fixed effects, and state-level
cluster-corrected standard errors. PDMP = prescription drug monitoring program.
*p < .05, **p < .01, ***p < .001.

Vuolo et al.
349
deployment of state control (Foucault 1982) given
that these trends were fueled by a rise in prescription
drug misuse where criminal justice approaches
became ineffective (Fischer et al. 2010). The over-
dose crisis also necessitated an expansion of the use
of biopower (Foucault 1990) as population health
became affected in adverse ways beyond the scope
of existing state intervention (Case and Deaton
2015; Dowell et al. 2017; Ho 2017; Imtiaz et al.
2018). Thus, U.S. states responded to these trends
by implementing policies that create and enhance
health care systems of surveillance and, in doing so,
introduced a more tacit form of governmentality.
With continued concern regarding overdose mortal-
ity, assessments of policy efficacy remain vital for
understanding the social dynamics of drug trends
and identifying equitable policymaking tools for
intervention. We contend that biopower in the form
of PDMP implementation constrains the choices of
prescribers, dispensers, and patients and ultimately
how people may misuse prescription drugs, as
inferred from reduced overdose mortality.
The results of these analyses indicate that PDMP
systems have been effective in reducing overdose
mortality. As hypothesized, net of other overdose-
related policies, PDMPs reduce the overall rate of
opioid overdose mortality within 2 years of imple-
mentation through the creation of a system of sur-
veillance and self-regulation that functions without
direct requirements for action. In this regard, these
policies highlight the role of unverifiable surveil-
lance in structuring behavioral routines (Foucault
1995). Beyond a general effect on opioid overdose
mortality, PDMPs reduce overdose rates specific to
prescription opioids, benzodiazepines, and psycho-
stimulants and also account for polydrug use.
Importantly, this analysis accounts for within-state
heterogeneity by assessing county-level mortality
and improves on prior limitations by accounting for
multiple policies across the nation over an extended
period of time.
Through the combination of lead and lag struc-
tures and fixed effects, the results also highlight that
the effects of PDMPs require time to reach efficacy,
but once they do, they have a sustained effect. Prior
research suggested that policy implementation may
require time to impact opioid prescribing (Bao et al.
2016). We contend that a system of surveillance and
self-governance builds via cultural shifts in institu-
tional practices, which may occur in a more incre-
mental manner (Luhrmann 1991). Because policies
focused on shifting routines within clinical encoun-
ters through a mechanism of surveillance and self-
scrutiny, these effects are not immediate. Considering
Foucault’s conception of the panopticon, it is per-
haps reasonable to expect that shifts in self-gover-
nance require time to become habituated under
possible but unverifiable surveillance, whereas the
proverbial immediacy of a guard standing in front of
a prisoner effects more immediate change (Foucault
1995). Because overdose mortality has grown over
decades, the problem likely will not be resolved
quickly by a single policy, but these results indicate
that such policies may lessen adverse mortality
trends. Generally, the research presented here indi-
cates that policy implementation expanding states’
biopower (Foucault 1990) can be a key means to
reduce mortality at the population level even if these
effects do not emerge immediately. Beyond the case
of fatal overdose, our results provide general evi-
dence that policies rooted in establishing a system of
surveillance within institutions can affect population-
level health outcomes.
The role of perceived agency within a system
of structured constraints is also highlighted
through these results, carrying forward Foucault’s
(1991) conception of governmentality. The imple-
mentation of surveillance itself is sufficient to pro-
duce changes in these outcomes even if they do
not directly coerce behavior. When we separated
PDMP systems that did or did not grant prescrib-
ers/dispensers access or require them to check the
system, we did not find significant effects. Thus,
following Foucault (1990, 1995), we argue that
the effect we observe is likely due to the system of
surveillance and self-regulation that initial imple-
mentation of PDMP systems created. The systems
successfully leveraged state biopower to influence
population-level health by disciplining relevant
actors to self-govern their behavior. As noted pre-
viously, the knowledge that one is potentially
being observed, but without certainty, and the pos-
sibility of sanctions from the state may alter
behavior among multiple actors such as prescrib-
ers, pharmacists, and patients, thus potentially
enhancing the effect of these policies. That is,
PDMPs create a form of surveillance whereby
multiple actors’ behavior is constrained (Bird and
Rieker 2008), which in the aggregate helps shift
drug-related mortality curves. This distinction is
important because governmental power is more
sustainable if actors perceive themselves as capa-
ble of choice rather than acting out of coercion
(Foucault 1982). Such constraints may be particu-
larly effective for actors operating within the con-
text of institutions.
Although we did not observe significant effects
for the requirement of prescribers/dispensers to

