# The OxyContin Reformulation Revisited: New Evidence From Improved
Definitions of Markets and Substitutes

Shiyu Zhang Zhang: California Institute of Technology. Corresponding author,
email<EMAIL_ADDRESS>Guth: California Institute of Technology Daniel
Guth

The opioid epidemic began with prescription pain relievers. In 2010 Purdue
Pharma reformulated OxyContin to make it more difficult to abuse. OxyContin
misuse fell dramatically, and concurrently heroin deaths began to rise.
Previous research overlooked generic oxycodone and argued that the
reformulation induced OxyContin users to switch directly to heroin. Using a
novel and fine-grained source of all oxycodone sales from 2006-2014, we show
that the reformulation led users to substitute from OxyContin to generic
oxycodone, and the reformulation had no overall impact on opioid or heroin
mortality. In fact, generic oxycodone, instead of OxyContin, was the driving
factor in the transition to heroin. Finally, we show that by omitting generic
oxycodone we recover the results of the literature. These findings highlight
the important role generic oxycodone played in the opioid epidemic and the
limited effectiveness of a partial supply-side intervention.

Keywords: Opioids, Drug Overdoses, Heroin, Drug Distributors

JEL Codes: I11, I12, I18

##  1 Introduction

Since 1999, the opioid epidemic has claimed more than 415,000 American lives
(CDC Wonder). What started with fewer than 6,000 opioid-related deaths in 1999
grew steadily every year until fatalities reached 47,573 deaths in 2017.
Following a small decline in fatal drug overdoses in 2018, deaths continue to
rise. Over the past two decades, millions of Americans have misused
prescription opioids or progressed to more potent opioids, first heroin and
later fentanyl. Many social scientists have tried to understand how this
crisis has grown over two decades despite significant public health efforts to
the contrary.

Doctors and health economists have long argued that the drug most responsible
for prescription opioid overdose deaths, and the key to understanding the
transition from prescription opioids to heroin starting in 2010, was
OxyContin. Previous research [41], court proceedings [27], and books ([28],
[24]) has documented how Purdue Pharma’s marketing campaign for OxyContin
downplayed the risk of addiction starting in 1996. Since then, according to
the National Survey on Drug Use and Health (NSDUH), millions of Americans have
misused it previously. A key question in this area is whether or not making
prescription opioids, especially OxyContin, more difficult to abuse will
reduce overdose deaths.

In this paper, we show that restricting access to OxyContin led many users to
switch to generic oxycodone but had no impact on opioid or heroin mortality.
Earlier analyses attributing opioid overdose deaths in the late 2000s and the
subsequent rise in heroin deaths to OxyContin are incomplete because they omit
generic oxycodone. Our analysis shows that the misuse of generic oxycodone was
prevalent before the reformulation that restricted OxyContin access, and was
even more so afterward. We also show that heroin overdose deaths increased in
areas with high generic oxycodone exposure, not high OxyContin exposure, two
years after the OxyContin reformulation. In addition, we show that omitting
generic oxycodone in our regressions recovers the results of the literature.

This analysis was not possible until one year ago when The Washington Post won
a court order and published the complete Automation of Reports and
Consolidated Orders System (ARCOS). The ARCOS tracks the manufacturer, the
distributor and the pharmacy of every pain pill sold in the United States. The
newly released data allow us to analyze what happened to sales of generic
oxycodone and OxyContin when OxyContin suddenly became more difficult to
abuse. The previous literature focused on analyzing OxyContin because of
Purdue’s notorious role in the opioid crisis. However, the new data shows that
the sales of OxyContin was only a small part of the sales of all prescription
opioids: in terms of the number of pills, OxyContin was 3% of all oxycodone
pills sold from 2006 to 2012; in terms of morphine milligram equivalents
(MME), OxyContin has closer to 20% market share over this period. The new
transaction-level ARCOS data allows us to track the sales of generic oxycodone
and fill in the narrative gaps of how the opioid crisis progressed in the
United States.

Following [5], [17] and [11], we treat the introduction of an abuse-deterrent
formulation (ADF) of OxyContin as an exogenous shock that should only affect
people who seek to bypass the extended-release mechanism for a more immediate
high. We construct measures of exposure by combining ARCOS sales and the NSDUH
data on drug misuse. The NSDUH is the best survey of people who use drugs at
the state level, and by combining it with local sales we can capture variation
in drug use within the state. We leverage this variation in OxyContin and
generic oxycodone exposure to examine how the reformulation affected OxyContin
sales, generic oxycodone sales, opioid mortality, and heroin mortality. Our
first contribution is that we fix the omitted-variable problem by
differentiating between OxyContin and generic oxycodone, and we show that this
leads to different conclusions than what previous literature suggests. Our
second contribution is disaggregating the data to metropolitan statistical
area (MSA), which allows us to address endogeneity at the state level.

To preview our results, we find strong evidence of substitution from OxyContin
to generic oxycodone immediately after the reformulation. This substitution
was larger in places that had more OxyContin misuse pre-reform, which is
consistent with our hypothesis that users would switch between oxycodones
rather than move on to heroin. Because this substitution should be
concentrated among people misusing OxyContin, the results imply large changes
in consumption at the individual level. Back-of-the-envelope calculation
suggests 68% of the decline in OxyContin sales was substituted to oxycodone in
MSAs with high OxyContin misuse. The findings are consonant with surveys like
[19], [14], and [9] who all document substitution to generic oxycodone after
the reformulation by people seeking to bypass the ADF. We also find suggestive
evidence of substitution from generic oxycodone to OxyContin after the
reformulation in places where generic oxycodone misuse was high, a channel
that has been unexplored in previous research.

Our event study approach also shows that generic oxycodone exposure is
predictive of future heroin overdose deaths whereas OxyContin exposure is not.
The results are not contingent on methodology or our construction of exposure
measures. Crucially, if we run the same exact regressions at the state or MSA
level and omit generic oxycodone, we recover the results of the literature
where OxyContin misuse appears to be significantly predictive of future heroin
overdose deaths. We find that every standard deviation increase in generic
oxycodone exposure pre-reformulation is associated with a 40.8% increase in
heroin mortality in 2012 from the 2009 baseline level. As further evidence
against the argument that there was immediate substitution from OxyContin to
heroin after the reformulation, we note that in all of our regressions the
increase in heroin deaths wasn’t statistically significant until 2012. As
suggested in [30], the rise in heroin deaths can be attributed in part to an
increase in the supply of heroin as well as the introduction of fentanyl into
heroin doses.

Our findings highlight the pitfalls of omitting important substitutes to
OxyContin in analyzing the prescription opioid crisis. Purdue Pharma has
received well-deserved attention over the years for its role in igniting the
crisis. The company has been involved in many lawsuits over the years, but
perhaps the most damaging were lesser-known cases that involved losing its
patent in 2004111Federal ruling, Risk management plan proposals for generic
oxycodone which cleared the way for a rapid increase in generic oxycodone
sales in the early 2000s. While Purdue Pharma was being sued and scrutinized,
several manufacturers took the opportunity to fill in the gaps of OxyContin.
By 2006, generic oxycodone outsold OxyContin by more than 3-to-1 after
accounting for pill dosage differences. This paper sheds lights on the role
generic oxycodone played and continues to play in the opioid crisis and helps
policy makers update their picture of the opioid use disorder (OUD) landscape.

The paper also calls attention to the limited effectiveness of a partial
supply-side intervention to curb OUD. Purdue Pharma was once a dominant player
in the opioid market, but by the time of the reformulation, that dominance had
vanished and it was only one of the many manufacturers whose drugs were
actively misused by Americans. Purdue was the first company to include abuse-
deterrent formulation (ADF) in their opioids, but it is not until recent years
that other brands started adding anti-deterrent compounds to their products
[32]. When substitutions to other abusable opioids are easy, cutting supplies
of one kind is less effective.

The rest of the paper runs as follows. Section 2 gives more background on the
opioid crisis and explains how previous research has characterized the
OxyContin reformulation. In Section 3 we describe the new ARCOS sales
database, the NSDUH misuse data, the NVSS mortality data, as well as our
constructed misuse measure and descriptive statistics. Section 4 describes our
empirical strategy for testing our hypotheses. Section 5 discusses our results
and what it means for our understanding of the transition between illicit
drugs, and Section 6 concludes.

##  2 Background and Literature Review

This section proceeds in chronological order. First, we provide a history of
oxycodone and its most important formulation, OxyContin. We then describe the
OxyContin reformulation in 2010 and what it meant for prescription opioid
misuse, as well as how the previous literature analyzed the reformulation.
Next, we present the nascent research on substitution between different
opioids and how our contribution fits in this strain of work. We conclude with
a summary of the literature on heroin mortality in the early 2010s and its
link with the prescription opioid crisis.

Oxycodone was first marketed in the United States as Percodan by DuPont
Pharmaceuticals in 1950. It quickly found to be as addictive as morphine [7],
and in 1965 California placed it on the triplicate prescription form
[33].222Triplicate programs required pharmacists to send a copy to the
government, and [3] show that these had a persisting effect on reducing the
number of opioid prescriptions. Before the 1990s, doctors were hesitant to
prescribe oxycodone to non-terminally ill patients due to its high abuse
potential [16]. The sales of oxycodone-based pain relievers did not take off
until the mass marketing of OxyContin, Purdue’s patented oxycodone-based
painkiller. OxyContin was first approved by the FDA in 1995. The drug’s
innovation was an ‘extended-release’ formula, which allowed the company to
pack a higher concentration of oxycodone into each OxyContin pill and the
patients to take the pills every 12 hours instead of 8 hours. OxyContin’s
original label, approved by the FDA, stated that the “delayed absorption, as
provided by OxyContin tablets, is believed to reduce the abuse liability of a
drug.” In 2001, the FDA changed OxyContin’s label to include stronger warnings
about the potential for abuse and Purdue agreed to implement a Risk Management
Program to try and reduce OxyContin misuse.333From the FDA Opioid Timeline.