350
Journal of Health and Social Behavior 63(3)
check, we are not advocating that these policies are
not worthwhile. As of yet, they are not having an
additional effect on overdose mortality. One possi-
bility is that they have not been in effect long
enough to observe effects, as was important in our
main analysis. However, we observed lead effects
for the overall overdose rate and heroin overdose
rate, implying that such requirements may have
been passed in places where overdose rates were
high. If heroin (and possibly fentanyl) were the
reason that U.S. states attempted to strengthen their
PDMP laws with requirements to check, we would
not anticipate finding an effect on illicit opioids
because these substances are less directly affected
by PDMPs. Furthermore, we found that presence
of a PDMP system had a significant effect across
multiple drug classes such that the systems were
working on their intended target of controlled pre-
scription drugs. As such, we must consider that
self-policing in the wake of surveillance had
already occurred by the time these requirements to
check were in place, and thus additional mandates
for action did not further enhance the effect of
these policies. Mandates to check may nonetheless
help retain the impact of these policies after the
news cycles have shifted away from concerns
about opioids and overdose.
The effects of PDMP systems on reduced benzo-
diazepine and stimulant overdose mortality highlight
the broad scope of such systems of institutional sur-
veillance; the policy is efficacious beyond a single
class of controlled substances. The findings for these
drug classes further support our central hypothesis
regarding the constraining power of surveillance sys-
tems because the expectation is that this oversight
would affect behavior generally and not necessarily
for opioids alone. Furthermore, as noted, these find-
ings are similar if we consider the case of polydrug
use between each class of substances and opioids.
Substantively, these analyses highlight the policy’s
impact on classes of substances that have received
less national attention than opioids but that have also
seen significant growth in dependence and overdose
during the past two decades (Rigg and Ford 2014;
Scholl et al. 2019). Although the findings on stimu-
lants are complicated by unclear distinctions in cause
of death codes between prescription stimulants and
illegal stimulants such as methamphetamine, the
effect is promising. Even regarding methamphet-
amine, PDMPs may reduce the ability to obtain pre-
cursors (e.g., pseudoephedrine) for methamphetamine
production, which is captured by surveillance sys-
tems in some U.S. states. Furthermore, within an
American political context in which calls for reduced
governmental involvement are common, our results
augment prior research indicating that the surveil-
lance effect of PDMPs has a measurable impact on
premature mortality and should be maintained even if
the United States reduces overdose mortality in future
years.
Although this study has numerous strengths,
we must consider some limitations. First, schol-
ars have identified that mortality data quality for
drug overdose varies across U.S. states and may
affect estimates of overdose mortality (Buchanich
et  al. 2018). Nonetheless, we follow standard
CDC protocols for definitions of overdoses by
substance (CDC 2014). Second, counties are
imperfect geographic units. Although the nar-
rower geographic unit is an improvement over
state-level measures, the size and number of
counties vary across U.S. states. Third, with
county-level assessments, we cannot include
individual-level characteristics as covariates.
Fourth, although we included a large battery of
county- and state-level controls and county and
year fixed effects, we recognize that other time-
varying factors may affect the overdose rate.
However, our methodology is robust to observ-
able and unobservable static factors; only factors
that differentially change over the period of
observation can bias the results. Finally, although
we used a robust statistical methodology, we note
that we cannot make direct observational conclu-
sions about individual actors’ behaviors. We
highly encourage additional qualitative work
among physicians, dispensers, and patients
regarding the surveillance effect of PDMPs and
other relevant policies (e.g., Chiarello 2015;
Rigg, March, and Inciardi 2010). Relatedly,
although we identify a pathway for the applica-
tion of the theory of constrained choices in the
aggregate (i.e., by inferring the impact of deci-
sions made by prescribers, dispensers, and
patients on mortality), we do not have data on
each of these decisions at the individual level.
We note that this is not a specific test of individ-
ual decision-making as completed elsewhere
(Vuolo et al. 2016).
Conclusion
PDMPs are effective policies to reduce fatal opioid
overdoses and have contributed to reductions in mor-
tality related to benzodiazepines and psychostimu-
lants as well. Notably, as shown with other research
on prescribing (Pauly et al. 2018), the effect is not
immediate because institutional and interpersonal

Vuolo et al.
351
shifts require time for the surveillance-driven effect
to take hold. Thus, efficacy of this policy requires
time as institutional and interpersonal shifts coalesce,
but they have a sustained effect. Additionally, our
analyses show that the system of state surveillance
created by PDMPs was enough to affect overdose
mortality rates across multiple drug classes without
explicitly mandating action by relevant actors. In
terms of impact, if we consider the effect of PDMPs
in place for 4 years and eliminate the reduction in opi-
oid overdose mortality in these states, we would
anticipate about 54,814 more deaths, an increase of
11.1%.3
Our findings have relevance beyond PDMPs for
public health, wherein surveillance is the main form
of state intervention. The area of occupational
health and safety is a clear example of surveillance
systems having an effect on self-governing of both
individuals and institutional actors leading to posi-
tive changes in outcomes. For example, firms
increased compliance with safety and health regula-
tions following a novel OSHA practice of issuing
press releases about facilities with violations
(Johnson 2020). Improved safety and health
conditions occurred once the threat of sanction due
to surveillance emerged—achieving the same
improvement in compliance from the press releases
that would require over 200 additional on-site
inspections. Importantly for Foucault (1995), these
systems of surveillance affect our everyday lives
beyond these institutions, whereby practitioners
and individuals make health-related choices that
align with the state’s interests due to the perception
of potential state intervention. In this manner,
choices constrained by policy implementation have
a measurable impact on health.
Although the promise of policy implementation
gives reason for hope in curbing prescription drug
overdoses, the problem of adulteration and syn-
thetic opioids, such as fentanyl, raises concern
because these substances may be less impacted by
policies such as PDMPs. Put simply, policy
approaches aimed at controlling pathways to drug
use through the medical system are less likely to
meet the challenges of evolving drug markets.
Foucault (1995) warns that state surveillance may
shift to the criminal justice system where other
institutions fail because criminal justice fills any
gap in the perfection of self-governing behavior.
Thus, the rise of fentanyl adulteration could mark a
resurgence in prior emphases on criminalization
approaches. However, even for illicit drugs, dispro-
portionate reliance on criminalization is not a fore-
gone conclusion. Expanding health systems for
early intervention with people who use drugs and
promoting access for drug-dependent persons to
receive needed treatment services while still creat-
ing surveillance are likely preferable to an expan-
sion of the coercive power of the criminal justice
system. We may only reduce morbidity and mortal-
ity from substance use through sustained and
comprehensive efforts of prevention, health promo-
tion, clinical care, and treatment alongside policy
implementation.

352
Journal of Health and Social Behavior 63(3)
Appendix A.  Fixed Effects Regressions of County-Level Overdose Rates.
All Opioid
Overdoses
All Prescription
Opioid Overdoses
Prescription
Opioid Overdoses
with Another
Opioid
All
Benzodiazepine
Overdoses
All
Psychostimulant
Overdoses
Year (vs. 2001)

2002
.761**
.416*
.232
.083
.070

(.238)
(.181)
(.141)
(.088)
(.060)
2003
1.305***
.822**
.432
.276
.090

(.351)
(.288)
(.244)
(.216)
(.079)
2004
1.841***
1.156**
.585
.554
.233*

(.470)
(.369)
(.356)
(.275)
(.101)
2005
2.612***
1.719**
.779
.775*
.270*

(.589)
(.496)
(.449)
(.308)
(.124)
2006
3.787***
2.497***
1.055
1.290**
.184

(.758)
(.618)
(.564)
(.369)
(.143)
2007
4.776***
3.123***
1.247
1.703***
.216

(.854)
(.720)
(.679)
(.436)
(.155)
2008
4.956***
3.684***
1.400
2.055***
.255

(.981)
(.846)
(.796)
(.538)
(.171)
2009
5.600***
4.193***
1.725
2.269***
.390

(1.129)
(.996)
(.951)
(.612)
(.207)
2010
5.485***
4.572***
1.727
2.585**
.480

(1.225)
(1.132)
(.978)
(.772)
(.257)
2011
6.457***
5.533***
2.059
3.038**
.575

(1.310)
(1.269)
(1.153)
(.958)
(.290)
2012
6.510***
5.549***
2.165
2.998**
.629

(1.446)
(1.366)
(1.300)
(.952)
(.344)
2013
6.651***
5.784***
2.323
3.220**
1.035**