OxyContin was one of the first opioids marketed specifically for non-cancer
pain. In the early 1990s, pain started to enter the medical discussion as the
‘fifth vital sign’ and something to be managed. As described in [28], [41],
and elsewhere, Purdue’s sales representatives pushed OxyContin and were told
to downplay the risk of addiction. [34] describes how Purdue cited a 1980
short letter published in the New England Journal of Medicine describing
extremely low rates of opioid addiction among hospital patients undergoing
hospital stays, but the company repeatedly implied this result extended to the
general population or to individuals who left the hospital with take-home
prescriptions of OxyContin. The short letter was uncritically or incorrectly
cited 409 times as evidence that addiction was rare with long-term opioid
therapy ([23]). As a result of Purdue’s aggressive marketing and downplaying
of the drug’s abuse potential, OxyContin was a huge financial success and
effectively catalyzed the prescription opioid crisis.

In May 2007, Purdue signed a guilty plea for misleading the public about the
risk of OxyContin and paid more than $600 million in fines. Less than six
months later, the company applied to the FDA for approval of a new
reformulated version of OxyContin that included a chemical to make it more
difficult to crush and misuse [35]. Although not completely effective in
reducing misuse, it was approved by the FDA and after August 2010 accounted
for all OxyContin sales in the United States. Until 2016, with Mallinrockdt’s
Xtampza ER, Purdue was the only prescription opioid manufacturer to make
abuse-deterrent oxycodone pills. The majority of all oxycodone sold over this
time was generic oxycodone that remained abusable.444Many other companies
attempted to make abuse-deterrent opioid pills at the same time, as shown in
[42], but Purdue was the first to market. [2] and [32] list other opioids with
an ADF.

Most research shows that OxyContin misuse fell following the reformulation. As
described in [11], although some users were able to circumvent the abuse-
deterrent formulation (ADF) to inject or ingest, the reformulation did reduce
misuse. [17] finds that the reformulation coincided almost exactly with a
structural break in aggregate oxycodone sales, which had previously been
increasing. Shortly after the OxyContin reformulation was implemented,
researchers began to notice illicit drug use moving towards other drugs such
as heroin or generic oxycodone ([12], [14], [4], [17], [19], [9]). Our paper
extends the analysis of the impact of reformulation on opioid use by
separately identifying the shifts in OxyContin and generic oxycodone misuse.

We build upon a rich literature that studies opioid misuse through surveys or
analysis of the aggregated ARCOS reports. Surveys mostly polled either
informants or users themselves (for details see [21]). The best surveys have
been of users in smaller samples at individual treatment facilities, like in
[20] and [39]. However, selection bias is a problem for surveying treatment
facilities, as that is a specific subset of patients whose habits may be
different from the overall drug-using population (particularly because they
are seeking treatment). Some researchers have also used the quarterly ARCOS
reports to study national trends in consumption, like in [5], [25], and [6].
The quaterly ARCOS reports have no information on the market share of each
brand of prescription opioid, thereby restricting any analysis to the
aggregate level only. Our work is closely connected to the second set of
papers, but we are able to leverage ARCOS’s transaction level data to
distinguish sales of OxyContin from generic oxycodone.

This newly released ARCOS data allows us to make two methodological
improvements. First, the literature treats the OxyContin reformulation as an
exogenous shock at the state level. This assumption is problematic because
each state’s dependency on OxyContin as well as exposure to the reformulation
is the result of the state’s regulatory environment ([3]). These regulatory
factors could have an impact on how people react to the reformulation, and
thus create a hidden link between OxyContin exposure and the reformulation
outcomes. Using the new ARCOS data, we can disaggregate to Metropolitan
Statistical Areas (MSAs), which allows our model to identify drug
substitutions using within-state variations in opioid sales and mortality
while controlling for across-state variations in policies and drug
enforcement.

The second benefit of the new ARCOS data set is that it allows us to
disaggregate different kinds of prescription opioid sales on a national scale.
Previous national studies were unable to distinguish between these drugs due
to limitations in existing data. The NSDUH survey, the primary data source for
drug misuse at the national level, only documented past year use of OxyContin.
Death certificates do not distinguish between OxyContin and generic oxycodone.
The aggregate ARCOS sales group all oxycodone sales into one bin. Because of
OxyContin’s unique role in fomenting the opioid epidemic, it has received most
of the attention of researchers. The literature assumes that the study of
OxyContin was equivalent to the study of all oxycodone. As a result, although
non-OxyContin oxycodone misuse is significant in size, it has been
understudied. One notable exception is [31], which notes that non-OxyContin
oxycodone was a better predictor of state opioid deaths than OxyContin.

The previous literature also attempts to link the misuse of prescription
opioids to the rise in heroin misuse. [38] are the first to suggest the
pathway from prescription opioids to heroin, and they further note a reverse
in trend where heroin users switched to prescription opioids when heroin was
unavailable. [13] describes how by the 21st century people who initiated
heroin use were very likely to have started by using prescription opioids non-
medically. The most recent works on OxyContin reformulation suggest that the
reformulation played an important part in reigniting the heroin epidemic since
2010. [11] and [26], who rely on smaller surveys, find the predominant drug
people switched to after reformulation was heroin. [17] identifies a
structural break in heroin deaths in August 2010 that was accompanied by
higher growth in heroin deaths in areas with greater pre-reformulation access
to heroin and opioids. Similarly, [5] shows that the rise in heroin deaths was
larger in places with higher OxyContin misuse pre-reformulation. However, the
evidence linking the reformulation to the rise in heroin death is not
conclusive: other researchers suggest the sharp rise in heroin use may have
predated the OxyContin reformulation by a few years ([15], [9]). With the new
ARCOS data, we are able to examine the claim that the OxyContin reformulation
caused the subsequent heroin epidemic in more detail. In particular, we
separate the impact of the reformulation on heroin use from the gradual shifts
in oxycodone misuse that are independent of the reformulation.

##  3 Data and Descriptive Statistics

To estimate the impact of the OxyContin reformulation on opioid use and
mortality, we combine several data sources including sales of OxyContin and
non-OxyContin alternatives from ARCOS, opioid and heroin mortality from the
NVSS, and self-reported OxyContin and Percocet misuse from the NSDUH. Our main
regression leverages variations in pre-reform exposure to OxyContin and
generic oxycodone to identify the impact of the reformulation on opioid sales
and mortality. We define a new measure of exposure by interacting the state-
level self-reported opioid misuse and MSA-level opioid sales. In this section,
we describe the three sources of data, the market definition, the construction
of the OxyContin and generic oxycodone exposure measure, and present summary
statistics of our data.

###  3.1 Data

####  3.1.1 ARCOS and the Sales of Prescription Opioid

As part of the Controlled Substances act, distributors and manufacturers of
controlled substances are required to report all transactions to the DEA. This
Automation of Reports and Consolidated Orders System (ARCOS) database contains
the record of every pain pill sold in the United States. The complete database
from 2006 to 2014 was recently released by a federal judge as a result of an
ongoing trial in Ohio against opioid manufacturers.555Link to the ARCOS Data
published by the Washington Post.

The ARCOS database has been used previously to study opioids, but only using
the publicly available quarterly aggregate weight of drugs sold ([6]) or via
special request to the DEA ([29]). The newly released full database reports
the manufacturer and the distributor for every pharmacy order. These data
allow us to track different brands of prescription opioids separately, and
calculate what fraction of oxycodone sold is OxyContin at any level of
geographic aggregation. We can thus construct what we believe is the first
public time-series of OxyContin and generic oxycodone sales from 2006-2014.

Note: We supplemented the 2006 to 2014 data with publicly available aggregate
data from 2000 to 2005. The publicly available aggregate data does not break
down the oxycodone sales by manufacturer.

Figure 1: Growth of oxycodone and OxyContin sales

As we can see from Figure 1, total oxycodone sales increased substantially
from 2000 to 2010, with per-person sales nearly quadrupling in the ten years
period. From 2010 to 2015, sales of oxycodone declined as a result of
aggressive measures taken by the states and the federal government to counter
opioid addiction ([22]).

The newly available ARCOS data suggests that the commonly held belief about
OxyContin’s dominance in the prescription opioid market at the time of
reformulation is incorrect. The last time OxyContin’s market was estimated was
in 2002 by [31], who acquired from the DEA a year’s worth of ARCOS data
aggregated at the state level. In that year, OxyContin was 68% of all
oxycodone sales by active ingredient weight and scholars have assumed that
Purdue’s market share stayed high until the OxyContin reformulation. However,
as Figure 1 shows, by 2006 when our data starts, OxyContin sales only
accounted for 18% of all oxycodone sold by weight and never got above 35%
during this period. The share is even smaller if we count the number of pills
sold, since the average OxyContin active ingredient weight is 5 to 10 times
higher than that of oxycodone from other brands. The share of OxyContin
decreased dramatically from 2002 to 2006 because Purdue lost the patent rights
in 2004. As a result, non-OxyContin oxycodone sales grew much faster in the
early 2000s than OxyContin sales. Figure 7 in Appendix presents the market
share for all oxycodone manufacturers by dosage strength, and Purdue Pharma is
only dominant at higher dosages ($\geq$ 40mg). The overestimation of
OxyContin’s importance in the pre-reform period explains why the previous
literature overlooked the role generic oxycodone played in the opioid
epidemic.

The ARCOS sales data are the primary variables in our main regressions. We
aggregate sales by MSA, year, and brand. To focus on the impact of the
reformulation on OxyContin and non-OxyContin alternatives, we group all
alternative oxycodone products into one measure, and we will refer to it as
generic oxycodone for the rest of the analysis.666We acknowledge some non-
OxyContin alternatives are branded and non-generic (i.e. Percocet and Percodan
or later Roxicodone), but the majority of them are generic products. Generic
oxycodone in this paper should be interpreted as all non-OxyContin oxycodone
products.

####  3.1.2 NVSS Mortality Data

The second outcome of interest in our main regression is opioid mortality. We
use the restricted-use multiple-cause mortality data from the National Vital
Statistics System (NVSS) to track opioid and heroin overdose. The dataset
covers all deaths in the United States from 2006-2014. We follow the
literature’s two step procedure to identify opioid-related deaths. First, we
code deaths with ICD-10 external cause of injury codes: X40–X44 (accidental
poisoning), X60–64 (intentional self-poisoning), X85 (assault by drugs), and
Y10–Y14 (poisoning) as overdose deaths. Second, we use the drug identification
codes, which provide information about the substances found in the body at
death, to restrict non-synthetic opioid fatalities to those with ICD-10 code
T40.2, and heroin deaths to those with code T40.1. Figure 2 shows the trend
over our period of study for the two series.

Figure 2: Mortality trend

The number of opioid fatalities grew in our sample period, from on average 600
deaths per month to 1000 per month. The number of heroin deaths was stable
from 2006 to 2009 at about 200 deaths per month, and then it rose sharply from
2011 to 2015. As we’ve stated in the literature review section, the cause of
the increase in heroin mortality is unclear. While some papers blame the
OxyContin reformulation, there is evidence indicating the availability of
heroin increased substantially after 2010 [30].

Since the number of drug overdose deaths with no drug specified accounts for
between one-fifth and one-quarter of the overdose cases ([36]), our measures
of opioid and heroin deaths likely underestimate the true number of
deaths.777Specifically, we omit ICD-10 code T50.9 (unspecified poisioning)
from our analysis, and some fraction of these deaths are due to opioids or
heroin but were not diagnosed or recorded as such. However, the
underestimation would not pose a problem for our regressions. There are
variations in how coroners attribute the cause of death across states, but
such variation would be captured by the state fixed effects. In addition, we
do not anticipate systematic changes to each state’s practices due to the
reformulation.

####  3.1.3 NSDUH and Measuring Misuse

We use state-level data from the National Survey on Drug Use and Health
(NSDUH) to measure nonmedical use of opioids. The NSDUH publishes an annual
measure of OxyContin misuse, asking the respondents whether they have ever
used OxyContin “only for the experience or feeling they caused” (NSDUH
Codebook). As first described in [5], the advantage of the NSDUH misuse
measure is that it seperates out misuse from medical use. However, only
OxyContin is reported in the NSDUH and there is no equivalent measure for
generic oxycodone.

Fortunately, the NSDUH reports PERCTYL2, which asks whether individuals ever
misused Percocet, Percodan, or Tylox.888Percocet Drug Information. Tylox was
discontinued in 2012 following the FDA regulations limiting acetaminophen.
These drugs are oxycodone hydrochloride with acetaminophen and have a maximum
dosage of 10mg of oxycodone per pill. The three drugs were popular among users
in the pre-OxyContin era [28]. In the present day, the PERCTYL2 variable
captures misuse of not only the three branded drugs but also other generic
oxycodone products that are popular on the street.

The most direct evidence supporting this claim is the fact that generic
oxycodone pills have often been referred to as ‘Percs’ colloquially in the
last decade. Many news report indicated that generic oxycodone has the street
name ‘Perc 30’ but is in fact not Percocet. The Patriot Ledger reported in a
2011 article999Patriot Ledger Link. Other references to generic non-OxyContin
oxycodone as Perc 30s: Pheonix House, Washington State Patrol, The Boston
Globe, The Salem News, Massachusetts Court Filing, Cape Cod Times, Pocono
Record, Bangor Daily News, Patch, CNN Op-Ed that ‘Perc 30s’ were the newest
drug of choice in South Shore of Massachusetts, saying:

> ‘Perc 30s are not Percocet — the brand name for oxycodone mixed with
> acetaminophen, the main ingredient in Tylenol — but a generic variety of
> quick-release oxycodone made by a variety of manufacturers. They are
> sometimes referred to as “roxys” after Roxane Laboratories, the first
> company to make the drug, or “blueberries,” because of their color.’

Since many generic oxycodone users wouldn’t know the name of the drug they use
other than by its street name, but could distinguish between immediate release
oxycodone and extended release OxyContin, it is likely that they answer
affirmatively to misusing Percocet when they are, in fact, using generic
oxycodone.101010In the ARCOS dataset these pills are simply listed as
‘Oxycodone Hydrochloride 30mg’

There are also several empirical observations that support this claim. The
first is that we continue to see increases in the lifetime misuse of Percocet,
Percodan, and Tylox even after they were replaced by OxyContin as the
preferred prescription opioid to misuse. The misuse rate of Percocet,
Percodan, and Tylox increased 30% from 4.1% to 5.6% from 2002 to 2009 (see
Figure 8 in Appendix), which would not have been possible if these drugs, or
what people believed were ‘Percs’, were not actively misused by new users
post-introduction of OxyContin.

The second observation is that, based on the average sales data from 2006 to
2014, a disproportionate number of people has reported misusing Percocet,
Percodan, or Tylox as compared to the actual sales of the three drugs. The
sales of Endo Pharma, the manufacturer of Percocet and Percodan111111Tylox not
included since it was discontinued., are orders of magnitude less than the
sales of Purdue while more than twice as many people reported misusing the
three drugs as compared to OxyContin (see Figure 9 in Appendix). A back-of-
the-envelope calculation shows that if PERCTYL2 misuse captures only the
misuse of Percocet and Percodan, then the proportion of pills misused out of
all pills sold is 29 times higher for Percocet and Percodan than than the same
proportion for OxyContin121212In terms of number of pills circulated,
OxyContin is 12.1 times Percocet and Percodan from 2006 to 2014. In terms of
misuse, OxyContin is 41% of Percocet and Percodan in the same period., an very
unlikely situation given the popularity of OxyContin on the street.

This deduction is further supported by misuse data reported in the NSDUH. We
know that generic oxycodone is commonly misused.131313Law enforcement and
journalists have previously identified the 30mg oxycodone pill as the most
commonly trafficked opioid, see DEA Link, ICE Link, and Palm Beach Post Link.
If oxycodone has any other drug names, the popularity of that drug name in the
NSDUH surveys should increase to reflect the increase in misuse in recent
years. In addition to inquiring about popular brands, the NSDUH survey asks
respondents to list any other prescription oxycodone that they have misused
before. Dozens of pain relievers are reported, but in 2010 “oxycodone or
unspecified oxycodone products” was only named by 0.10%141414NSDUH Codebook
variables ANALEWA through ANALEWE list the other pain relievers reported. Even
if we assumed all 2.49% of respondents saying they took a prescription pain
reliever not listed had taken generic oxycodone, it is still less than half of
the reported Percocet misuse. of the respondents. No other brand of oxycodone
pills are reported as commonly misused. We know from the reports in press and
documents in court that generic oxycodone is a popular opioid on the street,
and we know that Percocet is the only other commonly misused opioid documented
in the NSDUH survey. Thus, the only way to reconcile the discrepancy between
these two sources is that people mistakenly perceive generic oxycodone as
Percocet or respond to the NSDUH as if they do. Thus, we use lifetime
OxyContin and lifetime Percocet misuse for the construction of OxyContin and
generic oxycodone exposure measures in Section 3.3.

###  3.2 Market Definition and Endogeneity Problems

Previous studies of the OxyContin reformulation depend on state-level
variation to causally identify the impact of the reformulation. Treating
OxyContin reformulation as an exogenous shock at the state level is
potentially problematic. Although the timing of the reformulation is
exogenous, each state’s exposure to it is a result of a combination of the
state’s regulatory environment and Purdue’s initial marketing strategy ([3]).
These factors have substantial impact on how people in a state respond to the
reformulation, creating a hidden link between exposure to the reformulation,
the identifying variation, and subsequent drug use, the outcome variable.

One can limit the impact of endogenous regulation by disaggregation, but only
if there is substantial intra-state variation in exposure to the
reformulation. Both the ARCOS database and the NVSS mortality data have great
geographic detail. Conducting our analysis on metropolitan statistical areas
(MSAs), we find large variation in both OxyContin use and opioid mortality
across MSAs in the same state. At the aggregate level in 2009, the average
OxyContin market share in a state is 35.6%. 65 of the 379 MSAs (17.1%) in our
sample have an OxyContin market share that is 10% greater or smaller than
their state average. The average opioid mortality is 0.343 deaths per 100,000
population in 2008. The variation in death is even more significant. More than
310 (83%) MSAs have a mortality rate 20% higher or lower than their state
average, and more than 192 (51%) have a mortality rate 50% higher or lower
than their state average . We present the full distribution of deviations of
OxyContin market share and opioid mortality from state average in Figure 10
and Figure 11 in the Appendix.

Disaggregating to the MSA-level allows us to control for the state’s
regulatory environment and hence eliminate the most problematic source of
endogeneity. We use intra-state variation in exposure to the reformulation for
identification. Intra-state heterogeneity in opioid use is associated with
past economic conditions ([8]), location of hospitals and treatment centers
([40]), preferences of local physicians ([37]), and local policy, some of
which could still be correlated with the locality’s response to the
reformulation. Analysis at the MSA level clearly allows us to make a much
stronger claim than analysis at the state level.

In addition, as we will show in the next sections, the disaggregation
increases the statistical power of our regressions beyond the impact of the
tripled sample size. Our results indicate that defining the market at the MSA
level better captures the interaction between drug use and mortality than the
state level. The important variations in drug use, for example between Los
Angeles-Long Beach-Santa Ana at 4.4% of nonmedical use of pain relievers and
San Francisco-Oakland-Fremont at 5.6%, disappears when they’re aggregated to
the state level [1].