(1.470)
(1.421)
(1.377)
(.938)
(.373)
2014
7.993***
6.843**
3.310
3.266**
1.208**

(2.095)
(2.067)
(2.165)
(.934)
(.392)
2015
8.170***
6.347***
2.645
3.669**
1.712***

(1.694)
(1.573)
(1.450)
(1.060)
(.422)
2016
9.215***
6.792***
3.040
3.885***
2.298***

(1.778)
(1.639)
(1.620)
(1.103)
(.466)
PDMP (vs. year 0)

–5 or less
1.275
1.038
.164
1.021
.081

(.674)
(.583)
(.167)
(.567)
(.131)
–4
.395
.387
.054
.548
–.057

(.575)
(.444)
(.136)
(.391)
(.106)
–3
.701
.532
–.008
.453
–.085

(.443)
(.342)
(.110)
(.333)
(.082)
–2
.082
.140
–.015
.144
–.061

(.324)
(.237)
(.086)
(.230)
(.074)
–1
–.329
–.062
–.044
.136
–.128

(.226)
(.173)
(.065)
(.181)
(.068)
1
.208
.134
–.064
–.129
–.260***

(.283)
(.180)
(.061)
(.142)
(.065)
2
–.881*
–.344
–.340*
–.507*
–.279**

(.359)
(.246)
(.169)
(.252)
(.092)
3
–1.173*
–.547
–.302*
–.820*
–.330**

(.482)
(.353)
(.126)
(.359)
(.103)
(continued)

Vuolo et al.
353
All Opioid
Overdoses
All Prescription
Opioid Overdoses
Prescription
Opioid Overdoses
with Another
Opioid
All
Benzodiazepine
Overdoses
All
Psychostimulant
Overdoses
4
–1.594**
–.799*
–.304**
–.869*
–.452**

(.546)
(.359)
(.112)
(.385)
(.138)
5 or more
–1.175
–.710
–.393*
–.733
–.380*

(.596)
(.402)
(.161)
(.508)
(.170)
Naloxone access
.429
–.167
–.228
–.104
–.177

(.604)
(.381)
(.278)
(.315)
(.170)
Good Samaritan law
.155
.446
.395
.286
–.079

(.929)
(.429)
(.342)
(.275)
(.232)
Pain clinic law
.237
–.247
.078
.132
–.038

(1.021)
(.579)
(.242)
(.495)
(.158)
Medical marijuana
2.208*
–.129
.120
–.319
–.217

(.928)
(.427)
(.230)
(.352)
(.236)
Unemployment rate
.099
.088
–.006
.100
.001

(.069)
(.048)
(.019)
(.077)
(.015)
Population density
–.000
.000
.001
.000
–.000

(.002)
(.001)
(.001)
(.000)
(.000)
Median household
income
–.049
–.048
–.012
–.053
–.021

(.041)
(.027)
(.011)
(.029)
(.012)
Poverty rate
.044
–.067
–.085
–.014
.022

(.092)
(.082)
(.087)
(.043)
(.022)
% bachelor’s degree
–.048
–.050*
–.026
–.028
–.000

(.029)
(.025)
(.024)
(.021)
(.010)
% foreign born
.079
–.014
–.108
.077
–.037

(.193)
(.148)
(.132)
(.060)
(.033)
% Hispanic
–.602
–.524
–.364
–.193*
.017

(.301)
(.277)
(.259)
(.085)
(.021)
% Hispanic *
.002
.003
.005
–.001
–.001
% foreign-born
(.005)
(.004)
(.004)
(.001)
(.001)
% female-headed
households
.748
.680
.612
.070
.009
(.487)
(.508)
(.545)
(.043)
(.015)
% Black
–.009
–.105*
–.070
–.039
–.020

(.066)
(.052)
(.058)
(.034)
(.022)
State education
expenditures
–1.845
–.901
–.742
.260
–.462
(1.574)
(1.267)
(.851)
(.852)
(.318)
State public welfare
expenditures
1.570
–.085
–.065
.347
.860*
(1.126)
(.643)
(.368)
(.633)
(.332)
State hospital
expenditures
2.004
.499
.831
.373
.769
(2.564)
(2.246)
(.899)
(2.174)
(.712)
State health
expenditures
9.873***
4.661*
1.696*
1.035
1.250
(2.682)
(1.934)
(.687)
(1.672)
(1.025)
Constant
1.738
3.787*
1.653
2.219
.690

(2.494)
(1.853)
(1.311)
(1.267)
(.586)
Observations
48,239
48,239
48,239
48,239
48,239
Note: Data from Centers for Disease Control and Prevention, Prescription Drug Abuse Policy System, American
Community Survey, and decennial censuses. Models include state-level cluster corrected standard errors. PDMP =
Prescription drug monitoring program.
*p < .05, **p < .01, ***p < .001.
Appendix A.. (continued)

354
Journal of Health and Social Behavior 63(3)
Acknowledgments
We thank Rich Carpiano, Dave Melamed, Trent Mize,
Sarah Mustillo, Eric Schoon, and Dan Winchester for
valuable feedback on this article.
Funding
The authors disclosed receipt of the following financial
support for the research, authorship, and/or publication of
this article: This work was funded by the National Institute
on Drug Abuse (Grant No. R21DA046447). The funding
agency had no role in the research; the views expressed in
this article do not represent those of the funding agency.
ORCID iDs
Mike Vuolo
 https://orcid.org/0000-0002-2916-0046
Brian C. Kelly
 https://orcid.org/0000-0003-2039-1347
Notes
1.
Given that the extremes of 5 years in each direction
contain data for all years prior or after that point,
our figures display through 4 years in each direc-
tion. Alternative specifications of models with dif-
ferent top/bottom coding (e.g., ±7 years) revealed
no substantive differences in the results. We also
note that we found no differences by whether a
state was an early or late adopter, with late adoption
defined as 2007 or later.
2.
Although reverse causality is possible within the
models, as would be detected through significant
leads, reciprocation is not possible because pre-
scription drug monitoring programs (PDMPs)
remain in effect for the remainder of observations
once implemented. That is, the overdose rate cannot
affect PDMP adoption once in effect.
3.
This amount was calculated by taking all state-
years at the point where a PDMP was in effect for
4 years, increasing their crude opioid overdose
rate by the coefficient at the 4-year mark (shown
in Figure 3), and taking the difference between the
observed number of deaths and that expected from
this increased overdose rate. This estimate is con-
servative because it ignores the reduction in Years 2
and 3 and is only for opioids, thus not including any
additional lives lost to benzodiazepines or psycho-
stimulants when not in the presence of an opioid.
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Author Biographies
Mike Vuolo is an associate professor in the Department of
Sociology at The Ohio State University and current editor
of Sociological Methodology. His research interests
include crime, law, and deviance; health; work and educa-
tion; substance use; the life course; and statistics and
methodology.
Laura C. Frizzell is a PhD candidate in the Department of
Sociology at The Ohio State University. Her research centers
on the experiences of racial-ethnic, gender, and sexual minor-
ities within the criminal legal system, from involvement in
crime and substance use to reentry after incarceration.
Brian C. Kelly is a professor in the Department of
Sociology at Purdue University. His research focuses on
contextual influences of health, mainly substance use,
sexual health, and mental health.

    Policy Implications:

Answer: Prescription Drug Monitoring Programs have been an effective tool for reducing overdose mortality within the United States. Notably, their effects extend beyond opioids to mortality related to benzodiazepines and stimulants as well. Yet the effect is not immediate because the resulting changes in clinical practice require some time for the surveillance driven effect to take hold. Nonetheless, prescribers and dispensers need not be compelled to directly use these systems with each prescription for the general effect of surveillance to have an impact on prescribing in a manner that yields benefits for reducing drug overdose mortality.