###  3.3 OxyContin and Non-OxyContin Oxycodone Exposure

Since the OxyContin reformulation was a national event independent of local
conditions, we can estimate its impact by comparing the outcomes in areas of
high prior exposure to opioids with outcomes in areas of low exposure.
Ideally, we want to quantify exposure using the volume of OxyContin misused in
each region pre-reform while controlling for the volume of generic oxycodone
misused. In practice, we do not observe these quantities. The best proxy in
the literature is the self-reported misuse rate from the NSDUH.

Based on the NSDUH misuse, we create a new measure of OxyContin and non-
OxyContin oxycodone exposure by combining the NSDUH state-level misuse rate
with ARCOS MSA-level sales. Specifically, for each drug, we calculate:

$\text{Exposure}_{m}^{\text{pre-reform}}=\text{Lifetime
Misuse}_{s}^{2004-2009}\times\text{Sales}_{m}^{2009}$ (1)

Our measure is the interaction term of sales of OxyContin/generic oxycodone in
an MSA and the lifetime misuse rate of that drug in the corresponding state.
This new measure has two advantages over the conventional misuse rate from
NSDUH: it captures intra-state variation in misuse and it more accurately
reflects the current misuse of both OxyContin and generic oxycodone.

The NSDUH surveys approximately 70,000 respondents every year and uses
sophisticated reweighting techniques to get accurate state level estimates.
Once we get to the MSA level, the number of people surveyed as well as the
number of positive responses to questions on opioid misuse are extremely
small. As a result, most of the outcomes at the MSA level are censored by the
NSDUH to protect individual privacy. Using only the survey data means that we
would use same state misuse value for all MSAs and therefore forgo any intra-
state variation in drug use. In comparison, our proposed measure relies on
deviations from normal sales patterns to generate variations in exposure rates
for the MSAs. Our definition assumes that the percentage of people reported
misusing a particular drug in a state is equivalent to the proportion of sales
that are being misused. In a state where all the MSAs have identical sales,
all the MSAs will have identical exposure rates by definition. However, if one
MSA has higher sales of OxyContin compared with the rest of the state, our
OxyContin exposure measure in that MSA will be higher than the rest of the
state. This construction of exposure mirrors our intuitive understanding that
the misuse of a drug in a locality is a function of the overall misuse and the
availability of that particular drug in the area.

The NSDUH survey151515In all surveys prior to 2014. reports past-year misuse
of OxyContin but only lifetime misuse of generic oxycodone. Previous studies
did not focus on generic oxycodone misuse, so these studies rely on past-year
OxyContin misuse rate. In our case, to disentangle substitution among
prescription opioids, we have to make the comparison between OxyContin and
generic oxycodone equal. Resorting to lifetime misuse rates for both series
sacrifices the timely nature of the NSDUH misuse rates. By combining the
lifetime misuse rates with sales in the year before reformulation, we capture
recent changes in use of both drugs. To make our results comparable with
previous studies, in the Appendix section, we repeat our entire analysis with
OxyContin last-year misuse and generic oxycodone lifetime misuse. Most of our
conclusions stand despite giving OxyContin a more favorable treatment.

To construct our measure, we follow the precedent set in the literature by
using a six-years average state level lifetime misuse rate pre-reform (2004 -
2009) and sales in 2009. The goal of the time average is to reduce the
variance of the state-level misuse rates. We check the validity of our measure
by regressing opioid death on it and compare the results with the same
regressions on either only ARCOS sales or only NSDUH misuse. Results are
summarized in Table 2 in Appendix. The fit of the generic oxycodone regression
is much improved with the interacted variable ($R^{2}=0.187$) relative to
using only one with NSDUH misuse ($R^{2}=0.062$) or sales ($R^{2}=0.176$). The
improvement is even larger for the OxyContin regression ($R^{2}=0.128$)
relative to using only one with NSDUH ($R^{2}=0.084$) or with sales
($R^{2}=0.086$).

###  3.4 Descriptive Statistics

Table 1: Summary Statistics

|  |  All MSAs |  MSAs with low OxyContin exposure |  MSAs with high OxyContin exposure |  MSAs with low oxycodone exposure |  MSAs with high oxycodone exposure
---|---|---|---|---|---|---
NSDUH lifetime misuse rates (2004-2009)
|  OxyContin misuse rate (%) |  2.22 |  1.88 |  2.56 |  1.87 |  2.56
|  Oxycodone misuse rate (%) |  5.19 |  4.22 |  6.17 |  3.75 |  6.64
Annual ARCOS sales (all sample period)
|  Oxycontin sales per person |  65.71 |  43.47 |  88.06 |  50.70 |  80.79
|  Oxycodone sales per person |  181.84 |  112.50 |  251.55 |  99.24 |  264.88
Annual death per 100,000 (all sample period)
|  Opioid |  0.32 |  0.23 |  0.41 |  0.23 |  0.42
|  Heroin |  0.13 |  0.09 |  0.16 |  0.10 |  0.16
Census Demographics (2009)
|  Number of MSAs |  379 |  190 |  189 |  190 |  189
|  Population |  679878 |  745327 |  614082 |  663740 |  696101
|  Age |  36.13 |  34.68 |  37.59 |  34.84 |  37.43
|  Male (%) |  49.24 |  49.35 |  49.13 |  49.40 |  49.08
|  Separated (%) |  18.83 |  18.24 |  19.42 |  18.32 |  19.34
|  High school and above (%) |  84.20 |  82.79 |  85.61 |  83.68 |  84.72
|  Bachelor and above (%) |  25.36 |  24.77 |  25.96 |  24.85 |  25.87
|  Mean income |  64213 |  63414 |  65016 |  63058 |  65374
|  Low income (%) |  35.38 |  35.79 |  34.98 |  35.90 |  34.86
|  White (%) |  82.17 |  79.99 |  84.36 |  81.22 |  83.12
|  Black (%) |  11.20 |  13.09 |  9.30 |  11.80 |  10.60
|  Asian (%) |  3.03 |  3.47 |  2.60 |  3.52 |  2.54
|  Native American (%) |  0.18 |  0.20 |  0.17 |  0.20 |  0.17

  * •

Note: Simple average, not weighted by population.

Table 1 reports summary statistics for five groups of MSAs: All MSAs, MSAs
with high OxyContin exposure, MSAs with low OxyContin exposure, MSAs with high
generic oxycodone exposure, and MSAs with low generic oxycodone exposure. MSAs
with high OxyContin exposure and MSAs with high generic oxycodone exposure
have similar demographic summary statistics. These two groups of MSAs also are
not different statistically in their heroin mortality. Disentangling the
impact of various opioids on the rise in heroin mortality is impossible with
nationally aggregated or state level data due to the high correlation in
misuse. The high correlation also implies that regressing heroin death on
OxyContin without controlling for generic oxycodone use will likely lead to an
overestimation of OxyContin’s impact.

MSAs with high misuse differ from MSAs with low misuse. High misuse states
have higher sales of both types of prescription opioids (twice as much for
both types of opioids), higher mortality rate (twice as much for both opioid
and heroin overdose), smaller population, higher average age, higher median
income, higher percentage of white population, and lower percentage of black
population. The differences in racial composition repeat well established
findings in the literature: prescription opioid misuse was originally
concentrated among white users, and by 2010 new heroin users were almost
entirely white [10]. These differences in demographic variables motivate the
inclusion of control variables in our main regressions.

##  4 Empirical Strategies

Our goal is to investigate two questions. First, what was the reformulation’s
immediate impact on OxyContin and generic oxycodone use? Second, what was the
reformulation’s long-run effect on opioid mortality, heroin mortality, and on
the progression of opioid addiction?

We follow the event study framework from [5] to estimate the causal impact of
the OxyContin reformulation on OxyContin and generic oxycodone sales and
opioid and heroin mortality. We exploit variation in MSAs’ exposure to the
reformulation due to the differences in their pre-reform OxyContin use while
controlling for pre-reform generic oxycodone use. Our approach is similar to
[18], where the OxyContin reformulation has more “bite”, or more of an effect,
in areas where OxyContin misuse was higher than in places where generic
oxycodone was the preferred drug. The approach allows us to measure whether
MSAs with higher exposure to OxyContin experienced larger declines in
OxyContin sales, larger increases in alternative oxycodone, or larger
increases in opioid and heroin mortality. The empirical framework is:

$\begin{split}Y_{mt}&=\alpha_{s}+\delta_{t}+\sum_{i=2006}^{2014}\mathbbm{1}\\{i=t\\}\beta_{i}^{1}\times\text{OxyContin
Exp}^{Pre}_{m}\\\
+&\sum_{i=2006}^{2014}\mathbbm{1}\\{i=t\\}\beta_{i}^{2}\times\text{Oxycodone
Exp}^{Pre}_{m}+X^{\prime}_{mt}\gamma+\epsilon_{mt}\end{split}$ (2)

where $Y_{mt}$ are the outcome variables of interest in MSA $m$ at year $t$;
$\text{OxyContin Exp}^{Pre}_{m}$ and $\text{Oxycodone Exp}^{Pre}_{m}$ are
time-invariant measures of OxyContin and oxycodone exposure before the
reformulation (see Section 3.5 for construction), and are interacted with a
set of $\beta_{t}^{1}$ and $\beta_{t}^{2}$ for each year. We include state
fixed effects to control for regulatory differences among states and year
fixed effects to control for national changes in drug use. We also include a
full set of MSA-level demographic variables. We weight the regression by
population and exclude Florida.161616The literature excludes Florida because
it underwent massive increases in oxycodone sales over this period, some of
which was trafficked to other states. We show the full set of $\beta_{t}$
estimates graphically, normalizing by the 2009 coefficient. The $\beta_{t}$
identifies the differences in sales and death across MSAs due to their higher
or lower pre-reform OxyContin or oxycodone exposure. Standard errors are
clustered at the MSA level to account for serial correlation. In the Appendix
section, we present beta estimations from variations of our base model, which
include (1) using a MSA fixed effect instead of state fixed effect, (2)
replacing OxyContin lifetime misuse rate with OxyContin last-year misuse rate,
(3) regressing at the state level, and show that our conclusion are
insensitive to most of these variations.

To complement our results, we also use a strict difference-in-difference
framework to estimate effect of the reformulation conditioning on OxyContin
and non-OxyContin oxycodone exposure levels. Our specification is:

$\begin{split}Y_{mt}&=\alpha_{s}+\gamma_{t}+\delta_{1}\mathbbm{1}\\{t>2010\\}\\\
&+\delta_{2}\mathbbm{1}\\{m\in HighOxyContin\\}+\delta_{3}\mathbbm{1}\\{m\in
HighOxycodone\\}\\\
&+\delta_{4}\mathbbm{1}\\{t>2010\\}\times\mathbbm{1}\\{m\in
HighOxyContin\\}\\\
&+\delta_{5}\mathbbm{1}\\{t>2010\\}\times\mathbbm{1}\\{m\in
HighOxycodone\\}+X^{\prime}_{mt}\beta+\epsilon_{mt}\end{split}$ (3)

where $HighOxyContin$ and $HighOxycodone$ are the set of MSAs with higher than
median pre-reform exposure to OxyContin and oxycodone respectively. We
restrict the regression to include only the three years prior (2008 to 2010)
and the three years after (2011 to 2013) the reformulation. The advantage of
this specification is that it does not assume that OxyContin or oxycodone
exposure affects the outcome variable linearly. Instead of having a flexible
$\delta$ for each year, we have only one $\delta$ for each of the pre- or
post-reform period. In this specification, we simply test whether higher
exposure MSAs reacted differently to the reformulation as compared to lower
exposure MSAs (if $\delta_{4}$ and $\delta_{5}$ are significant). We include
state fixed effects to control for state-level heterogeneity, year fixed
effects for national trend, and a set of time-varying MSAs level covariates.
Again, standard errors are clustered at the MSA level.

##  5 Results

We proceed in two steps. First, we provide direct evidence that the OxyContin
reformulation caused OxyContin sales to decrease and generic oxycodone sales
to increase, and that the changes in sales are proportional to the pre-
reformulation level of OxyContin exposure. Second, we estimate the impact of
the reformulation on opioid and heroin mortality. We find that high pre-
reformulation levels of OxyContin exposure were not associated with high
opioid deaths, but there was a strong positive effect from generic oxycodone
exposure in both the pre- and post-reform period. We find that higher pre-
reform OxyContin and pre-reform oxycodone exposure were both positively but
not significantly associated with later heroin deaths, but the oxycodone
coefficient is larger. If we run the heroin regression separately with only
OxyContin exposure we recover the results of the literature, but running the
heroin regression with only oxycodone exposure better fits the data.

###  5.1 Reformulation’s Impact on Opioid Sales

We begin by showing graphically that OxyContin sales decreased and generic
oxycodone sales increased in high OxyContin misuse MSAs immediately after
reformulation. Figure 3 and Figure 4 present the full set of coefficients from
estimating the event study framework on OxyContin and generic oxycodone sales.
Each data point in the figure is the coefficient of the interactive term of
misuse and sale, which we call exposure, for OxyContin or generic oxycodone in
a specific year, and it captures any additional change in sales in that year
driven by high OxyContin or oxycodone exposure. In Figure 3, we observe a
larger decrease in OxyContin sales post-reform in MSAs with higher pre-reform
OxyContin exposure. As Figure 4 shows, higher OxyContin exposure MSAs saw
greater increases in generic oxycodone sales post-reform. The effects are well
identified at 95% confidence level. An one standard deviation increase in
OxyContin exposure translates into an additional 21.2 MME decrease in per
person OxyContin sales and 11.8 MME increase in per person oxycodone sales in
2011. These changes represents a 24% decrease in OxyContin sales and a 8.8%
increase in oxycodone sales from the 2009 level. The effects are economically
significant especially given that the reformulation should only affect the
population abusing OxyContin, so this drop in sales is driven by a fraction of
all users. The two observations combined support the hypothesis that
reformulation caused substantial substitution from OxyContin to generic
oxycodone.

Figure 3: Main regression on OxyContin sales. Shaded regions are the 95
percent confidence intervals with standard errors clustered at the MSA level.

Figure 4: Main regression on generic oxycodone sales. Shaded regions are the
95 percent confidence intervals with standard errors clustered at the MSA
level.

Figure 3 also documents that high pre-reform oxycodone misuse MSAs saw large
increases in OxyContin sales right after the reformulation. This phenomenon
has been unreported previously, but would be consistent with [37]’s physician
benevolence hypothesis where good physicians switch patients from oxycodone to
reformulated OxyContin to lower the future risk of abuse. Although the switch
toward OxyContin is smaller in magnitude than the switch from OxyContin, this
increase is the first documented positive impact of the OxyContin
reformulation in the literature. It seems both physicians and users saw the
two types of drugs as substitutes. Unfortunately, there are not enough MSAs
where the switch toward OxyContin is significant enough that it cancels out
the switch away from OxyContin to examine the possible substitution channel in
the other direction.

Because we include both OxyContin and generic oxycodone misuse in the same
regression, we can separate out the increases in oxycodone sales due to its
own popularity from the increases due to spillover effects from the OxyContin
reformulation. Figure 4 shows increasing growth in oxycodone sales in MSAs
with higher oxycodone misuse until 2011, and the growth rate declined after.
The smoothness of the oxycodone curve indicates that the OxyContin
reformulation had no impact on how oxycodone misuse affected oxycodone sales.
This trend corresponds well with many states tightening control over opioid
prescription policies in 2011 and 2012 in response to rising sales and and
increased awareness of opioid misuse.

Another way of estimating the impact of the reformulation is through
difference-in-difference regressions. Column (1) of Table 3 in Appendix shows
the regression on OxyContin sales. OxyContin sales in all MSAs decreased by
8.05 MME post-reform, a 9.4% decrease with respect to the average per person
sales of 85.6 MME in 2009. High OxyContin misuse MSAs had a higher level of
OxyContin sales to start with, but experienced an additional 15.1 MME drop (an
additional 17% decrease) post-reform. Given that only 2.46% of the population
ever misused OxyContin171717NSDUH, 2010. and the reformulation only affected
the people misusing it, a 17% additional decrease in all OxyContin sales would
translate into a very significant decrease in sales to the population that
misuses it. The negative and significant Post $\times$ High OxyContin
coefficient confirms previous findings that high OxyContin exposure MSAs saw
larger decreases in OxyContin sales post-reform.

Column (2) of the same table reports the regression on generic oxycodone
sales. Generic oxycodone sales per person increased 41.7 MME in the post
period, a 31.2% increase with respect to the average per person alternative
oxycodone sales of 133.5 MME in 2009. High OxyContin misuse MSAs experienced
an additional 10.3 MME increase, which translates to a 68% conversion from
OxyContin to generic oxycodone in those areas. Combining the findings from
columns (1) and (2), we see direct substitution from OxyContin to generic
oxycodone in local sales immediately after reformulation, and the substitution
pattern is more pronounced in MSAs with high OxyContin exposure as expected.

To help our readers visualize the trend of OxyContin and alternative oxycodone
sales, in Figure 12 in the Appendix, we break all MSAs into three bins by the
magnitude of the observed drop in OxyContin sales due to the reform. Then, we
plot the per person OxyContin and generic oxycodone sales for the three group
respectively. By definition, the high empirical drop group experienced the
largest decreases in OxyContin sales from 2009 to 2011 (-29%) and the low drop
group experienced an increase in OxyContin sales (+15%). Sales of generic
oxycodone started at different levels, but shared the same growth rate until
the reformulation in 2010. Since 2010, the higher the empirical drop in
OxyContin, the faster the growth in generic oxycodone. The high group saw in a
72 MME increase (46% from 2009) in generic oxycodone sales, while the low
group only saw an 29 MME increase (29% from 2009). The high growth rate of
generic oxycodone in high drop MSAs support the substitution story. The post-
reform level of OxyContin sales converges to the same level for all three
groups, suggesting that the remaining sales most likely represent non-
replaceable demand for medical OxyContin use.

###  5.2 Reformulation and Opioid and Heroin Mortality

Next, we estimate the impact of the reformulation on overdose mortality. In
Figure 5, we report the full set of coefficients from estimating the event
study framework on opioid mortality. Each data point in the figure is the
coefficient of the interactive term of misuse and sale for OxyContin or
generic oxycodone in a specific year, and it captures any additional change in
opioid mortality in that year driven by high OxyContin or oxycodone exposure.
The OxyContin coefficients are never significant, suggesting higher pre-reform
OxyContin misuse is not predictive of either higher or lower opioid death
post-reform. The lack of any trend indicates that any benefit of the OxyContin
reformulation on reducing OxyContin consumption is offset by the substitution
to generic oxycodone. In aggregate, the reformulation had no impact on non-
heroin opioid deaths.

Figure 5: Main regression on opioid mortality. Shaded regions are the 95
percent confidence intervals with standard errors clustered at the MSA level.

Figure 6: Main regression on heroin mortality. Shaded regions are the 95
percent confidence intervals with standard errors clustered at the MSA level.

In Figure 6, we report the event study coefficients on heroin mortality. Again
the OxyContin coefficients are tiny and insignificant, while the oxycodone
coefficients grow over time but never reach statistical significance at
conventional levels. The lack of statistical significance is due to the small
number of heroin moralities in the whole sample and high correlations between
OxyContin and oxycodone exposure. If we were to run the OxyContin and
oxycodone regression separately (See Figure 34 and Figure 38 in Appendix),
oxycodone exposure had a much larger and more significant impact on heroin
mortality. The results provide tentative evidence that the higher the generic
oxycodone exposure in an MSA, the higher the increases in heroin mortality.
However, the results do not support the alternative hypothesis that the
OxyContin reformulation was solely responsible for the increase in heroin
mortality.

The difference-in-difference results mirror our finding from the event study
framework. Column (3) of Table 3 in Appendix suggests that opioid deaths are
0.08 higher in high oxycodone exposure MSAs, which is equivalent to 27% of the
average opioid overdose of 0.29 per 10,000 people in 2009. Opioid mortality is
0.05 lower (17% of the 2009 average) in higher OxyContin exposure MSAs after
controlling for oxycodone use. Higher OxyContin exposure does not lead to
higher or lower opioid overdose post-reform, while higher generic oxycodone
exposure is associated with 0.06 (20.6% of 2009 average) more opioid death in
the post period.

Column (4) of the same table reports the difference-in-difference regression
on heroin death. Heroin mortality has increased by 0.14 in the post period in
all MSAs, which is equivalent to a 111% increase from the average 2009 level
of 0.126 heroin death per 10,000 population. High OxyContin exposure MSAs did
not experience additional jumps in heroin mortality, while high oxycodone
exposure MSAs did experience an additional 0.07 (56% with respect to 2009
average) increase in death. Again, the evidence from the difference-in-
difference regressions indicate that OxyContin was not responsible for the
rise in heroin mortality.

In Figure 13 in the Appendix, we show the average trend of the opioid and
heroin mortality for groups with high, medium and low observed drop in
Oxycontin sales. If the reformulation was responsible for the subsequent
heroin epidemic, then the MSAs mostly likely to have additional jumps in
heroin mortality would be the MSAs with the largest OxyContin drop. As shown
in the figure, the three groups went through the same explosive growth in
heroin mortality (around 38% from 2009 to 2011, and similar rate afterward),
indicating the rise in heroin was independent of the decrease in OxyContin
sales. This evidence conclusively rejects the hypothesis that the OxyContin
reformulation is solely responsible for the subsequent heroin epidemic.

###  5.3 Discussion

(A) The Reformulation’s Impact on Opioid Mortality

Until now, the literature has found mixed results for the effects of the
OxyContin reformulation on opioid mortality. In contrast to previous work, we
find no statistically significant impact of the reformulation on opioid
mortality as a result of substantial substitutions from OxyContin to generic
oxycodone post-reform. Increases in generic oxycodone sales compensated for
55% of the drop in OxyContin sales in high OxyContin misuse MSAs by our event
study framework, and 68% by our different-in-difference estimation. Opioid
mortality continued to increase in the post-reform period, but not was driven
by high OxyContin exposure.

(B) The Reformulation’s Impact on Heroin Mortality

Our results stand in direct contrast to the findings of the literature.
Instead of being the event that precipitated the heroin epidemic, the
OxyContin reformulation shifted misuse to other opioids, of which heroin was
only one. We cannot refute the hypothesis that some OxyContin users switched
to heroin due to the reformulation. Our analysis refutes the hypothesis that
the reformulation was the sole cause of the heroin epidemic. Instead of
OxyContin misuse, we identified generic oxycodone misuse as a much more
powerful driver of increases in heroin mortality post-2011. What prompted the
increases in heroin use is still an unresolved question. Previous research has
suggested an increase in the supply of heroin ([30]) around this time, as well
as crackdowns in Florida on pill-mills reducing the supply of oxycodone
([22]).

(C) Bridging the Differences between our Findings and the Literature

One of the innovations we’ve made in this paper is to shed light on a hidden
source of opioid misuse: the misuse of generic oxycodone. This segment of
prescription opioids was overlooked by other scholars because of OxyContin’s
dominance in opioid misuse in the early years as well as, we argue, the lack
of identifiable brand names for the generic products. Empirical studies based
on market data or interviews of opioid users noted that many people misused
generic oxycodone products ([31], [21]). Leaving out oxycodone misuse, an
important driver of opioid and heroin mortality that is positively correlated
with OxyContin misuse, would produce spurious regression results.

To show that the difference in findings is not driven by our constructed
misuse measure, or our choice of framework, we test whether we can reproduce
findings in the literature by running all of our regressions using only
OxyContin (see Section 7.4.4 in the Appendix). Our OxyContin misuse exposure
individually predicts an increase in opioid and heroin mortality post-reform
as the literature claims. This finding is the basis of previous studies
supporting the claim that the OxyContin reformulation is the main cause of the
subsequent heroin epidemic. However, if we run the same set of regressions
using only generic oxycodone (see Section 7.4.5), we were able to produce the
same findings. The only way to differentiate the impact of OxyContin from that
of generic oxycodone is to include both in the same regressions. Variations in
local OxyContin and oxycodone exposure allow us to identify the impact of both
series, if any exist. As we’ve shown in our main regressions, the impact of
OxyContin on heroin disappears after controlling for the effect of generic
oxycodone.

(D) Market Definition

Another innovation we’ve made in this paper is a finer definition of the
opioid market. It is important to consider what we gain from disaggregating to
the MSA level. The specific OxyContin market share in a state is endogenous to
a great many things, including advertising ([41]) and triplicate status ([3]).
Although the OxyContin reformulation was an exogenous shock, its
interpretation is made very complicated because its impact depended on each
state’s regulatory history and prescribing environment. We do our regressions
at the MSA level, where there are unobserved local conditions that affected
sales of OxyContin and generic oxycodone, while controlling for state-level
laws and restrictions. By comparing two different MSAs with the same
regulatory environment but different exposures to the reformulation, we can
get at the marginal effects of OxyContin and generic oxycodone exposure.
Contrasting the state-level regression estimates (see Section 7.4.3) with our
main results, our main results are larger in magnitude and more statistically
significant. The MSA level estimation of the effect of exposure on mortality
is more stable.

(E) Definition of OxyContin Misuse

The literature relies on NSDUH’s OxyContin past-year misuse. To make our
findings comparable with previous studies and robust to the choice of misuse
measure, we repeat our entire analysis with OxyContin last-year misuse and
generic oxycodone lifetime misuse (see Section 7.4.2 for results.) As noted in
Section 3.3, using last-year OxyContin misuse gives an unfair advantage to
OxyContin due to the timeliness of the measure. If our findings on oxycodone
persist despite the unequal treatment of the two misuse measures, then it is a
stronger indication of the essential role generic oxycodone played in the
opioid and heroin epidemic.

Comparing the two sets of results, we observe the same decline in OxyContin
sales and increase in generic oxycodone sales, although smaller in magnitude.
Both sets of coefficients on opioid mortality become positive but
insignificant. Finally, comparing the heroin result, at the state level we do
detect a positive effect on heroin mortality from OxyContin. In aggregate, our
results lose some significance when we replace lifetime OxyContin misuse with
last-year OxyContin misuse. The loss of significance, however, is in the
direction predicted by the unfair advantage given to OxyContin. This exercise
highlights the importance of treating the two misuse measures equally. When we
use measures that more accurately capture recent OxyContin misuse than recent
generic oxycodone misuse, we could mistakenly attribute effects of generic
oxycodone to OxyContin.

##  6 Conclusion

Researchers have attributed the prescription opioid opioid crisis and recent
increase in heroin use to OxyContin. Previous studies have documented how
Purdue Pharma’s marketing downplayed the risks of OxyContin’s abuse potential,
which fomented the prescription opioid crisis; recent studies identified the
OxyContin reformulation as the event that pushed users to switch to heroin,
which precipitated recent increase in heroin use. This paper revisits the
roles OxyContin and the Oxycontin reformulation played in the opioid crisis
with fine-grained sales data that includes OxyContin’s most immediate
substitute, generic oxycodone. We have three main findings.

First, we find direct evidence of substitution to from OxyContin to generic
oxycodone post-reformulation. Our difference-in-difference estimation
indicates a 68% substitution from OxyContin to generic oxycodone due to the
reform. Looking at the decline in OxyContin sales and rise in generic
oxycodone sales from 2002-2006, we believe this substitution (for different
reasons, namely Purdue’s loss of its patent) also happened years before the
reformulation. The size of this substitution, and indeed the size of the
generic oxycodone market pre-reform, may come as a surprise to researchers.
[31] estimate that in 2002 OxyContin’s market share was 68%. By the time of
the reformulation in 2010, it had fallen by more than half. OxyContin played
an essential part in igniting the prescription opioid crisis but, after losing
its patent in 2004, other companies took up the torch and surpassed Purdue by
selling generic oxycodone.

Our second main finding is that the OxyContin reformulation had no overall
effect on opioid mortality. In our estimation, the OxyContin coefficients are
not significant in the entire sample period, suggesting that higher OxyContin
exposure is not predictive of either higher or lower opioid death. The lack of
any trend indicates that the benefits of the OxyContin reformulation, if
exist, are offset by substitution to oxycodone. In addition, we do find that
high oxycodone exposure is predictive of rise in opioid mortality from 2011,
confirming the increasingly important role of generic oxycodone in the recent
prescription opioid crisis.

Third and most importantly, we show that the heroin overdose deaths after 2010
were predicted by generic oxycodone exposure, not OxyContin exposure. Our main
event-study model shows positive and significant effects from oxycodone
exposure on heroin deaths after 2012, but OxyContin exposure is not predictive
of heroin deaths once we control for oxycodone. The difference-in-difference
results are similar, showing that oxycodone exposure was predictive of heroin
deaths before or after the reformulation, and OxyContin exposure after the
reformulation is weakly positive but not statistically significant. We also do
not observe an additional rise in heroin deaths immediately after
reformulation in areas where OxyContin sales declined the most post-
reformulation. In particular, without including generic oxycodone in the
analysis, we recover the same results from the literature that OxyContin was
responsible for the rise in heroin deaths. The evidence shows that omitting
oxycodone, an important substitute to OxyContin, produces erroneous results.
This paper demonstrates the pernicious effects of generic oxycodone, which had
thus far escaped scrutiny until the Washington Post acquired data and reported
on it.

## References

  * [1] “2005-2010 NSDUH MSA Detailed Tables”, 2012  URL: https://web.archive.org/web/20130616122357/http://www.samhsa.gov/data/NSDUHMetroBriefReports/index.aspx
  * [2] Jeremy A Adler and Theresa Mallick-Searle  “An overview of abuse-deterrent opioids and recommendations for practical patient care”  In _Journal of multidisciplinary healthcare_ 11 Dove Press, 2018, pp. 323
  * [3] Abby Alpert, Evans, Lieber and Powell  “Origins of the Opioid Crisis and Its Enduring Impacts”, Working Paper Series 26500, 2019  DOI: 10.3386/w26500
  * [4] Abby Alpert, David Powell and Rosalie Liccardo Pacula  “Supply-Side Drug Policy in the Presence of Substitutes: Evidence from the Introduction of Abuse-Deterrent Opioids”, Working Paper Series 23031, 2017  DOI: 10.3386/w23031
  * [5] Abby Alpert, David Powell and Rosalie Liccardo Pacula  “Supply-side drug policy in the presence of substitutes: Evidence from the introduction of abuse-deterrent opioids”  In _American Economic Journal: Economic Policy_ 10.4, 2018, pp. 1–35
  * [6] Sairam Atluri, G Sundarshan and Laxmaiah Manchikanti  “Assessment of the trends in medical use and misuse of opioid analgesics from 2004 to 2011”  In _ASIPP_ 17, 2014, pp. E119–E28
  * [7] Edward R Bloomquist  “The addiction potential of oxycodone (Percodan®)”  In _California medicine_ 99.2 BMJ Publishing Group, 1963, pp. 127
  * [8] Christopher S Carpenter, Chandler B McClellan and Daniel I Rees  “Economic conditions, illicit drug use, and substance use disorders in the United States”  In _Journal of Health Economics_ 52 Elsevier, 2017, pp. 63–73
  * [9] Theresa A Cassidy et al.  “Changes in prevalence of prescription opioid abuse after introduction of an abuse-deterrent opioid formulation”  In _Pain Medicine_ 15.3, 2014, pp. 440–451
  * [10] Theodore J Cicero, Matthew S Ellis, Hilary L Surratt and Steven P Kurtz  “The changing face of heroin use in the United States: a retrospective analysis of the past 50 years”  In _JAMA psychiatry_ 71.7 American Medical Association, 2014, pp. 821–826
  * [11] Theodore Cicero and Matthew Ellis  “Abuse-Deterrent Formulations and the Prescription Opioid Abuse Epidemic in the United States: Lessons Learned From OxyContin”  In _JAMA Psychiatry_ 72.5, 2015, pp. 424–430  DOI: 10.1001/jamapsychiatry.2014.3043
  * [12] Theodore Cicero, Matthew Ellis and Hilary Surratt  “Effect of abuse-deterrent formulation of OxyContin”  In _New England Journal of Medicine_ 367.2 Mass Medical Soc, 2012, pp. 187–189
  * [13] Wilson M Compton, Christopher M Jones and Grant T Baldwin  “Relationship between nonmedical prescription-opioid use and heroin use”  In _New England Journal of Medicine_ 374.2 Mass Medical Soc, 2016, pp. 154–163
  * [14] Paul M Coplan et al.  “Changes in oxycodone and heroin exposures in the National Poison Data System after introduction of extended-release oxycodone with abuse-deterrent characteristics”  In _Pharmacoepidemiology and drug safety_ 22.12 Wiley Online Library, 2013, pp. 1274–1282
  * [15] Nabarun Dasgupta et al.  “Observed transition from opioid analgesic deaths toward heroin”  In _Drug and alcohol dependence_ 145 Elsevier, 2014, pp. 238–241
  * [16] Sarah DeWeerdt  “Tracing the US opioid crisis to its roots.”  In _Nature_ 573.7773 Nature Publishing Group, 2019, pp. S10–S10
  * [17] William Evans, Ethan Lieber and Patrick Power  “How the reformulation of OxyContin ignited the heroin epidemic”  In _Review of Economics and Statistics_ 101.1 MIT Press, 2019, pp. 1–15
  * [18] Amy Finkelstein  “The aggregate effects of health insurance: Evidence from the introduction of Medicare”  In _The quarterly journal of economics_ 122.1 MIT Press, 2007, pp. 1–37
  * [19] Jennifer R Havens et al.  “The impact of a reformulation of extended-release oxycodone designed to deter abuse in a sample of prescription opioid abusers”  In _Drug and alcohol dependence_ 139 Elsevier, 2014, pp. 9–17
  * [20] Lon R Hays  “A profile of OxyContin addiction”  In _Journal of Addictive Diseases_ 23.4 Taylor & Francis, 2004, pp. 1–9
  * [21] James Inciardi et al.  “The “black box” of prescription drug diversion”  In _Journal of addictive diseases_ 28.4 Taylor & Francis, 2009, pp. 332–347
  * [22] Alene Kennedy-Hendricks et al.  “Opioid overdose deaths and Florida’s crackdown on pill mills”  In _American journal of public health_ 106.2 American Public Health Association, 2016, pp. 291–297
  * [23] Pamela TM Leung et al.  “A 1980 letter on the risk of opioid addiction”  In _New England Journal of Medicine_ 376.22 Mass Medical Soc, 2017, pp. 2194–2195
  * [24] Beth Macy  “Dopesick: Dealers, doctors, and the drug company that addicted America”  Little, Brown, 2018
  * [25] Justine Mallatt  “The effect of prescription drug monitoring programs on opioid prescriptions and heroin crime rates”  In _Available at SSRN 3050692_ , 2018
  * [26] Sarah G Mars et al.  ““Every ‘never’I ever said came true”: transitions from opioid pills to heroin injecting”  In _International Journal of Drug Policy_ 25.2 Elsevier, 2014, pp. 257–266
  * [27] Barry Meier  “In guilty plea, OxyContin maker to pay $600 million”  In _New York Times_ 10, 2007
  * [28] Barry Meier  “Pain killer: A” wonder” drug’s trail of addiction and death”  Rodale, 2003
  * [29] F Modarai et al.  “Relationship of opioid prescription sales and overdoses, North Carolina”  In _Drug and alcohol dependence_ 132.1-2 Elsevier, 2013, pp. 81–86
  * [30] Julie O’Donnell, Matthew Gladden and Puja Seth  “Trends in deaths involving heroin and synthetic opioids excluding methadone, and law enforcement drug product reports, by census region—United States, 2006–2015”  In _MMWR. Morbidity and mortality weekly report_ 66.34 Centers for Disease ControlPrevention, 2017, pp. 897
  * [31] Leonard J Paulozzi and George W Ryan  “Opioid analgesics and rates of fatal drug poisoning in the United States”  In _American journal of preventive medicine_ 31.6 Elsevier, 2006, pp. 506–511
  * [32] Joseph Pergolizzi, Robert Raffa, Robert Taylor Jr and Steven Vacalis  “Abuse-deterrent opioids: an update on current approaches and considerations”  In _Current medical research and opinion_ 34.4 Taylor & Francis, 2018, pp. 711–723
  * [33] Wm F Quinn  “Percodan on Triplicate—The Background of the New Law”  In _California medicine_ 103.3 BMJ Publishing Group, 1965, pp. 212
  * [34] Sam Quinones  “Dreamland: The true tale of America’s opiate epidemic”  Bloomsbury Publishing USA, 2015
  * [35] Bob Rappaport  “APPLICATION NUMBER: 22-272 - MEDICAL REVIEW(S)”, 2009  URL: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2010/022272s000MedR.pdf
  * [36] Christopher J Ruhm  “Geographic variation in opioid and heroin involved drug poisoning mortality rates”  In _American journal of preventive medicine_ 53.6 Elsevier, 2017, pp. 745–753
  * [37] Molly Schnell  “Physician behavior in the presence of a secondary market: The case of prescription opioids”, 2017
  * [38] Harvey A Siegal, Robert G Carlson, Deric R Kenne and Maria G Swora  “Probable relationship between opioid abuse and heroin use”  In _American family physician_ 67.5, 2003, pp. 942
  * [39] Beth Sproule, Bruna Brands, Selina Li and Laura Catz-Biro  “Changing patterns in opioid addiction: characterizing users of oxycodone and other opioids”  In _Canadian Family Physician_ 55.1 The College of Family Physicians of Canada, 2009, pp. 68–69
  * [40] Isaac D Swensen  “Substance-abuse treatment and mortality”  In _Journal of Public Economics_ 122 Elsevier, 2015, pp. 13–30
  * [41] Art Van Zee  “The promotion and marketing of oxycontin: commercial triumph, public health tragedy”  In _American journal of public health_ 99.2 American Public Health Association, 2009, pp. 221–227
  * [42] Lynn Webster  “Update on abuse-resistant and abuse-deterrent approaches to opioid formulations”  In _Pain Medicine_ 10.suppl_2 Blackwell Publishing Inc Malden, USA, 2009, pp. S124–S133

##  7 Appendix

###  7.1 Additional Information

Note: We compute market share based on the average of 2006-2014 sales data. We
kept only the top twenty manufacturers for better readability of the table.
The rest of the 35 manufacturers combined contribute 0.18% of total sales.
During this sample period, Purdue Pharma was the dominant manufacturer of high
dosage oxycodone pills ($\geq$ 40mg). In the lower dosage market, three
manufacturers (SpecGx, Actavis Pharma and Phar Phamaceutical) had higher share
of the market than Purdue Pharma.

Figure 7: Market share of different opioid manufacturers

Note: The figure shows the misuse rate of OxyContin (OXYFLAG or OXYCONT2) and
the misuse rate of Percocet, Percodan and Tylox (PERCTYL2). Data obtained from
annual NSDUH. Percocet was a popular prescription oxycodone to misuse in the
pre-OxyContin period. We see in this graph that the PERCTYL2 misuse rate
increased 30% from 2002 to 2009, suggesting that the lifetime misuse rate
captures more than historical Percocet, Percodan and Tylox misuse.

Figure 8: NSDUH national lifetime misuse rate

Note: This graph shows the difference in oxycodone sales between Purdue and
Endo Pharma. The small market share of Endo Pharma leads us to believe that
individuals misreport the drugs they consume on the NSDUH.

Figure 9: Comparison of Sales of Purdue and Endo Pharma

Note: Left is the absolute difference in market share (0.1 means that MSA
share is 10% higher than the state average) and right is percentage difference
(10% means that MSA share is 1.1 times the state average).

Figure 10: Within-state variation in OxyContin Market Share

Note: Left is the absolute difference in opioid mortality (0.1 means that MSA
mortality per 10,000 people is 0.1 higher than the state average) and right is
percentage difference (10% means that MSA mortality per 10,000 people is 1.1
times the state average)

Figure 11: Within-state variation in opioid mortality

Note: We categorized all MSAs into high, mid, and low by the drop in the
observed per person OxyContin sales from 2009 to 2011. The series are
population weighted and Florida is excluded. The high group saw a 30% drop in
OxyContin sales, mid group a 3.9% drop, and low group a 15% increase. The high
group experienced a 46% increase in generic oxycodone sales, mid group a 34%
increase, and low group a 29% increase. The three groups share similar
oxycodone growth trends until the reformulation.

Figure 12: Opioid sales by empirical OxyContin drop

Note: Similarly to the previous figure, we categorized all MSAs into high,
mid, and low by the drop in the observed per person OxyContin sales from 2009
to 2011. The series are population weighted and Florida is excluded. No trend
break in opioid mortality in the high drop group. The high group saw an 35%
increase in heroin mortality, the mid group 38%, and the low group 37%. The
similar increases in heroin mortality post-reform indicates that drops in
OxyContin use post-reform did not lead to additional increase in heroin use.

Figure 13: Opioid mortality by empirical OxyContin drop

###  7.2 Tables

Table 2: Testing Constructed Exposure Measure Against Opioid Mortality

| Opioid overdose deaths per 100,000
---|---
| OxyContin | Generic Oxycodone
|  (1) |  (2) |  (3) |  (4) |  (5) |  (6)
NSDUH misuse |  10.235 |  |  |  2.909 |  |
|  (1.719) |  |  |  (0.570) |  |
ARCOS sales |  |  0.001 |  |  |  0.001 |
|  |  (0.0002) |  |  |  (0.0001) |
Combined exposure |  |  |  0.093 |  |  |  0.087
|  |  |  (0.012) |  |  |  (0.009)
Number of observations |  379 |  379 |  379 |  379 |  379 |  379
R-square |  0.086 |  0.089 |  0.130 |  0.065 |  0.178 |  0.189
Adjusted R-square |  0.084 |  0.086 |  0.128 |  0.062 |  0.176 |  0.187

  * •

Notes: Standard errors are in parentheses. We report coefficients from OLS
regressions of opioid mortality on misuse, sales or exposure. NSDUH misuse
rates is the 6-year average OxyContin or Percocet lifetime misuse rate from
pre-reform period (2004-2009). ACROS sales is Oxycontin or generic oxycodone
sales per person from 2009. Combined exposure is the product of the previous
two measures normalized (see equation 1). Overdose from 2009. Regressions are
weighted by MSA population.

Table 3: Difference in difference regression results

| Opioid sales per person | Overdose per 10,000
---|---|---
|  OxyContin |  Oxycodone |  Opioid |  Heroin
|  (1) |  (2) |  (3) |  (4)
Post |  -8.05 |  41.74 |  0.01 |  0.14
|  (2.86) |  (4.92) |  (0.02) |  (0.02)
High OxyContin |  47.24 |  56.46 |  -0.05 |  -0.07
|  (5.78) |  (13.36) |  (0.03) |  (0.02)
High Oxycodone |  26.84 |  95.90 |  0.14 |  0.08
|  (6.66) |  (15.47) |  (0.04) |  (0.05)
Post x High OxyContin |  -15.14 |  10.30 |  0.02 |  0.03
|  (6.39) |  (8.90) |  (0.02) |  (0.02)
Post x High Oxycodone |  -2.33 |  33.99 |  0.06 |  0.07
|  (6.37) |  (8.80) |  (0.02) |  (0.02)
Number of observations |  2148 |  2148 |  2148 |  2148
R-square |  0.665 |  0.737 |  0.517 |  0.469
Adjusted R-square |  0.654 |  0.728 |  0.501 |  0.452

  * •

Notes: We report coefficients from the difference-in-difference estimation
(see equation (3)). All MSAs in Florida are excluded. In all specifications,
we include MSA-level control variables, state fixed effects and year fixed
effects. Standard errors are clustered at the MSA level.

###  7.3 Map

Note: Data from 2004-2009 NSDUH lifetime OxyContin misuse rate (NSDUH ticker
OXXYR). 0.01 is interpreted as 1% of the state population have ever misused
OxyContin.

Figure 14: OxyContin lifetime misuse rate at state level

Note: Data from 2004-2009 NSDUH lifetime Percocet, Percodan, Tylox misuse rate
(NSDUH ticker PERCTYL2). 0.01 is interpreted as 1% of the state population
have ever misused one of the three drugs. Percocet lifetime misuse rate on
average is much higher than OxyContin lifetime misuse rate.

Figure 15: Percocet lifetime misuse rate at state level

Note: The figure plots the absolute difference in percentile ranking of the
two state level lifetime misuse rate. A 0.1 should be interpreted as a 10%
difference in percentile ranking between OxyContin lifetime misuse rate and
Percocet lifetime misuse rate. For example, Colorado’s OxyContin misuse rate
is 0.0063 (42 percentile) and it’s Percocet misuse rate is 0.092 (97
percentile), which is a 55% difference in percentile ranking. We rely on the
difference between two misuse rate to separately identify the impact of
OxyContin and oxycodone.

Figure 16: Difference in state level misuse rates

Note: This figure shows OxyContin exposure by MSA. We show Florida here, which
had very low OxyContin exposure/sales, but omit it from analysis because it
had abnormally high generic oxycodone sales with large amounts being
trafficked to other states.

Figure 17: OxyContin exposure at MSA level

Note: Florida is excluded in this analysis. MSAs grouped by high vs low
OxyContin exposure and high vs low generic oxycodone exposure.

Figure 18: Diff-in-diff regression categories

###  7.4 Alternative Regression Specifications

####  7.4.1 MSA FE

Figure 19: Regression on OxyContin sales with MSA FE. Shaded regions are the
95 percent confidence intervals with standard errors clustered at the MSA
level.

Figure 20: Regression on oxycodone sales with MSA FE. Shaded regions are the
95 percent confidence intervals with standard errors clustered at the MSA
level.

Figure 21: Regression on opioid mortality with MSA FE. Shaded regions are the
95 percent confidence intervals with standard errors clustered at the MSA
level.

Figure 22: Regression on heroin mortality with MSA FE. Shaded regions are the
95 percent confidence intervals with standard errors clustered at the MSA
level.

####  7.4.2 Last Year OxyContin Misuse

Figure 23: Regression on OxyContin sales with last-year OxyContin. Shaded
regions are the 95 percent confidence intervals with standard errors clustered
at the MSA level.

Figure 24: Regression on oxycodone sales with last-year OxyContin. Shaded
regions are the 95 percent confidence intervals with standard errors clustered
at the MSA level.

Figure 25: Regression on opioid mortality with last-year OxyContin. Shaded
regions are the 95 percent confidence intervals with standard errors clustered
at the MSA level.

Figure 26: Regression on heroin mortality with last-year OxyContin. Shaded
regions are the 95 percent confidence intervals with standard errors clustered
at the MSA level.

####  7.4.3 State Level Regression

Figure 27: Regression on OxyContin sales at state level. Shaded regions are
the 95 percent confidence intervals with standard errors clustered at the MSA
level.

Figure 28: Regression on oxycodone sales at state level. Shaded regions are
the 95 percent confidence intervals with standard errors clustered at the MSA
level.

Figure 29: Regression on opioid mortality at state level

Figure 30: Regression on heroin mortality at state level. Shaded regions are
the 95 percent confidence intervals with standard errors clustered at the MSA
level.

####  7.4.4 OxyContin Only

Figure 31: Regression on OxyContin sales with OxyContin only. Shaded regions
are the 95 percent confidence intervals with standard errors clustered at the
MSA level.

Figure 32: Regression on oxycodone sales with OxyContin only. Shaded regions
are the 95 percent confidence intervals with standard errors clustered at the
MSA level.

Figure 33: Regression on opioid mortality with OxyContin only. Shaded regions
are the 95 percent confidence intervals with standard errors clustered at the
MSA level.

Figure 34: Regression on heroin mortality with OxyContin only. Shaded regions
are the 95 percent confidence intervals with standard errors clustered at the
MSA level.

####  7.4.5 Oxycodone Only

Figure 35: Regression on OxyContin sales with oxycodone only. Shaded regions
are the 95 percent confidence intervals with standard errors clustered at the
MSA level.

Figure 36: Regression on oxycodone sales with oxycodone only. Shaded regions
are the 95 percent confidence intervals with standard errors clustered at the
MSA level.

Figure 37: Regression on opioid mortality with oxycodone only. Shaded regions
are the 95 percent confidence intervals with standard errors clustered at the
MSA level.

Figure 38: Regression on heroin mortality with oxycodone only. Shaded regions
are the 95 percent confidence intervals with standard errors clustered at the
MSA level.